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Learn, connect, and grow with 5,000+ data scientists and speakers, both in-person and virtually, at ODSC Europe 2023.
Over the course of 3 days, ODSC Europe will provide expert-led instruction in machine learning, deep learning, NLP, MLOps, and more through hands-on training sessions, immersive workshops, and talks. You’ll also have the chance to share insights and build new connections through a wide range of events from the ODSC Networking Reception to Book Signing Sessions with expert speakers.
With 100 hours of content, ODSC Europe has something for every data scientist from beginner to advanced.
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PAST VIRTUAL SPEAKERS
Henk Boelman
Henk is a Cloud Advocate specializing in Artificial intelligence and Azure with a background in application development. He is currently part of the AI cloud advocate team and based in the Netherlands. Before joining Microsoft, he was a Microsoft AI MVP and worked as a software developer and architect building lots of AI powered platforms on Azure.
He loves to share his knowledge about topics such as DevOps, Azure and Artificial Intelligence by providing training courses and he is a regular speaker at user groups and international conferences.
Build and Deploy PyTorch models with Azure Machine Learning (Keynote)
Carles Sierra, PhD
Carles Sierra is Director of the Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council (CSIC) located in Barcelona. He is the President of EurAI, the European Association of Artificial Intelligence. He has been contributing to Artificial Intelligence research since 1985 in the areas of Knowledge Representation, Auctions, Electronic Institutions, Autonomous Agents, Multiagent Systems and Agreement Technologies. He is or has been a member of several editorial boards of journals, including AIJ and JAIR, two of the most prestigious generalist journals, and was the editor in chief of the JAAMAS journal, specialized in autonomous agents. He organized IJCAI, the most important international artificial intelligence conference in 2011 in Barcelona and was the President of the IJCAI Program Committee in 2017 in Melbourne. He is a Fellow of the European Association of AI, EurAI, and recipient of the ACM/SIGAI Autonomous Agents Research Award 2019.
Sophia Ananiadou
Sophia Ananiadou is Professor in Computer Science, Department of Computer Science, the University of Manchester. She is also Director of the National Centre for Text Mining (NaCTeM)); Deputy Director of the University’s Institute of Data Science and AI (IDSAI); Distinguished Research Fellow at the AI Research Centre of the National Institute of Advanced Industrial Science and Technology, Japan; Alan Turing Institute Fellow; Honorary Professor, University of the Aegean and Member of European Laboratory for Learning and Intelligent Systems Society. Her research interests evolved from abstract work on fragments of linguistic theory and logic to exploration of how AI systems could acquire and exploit knowledge of language, particularly in specialised domains (biomedicine, chemistry, exposome, law, public health). Research contributions include neural information extraction, text summarisation and simplification, emotion detection, terminology, development of resources (lexica, terminologies and labelled data), annotation tools and interoperable platforms for NLP workflows. She has developed tools such as the RobotAnalyst to improve evidence-based decisions, cut costs and improve efficiency and robustness of key policy decisions in public health.
Dr.-Ing. Thomas Albin
Thomas is a Senior Machine Learning engineer, working in the automotive industry since 2019. Before joining the Research & Development department of a large manufacturer he was conducting research activities in space science. In parallel to his studies in Astro- and Geo-Physics and later PhD program, he participated in 2 major missions: ESA’s comet mission Rosetta/Philae and NASA’s & ESA’s Saturn spacecraft Cassini/Huygens; always with a special focus on cosmic dust. Additionally, he applies Machine Learning algorithms to analyse astronomy- and space-related data to derive new scientific insights or to create new methods for calibrating instruments. Besides his industry work, Thomas is a guest scientist at the Free University of Berlin, where he continues working on the Cassini-related datasets using Deep Learning. On his active YouTube channel Astroniz he shares his Python + Space Science + Machine Learning knowledge with a small community.
Space Science with Python – Enabling Citizen Scientists(Workshop)
Matthias Seeger, PhD
Matthias W. Seeger is a principal applied scientist at Amazon. He received a Ph.D. from the School of Informatics, Edinburgh university, UK, in 2003 (advisor Christopher Williams). He was a research fellow with Michael Jordan and Peter Bartlett, University of California at Berkeley, from 2003, and with Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems, Tuebingen, Germany, from 2005. He led a research group at the University of Saarbruecken, Germany, from 2008, and was assistant professor at the Ecole Polytechnique Federale de Lausanne from fall 2010. He joined Amazon as machine learning scientist in 2014. He received the ICML Test of Time Award in 2020.
