2-for-1 Offer | Ends Soon
We are delighted to announce our Europe 2023 Preliminary Schedule!
90+ Additional Sessions Coming Soon
All sessions are scheduled in GMT time zone (UK time zone)
- ODSC Talks schedule includes Wednesday, June 14th – Thursday, June 15th. In-person sessions are available to Platinum and Mini-Bootcamp pass holders. Virtual Sessions are available to Virtual Premium, Virtual Platinum & Virtual Mini-Bootcamp pass holders.
- ODSC Trainings are scheduled from June 14th – Thursday, June 15th. In-person sessions are available to Platinum & Mini-Bootcamp Pass holders. Virtual Sessions are available to Virtual Platinum & Virtual Mini-Bootcamp pass holders.
- ODSC Workshop/Tutorials are scheduled from June 14th to Thursday, June 15th. All in-person sessions are available to Platinum & Mini-Bootcamp holders. Virtual Sessions are available for Virtual Premium, Virtual Platinum & Virtual Mini-Bootcamp pass holders.
- ODSC Bootcamp Sessions are scheduled VIRTUALLY on Tuesday, June 13 as pre conference training. They are ONLY available for In person Mini-Bootcamp, and VIP Pass and Virtual Mini-Bootcamp holders.
Speaker and speaker schedule times are subject to change.
Please Note: In-Persons attendees will have access to virtual sessions. If you have a virtual Pass, please note that we will not live-stream any in-person sessions. Only virtual sessions will be recorded.
Virtual | Bootcamp | Beginner
In this class students will install Anaconda Python and Jupyter Labs. Using this Jupyter Lab interface I will cover the basics of Python programming. Topics will include built in data structures, functions, looping, decisions, and importing other libraries…more details
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.
In-person | Bootcamp | Machine Learning | Beginner
In this workshop, you will get acquainted with the pandas library, which is the most widely used package for reading, analyzing and exporting datasets in Python. You will also learn how to visualize many kinds of tabular data using the plotnine package, along with some tips and tricks on how to make your visualizations stand out. Lastly, you will have the opportunity make predictions and take decisions using data, based on basic statistical methods…more details
Leonidas (Leo) is a Senior Data Scientist at Astrazeneca. His work is focused around machine learning in oncology, including clinical and non clinical applications. He is also enthusiastic about NLP applications in oncology and how this can be used to leverage patient treatment. He is also a workshop facilitator in the European Leadership University (ELU), NL and has also been a data science educator at DataCamp. He holds a PhD from the University of Warwick, UK. in bioinformatics and ML, an MSc in statistics from Imperial College London, UK and a BSc in Statistics and Insurance Science from the University of Piraeus, GR.
Virtual | Talk | NLP | Machine Learning | Deep Learning | Generative AI | All Levels
In this talk, I will first introduce the field of semantics and the task of semantic analysis, a.k.a, semantic parsing from a multilingual perspective. In particular, we will first discuss the layers of meaning, from morphology to pragmatics, and then define the scope of semantics as a field…more details
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.
In-person | Talk | NLP | Machine Learning | Intermediate
In this talk, I will first give an overview of the built-in functionality available in spaCy, using pretrained models. I will showcase how linguistic information such as part-of-speech tags and dependency parses can help you identify interesting patterns or phrases in your documents and ultimately perform document classification or other information retrieval tasks…more details
Sofie is a machine learning and NLP engineer who firmly believes in the power of data to transform decision making in industry. She has a Master in Computer Science (software engineering) and a PhD in Sciences (Bioinformatics), and more than 16 years of experience in Natural Language Processing and Machine Learning, including in the pharmaceutical industry and the food industry. In 2019, she joined Explosion to work on the open-source NLP library spaCy. She is currently leading the open-source team developing and maintaining spaCy, as well as various other open-source developer tools for data scientists.
