May 9th - 11th, 2023

Responsible AI and Social Good

Applying AI to help solve social good challenges
First 100 Tickets | Register now

Responsible AI and Social Good

The Responsible AI track brings together top data ethicists to provide a practical, ethical framework for technologists to develop machine learning systems.

Using case studies and existing frameworks, we’ll give you the tools to build out your own ethical approach to realize the best outcomes while deploying machine learning in the real world.

You will be able to responsibly design human-in-the-loop review processes, monitor bias, build trust transparency, and develop explainable machine learning systems to ensure data and model security.

What You'll Learn

Talks + Workshops + Special Events on these topics:

  • AI Ethics and Bias

  • Federated Analytics

  • Federated Learning for User Privacy

  • AI for Climate Change, Social Good

  • Reproducibility

  • Explainable AI: heatmap-based explanations

  • Explainable AI: human in the loop

  • Uncertainty in AI

  • Fairness in Machine Learning

  • Algorithmic Decision Making

  • Fairness in Predictive Modeling

  • and more…

ODSC EAST 2023 | May 9th-11th

First 100 Tickets | Register now

Some of Our Past Responsible AI Speakers

Professor, Computer ScienceNortheastern University

Tina Eliassi-Rad, PhD

Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University. She is also a core faculty member at Northeastern University’s Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of big data from networked representations of physical and social phenomena. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awardees). Tina’s work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  and as the program co-chair for the International Conference on Network Science . In 2020, she is serving as the program co-chair for the International Conference on Computational Social Science. Tina received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010; and became a Fellow of the ISI Foundation in Turin Italy in 2019. 

Just Machine Learning(Talk)

 

Avanessians Director, Data Science Institute | Professor of Computer ScienceColumbia University

Jeannette M. Wing, PhD

Jeannette M. Wing is the Executive Vice President for Research at Columbia University and Professor of Computer Science. In her EVPR role, she has overall responsibility for the University’s research enterprise at all New York locations and internationally. The New York locations include the Morningside and Manhattanville campuses, Columbia University Irving Medical Center, Lamont-Doherty Earth Observatory, and Nevis Laboratories. She joined Columbia in 2017 as the inaugural Avanessians Director of the Data Science Institute.

Prior to Columbia, Dr. Wing was Corporate Vice President of Microsoft Research, served on the faculty and as department head in computer science at Carnegie Mellon University, and served as Assistant Director for Computer and Information Science and Engineering at the National Science Foundation.

Dr. Wing’s research contributions have been in the areas of trustworthy AI, security and privacy, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her 2006 seminal essay, titled “Computational Thinking,’’ is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced, and thereby influencing K-12 and university curricula worldwide.

She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers. She received distinguished service awards from the ACM and the Computing Research Association and an honorary doctorate degree from Linköping University, Sweden. She earned her bachelor’s, master’s, and doctoral degrees in computer science, all from the Massachusetts Institute of Technology.

Trustworthy AI(Talk)

Applied Data Ethicist | Visiting ResearcherGoogle

Razvan Amironesei, PhD

Razvan Amironesei is a Visiting Researcher in the Ethical AI team. While at Google’s Center for Responsible AI, his research and publications focus on developing a pluralistic data ethics framework by using responsible interpretive methods to analyze the construction of benchmark datasets. He is also researching the relationship between computer science pedagogy and humanistic social science, specific issues related to data annotation, the constitution of offensiveness in ML datasets, and the topic of algorithmic conservation. Previously, Razvan has done research and published on sociotechnical impacts of benchmark datasets at the Center for Applied Data Ethics at the University of San Francisco, and on the political and ethical formation of algorithms at the Institute for Practical Ethics at UC San Diego. Razvan has taught classes in English and French in Applied Ethics for Engineers, Bioethics, Political Theory, and on Religion and Politics in the US. His educational background is international and situated at the intersection of social sciences and the humanities. He completed postdoctoral studies at the Center on Global Justice at UC San Diego, a PhD in philosophy at Laval University in Canada, an MA in the history of science and technology in France and a Bachelor’s degree in the history of philosophy in Romania.

ImageNet and its Discontents. The Case for Responsible Interpretation in ML (Talk)

Senior Data Scientist | Vice-chairABN Amro Bank | The Foundation for Best Practices in ML

Violeta Misheva, PhD

Violeta has been interested in understanding the causes of social inequalities and to what extent bad experiences early in life propagate to negative outcomes later. When she realized ML can result in widening already existing social gaps, she became an advocate for the responsible development and deployment of ML. Violeta currently works as a data scientist at ABN Amro. Before that, she worked in consultancy and obtained her PhD in applied econometrics. Violeta likes sharing her knowledge with others by the form of workshops on data science and online courses. Violeta proposes that developers of ML solutions alone cannot ensure their safety but, rather, that the additional efforts of multidisciplinary experts as well as proper regulation is also needed.