His interests center around Bayesian learning and decision making with probabilistic models, from gaining understanding to making it work in large scale practice. He has been working on theory and practice of Gaussian processes and Bayesian optimization, scalable variational approximate inference algorithms, Bayesian compressed sensing, and active learning for medical imaging. More recently, he worked on demand forecasting, hyperparameter tuning (Bayesian optimization) applied to deep learning (NLP), and AutoML.
Distributed Hyperparameter Tuning: Finding the Right Model can be Fast and Fun(Tutorial)
Dr. Gözde Gül Şahin
Dr. Gözde Gül Şahin is an Assistant Prof. at Koç University and a KUIS AI Fellow since February 2022. Previously, she was a postdoctoral researcher in the Ubiquitous Knowledge Processing (UKP) Lab at the Technical University of Darmstadt, Germany. Her research spans the fields of linguistics and machine learning, in particular semantics, multilingual representations and large language models. She completed her PhD studies in Istanbul Technical University (İTÜ) Computer Engineering department in 2018. She was a visiting researcher at the Institute for Language, Cognition and Computation (ILCC) of the University of Edinburgh in 2017. Before her Ph.D., she received her Masters and Bachelor degrees from Sabancı University in 2011 and İTÜ in 2009, respectively. She regularly serves as a PC member for *ACL conferences and is a co-organizer for the Workshop on Multilingual Representation Learning (MRL). Her research on NLP has been funded by Tübitak 2232, and 2236 grant programs that are granted to outstanding young principal investigators.
Semantic Analysis and Procedural Language Understanding in the Era of Large Language Models(Talk)
Luc De Raedt, PhD
Prof. Dr. Luc De Raedt is currently Director of Leuven.AI, the KU Leuven Institute for AI, full professor of Computer Science at KU Leuven, and guestprofessor at Örebro University (Sweden) at the Center for Applied Autonomous Sensor Systems in the Wallenberg AI, Autonomous Systems and Software Program.
Luc De Raedt obtained his PhD in Computer Science from the KU Leuven (1991), was post-doctoral researcher of the Fund for Scientific Research, Flanders (FWO) (1991-99) and part-time assistant/associate professor (1993-1999) KU Leuven; full professor (C4) and Chair of the Machine Learning and Natural Language Processing Lab at the Albert-Ludwigs-University Freiburg, Germany (1999-2006); head of the Lab for Declarative Languages and Artificial intelligence at KU Leuven from (2015-2019).
Luc De Raedt’s research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is well known for his contributions in the areas of learning and reasoning, in particular, for his contributions to statistical relational learning, probabilistic and inductive programming. Today he is working on the next generation of programming languages, which can automatically learn from data, on combining probabilistic and logical reasoning and learning, on the automation of (data) science, and on verifying learning artificial intelligence systems and robotics. He is also now also focusing on integrating the probabilistic logics with neural networks and wants to apply these to reinforcement learning as well as program induction.
Jutta Treviranus
Jutta Treviranus is the Director of the Inclusive Design Research Centre (IDRC) and professor in the faculty of Design at OCAD University in Toronto (http://idrc.ocadu.ca ). Jutta established the IDRC in 1993 as the nexus of a growing global community that proactively works to ensure that our digitally transformed and globally connected society is designed inclusively. Dr. Treviranus also founded an innovative graduate program in inclusive design at OCAD University. Jutta is credited with developing an inclusive design methodology that has been adopted by large enterprise companies such as Microsoft, as well as public sector organizations internationally. In 2022 Jutta was recognized for her work in AI by Women in AI with the AI for Good – DEI AI Leader of the Year award.
Brent Mittelstadt, PhD
Professor Brent Mittelstadt is an Associate Professor, Senior Research Fellow, and Director of Research at the Oxford Internet Institute, University of Oxford. He leads the Governance of Emerging Technologies (GET) research programme which works across ethics, law, and emerging information technologies. He is a prominent data ethicist and philosopher specializing in AI ethics, algorithmic fairness and explainability, and technology law and policy. Prof. Mittelstadt is the author of foundational works addressing the ethics of algorithms, AI, and Big Data; fairness, accountability, and transparency in machine learning; data protection and non-discrimination law; group privacy; ethical auditing of automated systems; and digital epidemiology and public health ethics. His contributions in these areas are widely cited and have been implemented by researchers, policy-makers, and companies internationally, featuring in policy proposals and guidelines from the UK government, Information Commissioner’s Office, and European Commission, as well as products from Google, Amazon, and Microsoft.