Virtual | Business Talk | Cross Industry | Intermediate
On average, most generative models are truthful only 25% of the time, according to Stanford Artificial Intelligence Index Report 2022 . The downstream impact is not just limited to ML or business teams as it trickles down to the end customer who faces the first-hand repercussions of inaccurate and incorrect predictions. With the adoption of AI Observability tools, pioneering tech giants have managed to quickly catch model issues, diagnose root causes, and perform continuous improvement to optimize the model’s performance…more details
Ayush is the co-founder of TwelveFold, an AI start-up studio, where he manages a portfolio of MLOps and Generative AI companies with entrepreneurs. He also works as the CEO of Censius, an AI Observability platform that helps to optimize AI models' real-world performance. As a seasoned professional, he has closely worked with customers across industry verticals, AI teams, and research projects to build reliable and compliant AI solutions to solve everyday business problems and scale models at production.
In-person | Talk | Machine Learning | Deep Learning | NLP | Beginner-Intermediate
During the talk, we’ll show how Ludwig’s novel compositional model architecture referred to as encoder-combiner-decoder makes it possible to easily mix multiple modalities of data such as text, images, audio with structured data in a way that is consistently easy across tasks like regressions, classification, and even generation…more details
Dev is co-founder and Chief Product Officer for Predibase, a company looking to redefine how data scientists and engineers build models with a declarative approach. Prior to Predibase, he was a ML PM at Google working across products like Firebase, Google Research and the Google Assistant as well as Vertex AI. While there, Dev was also the first product manager for Kaggle – a data science and machine learning community with over 8 million users worldwide. Dev’s academic background is in computer science and statistics, and he holds a masters in computer science from Harvard University focused on machine learning.
Virtual | Talk | Machine Learning | Deep Learning | Intermediate
A common problem in the cybersecurity industry is how to detect and track botnets when there are billions of daily attacks. Botnets are internet connected devices that perform repetitive tasks, such as Distributed Denial of Service (DDoS). In many cases, these consumer devices are infected with malicious malware that is controlled by an external entity, often without the owner’s knowledge…more details
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.
In-person | Talk | MLOps & Data Engineering | Responsible AI | Beginner
From this talk you will learn:
– What ML Governance is meant to achieve
– How to get started with a template process
– The role of documentation (and especially Google Model Cards)
– Which roles have what responsibilities
– The relevance of a governance board
Ryan Dawson is a technologist passionate about data. Ryan works with clients on large-scale data and AI initiatives, helping organizations get more value from data. His work includes strategies to productionize machine learning, organizing the way data is captured and shared, selecting the right data technologies and optimal team structures, as well as writing the code to make it happen. He has over 15 years of experience and, as well as many widely read articles about MLOps, software design, and delivery. is author of the Thoughtworks Guide to Evaluating MLOps Platforms.
Meissane Chami serves ThoughtWorks, Inc. as a Senior ML Engineer, advising and developing innovative data science and machine learning solutions from proof of concept to production. She has gained expertise setting up innovation frameworks and conducting fast cycle proof of concepts. Her primary areas of expertise are in Natural Language processing, MLOps, DevOps, cloud computing, containerisation and Python. She holds a MSc degree in Machine Learning and Data Science form University College London School of Engineering.
Virtual | Talk | Machine Learning for Finance | All Levels
The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. The application of PCA in a financial setting is associated with several difficulties, such as numerical instability and nonstationarity. We attempt to resolve them by proposing two new variants of PCA: an iterated principal component analysis (IPCA) and an exponentially weighted moving principal component analysis (EWMPCA). Both variants rely on the Ogita-Aishima iteration as a crucial step…more details
Bio Coming Soon!
In-person | Talk | Machine Learning | Deep Learning | Intermediate
In this session, we’ll explore and discuss the following:
– Why and what is Ray
– How AIR, built atop Ray, allows you to program and scale your machine learning workloads easily
– AIR’s interoperability and easy integration points with other systems for storage and metadata needs
– AIR’s cutting-edge features for accelerating the machine learning lifecycle such as data preprocessing, last-mile data ingestion, tuning and training, and serving at scale..more details
Kai Fricke is a senior software engineer at Anyscale. As a core maintainer of the Ray AI Runtime he is building software for distributed machine learning training and tuning. During his postdoc at Cambridge he utilized reinforcement learning to optimize large graph structures and co-authored two open source reinforcement learning libraries.
