ODSC APAC 2023 | VIRTUAL CONFERENCE
AI EXPO & DEMO HALL
AI Solutions Showcase l Networking
Product Demos
Partners
Networking Events
Attendees
Learn how to Build AI Better
Want to keep up with the latest AI developments, trends, and insights? Dealing with the build or buy dilemma to grow your business? Seeking to interact with data-obsessed peers and build your network?
Look no further: The ODSC AI Expo & Demo Hall is the right destination for you
Expo Hall Topics
Partner sessions offer compelling insights on how to make data science and AI work for your industry. Here are some of the topics you can expect at AI Expo & Demo Hall. The full agenda is coming soon.
Visionaries and Thought Leaders
With an AI Expo Pass, you can take advantage of 15+ sessions and ODSC Keynotes. Our Speakers will provide compelling insights on how to make data science and AI work for your industry.
Past Keynote Speakers
Past Expo Speakers
Sudeep George
Sudeep George is the Vice President of Engineering at iMerit, where he develops production-ready frameworks for a data-centric approach to machine learning. He has a strong background in imaging sensors, computer vision and has built and manufactured multi-sensor computational imaging platforms for several market verticals.
Annotated Data: The Bedrock of Successful AI Deployments(Talk)
Glen Ford
Glen Ford is VP of Product at iMerit — a leading AI data solutions company — where he leads the product management and design teams. Glen holds more than two decades of product development experience across the technology sector. A Graduate of Texas A&M University—Commerce, Glen began his career as a consultant where he handled full-stack web programming and architecture for clients including Time Warner and AIM Funds. Over the years, he has held senior and director-level product management roles at several companies including Demand Media, WP Engine and Humanify. Most recently, Glen spent four years at Alegion — an ML-powered data annotation platform — where he helped the company grow from eight full-time employees to more than 100 in a challenging, emerging market.
The Hidden Layers of Tech Behind Successful Data Labeling(Demo Talk)
Rohan Maheshwari
Rohan Maheshwari is a student at RV College of Engineering in Bengaluru, India with a keen interest in Deep Learning, Natural Language Processing, Graph modelling and Machine Learning as well as their applications in finance and sentiment analysis. He has worked under the Samsung PRISM program to create a code-mixed multi-intent classification system. He has also worked with SCII to create an invoice extraction system. He is actively working with the LexisNexis® Risk Solutions HPCC Systems® team and the RV College of Engineering Centre of Excellence on Cognitive Intelligent Systems for Sustainable Solutions to investigate block data stored on the blockchain to gain insight and build relationships between transactions that can shed light on potential criminal transactions . Rohan is pursuing a Bachelor of Computer Science and Engineering at RV College of Engineering.
Analyzing Blockchain Data to Detect Illicit Transactions made Using Bitcoin(Talk)
Dr. G Shobha
Dr. G Shobha, Professor, Computer Science, and Engineering Department, R.V College of Engineering, Bengaluru, India has teaching experience of 28 years, her specialization includes Data mining, Machine Learning, and Image processing. She has published more than 150 papers in reputed journals/conferences. She has also executed sponsored projects worth INR 200 lakhs funded by various agencies nationally and internationally. She is a recipient of various awards such as the Career Award for young teachers 2007-08 constituted by All India Council of Technical Education, Best Researcher award from Cognizant 2017, GHC Faculty Scholar for Women in Computing in 2018, IBM Shared University Research Award in 2019, HPCC Systems community recognition award 2020.
Leveraging HPCC Systems Platform for Machine Learning Applications(Demo Talk)
Prof. Jyoti Shetty
Prof. Jyoti Shetty, Assistant Professor, Computer Science and Engineering Department, RV College of Engineering, Bengaluru, India has 16 years teaching and 2 year industry experience. Her specialization includes Data Mining, Machine Learning and Cloud Computing. She has published research papers in reputed journals and conferences. She has also executed sponsored projects funded from various agencies nationally and internationally. She was the recipient of awards such as SAP Award of excellence from IIT Bombay for demonstrating ICT in education in 2016 and HPCC Systems Mentor Badge Award in 2021 for providing guidance and direction towards the successful completion of intern open source projects.
Leveraging HPCC Systems Platform for Machine Learning Applications(Demo Talk)
Chenlu Jiao
Chenlu Jiao is a Product Manager from Azure Machine Learning Platform, and currently driving AzureML with Kubernetes anywhere.
Luis Vargas, PhD
Luis Vargas is a Partner Technical Advisor to the CTO of Microsoft. Responsible for Microsoft’s AI at Scale initiative coordinating efforts across infrastructure, systems software, models, and products. He bootstrapped the productization of Automated ML and Reinforcement Learning in the Azure AI Platform, worked on the launch of Azure Database Services, and lead the high-availability area for SQL Server. Luis has a PhD in Computer Science from Cambridge University.
The Big Wave of AI at Scale(Keynote)
Setu Chokshi
Setu is a senior technical leader, innovator and specialist in machine learning and artificial intelligence. He has led and implements machine learning products at scale for various companies.
Microsoft’s Accelerator for MLOps(Workshop)
Chanran Kim
Passionate about solving human problems, Chanran’s main interests are computer vision and natural language processing while using machine learning and deep learning. He’s written a book on transfer learning and he often gives lectures on it. Today, Chanran is running Pseudo Labs, a machine learning community he founded in 2020. Pseudo Labs is all about sharing knowledge on machine learning and deep learning studies for free, offering Kaggle meetups, code sharing and other events in the Republic of Korea.
When Chanran isn’t running hackathons for Pseudo Labs, you’ll find him watching Premier League soccer matches or honing his food photography (pop over to his Instagram to see some mouth-watering shots).
Introduction to WSL2 for Data Science with Z by HP(Demo Talk)
Ken Jee
As the Head of Data Science at Scouts Consulting Group, Ken spends his workdays improving the performance of athletes and teams by analyzing the data collected on them. He also dabbles in entrepreneurship and content creation, best known for his YouTube channel where he helps over 80,000 people navigate the data science landscape. More recently, Ken is focused on project-based learning through Kaggle. He hopes to share the processes that data scientists take when approaching Kaggle competitions and new datasets. He started the #66DaysOfData challenge to help people create the habit of learning and working on projects every day.
Bridging the Gap Between Data Scientists and Decision Makers(Keynote)
Prerna Daga
Prerna leads Business Development for Data Science at HP India. Prior to joining HP, she worked in IT industry for almost 9 years and has multi-disciplinary experience cutting across business development and data science & analytics. She has an MBA from SPJIMR, Mumbai and BTech in Computer Science.
Data Science Innovation with Z by HP Workstations, Remote Collaboration and Software Stack(Talk)
Chip Kent
Chip Kent is the chief data scientist at Deephaven Data Labs. He holds a Ph.D. from CalTech, with decades of quantitative, mathematical, and computer science experience. Chip comes from a background in quantitative private investment, using data to make investments at Walleye Capital.
Pete Goddard
Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.
Real-time Analytics, AI&Apps with Deephaven Data Labs(Demo Talk)
Florian Jacta
Florian Jacta is a specialist of Taipy, a low-code open-source Python package enabling any Python developers to easily develop a production-ready AI application. Package pre-sales and after-sales functions. He is data Scientist for Groupe Les Mousquetaires (Intermarche) and ATOS. He developed several Predictive Models as part of strategic AI projects. Also, Florian got his master’s degree in Applied Mathematics from INSA, Major in Data Science and Mathematical Optimization.
How to Build Stunning Data Science Web applications in Python – Taipy Tutorial(Workshop)
Bringing AI to Retail and Fast Food with Taipy’s Applications(Track Keynote)
Demo Session Title: Turning your Data/AI algorithms into full web apps in no time with Taipy
Abstract:
In the Python open-source ecosystem, many packages are available that cater to:
– the building of great algorithms
– the visualization of data
Despite this, over 85% of Data Science Pilots remain pilots and do not make it to the production
stage.
With Taipy, a new open-source Python framework, Data Scientists/Python Developers are able to
build great pilots as well as stunning production-ready applications for end-users.
Taipy provides two independent modules: Taipy GUI and Taipy Core.
In this talk, we will demonstrate how:
1. Taipy-GUI goes way beyond the capabilities of the standard graphical stack: Gradio,
Streamlit, Dash, etc.
2. Taipy Core fills a void in the standard Python back-end stack.
Zaccheus Sia
Zaccheus has strong experience in development of time series machine learning models which power workforce scheduling systems. He is skilled in data science workflow and solution delivery by developing end-user applications. Zaccheus has designed/modeled AI projects and has big interest in Python’s ubiquity in diverse settings to explore fields like computer vision, NLP and bioinformatics. His main objective is discovering untapped potential across wide disciplines as big data becomes increasingly appreciated and available. He received his BSc. (Hons) in Computer Science from Sunway University. His Bachelor Thesis was on “Application of Blockchain to Trust Models for Secure Cognitive Radio Networks”.