The Unfairness of Fair Machine Learning: Levelling Down and Strict Egalitarianism by Default(Talk)
Andras Zsom, PhD
Andras Zsom is an Assistant Professor of the Practice and Director of Graduate Studies at the Data Science Initiative at Brown University, Providence, RI. He is teaching two mandatory courses in the data science master’s program, and helps the students navigate through their studies and curriculum. He also supervises interns on various research projects related to missing data, interpretability, and developing machine learning pipelines.
Alexandra Ebert
Alexandra Ebert is a Responsible AI, synthetic data & privacy expert and serves as Chief Trust Officer at MOSTLY AI. As a member of the company’s senior leadership team, she is engaged in public policy issues in the emerging field of synthetic data and Ethical AI and is responsible for engaging with the privacy community, with regulators, the media, and with customers. She regularly speaks at international conferences on AI, privacy, and digital banking and hosts The Data Democratization Podcast, where she discusses emerging digital policy trends as well as Responsible AI and privacy best practices with regulators, policy experts and senior executives.
Apart from her work at MOSTLY AI, she serves as the chair of the IEEE Synthetic Data IC expert group and was pleased to be invited to join the group of AI experts for the #humanAIze initiative, which aims to make AI more inclusive and accessible to everyone.
Before joining the company, she researched GDPR’s impact on the deployment of artificial intelligence in Europe and its economic, societal, and technological consequences. Besides being an advocate for privacy protection, Alexandra is deeply passionate about Ethical AI and ensuring the fair and responsible use of machine learning algorithms. She is the co-author of an ICLR paper and a popular blog series on fairness in AI and fair synthetic data, which was featured in Forbes, IEEE Spectrum, and by distinguished AI expert Andrew Ng.
When Privacy Meets AI – Your Kick-Start Guide to Machine Learning with Synthetic Data(Tutorial)
Sara Khalid
Sara is a Senior Research Associate in Biomedical Data Science and University Research Lecturer at the University of Oxford, where she is the Machine Learning Lead in the Centre for Statistics in Medicine. She has 12 years of experience in machine learning, signal processing, and intelligent remote monitoring research, with applications in biomedical and planetary health informatics. Sara has served on the NASA Frontier Development Lab Artificial Intelligence Panel and the NASA Climate Challenge Big Think. She is a National Geographic Society Explorer in Tracking Plastic Pollution with Remote Monitoring and Machine Learning. Sara is also a University of Oxford Ambassador for Women in Data Science.
Todd Cioffi
For more than 20 years, Todd has been highly respected as both a technologist and a trainer. As a tech, he has seen that world from many perspectives: “data guy” and developer; architect, analyst, and consultant. As a trainer, he has designed and covered subject matter from operating systems to databases to machine learning / AI to end-user applications, with an emphasis on data, programming, and results that matter.
As a strong advocate for knowledge sharing, he combines his experience in technology and education to impart real-world use cases to students and users of analytics solutions across multiple industries. He has been a regular contributor to the community of analytics and technology user groups in the Boston area and beyond, writes and teaches on many topics, and looks forward to the next time he can strap on a dive mask and get wet.
Julien Simon
Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.
Hyper-productive NLP with Hugging Face Transformers(Workshop)
Sandra Wachter, PhD
Professor Sandra Wachter is Professor of Technology and Regulation at the Oxford Internet Institute at the University of Oxford where she researches the legal and ethical implications of AI, Big Data, and robotics as well as Internet and platform regulation. At the OII, Professor Sandra Wachter leads and coordinates the Governance of Emerging Technologies (GET) Research Programme that investigates legal, ethical, and technical aspects of AI, machine learning, and other emerging technologies.
Professor Wachter is also an affiliate and member at numerous institutions, such as the Berkman Klein Center for Internet & Society at Harvard University, World Economic Forum’s Global Futures Council on Values, Ethics and Innovation, the European Commission’s Expert Group on Autonomous Cars, the Law Committee of the IEEE, the World Bank’s Task Force on Access to Justice and Technology, the United Kingdom Police Ethics Guidance Group, the British Standards Institution, the Bonavero Institute of Human Rights at Oxford’s Law Faculty and the Oxford Martin School. Professor Wachter also serves as a policy advisor for governments, companies, and NGO’s around the world on regulatory and ethical questions concerning emerging technologies.