Open-source Best Practices in Responsible AI(Talk)

Engineering Director, Machine LearningSeldon

Alejandro Saucedo

Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he contributes to policy and industry standards on the responsible design, development and operation of AI, including the fields of explainability, GPU acceleration, privacy preserving ML and other key machine learning research areas. Alejandro Saucedo is also the Director of Engineering at Seldon Technologies, where he leads teams of machine learning engineers focused on the scalability and extensibility of machine learning deployment and monitoring products. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has a strong track record building cross-functional teams of software engineers. He is currently appointed as governing council Member-at-Large at the Association for Computing Machinery, and is currently the Chairperson of the GPU Acceleration Kompute Committee at the Linux Foundation.

Flawed Machine Learning Security: The Top Security Flaws in the ML Lifecycle (and how to avoid them)(Talk)

Director of Machine Learning Intelinair

Jennifer Hobbs, PhD

Jennifer Hobbs is the Director of Machine Learning at Intelinair. Her team is responsible for the development and delivery of computer vision and machine learning models to deliver intelligence and insights to the agriculture industry. She completed her PhD in Physics and Astronomy at Northwestern University. Throughout her career she has been involved in all phases of the machine learning lifecycle, transforming raw data into compelling technology products through data modeling and architecture, pipeline design and management, machine learning, and visualization.

Deep Learning Enables a New View in the Agriculture Industry(Talk)

Security Data ScientistChainguard

John Speed Meyers, PhD

John Speed Meyers is a security data scientist at Chainguard. His interests include software supply chain security, open source software security and applications of data science to cybersecurity. He has a.PhD in policy analysis from the Pardee RAND Graduate School.

You Too Can Be a Cybersecurity Data Scientist!(Tutorial)

Manager | Data Science Product OwnerHighmark Health

Evie Fowler

Evie Fowler is a data scientist based in Pittsburgh, Pennsylvania. She currently works in the healthcare sector leading a team of data scientists who develop predictive models centered on the patient care experience. She holds a particular interest in the ethical application of predictive analytics and in exploring how qualitative methods can inform data science work. She holds an undergraduate degree from Brown University and a master’s degree from Carnegie Mellon.

Data Science and Contextual Approaches to Palliative Care Need Prediction(Talk)

Legal Counsel for AI & Data ScienceH&M Group

Daniel Vale

Daniel has long been interested in the intersection between the law, technology and society. Unsurprisingly, this drew him into the field of data science and law. Daniel currently works as legal counsel for AI & data science at the H&M Group: where his principal focus is on developing and maturing the company’s MLOps (business, governance, and regulatory) capacities. Daniel is also completing his PhD in law, MLOps, & finance at Leiden University. His education is in behavioural science, statistics, and law. Having worked at corporate law firms and as a consultant, Daniel has practical legal and commercial experience in the field. He proposes that responsible ML is centred around two essential themes – (a) a constant appreciation of context, and (b) prudent MLOps & project management.

Open-source Best Practices in Responsible AI(Talk)

Sr. Engineering Leader - Reliability and Observability EngineeringAdobe Systems Inc

Ravi Kumar Buragapu

Ravi Kumar Buragapu is a Sr. Engineering Leader – Reliability and Observability Engineering at Adobe Systems Inc. Ravi is a strategic thinker and technology leader with a strong background in Artificial Intelligence, Machine Learning, Systems Engineering, Site Reliability Engineering, Infrastructure Architecture, and DevOps Engineering. He is heading Platform and Reliability Engineering Center Of Excellence in building cutting edge strategies for End-2-End Resiliency of applications and Infrastructure.

Kubernetes – Observability Engineering (Talk)

Principal Data ScientistOracle

John Peach

A modern polymath, John holds advanced degrees in mechanical engineering, kinesiology and data science, with a focus on solving novel and ambiguous problems. As a senior applied data scientist at Amazon, John worked closely with engineering to create machine learning models to arbitrate chatbot skills, entity resolution, search, and personalization.