How to Build Stunning Data Science Web applications in Python – Taipy Tutorial(Workshop)
Vincent Gosselin
Vincent has 30+ years as AI specialist with ILOG and IBM. He has mentored several Data Science teams. Vincent has designed/modeled several major AI projects for customers such as Samsung. Electronics, McDonald’s, Dassault Aviation, Carhartt, Toyota, TSMC, Disney, etc. He is skilled in Mathematical Modeling, Machine Learning, Time Series prediction. He has strong experience in Manufacturing, Retail & Logistics industries. His main objective is to “Help companies go beyond AI pilots and be successful in bringing AI to their end-users”. He received his Msc in Comp. Science & AI from Paris-Saclay University.
How to Build Stunning Data Science Web applications in Python – Taipy Tutorial(Tutorial)
Bringing AI to Retail and Fast Food with Taipy’s Applications(Track Keynote)
AI Expo & Demo Hall Schedule
Keynote | Machine Learning | Big Data Analytics | All Levels
Session Abstract Coming Soon!
Dr. Jim Webber is Neo4j’s Chief Scientist and Visiting Professor at Newcastle University. At Neo4j, Jim leads the Systems Research Group, working on a variety of database research topics with a focus on fault-tolerance. He also co-wrote Graph Databases (1st and 2nd editions, O’Reilly) and Graph Databases for Dummies (Wiley).
Prior to Neo4j, Jim worked on fault-tolerant distributed systems. First at Newcastle University startup Arjuna and then for a variety of clients for global consulting firm ThoughtWorks. Along the way Jim co-authored the books REST in Practice (O’Reilly) and Developing Enterprise Web Services – An Architect’s Guide (Prentice Hall).
Jim’s blog is located at https://jimwebber.org and he tweets sometimes at @jimwebber.
Keynote | Machine Learning | All Levels
Everyone seeks to be understood. Whether at home or in the workplace, this is a central part of the human experience. From speaking with hundreds of data scientists, I find that many of them don’t feel like their work is understood by the decision makers at their organizations. When speaking with IT decision makers, I get the same feeling. Many of them feel like they have highly qualified teams, but their data scientists don’t quite get the business. In this keynote we debunk the common assumptions of both data scientists and ITDMs so they can get the most out of their roles…more details
As the Head of Data Science at Scouts Consulting Group, Ken spends his workdays improving the performance of athletes and teams by analyzing the data collected on them. He also dabbles in entrepreneurship and content creation, best known for his YouTube channel where he helps over 80,000 people navigate the data science landscape. More recently, Ken is focused on project-based learning through Kaggle. He hopes to share the processes that data scientists take when approaching Kaggle competitions and new datasets. He started the #66DaysOfData challenge to help people create the habit of learning and working on projects every day.
Keynote | Machine Learning | Big Data Analytics | All Levels
In this presentation, we will present an overview of a modern class of techniques that are suitable for generating synthetic tabular data. We will focus in particular on deep generative models that can fit complex probability distributions and efficiently generate samples to construct synthetic datasets. We will discuss a variety of deep generative models, how their output can ensure privacy and how to assess the fidelity of the synthetic samples…more details
Kerrie Mengersen is a Distinguished Professor of Statistics and Director of the Centre for Data Science at QUT. Her career in statistical consulting and academic research has taken her across three states of Australia, the USA and France. Kerrie is a Fellow of the Australian Academy of Science, the Australian Academy of Social Sciences, and the Queensland Academy of the Arts and Sciences. Her overall ambition is to ‘use data better’, particularly in the fields of health, environment and industry. To this end, she has led over 30 major projects such as the current Long-term Benefits and Impacts Study with Queens Wharf Brisbane, the online interactive Australian Cancer Atlas and the Virtual Reef Diver program.
Keynote | Responsible AI | All Levels
Session Abstract Coming Soon!
Professor Mary-Anne Williams is the Michael J Crouch Chair in Innovation at UNSW where she collaborates with business, government and societal organisations to grow entrepreneurship and accelerate innovation in Australia. Mary-Anne has a PhD in Computer Science (University of Sydney) and Master of Laws (University of Edinburgh). She is a Fellow at Stanford University, the Australian Academy of Technological Sciences and Engineering, the Australian Computer Society and the Association for the Advancement of Artificial Intelligence (AAAI). Mary-Anne is a leading authority on AI with transdisciplinary strengths in AI for Business, Disruptive Innovation, Entrepreneurship, AI Ethics and Law. She has received multiple awards including the 2019 Australasian Distinguished Artificial Intelligence Contribution Award from the Australian Computer Society; two Google Faculty Machine Learning Awards in 2019 and 2021; and an IBM Faculty Award in 2008. She is a member of the Editorial Boards for AAAI/MIT Press; the Information Systems Journal; and the International Journal of Social Robotics. She was Chair of the International Conference on Social Robotics in 2014; Review Editor for Artificial Intelligence Journal; and served on the ACM Eugene L. Lawler Award Committee for Humanitarian Contributions within Computer Science and Informatics. Mary-Anne was Conference Chair for the 2021 Australasian Joint Conference on Artificial Intelligence and invited speaker for the Australian government at World Expo in Dubai in 2022.
Demo Talk | Machine Learning | All Levels
This introductory session aims to provide answers to these questions, get you introduced to WSL2 and get you started by configuring your machine and running your first code…more details
Passionate about solving human problems, Chanran’s main interests are computer vision and natural language processing while using machine learning and deep learning. He’s written a book on transfer learning and he often gives lectures on it. Today, Chanran is running Pseudo Labs, a machine learning community he founded in 2020. Pseudo Labs is all about sharing knowledge on machine learning and deep learning studies for free, offering Kaggle meetups, code sharing and other events in the Republic of Korea.
When Chanran isn’t running hackathons for Pseudo Labs, you’ll find him watching Premier League soccer matches or honing his food photography (pop over to his Instagram to see some mouth-watering shots).
Talk | Big Data & Data Analytics | All Focus Areas | All Levels
How far can we take democratization of data and AI? As an eccentric progression of my quest to answer this question, I currently work at Qosmo, Inc. whose mission is to advance human creativity with AI. In this talk I will talk about AI for music. In recent years, there has been some remarkable progresses in AI for music and I will cover these developments in summary of the recent white paper I published. It may not be a surprise to find out that musicians are not the most technical users. We therefore released Neutone, an AI audio plugin & community, in the aim of bridging the gap between AI research and musical creativity…more details
Akira is a renowned data scientist in Japan who led the growth of DataRobot Japan as CEO until June 2021. His background in entrepreneurship (Shiroyagi Corporation), strategy consulting (BCG), experimental particle physicist (LHC, CERN) gives him a unique edge to develop business potential of AI and data technologies. He has worked with over a hundred companies in deploying advanced analytics and digital transformation projects. His active podcast and blog can be found through the links.
Talk | Big Data Analytics | Machine Learning | Intermediate
In this talk, Fatemeh overviews the main themes in her research program from minimally-invasive biomarker discovery for personalised medicine and single-cell sequencing data analysis to computational drug repositioning and network pharmacology. Across all themes, Dr Vafaee’s research heavily relies on multidisciplinary expertise and cross-faculty collaborations to generate translatable outcomes impacting upon biomedicine of the future…more details
Dr Fatemeh Vafaee is the Deputy Director of the Data Science Centre at the University of New South Wales (UNSW Sydney) and leads the ‘Health Data Science’ priority area. She launched and leads Artificial Intelligence in Biomedicine Laboratory (VafaeeLab.com) at UNSW and is the founder of OmniOmics.ai proprietary limited company (OmniOmics.ai) with the mission to develop and deploy AI technologies to enhance disease diagnosis and accelerate drug development. Dr Vafaee received her PhD in Artificial Intelligence from the School of Computer Science at the University of Illinois at Chicago, USA (2011) followed by 2 multidisciplinary postdoctoral fellowships at the University of Toronto, Canada, and the University of Sydney, Australia (2012 – 2017) on computational biomedicine. Dr Vafaee has a strong track record of multidisciplinary research leadership and industrial engagement. Her research has attracted over $10.5M across >12 research and industry-based project grants and has been published in top-tier journals in the field.