Danushka Bollegala, PhD
Danushka Bollegala is a Professor in the Department of Computer Science, University of Liverpool, UK. He obtained his PhD from the University of Tokyo in 2009 and worked as an Assistant Professor before moving to the UK. He has worked on various problems related to Natural Language Processing and Machine Learning. He has received numerous awards for his research excellence such as the IEEE Young Author Award, best paper awards at GECCO and PRICAI. His research has been supported by various research council and industrial grants such as EU, DSTL, Innovate UK, JSPS, Google and MSRA. He is an Amazon Scholar.
Towards Socially Unbiased Generative Artificial Intelligence(Talk)
Elisa Fromont
Elisa Fromont is a full professor at Université de Rennes France, since 2017 and a Junior member of the Institut Universitaire de France (IUF). She works at IRISA research institute in the INRIA LACODAM (“Large Scale Collaborative Data Mining”) team. From 2008 until 2017, she was associate professor at Université Jean Monnet in Saint-Etienne, France. She worked at the Hubert Curien research institute in the Data Intelligence team. Elisa received her Research Habilitation (HDR) in December 2015 from the University of Saint-Etienne. Her research interests lie in (explainable) machine learning, data mining and, in particular, time series analysis.
Explainable Time Series Classification (Tutorial)
Dr Paul A. Bilokon
Bio Coming Soon!
Iterated and Exponentially Weighted Moving Principal Component Analysis(Talk)
Daniel Voigt Godoy
Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers. He writes regularly for Towards Data Science. His blog post “Understanding PyTorch with an example: a step-by-step tutorial” reached more than 220,000 views since it was published. The positive feedback from the readers motivated him to write the book Deep Learning with PyTorch Step-by-Step, which covers a broader range of topics. Daniel is also the main contributor of two python packages: HandySpark and DeepReplay. His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.
Diffusion Models 101(Workshop)
Julia Lintern
Julia Lintern currently works as a Director of Data Science at Gartner. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.
Introduction to Machine Learning(Bootcamp)
Philip Tracton
Phil Tracton is an IC design engineer at Medtronic and an instructor at UCLA Extension. He has worked at Medtronic for over 20 years and has experience in implementing firmware, FPGAs, and custom ASICs. Many thousands of people have his work implanted in them. Most of these devices are focused on Neuromodulation. He has recently joined an internal team focused on long term research for implantable devices.
At UCLA he teaches multiple Python based courses including Learning Python and Python on the Raspberry Pi.
He is interested in low power AI on edge devices.
He will be running the Fundamentals of Python training class. This is his second time teaching at an ODSC event.
Python Fundamentals(Bootcamp)
Hossam Amer, PhD
Hossam Amer joined Microsoft as a scientist in 2021. His research interests are Image/Video Compression, Computer Vision, and most recently Natural Language Processing. Hossam is contributing to many products including Microsoft Translator and Microsoft SwiftKey. Prior to joining Microsoft, Hossam was a Postdoctoral-Fellow at the Multimedia Communications Lab at the University of Waterloo (UW), where he mentored several MSc and PhD students. He obtained his PhD from the same lab, where he received the prestigious annual UW teaching award based on students’ and instructors’ nominations as well as published papers in top venues. Hossam also acts as a reviewer in several IEEE conferences and journals and supervises students in research and teaching. In addition, Hossam was the Chair of the ECE Graduate Student Association at UW. Hossam is a strong believer in constantly transferring his knowledge in order to make a difference.
Deep Learning and Comparisons between Large Language Models(Talk)
Colin Priest
Colin is a seasoned data scientist who has worked in the finance, healthcare, security, oil and gas, government, telecommunications, and marketing industries. He has a keen interest in exploring the relationship between humans and AI and has contributed to projects on AI ethics, governance, and the future of work. His work has gained global recognition from the World Economic Forum, and he has contributed to several important initiatives, including the Singapore government’s official AI strategy, PDPC AI Governance and Ethics Guidelines, and the Monetary Authority of Singapore Veritas Initiative. In addition to his professional work, Colin is a dedicated healthcare advocate who volunteers for cancer research.
Feature Engineering With Signal Types(Workshop)
Moez Ali
Innovator, Technologist, and a Data Scientist turned Product Manager with proven track record of building and scaling data products, platforms, and communities. Experienced in building and leading teams of data scientists, data engineers, and product managers. Strongly opinionated tech visionary and a thought partner to C-level leadership.