In-person | Talk | Machine Learning | Machine Learning Safety and Security | Data Engineering & Big Data | Responsible AI | Intermediate
If you’ve ever asked one of the questions above, then this talk is for you! You’ll learn how the ability to interpret a model can identify poor model performance or, worse, bias that could ultimately impact the fairness of your machine learning applications. You’ll learn about some of the most common algorithms, how they work and how you can get started using them yourself…more details
Ed Shee, Head of Developer Relations at Seldon. Having previously led a tech team at IBM, Ed comes from a cloud computing background and is a strong believer in making deployments as easy as possible for developers. With an education in computational modelling and an enthusiasm for machine learning, Ed has blended his work in ML and cloud native computing together to cement himself firmly in the emerging field of MLOps.
Virtual | Talk | Machine Learning for Finance | Intermediate
The objective of this session is to make attendees familiar with the reasons why probabilistic machine learning is the next generation of AI in finance and investing…more details
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).
Virtual | Talk | Responsible Ai | Machine Learning | Beginner
This talk will introduce the audience to challenges in AI for health equity with a particular focus on race and ethnicity data. We will explore real-world ethnicity data collected routinely in healthcare settings in the form of electronic health records. We will examine issues with completeness, correctness, and granularity of these data, implications for healthcare AI, and finally highlight opportunities towards “better data, better models, better healthcare”…more details
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.
In-person | Talk | Generative AI | Machine Learning, Deep Learning | All Levels
The session will cover the importance of explaining AI models and their limitations, building effective next-gen data products, evaluating audience and user needs, and the aspects of visualisation that will always require human input. It will focus on the practical implications of AI tools on the roles of data professionals – and look at how we can thrive in this exciting new era…more details
Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Riskified,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.
In-person | Talk | Machine Learning for Finance
In this talk, we will look at how deep learning techniques can be used for building fast option pricers. A large set of representative training data is generated by using the numerical pricers. Then deep neural networks are used to learn the non-linear pricing functions…more details
Chakri Cherukuri is a senior researcher in the Quantitative Financial Research Group at Bloomberg LP in NYC. His research interests include quantitative portfolio management, algorithmic trading strategies, and applied machine learning. He has extensive experience in scientific computing and software development. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. He holds an undergraduate degree in mechanical engineering from the Indian Institute of Technology (IIT) Madras, India, and an MS in computational finance from Carnegie Mellon University.
In-person | Talk and Career Talk
In this lightning overview, we’ll discuss the most impactful changes you can make to your data science practice. Topics include running a model in shadow mode, data versioning, estimating costs, and communicating impact to a non-technical audience…more details
Kerstin is CEO and Co-founder of SuperUse, a collaboration platform. She has led data science initiatives at startups across industries, from healthcare to CPG. She takes pride in mentoring fantastic data scientists and nurturing talent. A builder at heart, she regularly pushes code, trains models, and uncovers insights. She has Masters degrees in Mathematical Computer Science and Mathematical Statistics. She is expecting her PhD from Cornell in early 2023. She spends her free time going on long hikes with her two small dogs through the big mountains outside Seattle.
In-person | Workshop | Machine Learning | Deep Learning | Intermediate
In this workshop we will illustrate both approaches using a consistent single example. We will use TensorFlow in a Colab notebooks, so all you need is a recent version of Chrome and a Google login. You will not need prior knowledge with TensorFlow, but need a good understanding of how training neural networks work as a prerequisite…more details
Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.
In-person | Tutorial | Generative AI | Machine Learning | Deep Learning | NLP | Intermediate-Advanced
In the first part of the talk I will provide an overview of the latest generative AI models and how they work. This will include discussing the various types of generative AI models, such as diffusion models for image generation and transformer (GPT-like) models for text generation and their underlying architectures and key concepts…more details
Heiko Hotz is a Senior Solutions Architect for AI & Machine Learning at AWS with a special focus on Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI. He is also the founder of the NLP London Meetup group, bringing together NLP enthusiasts and industry experts.
In-person | Half-Day Training | Machine Learning for Finance | Intermediate
This half-day trading session covers the most important Python topics and skills to apply AI and Machine Learning (ML) to Algorithmic Trading. The session shows how to make use of the Oanda trading API (via a demo account) to retrieve data, to stream data, to place orders, etc. Building on this, a ML-based trading strategy is formulated and backtested. Finally, the trading strategy is transformed into an online trading algorithm and is deployed for real-time trading on the Oanda trading platform…more details
Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (http://tpq.io), a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading, and computational finance. He is also founder and CEO of The AI Machine (http://aimachine.io), a company focused on AI-powered algorithmic trading based on a proprietary strategy execution platform.