Talk | MLOps & Data Engineering | All Levels
Z by HP is bringing exceptional technology -both hardware and software- to data scientists, analysts, and creatives that is built for the demands of heavy data processing workloads, right out of the box. With Z laptops, desktops, or rack-mounted data science computers, you get high-performance computers that complement your cloud infrastructure, reduce latency of heavy workloads, make collaboration more efficient, and secure your data end to end…more details
Prerna leads Business Development for Data Science at HP India. Prior to joining HP, she worked in IT industry for almost 9 years and has multi-disciplinary experience cutting across business development and data science & analytics. She has an MBA from SPJIMR, Mumbai and BTech in Computer Science.
Ruchi Bhatia is a Computer Engineering graduate from India and is currently pursuing her Master’s degree at Carnegie Mellon University. She is the youngest 3x Kaggle Grandmaster in the Notebooks, Datasets, and Discussion category, the Leader of Data Science at OpenMined, and one of the 21 Data Science Global Ambassadors at Z by HP. Her passion lies in utilizing data-driven techniques in conjunction with a sound knowledge of business processes to drive meaningful insights and impact.
Paras Varshney is a Data Scientist at LogicAI and an Ex-Data Scientist from IISc. Bangalore. He has been working on building competitive machine learning pipelines and automating the competitive ecosystem for the “Kaggle Days x Z by HP Global Data Science Championship”. He also developed the systems to manage the high throughput data ingestion pipelines for smart cities’ big data. Recognized within the top 1% of Kaggle worldwide, Paras likes to share his passion for data science by helping junior coders develop programming skills through an online classroom. He also loves writing, and his works can be found in top digital publications such as Towards Data Science.
Demo Talk | All Levels
In this talk, we discuss the contents of the PyTorch on Azure container and share the results seen by early adopters. In addition, we will show demos of how you can get started with the container and how to use the Microsoft acceleration technologies it comes with…more details
Parinita Rahi is a Principal PM lead in Azure AI Platform organization at Microsoft. She works on various AI frameworks (PyTorch, ORT Training and ONNX Converters), in addition to driving GitHub CoPilot quality initiatives. She has 10+ years of Product Management experience, previously in the Online Advertising domain. She has driven various ML based solutions and products with a strong focus on solving customer problems.
Jambay Kinley is a Software Engineer in Azure AI Platform organization at Microsoft. He is a member of a team that works on various Pytorch related initiatives for the AI Frameworks team. Prior to joining Microsoft, he was involved in Machine Learning research in a Natural Language Processing lab at Cornell Tech.
Talk | Machine Learning | Big Data Analytics | Intermediate
Apart from the performance , interpretability part kicks in when there is a change in the prediction by model in between the journey of an object. The most anticipated and important question is “What are the reasons the prediction was changed from x to y in the middle of the journey?”. Not able to provide reasons and answers to the questions above could easily lead to loss of trust in the system. In this talk, you will learn about different KPIs, visualisation techniques that can capture the behaviour of ETA models. We will also walk through why it is important to have interpretable model by design to be able to answer some of the questions…more details
Urvesh, currently Head of Data Science at Portcast.io (real time predictive visibility and demand forecasting to optimize supply-chain), has more than 8 years of hands-on and 4 years of leadership experience overall across Machine Learning, Software product development, management, and cross-functional collaboration. Urvesh started his career as a software engineer at Cisco, writing and fixing protocols for firewalls and exploring use of machine learning in malware classification. He later joined noodle.ai as a Data Scientist and worked on numerous ML projects involving government, manufacturing industry, and airlines. He has spent some time mentoring Data Science Students at SpringBoard and is currently a fellow at On Deck Data Science (ODDS).
Ghassen, currently data scientist at Portcast.io (real time predictive visibility and demand forecasting to optimize supply-chain), has close to four years of experience and extensive expertise in leading full-spectrum descriptive and predictive analyses towards supporting high-level decision-making.
Talk | Responsible Ai | Intermediate
The desire to embody Responsible AI practices requires an understanding of, the context around, and the impact on the end user. To achieve this, design and research are just as pivotal to the RAI conversation as ML. There is no bigger risk, and no greater irresponsibility, than to not interface with those who will be affected by your design. In this talk, Bujuanes Livermore will share how she navigates customer relationships to encourage end user contact and how this process can help mitigate assumptions and therefore risk. She will also recommend next steps for when that first conversation meets with too much resistance, as well as how to navigate those waters effectively and professionally through designing experiments and highlighting risks in an assumptions grid. Learn some of the lessons and ‘stealth mode’ tactics in designing that conversation with the client from one of the leaders in the field, the Head of Research & Design in Data, Intelligence & Design at Microsoft with over 15 years of experience…more details
Bujuanes Livermore is the Head of Research and Design in Data, Intelligence, and Design in Commercial Software Engineering (CSE) at Microsoft and she leads the Worldwide Community for Design & Experience. CSE is a global engineering organization that works directly with the largest companies and not-for-profits in the world to tackle their most significant technical challenges. Bringing human-centered design to the forefront and creating a more harmonious balance between the technical and the human drives her work. She is keenly interested in the influences and effects of digital and augmented environments, the human experience of service and product design, and challenging traditional business models and service offerings.
Talk | Machine Learning | Deep Learning | All Levels
In this talk, I am going to highlight some of the best practices around data annotation and ML DataOps that help companies quickly develop, deploy and continuously update their ML models…more details
Sudeep George is the Vice President of Engineering at iMerit, where he develops production-ready frameworks for a data-centric approach to machine learning. He has a strong background in imaging sensors, computer vision and has built and manufactured multi-sensor computational imaging platforms for several market verticals.
Talk | NLP | Machine Learning | Intermediate
In this talk, I will present two applications on one of the most popular e-commerce platforms: Amazon. We will first look at generating product descriptions, and show how we train a multimodal model that takes product images and metadata as input to generate descriptions for 23 product types. Although we found that the generated descriptions are fluent and persuasive, they are not always faithful (i.e. the details are not real)…more details
Dr Lau is a lecturer in the School of Computing and Information Systems at the University of Melbourne. His research is in Natural Language Processing — a sub-field of Artificial Intelligence — where the goal is to develop computational models to understand human languages. A common theme of Dr Lau’s research is that it involves building computational models in an unsupervised or semi-supervised setting, i.e. a learning scenario where the supervision signal for model training is not available or scarce, and is characterised by a diverse flavour of applications, e.g. topic models, lexical semantics, text generation and misinformation detection. Some of his research in text generation and state-sponsored influence operations has been covered by popular science magazines (New Scientist) and mainstream news media (BBC and Guardian).
Workshop | MLOps & Data Engineering | Machine Learning | All Levels
MLOps means different things to different people, however, the fundamental essence of MLOps is to deliver models into productions faster with a consistent, repeatable and reliable approach. Based on our experience of working with various large and small customers across the world, Microsoft has developed an accelerator to do exactly what the word suggests – accelerate our customer’s journey to production. Simplicity and segregation of duties are key pillars of this accelerator, which means that our intention is that Data Scientists, ML Engineers and IT teams don’t need significant upskilling before they can do MLOps…more details
Setu is a senior technical leader, innovator and specialist in machine learning and artificial intelligence. He has led and implements machine learning products at scale for various companies.
Demo Talk
In this session, we will be walking through the way that RVCE-HPCC Systems Centre of Excellence on Cognitive Intelligent Systems for Sustainable solutions have leveraged HPCC Systems for Big Data analysis. Demonstration and visualization of few applications such as Analysis of corvid 19 data, Analysis, Classification of Surface water will be showcased for the audience to understand various components involved in the architecture of HPCC Systems. We intend to discuss the comparison with other distributed platforms such as Hadoop to show the applicability of HPCC Systems platform to solve big data problems…more details
Dr. G Shobha, Professor, Computer Science, and Engineering Department, R.V College of Engineering, Bengaluru, India has teaching experience of 28 years, her specialization includes Data mining, Machine Learning, and Image processing. She has published more than 150 papers in reputed journals/conferences. She has also executed sponsored projects worth INR 200 lakhs funded by various agencies nationally and internationally. She is a recipient of various awards such as the Career Award for young teachers 2007-08 constituted by All India Council of Technical Education, Best Researcher award from Cognizant 2017, GHC Faculty Scholar for Women in Computing in 2018, IBM Shared University Research Award in 2019, HPCC Systems community recognition award 2020.
Prof. Jyoti Shetty, Assistant Professor, Computer Science and Engineering Department, RV College of Engineering, Bengaluru, India has 16 years teaching and 2 year industry experience. Her specialization includes Data Mining, Machine Learning and Cloud Computing. She has published research papers in reputed journals and conferences. She has also executed sponsored projects funded from various agencies nationally and internationally. She was the recipient of awards such as SAP Award of excellence from IIT Bombay for demonstrating ICT in education in 2016 and HPCC Systems Mentor Badge Award in 2021 for providing guidance and direction towards the successful completion of intern open source projects.