Moez Ali is an inventor and creator of PyCaret. PyCaret is an open-source, low-code, machine learning software. Ranked in top 1%, 8M+ downloads, 7K+ GitHub stars, 100+ contributors, and 1000+ citations.
Globally recognized personality for open-source work on PyCaret. Keynote speaker and top ten most-read writer in the field of artificial intelligence. Teaching AI and ML courses at Cornell, NY and Queens University, CA. Currently building world’s first hyper-focused Data and ML Platform.
Automate Machine Learning Workflows with PyCaret 3.0(Workshop)
Deepak Kanungo
Deepak Kanungo is the founder and CEO of Hedged Capital LLC, an AI-powered, proprietary trading and analytics firm built around probabilistic machine learning technologies. In 2005, long before machine learning was an industry buzzword, Deepak invented a probabilistic machine learning method and software system for managing the risks and returns of project portfolios. It is a unique framework that has been cited by IBM and Accenture, among others. Previously, Deepak was a financial advisor at Morgan Stanley, a Silicon Valley fintech entrepreneur, and a director in the Global Planning Department at Mastercard International. He was educated at Princeton University (astrophysics) and the London School of Economics (finance and information systems).
Probabilistic Machine Learning for Finance and Investing(Talk)
Ori Nakar
Ori Nakar is a principal cyber-security researcher, a data engineer, and a data scientist at Imperva Threat Research group. Ori has many years of experience as a software engineer and engineering manager, focused on cloud technologies and big data infrastructure. Ori also has an AWS Data Analytics certification. In the Threat Research group, Ori is responsible for the data infrastructure and involved in analytics projects, machine learning, and innovation projects.
Botnets Detection at Scale – Lesson Learned from Clustering Billions of Web Attacks into Botnets(Talk)
Emanuele Fabbiani, PhD
Emanuele is Engineer by education, Data Scientist by choice, researcher and lecturer by passion. During his PhD in ML, he got invited to EPFL Lausanne for a 6-month visit and published 9 papers in top journals.He is the co-founder of xtream, an AI boutique applying academic research to business. Contributing to the community is part of their mission: He was a speaker and track organizer at eRum, AMLD, and PyCon and he lectured at Italian, Swiss, and Polish universities.
Should You Trust Your Copilot? Limitations and Merits of AI Coding Assistants(Talk)
Damian Bogunowicz
Damian is engineer, roboticist, software developer, and problem solver. Previous experience in autonomous driving (Argo AI), AI in industrial robotics (Arrival), and building machines that build machines (Tesla). Currently working in Neural Magic, focusing on the sparse future of AI computation. Works towards unlocking creative and economic potential with intelligent robotics while avoiding the uprising of sentient machines.
:https://dtransposed.github.io
Konstantin Berlin
Konstantin Berlin is currently the Head of AI at Sophos, where he manages a team of machine learning researchers and big data engineers. His group is responsible for developing and maintaining headline ML models that are actively deployed and used by millions of Sophos customers every day. His areas of interests and work cover all aspects of the ML development cycle. This includes leading a team of research in developing novel ML cybersecurity models, working across organizations to integrate the models into products, and expanding and architecting Sophos AI infrastructure and MLOps capabilities.
Dillon Bostwick
Dillon Bostwick is a Solutions Architect at Databricks, where he’s spent the last five years advising customers ranging from startups to Fortune 500 enterprises. He currently helps lead a team of field ambassadors for streaming products and is interested in improving industry awareness of effective streaming patterns for data integration and production machine learning. He previously worked as a product engineer in infrastructure automation.
Avinash Sooriyarachchi
Avinash Sooriyarachchi is a Senior Solutions Architect at Databricks. His current work involves working with large Retail and Consumer Packaged Goods organizations across the United States and enabling them to build Machine Learning based systems. His specific interests include streaming machine learning systems and building applications leveraging foundation models. Avi holds a Master’s degree in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania.
Felipe de Pontes Adachi
Felipe is a Data Scientist at WhyLabs. He is a core contributor to whylogs, an open-source data logging library, and focuses on writing technical content and expanding the whylogs library in order to make AI more accessible, robust, and responsible. Previously, Felipe was an AI Researcher at WEG, where he researched and deployed Natural Language Processing approaches to extract knowledge from textual information about electric machinery. He is also a Master in Electronic Systems Engineering from UFSC (Universidade Federal de Santa Catarina), with research focused on developing and deploying fault detection strategies based on machine learning for unmanned underwater vehicles. Felipe has published a series of blog articles about MLOps, Monitoring, and Natural Language Processing in publications such as Towards Data Science, Analytics Vidhya, and Google Cloud Community.