Yves has a Diploma in Business Administration, a Ph.D. in Mathematical Finance and is Adjunct Professor for Computational Finance at Miami Herbert Business School.
Virtual | Bootcamp | Beginner
In this class students will install Anaconda Python and Jupyter Labs. Using this Jupyter Lab interface I will cover the basics of Python programming. Topics will include built in data structures, functions, looping, decisions, and importing other libraries…more details
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.
In-person | Bootcamp | Machine Learning | Beginner
In this workshop, you will get acquainted with the pandas library, which is the most widely used package for reading, analyzing and exporting datasets in Python. You will also learn how to visualize many kinds of tabular data using the plotnine package, along with some tips and tricks on how to make your visualizations stand out. Lastly, you will have the opportunity make predictions and take decisions using data, based on basic statistical methods…more details
Leonidas (Leo) is a Senior Data Scientist at Astrazeneca. His work is focused around machine learning in oncology, including clinical and non clinical applications. He is also enthusiastic about NLP applications in oncology and how this can be used to leverage patient treatment. He is also a workshop facilitator in the European Leadership University (ELU), NL and has also been a data science educator at DataCamp. He holds a PhD from the University of Warwick, UK. in bioinformatics and ML, an MSc in statistics from Imperial College London, UK and a BSc in Statistics and Insurance Science from the University of Piraeus, GR.
Virtual | Talk | NLP | Machine Learning | Deep Learning | Generative AI | All Levels
In this talk, I will first introduce the field of semantics and the task of semantic analysis, a.k.a, semantic parsing from a multilingual perspective. In particular, we will first discuss the layers of meaning, from morphology to pragmatics, and then define the scope of semantics as a field…more details
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.
In-person | Talk | NLP | Machine Learning | Intermediate
In this talk, I will first give an overview of the built-in functionality available in spaCy, using pretrained models. I will showcase how linguistic information such as part-of-speech tags and dependency parses can help you identify interesting patterns or phrases in your documents and ultimately perform document classification or other information retrieval tasks…more details
Sofie is a machine learning and NLP engineer who firmly believes in the power of data to transform decision making in industry. She has a Master in Computer Science (software engineering) and a PhD in Sciences (Bioinformatics), and more than 16 years of experience in Natural Language Processing and Machine Learning, including in the pharmaceutical industry and the food industry. In 2019, she joined Explosion to work on the open-source NLP library spaCy. She is currently leading the open-source team developing and maintaining spaCy, as well as various other open-source developer tools for data scientists.
Virtual | Business Talk | Cross Industry | Intermediate
On average, most generative models are truthful only 25% of the time, according to Stanford Artificial Intelligence Index Report 2022 . The downstream impact is not just limited to ML or business teams as it trickles down to the end customer who faces the first-hand repercussions of inaccurate and incorrect predictions. With the adoption of AI Observability tools, pioneering tech giants have managed to quickly catch model issues, diagnose root causes, and perform continuous improvement to optimize the model’s performance…more details
Ayush is the co-founder of TwelveFold, an AI start-up studio, where he manages a portfolio of MLOps and Generative AI companies with entrepreneurs. He also works as the CEO of Censius, an AI Observability platform that helps to optimize AI models' real-world performance. As a seasoned professional, he has closely worked with customers across industry verticals, AI teams, and research projects to build reliable and compliant AI solutions to solve everyday business problems and scale models at production.
In-person | Talk | Machine Learning | Deep Learning | NLP | Beginner-Intermediate
During the talk, we’ll show how Ludwig’s novel compositional model architecture referred to as encoder-combiner-decoder makes it possible to easily mix multiple modalities of data such as text, images, audio with structured data in a way that is consistently easy across tasks like regressions, classification, and even generation…more details
Dev is co-founder and Chief Product Officer for Predibase, a company looking to redefine how data scientists and engineers build models with a declarative approach. Prior to Predibase, he was a ML PM at Google working across products like Firebase, Google Research and the Google Assistant as well as Vertex AI. While there, Dev was also the first product manager for Kaggle – a data science and machine learning community with over 8 million users worldwide. Dev’s academic background is in computer science and statistics, and he holds a masters in computer science from Harvard University focused on machine learning.