Demo Talk | MLOps & Data Engineering | All Levels
In this talk, we will demonstrate how:
Taipy-GUI goes way beyond the capabilities of the standard graphical stack: Gradio, Streamlit, Dash, etc.
Taipy Core is simpler yet more powerful than the standard Python back-end stack: Airflow, MLFlow, Luigi, etc.
Vincent has 30+ years as AI specialist with ILOG and IBM. He has mentored several Data Science teams. Vincent has designed/modeled several major AI projects for customers such as Samsung. Electronics, McDonald’s, Dassault Aviation, Carhartt, Toyota, TSMC, Disney, etc. He is skilled in Mathematical Modeling, Machine Learning, Time Series prediction. He has strong experience in Manufacturing, Retail & Logistics industries. His main objective is to “Help companies go beyond AI pilots and be successful in bringing AI to their end-users”. He received his Msc in Comp. Science & AI from Paris-Saclay University.
Talk | MLOps & Data Engineering | All Levels
Machine learning models are being deployed extensively in many important areas to assist humans in making important decisions. But there is no guarantee a model will always perform well after deployment as its developers intended. Understanding the correctness of a model is thus crucial to prevent potential failures that may have a significant detrimental impact in critical application areas. In this talk, I will discuss the challenges we face to ensure the correctness of deployed machine learning models and introduce some works on how to efficiently test a machine learning model using only a small amount of labelled test data.
Dr. Huong Ha is currently a Lecturer at the Artificial Intelligence Discipline, School of Computing Technologies, RMIT University, Melbourne, Australia. Her research is in the areas of Artificial Intelligence and Software Engineering, particularly trustworthy machine learning, automated machine learning, and data-driven software engineering. She regularly publishes her works in the leading international research venues in these areas including NeurIPS, ICML, AAAI, AISTATS, ICSE, and ICSME. In addition to her current role in academia, Huong has previous working experience in the industry as a data scientist and a product development engineer.
Talk | MLOps & Data Engineering | Big Data Analytics | Beginner
In this talk, we will demonstrate how we process our Big Data, gain insights of our Smart Grid and Innovation applications, how we do detect faults from smart meter power quality data and meter channel alerts that resulted in reducing shock incidents in our network, how we automate monitoring of customer sites for immediate hazard detection, how we balance the grid from distributed energy resource management system, and how we automate fault detection analytics and jobs dispatch with no human intervention…more details
Thilaksha Silva has obtained a Doctor of Philosophy (PhD) in Statistics from Monash University, Australia. Thilaksha is skilled in data science for electricity distribution, statistics, time series forecasting, predictive modelling and big data analytics. She is adept at advanced data analytics with 10+ years of experience and has mastered in communicating the business value across the business and engaging audience with data science on a deeper level.
Shashank Kundapur has obtained a Master of Data Science in Advance Analytics from Monash University, Australia. Shashank is a skilled analyst and software engineer, highly adept at ETL processing and building data science solutions, with a proven track record in managing complex data analytics project gained from over 9 years of professional experience within the Telecom and Energy sectors. Extensively skilled in building Business Intelligence and Data Modelling solutions.
Talk | Machine Learning | Deep Learning | All Levels
In this talk Rohan will take you through how clustering and transaction times series fingerprinting can give insights into the underlying transaction behaviour of users and help find the criminals utilizing the bitcoin network for illicit activities..more details
Rohan Maheshwari is a student at RV College of Engineering in Bengaluru, India with a keen interest in Deep Learning, Natural Language Processing, Graph modelling and Machine Learning as well as their applications in finance and sentiment analysis. He has worked under the Samsung PRISM program to create a code-mixed multi-intent classification system. He has also worked with SCII to create an invoice extraction system. He is actively working with the LexisNexis® Risk Solutions HPCC Systems® team and the RV College of Engineering Centre of Excellence on Cognitive Intelligent Systems for Sustainable Solutions to investigate block data stored on the blockchain to gain insight and build relationships between transactions that can shed light on potential criminal transactions . Rohan is pursuing a Bachelor of Computer Science and Engineering at RV College of Engineering.
Talk | Machine Learning | Beginner
In this session, we will learn about specific video analytics & image processing impactful use cases. Jakarta Smart City has been closely collaborating with several key organizations such as agency of transportation, transportation operators (MRT, LRT, train, BRT), police force, etc…more details
Juan Kanggrawan is the current Head of Data Analytics at Jakarta Smart City. His key role is to fully utilize data to formulate public policy and to improve quality of public services. Juan is currently working on several city-scale strategic analytics initiatives. He is actively analyzing complex, diverse and exciting urban data on a daily basis: citizen complaint/aspiration, transportation/mobility, health (COVID-19), CCTV, Open Data, weather-flood-river bank, subsidy utilization, food commodities price elasticity, etc. He is also developing and aligning strategic partnership framework between Jakarta Smart City with other government agencies, business enterprises, research agencies and universities.
Talk | NLP | Machine Learning | All Levels
In this talk we “organize” chatbots across a “spectrum of sophistication” and present as case-studies two examples of next generation reasoning based chatbots. First a higher order question answering (level 4) chatbot (that goes beyond factoid question answering) that we believe is one of the keys to artificial general intelligence. Second, a (level 7) Diagnosis chatbot that can mimic a doctor’s differential diagnosis conversation that infers, with a very high precision and recall, the potential diseases given the symptoms of a patient while via a series of follow-up questions…more details
Dr. Shailesh Kumar is currently the Chief Data Scientist at the Centre of Excellence in AI/ML, Reliance Jio. Prior to this he worked as a Distinguished Scientist at Ola cabs, Chief Scientist and Co-founder of Third Leap, an EdTech startup, Researcher in the Google Brain team, Sr. Scientist at Yahoo! Labs and Principal Scientist at Fair Isaac Research.
Dr. Kumar has 18 years of experience in building AI solutions in a variety of domains including Web, Retail, Finance, Remote Sensing, Fleet Management, Computer Vision, Knowledge Graph, and Conversational computing. He has published over 20 international papers and book chapters and holds more than 20 patents in AI/ML. He was recognised as one of the top 10 data scientists in India in 2015 by Analytics India Magazine. Dr. Kumar holds a Masters and PhD in AI from UT-Austin and B.Tech. in Computer Science from IIT-Varanasi.
Talk | Responsible Ai | Beginner-Intermediate
Machine Learning and AI models are often considered as black boxes, and the lack of transparency in the working of these models lead to lack of trust and adoption. In order to promote end user trust Explainable AI (XAI) and model explainability techniques like LIME, SHAP are widely used. But are these methods really human friendly? Do we have any existing method or framework that can provide explainability to non-technical users? How can we use XAI to bridge the gap between AI and end users to promote AI adoption?…more details
Aditya Bhattacharya is an Explainable AI Researcher at KU Leuven with an overall experience of 7 years in Data Science, Machine Learning, IoT & Software Engineering. Prior to his current role, Aditya has worked in various roles in organizations like West Pharma, Microsoft & Intel to democratize AI adoption for industrial solutions. As the AI Lead at West Pharma, he had contributed to forming the AI Centre of Excellence, managing & leading a global team of 10+ members focused on building AI products. He also holds a Master’s degree from Georgia Tech in Computer Science with ML and a Bachelor’s degree from VIT University in ECE. Aditya is passionate about bringing AI closer to end- users through his various initiatives for the AI community. He has also authored a book “Applied Machine Learning Explainability Techniques”
Talk | NLP | Machine Learning | Intermediate
This talk is for those who want to learn the introduction of sentiment analysis task and its different model choices to implement it. By listening this talk, you will become familiar and comfortable with the sentiment analysis and its different methodological approaches. Plus, I will bring up more advanced topics in this area such as issues and challenges in sentiment analysis or Aspect-based Sentiment Analysis (ABSA)…more details
Sunny is a seasoned professional data scientist, with over 15 years of relevant experience, and successful completion of significant company-onsite projects for many respected companies in South Korea and the US. Significant experience and dynamic practitioner in various domains, including NLP project lead, credit risk modeling, financial distress modeling, customer marketing prediction, and ML service provider consultation. She is passionate about creating and building AI solutions applying a variety of NLP technologies including sentiment analysis, conversational computing, topic modeling, etc. to support AI real-world usages for SME businesses. She is currently putting her efforts into her own AI start-up company – ReviewMind Inc. In 2020, her company was identified as an excellent start-up case by Korea Women in Science and Technology Support Center. Sunny and her team also won the best award in the 2021 Start-up Demo Day from the Korea Institute of Startup & Entrepreneurship Development. Sunny holds both a Masters in Data Science (Information Systems) and an MBA from the US and South Korea respectively.