Data Validation at Scale – Detecting and Responding to Data Misbehavior(Workshop)
Daniel Whitenack, PhD
Daniel Whitenack (aka Data Dan) is a Ph.D. trained data scientist working with SIL International on NLP and speech technology for local languages in emerging markets. He has more than ten years of experience developing and deploying machine learning systems at scale. Daniel co-hosts the Practical AI podcast, has spoken at conferences around the world (Applied Machine Learning Days, O’Reilly AI, QCon AI, GopherCon, KubeCon, and more), and occasionally teaches data science/analytics at Purdue University.
Modern NLP: Pre-training, Fine-tuning, Prompt Engineering, and Human Feedback(Workshop)
Christian Ramirez
Christian is Machine Learning Technical Leader at Mercado Libre, the largest e-commerce/fintech company in Latin America, where he dedicates his efforts to creating tools for monitoring and quality of learning models. He is a Computer Engineer and Master in Science with a major in Astronomy from UNAM (Universidad Nacional Autonoma de Mexico). He is a “Xoogler” and has more than 15 years of experience in the field of machine learning. He has lectured in almost a dozen countries.
Introduction to Topological Data Analysis Workshop(Tutorial)
Kai Waehner
Kai Waehner is Field CTO at Confluent. He works with customers and partners across the globe and with internal teams like engineering and marketing. Kai’s main area of expertise lies within the fields of Data Streaming, Analytics, Hybrid Cloud Architectures and Internet of Things. Kai is a regular speaker at international conferences, writes articles for professional journals, and shares his experiences with industry use cases and new technologies on his blog: www.kai-waehner.de. Contact: kai.waehner@confluent.io / @KaiWaehner / linkedin.com/in/kaiwaehner.
Apache Kafka for Real-Time Machine Learning Without a Data Lake(Talk)
Dipanjan (DJ) Sarkar
Dipanjan (DJ) Sarkar is an acknowledged Data Scientist, published Author and Consultant with over nine years of industry experience in all things data. He was recognized as a Google Developer Expert in Machine Learning by Google in 2019, and a Champion Innovator in Cloud AI\ML by Google in 2022. He currently works as a Lead Data Scientist at Constructor Learning (formerly Schaffhausen Institute of Technology (SIT) Learning), Zurich.
Dipanjan has led advanced analytics initiatives working with Fortune 500 companies like Intel, Applied Materials, Red Hat / IBM. He works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. Dipanjan also works as an independent consultant, mentor and AI advisor in his spare time collaborating with multiple universities, organizations and startups across the globe. His passion includes solving challenging data problems as well as educating and helping people upskill in all things data. Find more about him at https://djsarkar.com
Tanvir Ahmed Shaikh
Tanvir Ahmed Shaikh is a highly entrepreneurial and visionary data strategist with a passion for driving business growth through innovative data-driven solutions. With a track record of success in data science and digital transformation, Tanvir has been instrumental in developing and implementing strategies that improve efficiency, quality, and compliance. He possesses strong collaboration skills and effectively communicates technical concepts to non-technical stakeholders.
Currently serving as a Data Strategist (Director) at Genentech Inc., Tanvir leads the digital roadmap for the Global Pharma Manufacturing Quality organization. His expertise in prioritizing digital initiatives, building consensus, and driving change management has resulted in significant positive impacts on the organization.
Tanvir’s leadership abilities are exemplified through his role as the Founder and Digital Strategy Lead of the Roche Intrapreneur Network, a global network of over 350+ Roche technologists focused on executive capabilities and experiential learning. Through this network, he fosters a culture of entrepreneurship, product management, and storytelling, encouraging innovation and empowering individuals to think like CEOs of their products.