Virtual | Talk | Machine Learning | Deep Learning | Intermediate
A common problem in the cybersecurity industry is how to detect and track botnets when there are billions of daily attacks. Botnets are internet connected devices that perform repetitive tasks, such as Distributed Denial of Service (DDoS). In many cases, these consumer devices are infected with malicious malware that is controlled by an external entity, often without the owner’s knowledge…more details
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.
In-person | Talk | MLOps & Data Engineering | Responsible AI | Beginner
From this talk you will learn:
– What ML Governance is meant to achieve
– How to get started with a template process
– The role of documentation (and especially Google Model Cards)
– Which roles have what responsibilities
– The relevance of a governance board
Ryan Dawson is a technologist passionate about data. Ryan works with clients on large-scale data and AI initiatives, helping organizations get more value from data. His work includes strategies to productionize machine learning, organizing the way data is captured and shared, selecting the right data technologies and optimal team structures, as well as writing the code to make it happen. He has over 15 years of experience and, as well as many widely read articles about MLOps, software design, and delivery. is author of the Thoughtworks Guide to Evaluating MLOps Platforms.
Meissane Chami serves ThoughtWorks, Inc. as a Senior ML Engineer, advising and developing innovative data science and machine learning solutions from proof of concept to production. She has gained expertise setting up innovation frameworks and conducting fast cycle proof of concepts. Her primary areas of expertise are in Natural Language processing, MLOps, DevOps, cloud computing, containerisation and Python. She holds a MSc degree in Machine Learning and Data Science form University College London School of Engineering.
Virtual | Talk | Machine Learning for Finance | All Levels
The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. The application of PCA in a financial setting is associated with several difficulties, such as numerical instability and nonstationarity. We attempt to resolve them by proposing two new variants of PCA: an iterated principal component analysis (IPCA) and an exponentially weighted moving principal component analysis (EWMPCA). Both variants rely on the Ogita-Aishima iteration as a crucial step…more details
Bio Coming Soon!
In-person | Talk | Machine Learning | Deep Learning | Intermediate
In this session, we’ll explore and discuss the following:
– Why and what is Ray
– How AIR, built atop Ray, allows you to program and scale your machine learning workloads easily
– AIR’s interoperability and easy integration points with other systems for storage and metadata needs
– AIR’s cutting-edge features for accelerating the machine learning lifecycle such as data preprocessing, last-mile data ingestion, tuning and training, and serving at scale..more details
Kai Fricke is a senior software engineer at Anyscale. As a core maintainer of the Ray AI Runtime he is building software for distributed machine learning training and tuning. During his postdoc at Cambridge he utilized reinforcement learning to optimize large graph structures and co-authored two open source reinforcement learning libraries.
In-person | Talk | Machine Learning | Machine Learning Safety and Security | Data Engineering & Big Data | Responsible AI | Intermediate
If you’ve ever asked one of the questions above, then this talk is for you! You’ll learn how the ability to interpret a model can identify poor model performance or, worse, bias that could ultimately impact the fairness of your machine learning applications. You’ll learn about some of the most common algorithms, how they work and how you can get started using them yourself…more details
Ed Shee, Head of Developer Relations at Seldon. Having previously led a tech team at IBM, Ed comes from a cloud computing background and is a strong believer in making deployments as easy as possible for developers. With an education in computational modelling and an enthusiasm for machine learning, Ed has blended his work in ML and cloud native computing together to cement himself firmly in the emerging field of MLOps.
Virtual | Talk | Machine Learning for Finance | Intermediate
The objective of this session is to make attendees familiar with the reasons why probabilistic machine learning is the next generation of AI in finance and investing…more details
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).
Virtual | Talk | Responsible Ai | Machine Learning | Beginner
This talk will introduce the audience to challenges in AI for health equity with a particular focus on race and ethnicity data. We will explore real-world ethnicity data collected routinely in healthcare settings in the form of electronic health records. We will examine issues with completeness, correctness, and granularity of these data, implications for healthcare AI, and finally highlight opportunities towards “better data, better models, better healthcare”…more details
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.