Talk | Machine Learning | All Focus Areas | All Levels
As Gartner notes in the “State of Data Science and Machine Learning” report, DSML platforms are now addressing two growing and equally important markets: (i) A ‘multipersona’ market with a central focus on time to value, ease of use, and collaboration between multiple technical and nontechnical personas, and (ii) an ‘engineering’ market focused on primarily technical personas whose primary aim is to engineer (design, develop, deploy, monitor, and maintain) scalable, enterprise-wide AI solutions…more details
Hui Xiang Chua is Senior Data Scientist at Dataiku, helping enterprises with data democratization and enabling them to build their own path to AI. Dataiku is a 2x Gartner Magic Quadrant Leader for Data Science and Machine-Learning Platforms (as of 2021). She has both public and private experiences solving problems using data, namely over six years in the public service and two years in the media industry. She was also previously an instructor with General Assembly.
In 2017, she was accepted to the Data Science for Social Good Fellowship and was mentored by Rayid Ghani, Chief Scientist of the Obama for America campaign in 2012. For bringing data science into a high school’s curriculum, Hui Xiang was a recipient of the KDD Impact Program award by SIGKDD, the Association for Computing Machinery’s special interest group on knowledge discovery and data mining. She also runs a data science blog called Data Double Confirm that was recognised as 2018/2019 Top 100 Data Science Resources on MastersInDataScience.com.
Hui Xiang holds a B.Sc.(Hons) in Statistics and M.Sc. in Business Analytics from National University of Singapore.
Demo Talk | All Levels
This demonstration of Deephaven will show you how to: 1. Launch Deephaven from Jupyter and your Python IDE. 2. Access and manipulate Kafka and Parquet with just a few lines of code. 3. Filter, aggregate, join, select, and project ticking, updating tables in real-time. 4. Create and interact with table and plot widgets in web dashboards and Jupyter. 5. Integrate Deephaven with matplotlib, PyTorch, and other popular tools.
Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.
Talk | Deep Learning| Intermediate
This talk will introduce you to the world of Generative Models through the GAN lens. We will discuss how a simple game of adversaries leads to such powerful models. We will cover the evolution and advancement in the architecture of GANs along with some exciting use-cases. Towards the end we will go through a quick hands-on to generate images with GANs ourselves…more details
Raghav is a seasoned Data Science professional with over a decade’s experience of research & development of large-scale solutions in Finance, Digital Experience, IT Infrastructure and Healthcare for giants such as Intel, American Express, United HealthGroup and DeliverHero. He is an innovator with 10+ patents, a published author of multiple well received books & peer-reviewed papers and a regular speaker in leading conferences on topics in the areas of Generative AI, Recommendation Systems, Computer Vision, NLP, Deep Learning, Machine Learning and Augmented Reality.
Talk | Deep Learning | Intermediate
The Fourth Industrial Revolution (Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using modern smart technology. Large-scale machine-to-machine communication (M2M), the internet of things (IoT), Augmented Reality, Machine Vision, and Deep Learning are integrated for increased automation, improved communication and self-monitoring, and the production of smart machines that can analyze and diagnose issues. Designing a humanless system sometimes poses the biggest risk if something goes wrong. This is where Augmented Reality will add the human into the automation loop in the world of Automation…more details
Talk | MLOps & Data Engineering | Intermediate
In this talk, Ray Reed will explore the plethora of problems impacting data and model health for computer vision systems specifically, and construct an approach for detecting, debugging, and reacting to these issues at scale. In the case of tabular data, powerful monitoring capabilities can be achieved by collecting telemetric data such as missing value ratios, cardinality of discrete features, descriptive statistics, etc. For image and video data, however, metrics of interest must be derived…more details
Ray is a Customer Success Data Scientist at WhyLabs, the AI Observability company. He has a long held passion for machine learning and loves helping customers save time and money by monitoring their ML systems at scale. Ray was formerly a Senior Success Engineer at Datorama, a Salesforce Company, where he drove success for large enterprise customers with a focus on improving query performance across the company. With his spare time, Ray enjoys hiking, music, and more hiking.
Demo Talk | All Levels
In this session, attendees will learn how iMerit is solving the problem of scaling data pipelines with accuracy using unique technology. Join iMerit’s VP of Product, Glen Ford, as he uncovers the invisible technology building successful data labeling workflows and discovering anomalous and novel classes for customers using iMerit’s Edge Case technology…more details
Glen Ford is VP of Product at iMerit — a leading AI data solutions company — where he leads the product management and design teams. Glen holds more than two decades of product development experience across the technology sector. A Graduate of Texas A&M University—Commerce, Glen began his career as a consultant where he handled full-stack web programming and architecture for clients including Time Warner and AIM Funds. Over the years, he has held senior and director-level product management roles at several companies including Demand Media, WP Engine and Humanify. Most recently, Glen spent four years at Alegion — an ML-powered data annotation platform — where he helped the company grow from eight full-time employees to more than 100 in a challenging, emerging market.
Keynote | Machine Learning | Big Data Analytics | All Levels
Session Abstract Coming Soon!
Dr. Jim Webber is Neo4j’s Chief Scientist and Visiting Professor at Newcastle University. At Neo4j, Jim leads the Systems Research Group, working on a variety of database research topics with a focus on fault-tolerance. He also co-wrote Graph Databases (1st and 2nd editions, O’Reilly) and Graph Databases for Dummies (Wiley).
Prior to Neo4j, Jim worked on fault-tolerant distributed systems. First at Newcastle University startup Arjuna and then for a variety of clients for global consulting firm ThoughtWorks. Along the way Jim co-authored the books REST in Practice (O’Reilly) and Developing Enterprise Web Services – An Architect’s Guide (Prentice Hall).
Jim’s blog is located at https://jimwebber.org and he tweets sometimes at @jimwebber.
Keynote | Machine Learning | All Levels
Everyone seeks to be understood. Whether at home or in the workplace, this is a central part of the human experience. From speaking with hundreds of data scientists, I find that many of them don’t feel like their work is understood by the decision makers at their organizations. When speaking with IT decision makers, I get the same feeling. Many of them feel like they have highly qualified teams, but their data scientists don’t quite get the business. In this keynote we debunk the common assumptions of both data scientists and ITDMs so they can get the most out of their roles…more details
As the Head of Data Science at Scouts Consulting Group, Ken spends his workdays improving the performance of athletes and teams by analyzing the data collected on them. He also dabbles in entrepreneurship and content creation, best known for his YouTube channel where he helps over 80,000 people navigate the data science landscape. More recently, Ken is focused on project-based learning through Kaggle. He hopes to share the processes that data scientists take when approaching Kaggle competitions and new datasets. He started the #66DaysOfData challenge to help people create the habit of learning and working on projects every day.
Keynote | Machine Learning | Big Data Analytics | All Levels
In this presentation, we will present an overview of a modern class of techniques that are suitable for generating synthetic tabular data. We will focus in particular on deep generative models that can fit complex probability distributions and efficiently generate samples to construct synthetic datasets. We will discuss a variety of deep generative models, how their output can ensure privacy and how to assess the fidelity of the synthetic samples…more details
Kerrie Mengersen is a Distinguished Professor of Statistics and Director of the Centre for Data Science at QUT. Her career in statistical consulting and academic research has taken her across three states of Australia, the USA and France. Kerrie is a Fellow of the Australian Academy of Science, the Australian Academy of Social Sciences, and the Queensland Academy of the Arts and Sciences. Her overall ambition is to ‘use data better’, particularly in the fields of health, environment and industry. To this end, she has led over 30 major projects such as the current Long-term Benefits and Impacts Study with Queens Wharf Brisbane, the online interactive Australian Cancer Atlas and the Virtual Reef Diver program.
Keynote | Responsible AI | All Levels
Session Abstract Coming Soon!
Professor Mary-Anne Williams is the Michael J Crouch Chair in Innovation at UNSW where she collaborates with business, government and societal organisations to grow entrepreneurship and accelerate innovation in Australia. Mary-Anne has a PhD in Computer Science (University of Sydney) and Master of Laws (University of Edinburgh). She is a Fellow at Stanford University, the Australian Academy of Technological Sciences and Engineering, the Australian Computer Society and the Association for the Advancement of Artificial Intelligence (AAAI). Mary-Anne is a leading authority on AI with transdisciplinary strengths in AI for Business, Disruptive Innovation, Entrepreneurship, AI Ethics and Law. She has received multiple awards including the 2019 Australasian Distinguished Artificial Intelligence Contribution Award from the Australian Computer Society; two Google Faculty Machine Learning Awards in 2019 and 2021; and an IBM Faculty Award in 2008. She is a member of the Editorial Boards for AAAI/MIT Press; the Information Systems Journal; and the International Journal of Social Robotics. She was Chair of the International Conference on Social Robotics in 2014; Review Editor for Artificial Intelligence Journal; and served on the ACM Eugene L. Lawler Award Committee for Humanitarian Contributions within Computer Science and Informatics. Mary-Anne was Conference Chair for the 2021 Australasian Joint Conference on Artificial Intelligence and invited speaker for the Australian government at World Expo in Dubai in 2022.