In his previous role as a Principal Data Scientist, Tanvir spearheaded cross-functional projects, driving operational excellence in forecasting, automation, and AI education. His contributions have led to substantial cost savings and increased efficiency within the organization. Tanvir’s passion for education and continuous learning is evident in his role as an Adjunct Professor at Carnegie Mellon University. He teaches courses on Time Series Forecasting in Python, AI Product Management, and Storytelling with Data, inspiring students to think holistically and take an end-to-end view of problem-solving. He actively promotes a culture of continuous learning, inclusive community building, and inspirational storytelling. Beyond his professional pursuits, Tanvir embraces a diverse range of interests. He finds joy in the culinary arts, experimenting with new recipes and creating culinary delights. Music also holds a special place in his heart, and he enjoys singing and playing the ukulele in his free time. Tanvir’s curiosity extends to the financial world, where he actively researches stocks and shares his knowledge, promoting personal finance education. Additionally, he stays active through the sport of tennis, both in competitive settings and for leisure. Tanvir’s dedication to data-driven strategies, love for storytelling, and commitment to personal growth and education make him a versatile and accomplished professional. He embodies the values of continuous learning, community building, and innovative thinking, making a significant impact in the field of data science and beyond.
Time Series Forecasting for Managers – All forecasts are wrong but some are useful (Talk)
FEATURED SESSIONS
June 14th
9:50 AM – 10:20 AM BST
ODSC Keynote: Build and Deploy PyTorch models with Azure Machine Learning
Speaker: Henk Boelman, Senior Cloud Advocate at Microsoft
June 14th
11:20 – AM 11:50 AM BST
Session Title: Driving AI Forward: Continental Tire’s Journey to MLOps Excellence
Speaker: Drazen Dodik, Customer Success Lead at Valohai
June 14th
12:00 PM – 12:30 PM BST
Session Title: The Tangent Information Modeler, time series modeling reinvented
Speaker: Philip Wauters, Customer Success Manager, and Value Engineer at Tangent Works
June 14th
10:40 AM – 11:25 AM BST
Session Title: Me, my health, and AI: applications in medical diagnostics and prognostics
Speaker: Sara Khalid, Associate Professor | Senior Research Fellow, Biomedical Data Science and Health Informatics at the University of Oxford
June 14th
10:40 AM – 11:10 AM BST
Session Title: Ask the Experts! ML Pros Deep-Dive into Machine Learning Techniques and MLOps
Speaker: Seth Juarez, Principal Program Manager at Microsoft
June 14th
11:35 AM – 12:20 PM BST
Session Title: Iterated and Exponentially Weighted Moving Principal Component Analysis
Speaker: Dr Paul A. Bilokon, Visiting Lecturer | CEO and Founder at Imperial College London | Thalesians Ltd
June 14th
11:35 AM – 12:20 PM BST
Session Title: Apache Kafka for Real-Time Machine Learning Without a Data Lake
Speaker: Kai Waehner, Global Field CTO | Author | International Speaker
June 14th
12:30 PM – 1:15 PM BST
Session Title: Apache Kafka for Real-Time Machine Learning Without a Data Lake
Speaker: Dr. Gözde Gül Şahin, Assistant Professor | KUIS AI Fellow at KOC University
June 14th
12:30 PM – 1:15 PM BST
Session Title: Fraud Detection with Machine Learning
Speaker: Laura Mitchell, Senior Data Science Manager at MoonPay
June 14th
1:45 PM – 2:30 PM BST
Session Title: Deep Learning and Comparisons between Large Language Models
Speaker: Hossam Amer, PhD, Applied Scientist at Microsoft
June 14th
1:45 PM – 2:30 PM BST
Session Title: Multimodal Video Representations and Their Extension to Visual Language Navigation
Speaker: Cordelia Schmid, Research Director | Research Scientist at Inria | Google
June 14th
2:40 PM – 3:25 PM BST
Session Title: Why GPU Clusters Don’t Need to Go Brrr? Leverage Compound Sparsity to Achieve the Fastest Inference Performance on CPUs
Speaker: Damian Bogunowicz and
Konstantin Gulin, Machine Learning Engineer at Neural Magic
June 14th
2:40 PM – 3:25 PM BST
Session Title: Few-shot Learning for Natural Language Understanding
Speaker: Helen Yannakoudakis, Assistant Professor at King’s College London
June 14th
3:35 PM – 4:20 PM BST
Session Title: Probabilistic Machine Learning for Finance and Investing
Speaker: Deepak Kanungo, Founder and CEO | Advisory Board Member at Hedged Capital LLC | AIKON
June 14th
3:35 PM – 4:20 PM BST
Session Title: Why the Jagged Edge Matters
Speaker: Jutta Treviranus, Director and Professor at Inclusive Design Research Centre at OCAD University
Partnering With ODSC
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