In-person | Talk | Generative AI | Machine Learning, Deep Learning | All Levels
The session will cover the importance of explaining AI models and their limitations, building effective next-gen data products, evaluating audience and user needs, and the aspects of visualisation that will always require human input. It will focus on the practical implications of AI tools on the roles of data professionals – and look at how we can thrive in this exciting new era…more details
Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Riskified,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.
In-person | Talk | Machine Learning for Finance
In this talk, we will look at how deep learning techniques can be used for building fast option pricers. A large set of representative training data is generated by using the numerical pricers. Then deep neural networks are used to learn the non-linear pricing functions…more details
Chakri Cherukuri is a senior researcher in the Quantitative Financial Research Group at Bloomberg LP in NYC. His research interests include quantitative portfolio management, algorithmic trading strategies, and applied machine learning. He has extensive experience in scientific computing and software development. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. He holds an undergraduate degree in mechanical engineering from the Indian Institute of Technology (IIT) Madras, India, and an MS in computational finance from Carnegie Mellon University.
In-person | Talk and Career Talk
In this lightning overview, we’ll discuss the most impactful changes you can make to your data science practice. Topics include running a model in shadow mode, data versioning, estimating costs, and communicating impact to a non-technical audience…more details
Kerstin is CEO and Co-founder of SuperUse, a collaboration platform. She has led data science initiatives at startups across industries, from healthcare to CPG. She takes pride in mentoring fantastic data scientists and nurturing talent. A builder at heart, she regularly pushes code, trains models, and uncovers insights. She has Masters degrees in Mathematical Computer Science and Mathematical Statistics. She is expecting her PhD from Cornell in early 2023. She spends her free time going on long hikes with her two small dogs through the big mountains outside Seattle.
In-person | Workshop | Machine Learning | Deep Learning | Intermediate
In this workshop we will illustrate both approaches using a consistent single example. We will use TensorFlow in a Colab notebooks, so all you need is a recent version of Chrome and a Google login. You will not need prior knowledge with TensorFlow, but need a good understanding of how training neural networks work as a prerequisite…more details
Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.
In-person | Tutorial | Generative AI | Machine Learning | Deep Learning | NLP | Intermediate-Advanced
In the first part of the talk I will provide an overview of the latest generative AI models and how they work. This will include discussing the various types of generative AI models, such as diffusion models for image generation and transformer (GPT-like) models for text generation and their underlying architectures and key concepts…more details
Heiko Hotz is a Senior Solutions Architect for AI & Machine Learning at AWS with a special focus on Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI. He is also the founder of the NLP London Meetup group, bringing together NLP enthusiasts and industry experts.
In-person | Half-Day Training | Machine Learning for Finance | Intermediate
This half-day trading session covers the most important Python topics and skills to apply AI and Machine Learning (ML) to Algorithmic Trading. The session shows how to make use of the Oanda trading API (via a demo account) to retrieve data, to stream data, to place orders, etc. Building on this, a ML-based trading strategy is formulated and backtested. Finally, the trading strategy is transformed into an online trading algorithm and is deployed for real-time trading on the Oanda trading platform…more details
Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (http://tpq.io), a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading, and computational finance. He is also founder and CEO of The AI Machine (http://aimachine.io), a company focused on AI-powered algorithmic trading based on a proprietary strategy execution platform.
Yves has a Diploma in Business Administration, a Ph.D. in Mathematical Finance and is Adjunct Professor for Computational Finance at Miami Herbert Business School.

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Virtual: How It Works
In-Person attendees also have access to virtual sessions and platform


Virtual conference experience includes networking lounge area, speaker auditorium, expo halls, and prizes
Access multiple Virtual tracks on Tuesday, Wednesday, Thursday
Switch between sessions or tracks as your interests dictate
Multiple focus areas including deep learning, machine learning, NLP, research frontiers, AI X for business, and more
Sessions you missed can be viewed on demand at your leisure
Engage virtually with fellow attendees, speakers, and Expo partners
Participate in Q&A sessions with your speaker over live chat
Directly download slides and other session materials
(Training only) Access training and workshops prerequisites, notebooks, and other materials prior to training session starting
(Training only) Access hands-on training and workshops with instructor-led code labs and notebooks.