Demo Talk | Machine Learning | All Levels
This introductory session aims to provide answers to these questions, get you introduced to WSL2 and get you started by configuring your machine and running your first code…more details
Passionate about solving human problems, Chanran’s main interests are computer vision and natural language processing while using machine learning and deep learning. He’s written a book on transfer learning and he often gives lectures on it. Today, Chanran is running Pseudo Labs, a machine learning community he founded in 2020. Pseudo Labs is all about sharing knowledge on machine learning and deep learning studies for free, offering Kaggle meetups, code sharing and other events in the Republic of Korea.
When Chanran isn’t running hackathons for Pseudo Labs, you’ll find him watching Premier League soccer matches or honing his food photography (pop over to his Instagram to see some mouth-watering shots).
Talk | Big Data & Data Analytics | All Focus Areas | All Levels
How far can we take democratization of data and AI? As an eccentric progression of my quest to answer this question, I currently work at Qosmo, Inc. whose mission is to advance human creativity with AI. In this talk I will talk about AI for music. In recent years, there has been some remarkable progresses in AI for music and I will cover these developments in summary of the recent white paper I published. It may not be a surprise to find out that musicians are not the most technical users. We therefore released Neutone, an AI audio plugin & community, in the aim of bridging the gap between AI research and musical creativity…more details
Akira is a renowned data scientist in Japan who led the growth of DataRobot Japan as CEO until June 2021. His background in entrepreneurship (Shiroyagi Corporation), strategy consulting (BCG), experimental particle physicist (LHC, CERN) gives him a unique edge to develop business potential of AI and data technologies. He has worked with over a hundred companies in deploying advanced analytics and digital transformation projects. His active podcast and blog can be found through the links.
Talk | Big Data Analytics | Machine Learning | Intermediate
In this talk, Fatemeh overviews the main themes in her research program from minimally-invasive biomarker discovery for personalised medicine and single-cell sequencing data analysis to computational drug repositioning and network pharmacology. Across all themes, Dr Vafaee’s research heavily relies on multidisciplinary expertise and cross-faculty collaborations to generate translatable outcomes impacting upon biomedicine of the future…more details
Dr Fatemeh Vafaee is the Deputy Director of the Data Science Centre at the University of New South Wales (UNSW Sydney) and leads the ‘Health Data Science’ priority area. She launched and leads Artificial Intelligence in Biomedicine Laboratory (VafaeeLab.com) at UNSW and is the founder of OmniOmics.ai proprietary limited company (OmniOmics.ai) with the mission to develop and deploy AI technologies to enhance disease diagnosis and accelerate drug development. Dr Vafaee received her PhD in Artificial Intelligence from the School of Computer Science at the University of Illinois at Chicago, USA (2011) followed by 2 multidisciplinary postdoctoral fellowships at the University of Toronto, Canada, and the University of Sydney, Australia (2012 – 2017) on computational biomedicine. Dr Vafaee has a strong track record of multidisciplinary research leadership and industrial engagement. Her research has attracted over $10.5M across >12 research and industry-based project grants and has been published in top-tier journals in the field.
Talk | MLOps & Data Engineering | All Levels
Z by HP is bringing exceptional technology -both hardware and software- to data scientists, analysts, and creatives that is built for the demands of heavy data processing workloads, right out of the box. With Z laptops, desktops, or rack-mounted data science computers, you get high-performance computers that complement your cloud infrastructure, reduce latency of heavy workloads, make collaboration more efficient, and secure your data end to end…more details
Prerna leads Business Development for Data Science at HP India. Prior to joining HP, she worked in IT industry for almost 9 years and has multi-disciplinary experience cutting across business development and data science & analytics. She has an MBA from SPJIMR, Mumbai and BTech in Computer Science.
Ruchi Bhatia is a Computer Engineering graduate from India and is currently pursuing her Master’s degree at Carnegie Mellon University. She is the youngest 3x Kaggle Grandmaster in the Notebooks, Datasets, and Discussion category, the Leader of Data Science at OpenMined, and one of the 21 Data Science Global Ambassadors at Z by HP. Her passion lies in utilizing data-driven techniques in conjunction with a sound knowledge of business processes to drive meaningful insights and impact.
Paras Varshney is a Data Scientist at LogicAI and an Ex-Data Scientist from IISc. Bangalore. He has been working on building competitive machine learning pipelines and automating the competitive ecosystem for the “Kaggle Days x Z by HP Global Data Science Championship”. He also developed the systems to manage the high throughput data ingestion pipelines for smart cities’ big data. Recognized within the top 1% of Kaggle worldwide, Paras likes to share his passion for data science by helping junior coders develop programming skills through an online classroom. He also loves writing, and his works can be found in top digital publications such as Towards Data Science.
Demo Talk | All Levels
In this talk, we discuss the contents of the PyTorch on Azure container and share the results seen by early adopters. In addition, we will show demos of how you can get started with the container and how to use the Microsoft acceleration technologies it comes with…more details
Parinita Rahi is a Principal PM lead in Azure AI Platform organization at Microsoft. She works on various AI frameworks (PyTorch, ORT Training and ONNX Converters), in addition to driving GitHub CoPilot quality initiatives. She has 10+ years of Product Management experience, previously in the Online Advertising domain. She has driven various ML based solutions and products with a strong focus on solving customer problems.
Jambay Kinley is a Software Engineer in Azure AI Platform organization at Microsoft. He is a member of a team that works on various Pytorch related initiatives for the AI Frameworks team. Prior to joining Microsoft, he was involved in Machine Learning research in a Natural Language Processing lab at Cornell Tech.
Talk | Machine Learning | Big Data Analytics | Intermediate
Apart from the performance , interpretability part kicks in when there is a change in the prediction by model in between the journey of an object. The most anticipated and important question is “What are the reasons the prediction was changed from x to y in the middle of the journey?”. Not able to provide reasons and answers to the questions above could easily lead to loss of trust in the system. In this talk, you will learn about different KPIs, visualisation techniques that can capture the behaviour of ETA models. We will also walk through why it is important to have interpretable model by design to be able to answer some of the questions…more details
Urvesh, currently Head of Data Science at Portcast.io (real time predictive visibility and demand forecasting to optimize supply-chain), has more than 8 years of hands-on and 4 years of leadership experience overall across Machine Learning, Software product development, management, and cross-functional collaboration. Urvesh started his career as a software engineer at Cisco, writing and fixing protocols for firewalls and exploring use of machine learning in malware classification. He later joined noodle.ai as a Data Scientist and worked on numerous ML projects involving government, manufacturing industry, and airlines. He has spent some time mentoring Data Science Students at SpringBoard and is currently a fellow at On Deck Data Science (ODDS).
Ghassen, currently data scientist at Portcast.io (real time predictive visibility and demand forecasting to optimize supply-chain), has close to four years of experience and extensive expertise in leading full-spectrum descriptive and predictive analyses towards supporting high-level decision-making.
Talk | Responsible Ai | Intermediate
The desire to embody Responsible AI practices requires an understanding of, the context around, and the impact on the end user. To achieve this, design and research are just as pivotal to the RAI conversation as ML. There is no bigger risk, and no greater irresponsibility, than to not interface with those who will be affected by your design. In this talk, Bujuanes Livermore will share how she navigates customer relationships to encourage end user contact and how this process can help mitigate assumptions and therefore risk. She will also recommend next steps for when that first conversation meets with too much resistance, as well as how to navigate those waters effectively and professionally through designing experiments and highlighting risks in an assumptions grid. Learn some of the lessons and ‘stealth mode’ tactics in designing that conversation with the client from one of the leaders in the field, the Head of Research & Design in Data, Intelligence & Design at Microsoft with over 15 years of experience…more details
Bujuanes Livermore is the Head of Research and Design in Data, Intelligence, and Design in Commercial Software Engineering (CSE) at Microsoft and she leads the Worldwide Community for Design & Experience. CSE is a global engineering organization that works directly with the largest companies and not-for-profits in the world to tackle their most significant technical challenges. Bringing human-centered design to the forefront and creating a more harmonious balance between the technical and the human drives her work. She is keenly interested in the influences and effects of digital and augmented environments, the human experience of service and product design, and challenging traditional business models and service offerings.
Talk | Machine Learning | Deep Learning | All Levels
In this talk, I am going to highlight some of the best practices around data annotation and ML DataOps that help companies quickly develop, deploy and continuously update their ML models…more details
Sudeep George is the Vice President of Engineering at iMerit, where he develops production-ready frameworks for a data-centric approach to machine learning. He has a strong background in imaging sensors, computer vision and has built and manufactured multi-sensor computational imaging platforms for several market verticals.
Talk | NLP | Machine Learning | Intermediate
In this talk, I will present two applications on one of the most popular e-commerce platforms: Amazon. We will first look at generating product descriptions, and show how we train a multimodal model that takes product images and metadata as input to generate descriptions for 23 product types. Although we found that the generated descriptions are fluent and persuasive, they are not always faithful (i.e. the details are not real)…more details
Dr Lau is a lecturer in the School of Computing and Information Systems at the University of Melbourne. His research is in Natural Language Processing — a sub-field of Artificial Intelligence — where the goal is to develop computational models to understand human languages. A common theme of Dr Lau’s research is that it involves building computational models in an unsupervised or semi-supervised setting, i.e. a learning scenario where the supervision signal for model training is not available or scarce, and is characterised by a diverse flavour of applications, e.g. topic models, lexical semantics, text generation and misinformation detection. Some of his research in text generation and state-sponsored influence operations has been covered by popular science magazines (New Scientist) and mainstream news media (BBC and Guardian).
Workshop | MLOps & Data Engineering | Machine Learning | All Levels
MLOps means different things to different people, however, the fundamental essence of MLOps is to deliver models into productions faster with a consistent, repeatable and reliable approach. Based on our experience of working with various large and small customers across the world, Microsoft has developed an accelerator to do exactly what the word suggests – accelerate our customer’s journey to production. Simplicity and segregation of duties are key pillars of this accelerator, which means that our intention is that Data Scientists, ML Engineers and IT teams don’t need significant upskilling before they can do MLOps…more details
Setu is a senior technical leader, innovator and specialist in machine learning and artificial intelligence. He has led and implements machine learning products at scale for various companies.
Demo Talk
In this session, we will be walking through the way that RVCE-HPCC Systems Centre of Excellence on Cognitive Intelligent Systems for Sustainable solutions have leveraged HPCC Systems for Big Data analysis. Demonstration and visualization of few applications such as Analysis of corvid 19 data, Analysis, Classification of Surface water will be showcased for the audience to understand various components involved in the architecture of HPCC Systems. We intend to discuss the comparison with other distributed platforms such as Hadoop to show the applicability of HPCC Systems platform to solve big data problems…more details
Dr. G Shobha, Professor, Computer Science, and Engineering Department, R.V College of Engineering, Bengaluru, India has teaching experience of 28 years, her specialization includes Data mining, Machine Learning, and Image processing. She has published more than 150 papers in reputed journals/conferences. She has also executed sponsored projects worth INR 200 lakhs funded by various agencies nationally and internationally. She is a recipient of various awards such as the Career Award for young teachers 2007-08 constituted by All India Council of Technical Education, Best Researcher award from Cognizant 2017, GHC Faculty Scholar for Women in Computing in 2018, IBM Shared University Research Award in 2019, HPCC Systems community recognition award 2020.
Prof. Jyoti Shetty, Assistant Professor, Computer Science and Engineering Department, RV College of Engineering, Bengaluru, India has 16 years teaching and 2 year industry experience. Her specialization includes Data Mining, Machine Learning and Cloud Computing. She has published research papers in reputed journals and conferences. She has also executed sponsored projects funded from various agencies nationally and internationally. She was the recipient of awards such as SAP Award of excellence from IIT Bombay for demonstrating ICT in education in 2016 and HPCC Systems Mentor Badge Award in 2021 for providing guidance and direction towards the successful completion of intern open source projects.
Demo Talk | MLOps & Data Engineering | All Levels
In this talk, we will demonstrate how:
Taipy-GUI goes way beyond the capabilities of the standard graphical stack: Gradio, Streamlit, Dash, etc.
Taipy Core is simpler yet more powerful than the standard Python back-end stack: Airflow, MLFlow, Luigi, etc.
Vincent has 30+ years as AI specialist with ILOG and IBM. He has mentored several Data Science teams. Vincent has designed/modeled several major AI projects for customers such as Samsung. Electronics, McDonald’s, Dassault Aviation, Carhartt, Toyota, TSMC, Disney, etc. He is skilled in Mathematical Modeling, Machine Learning, Time Series prediction. He has strong experience in Manufacturing, Retail & Logistics industries. His main objective is to “Help companies go beyond AI pilots and be successful in bringing AI to their end-users”. He received his Msc in Comp. Science & AI from Paris-Saclay University.
Talk | MLOps & Data Engineering | All Levels
Machine learning models are being deployed extensively in many important areas to assist humans in making important decisions. But there is no guarantee a model will always perform well after deployment as its developers intended. Understanding the correctness of a model is thus crucial to prevent potential failures that may have a significant detrimental impact in critical application areas. In this talk, I will discuss the challenges we face to ensure the correctness of deployed machine learning models and introduce some works on how to efficiently test a machine learning model using only a small amount of labelled test data.
Dr. Huong Ha is currently a Lecturer at the Artificial Intelligence Discipline, School of Computing Technologies, RMIT University, Melbourne, Australia. Her research is in the areas of Artificial Intelligence and Software Engineering, particularly trustworthy machine learning, automated machine learning, and data-driven software engineering. She regularly publishes her works in the leading international research venues in these areas including NeurIPS, ICML, AAAI, AISTATS, ICSE, and ICSME. In addition to her current role in academia, Huong has previous working experience in the industry as a data scientist and a product development engineer.
Talk | MLOps & Data Engineering | Big Data Analytics | Beginner
In this talk, we will demonstrate how we process our Big Data, gain insights of our Smart Grid and Innovation applications, how we do detect faults from smart meter power quality data and meter channel alerts that resulted in reducing shock incidents in our network, how we automate monitoring of customer sites for immediate hazard detection, how we balance the grid from distributed energy resource management system, and how we automate fault detection analytics and jobs dispatch with no human intervention…more details
Thilaksha Silva has obtained a Doctor of Philosophy (PhD) in Statistics from Monash University, Australia. Thilaksha is skilled in data science for electricity distribution, statistics, time series forecasting, predictive modelling and big data analytics. She is adept at advanced data analytics with 10+ years of experience and has mastered in communicating the business value across the business and engaging audience with data science on a deeper level.
Shashank Kundapur has obtained a Master of Data Science in Advance Analytics from Monash University, Australia. Shashank is a skilled analyst and software engineer, highly adept at ETL processing and building data science solutions, with a proven track record in managing complex data analytics project gained from over 9 years of professional experience within the Telecom and Energy sectors. Extensively skilled in building Business Intelligence and Data Modelling solutions.
Talk | Machine Learning | Deep Learning | All Levels
In this talk Rohan will take you through how clustering and transaction times series fingerprinting can give insights into the underlying transaction behaviour of users and help find the criminals utilizing the bitcoin network for illicit activities..more details
Rohan Maheshwari is a student at RV College of Engineering in Bengaluru, India with a keen interest in Deep Learning, Natural Language Processing, Graph modelling and Machine Learning as well as their applications in finance and sentiment analysis. He has worked under the Samsung PRISM program to create a code-mixed multi-intent classification system. He has also worked with SCII to create an invoice extraction system. He is actively working with the LexisNexis® Risk Solutions HPCC Systems® team and the RV College of Engineering Centre of Excellence on Cognitive Intelligent Systems for Sustainable Solutions to investigate block data stored on the blockchain to gain insight and build relationships between transactions that can shed light on potential criminal transactions . Rohan is pursuing a Bachelor of Computer Science and Engineering at RV College of Engineering.
Talk | Machine Learning | Beginner
In this session, we will learn about specific video analytics & image processing impactful use cases. Jakarta Smart City has been closely collaborating with several key organizations such as agency of transportation, transportation operators (MRT, LRT, train, BRT), police force, etc…more details
Juan Kanggrawan is the current Head of Data Analytics at Jakarta Smart City. His key role is to fully utilize data to formulate public policy and to improve quality of public services. Juan is currently working on several city-scale strategic analytics initiatives. He is actively analyzing complex, diverse and exciting urban data on a daily basis: citizen complaint/aspiration, transportation/mobility, health (COVID-19), CCTV, Open Data, weather-flood-river bank, subsidy utilization, food commodities price elasticity, etc. He is also developing and aligning strategic partnership framework between Jakarta Smart City with other government agencies, business enterprises, research agencies and universities.
Talk | NLP | Machine Learning | All Levels
In this talk we “organize” chatbots across a “spectrum of sophistication” and present as case-studies two examples of next generation reasoning based chatbots. First a higher order question answering (level 4) chatbot (that goes beyond factoid question answering) that we believe is one of the keys to artificial general intelligence. Second, a (level 7) Diagnosis chatbot that can mimic a doctor’s differential diagnosis conversation that infers, with a very high precision and recall, the potential diseases given the symptoms of a patient while via a series of follow-up questions…more details
Dr. Shailesh Kumar is currently the Chief Data Scientist at the Centre of Excellence in AI/ML, Reliance Jio. Prior to this he worked as a Distinguished Scientist at Ola cabs, Chief Scientist and Co-founder of Third Leap, an EdTech startup, Researcher in the Google Brain team, Sr. Scientist at Yahoo! Labs and Principal Scientist at Fair Isaac Research.
Dr. Kumar has 18 years of experience in building AI solutions in a variety of domains including Web, Retail, Finance, Remote Sensing, Fleet Management, Computer Vision, Knowledge Graph, and Conversational computing. He has published over 20 international papers and book chapters and holds more than 20 patents in AI/ML. He was recognised as one of the top 10 data scientists in India in 2015 by Analytics India Magazine. Dr. Kumar holds a Masters and PhD in AI from UT-Austin and B.Tech. in Computer Science from IIT-Varanasi.
Talk | Responsible Ai | Beginner-Intermediate
Machine Learning and AI models are often considered as black boxes, and the lack of transparency in the working of these models lead to lack of trust and adoption. In order to promote end user trust Explainable AI (XAI) and model explainability techniques like LIME, SHAP are widely used. But are these methods really human friendly? Do we have any existing method or framework that can provide explainability to non-technical users? How can we use XAI to bridge the gap between AI and end users to promote AI adoption?…more details
Aditya Bhattacharya is an Explainable AI Researcher at KU Leuven with an overall experience of 7 years in Data Science, Machine Learning, IoT & Software Engineering. Prior to his current role, Aditya has worked in various roles in organizations like West Pharma, Microsoft & Intel to democratize AI adoption for industrial solutions. As the AI Lead at West Pharma, he had contributed to forming the AI Centre of Excellence, managing & leading a global team of 10+ members focused on building AI products. He also holds a Master’s degree from Georgia Tech in Computer Science with ML and a Bachelor’s degree from VIT University in ECE. Aditya is passionate about bringing AI closer to end- users through his various initiatives for the AI community. He has also authored a book “Applied Machine Learning Explainability Techniques”
Talk | NLP | Machine Learning | Intermediate
This talk is for those who want to learn the introduction of sentiment analysis task and its different model choices to implement it. By listening this talk, you will become familiar and comfortable with the sentiment analysis and its different methodological approaches. Plus, I will bring up more advanced topics in this area such as issues and challenges in sentiment analysis or Aspect-based Sentiment Analysis (ABSA)…more details
Sunny is a seasoned professional data scientist, with over 15 years of relevant experience, and successful completion of significant company-onsite projects for many respected companies in South Korea and the US. Significant experience and dynamic practitioner in various domains, including NLP project lead, credit risk modeling, financial distress modeling, customer marketing prediction, and ML service provider consultation. She is passionate about creating and building AI solutions applying a variety of NLP technologies including sentiment analysis, conversational computing, topic modeling, etc. to support AI real-world usages for SME businesses. She is currently putting her efforts into her own AI start-up company – ReviewMind Inc. In 2020, her company was identified as an excellent start-up case by Korea Women in Science and Technology Support Center. Sunny and her team also won the best award in the 2021 Start-up Demo Day from the Korea Institute of Startup & Entrepreneurship Development. Sunny holds both a Masters in Data Science (Information Systems) and an MBA from the US and South Korea respectively.
Talk | Machine Learning | All Focus Areas | All Levels
As Gartner notes in the “State of Data Science and Machine Learning” report, DSML platforms are now addressing two growing and equally important markets: (i) A ‘multipersona’ market with a central focus on time to value, ease of use, and collaboration between multiple technical and nontechnical personas, and (ii) an ‘engineering’ market focused on primarily technical personas whose primary aim is to engineer (design, develop, deploy, monitor, and maintain) scalable, enterprise-wide AI solutions…more details
Hui Xiang Chua is Senior Data Scientist at Dataiku, helping enterprises with data democratization and enabling them to build their own path to AI. Dataiku is a 2x Gartner Magic Quadrant Leader for Data Science and Machine-Learning Platforms (as of 2021). She has both public and private experiences solving problems using data, namely over six years in the public service and two years in the media industry. She was also previously an instructor with General Assembly.
In 2017, she was accepted to the Data Science for Social Good Fellowship and was mentored by Rayid Ghani, Chief Scientist of the Obama for America campaign in 2012. For bringing data science into a high school’s curriculum, Hui Xiang was a recipient of the KDD Impact Program award by SIGKDD, the Association for Computing Machinery’s special interest group on knowledge discovery and data mining. She also runs a data science blog called Data Double Confirm that was recognised as 2018/2019 Top 100 Data Science Resources on MastersInDataScience.com.
Hui Xiang holds a B.Sc.(Hons) in Statistics and M.Sc. in Business Analytics from National University of Singapore.
Demo Talk | All Levels
This demonstration of Deephaven will show you how to: 1. Launch Deephaven from Jupyter and your Python IDE. 2. Access and manipulate Kafka and Parquet with just a few lines of code. 3. Filter, aggregate, join, select, and project ticking, updating tables in real-time. 4. Create and interact with table and plot widgets in web dashboards and Jupyter. 5. Integrate Deephaven with matplotlib, PyTorch, and other popular tools.
Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.
Talk | Deep Learning| Intermediate
This talk will introduce you to the world of Generative Models through the GAN lens. We will discuss how a simple game of adversaries leads to such powerful models. We will cover the evolution and advancement in the architecture of GANs along with some exciting use-cases. Towards the end we will go through a quick hands-on to generate images with GANs ourselves…more details
Raghav is a seasoned Data Science professional with over a decade’s experience of research & development of large-scale solutions in Finance, Digital Experience, IT Infrastructure and Healthcare for giants such as Intel, American Express, United HealthGroup and DeliverHero. He is an innovator with 10+ patents, a published author of multiple well received books & peer-reviewed papers and a regular speaker in leading conferences on topics in the areas of Generative AI, Recommendation Systems, Computer Vision, NLP, Deep Learning, Machine Learning and Augmented Reality.
Talk | Deep Learning | Intermediate
The Fourth Industrial Revolution (Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using modern smart technology. Large-scale machine-to-machine communication (M2M), the internet of things (IoT), Augmented Reality, Machine Vision, and Deep Learning are integrated for increased automation, improved communication and self-monitoring, and the production of smart machines that can analyze and diagnose issues. Designing a humanless system sometimes poses the biggest risk if something goes wrong. This is where Augmented Reality will add the human into the automation loop in the world of Automation…more details
Talk | MLOps & Data Engineering | Intermediate
In this talk, Ray Reed will explore the plethora of problems impacting data and model health for computer vision systems specifically, and construct an approach for detecting, debugging, and reacting to these issues at scale. In the case of tabular data, powerful monitoring capabilities can be achieved by collecting telemetric data such as missing value ratios, cardinality of discrete features, descriptive statistics, etc. For image and video data, however, metrics of interest must be derived…more details
Ray is a Customer Success Data Scientist at WhyLabs, the AI Observability company. He has a long held passion for machine learning and loves helping customers save time and money by monitoring their ML systems at scale. Ray was formerly a Senior Success Engineer at Datorama, a Salesforce Company, where he drove success for large enterprise customers with a focus on improving query performance across the company. With his spare time, Ray enjoys hiking, music, and more hiking.
Demo Talk | All Levels
In this session, attendees will learn how iMerit is solving the problem of scaling data pipelines with accuracy using unique technology. Join iMerit’s VP of Product, Glen Ford, as he uncovers the invisible technology building successful data labeling workflows and discovering anomalous and novel classes for customers using iMerit’s Edge Case technology…more details
Glen Ford is VP of Product at iMerit — a leading AI data solutions company — where he leads the product management and design teams. Glen holds more than two decades of product development experience across the technology sector. A Graduate of Texas A&M University—Commerce, Glen began his career as a consultant where he handled full-stack web programming and architecture for clients including Time Warner and AIM Funds. Over the years, he has held senior and director-level product management roles at several companies including Demand Media, WP Engine and Humanify. Most recently, Glen spent four years at Alegion — an ML-powered data annotation platform — where he helped the company grow from eight full-time employees to more than 100 in a challenging, emerging market.
Schedule will be updated frequently. More sessions coming soon.
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