As a principal data scientist for Oracle Cloud Infrastructure, he is now defining tooling for data science at scale. John frequently gives talks on best practices and reproducible research. To that end, he has developed an approach to improve validation and reliability by using data unit tests and has pioneered Data Science Design Thinking. He also coordinates SoCal RUG, the largest R meetup group in Southern California.

Tired of Cleaning your Data? Have Confidence in Data with Feature Types(Workshop)

CEO, Security & Forensics Analyst, Incident Responder | Senior InstructorOne eSecurity | SANS Institute

Jess Garcia

Jess Garcia is the Founder of the global Cybersecurity/DFIR firm One eSecurity and a Senior Instructor with the SANS Institute.

During his 25 years in the field, Jess has led a myriad of complex multinational investigations for Fortune 500 companies and global organizations. As a SANS Instructor, Jess stands as one of the most prolific and veteran ones, having taught 10+ different highly technical Cybersecurity/DFIR courses in hundreds of conferences world-wide over the last 19 years.

Jess is also an active Cybersecurity/DFIR Researcher. With the mission of bringing Data Science/AI to the DFIR field, Jess launched in 2020 the DS4N6 initiative (www.ds4n6.io), under which he is leading the development of multiple open source tools, standards and analysis platforms for DS/AI+DFIR interoperability.

DS/AI for Incident Response & Threat Hunting with CHRYSALIS & DAISY(Talk)

Product OwnerOne eSecurity

David Contreras

David Contreras is a Senior Forensic Analyst in One eSecurity, working in Incident Response, leading the Research team and Internal products development. David has more than six years in DFIR, working in multiple remarkable incidents in international organizations and many other projects related to Threat Hunting, SOCs, etc. He also collaborates in the research of the DS4N6 project (www.ds4n6.io), helping to provide Data Science and Machine Learning content to the Cybersecurity community.

Data Science for Digital Forensics & Incident Response (DFIR)(Training)

Associate Professor | Co-FounderDuke University | Tumult Labs

Ashwin Machanavajjhala, PhD

Ashwin Machanavajjhala is an Assistant Professor in the Department of Computer Science, Duke University and an Associate Director at the Information Initiative@Duke (iiD). Previously, he was a Senior Research Scientist in the Knowledge Management group at Yahoo! Research. His primary research interests lie in algorithms for ensuring privacy in statistical databases and augmented reality applications. He is a recipient of the National Science Foundation Faculty Early CAREER award in 2013, and the 2008 ACM SIGMOD Jim Gray Dissertation Award Honorable Mention. Ashwin graduated with a Ph.D. from the Department of Computer Science, Cornell University and a B.Tech in Computer Science and Engineering from the Indian Institute of Technology, Madras.

Analyzing Sensitive Data Using Differential Privacy(Tutorial)

Associate Professor | Founder & CTOColgate University | Tumult Labs

Michael Hay, PhD

Michael Hay is an Associate Professor of Computer Science at Colgate University and founder/CTO of Tumult Labs, a startup that helps organizations safely release data using differential privacy. His research interests include data privacy, databases, data mining, machine learning, and social network analysis. He was previously a Research Data Scientist at the US Census Bureau and a Computing Innovation Fellow at Cornell University. He holds a Ph.D. from the University of Massachusetts Amherst and a bachelor’s degree from Dartmouth College. His research is supported by grants from DARPA and NSF.

Analyzing Sensitive Data Using Differential Privacy(Tutorial)

MLOps ArchitectWhyLabs

Danny D. Leybzon

Danny D. Leybzon has worn many hats, all of them related to data. He studied computational statistics at UCLA and has worked in the data and ML space ever since. In his role as MLOps architect, he has worked to evangelize machine learning best practices, talking on subjects such as distributed deep learning, productionizing machine learning models, automated machine learning, and lately has been talking about AI observability and data logging. When Danny’s not researching, practicing, or talking about data science, he’s usually doing one of his numerous outside hobbies: rock climbing, backcountry backpacking, skiing, etc.

Achieving Better Models Through Monitoring(Demo Talk)

 

Climate & Agronomic Data ScientistSyngenta

Serg Masis

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he’s a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making. He wrote the bestselling book “Interpretable Machine Learning with Python” and is currently working on a new book titled “DIY AI” for Addison-Wesley for a broader audience of curious developers, makers, and hackers.

Enhance Trust with Machine Learning Model Error Analysis(Workshop)

 

ProfessorBentley University

Noah Giansiracusa, PhD

Noah Giansiracusa received a Ph.D. in mathematics from Brown University and is an Assistant Professor of Mathematics and Data Science at Bentley University, a business school near Boston. He previously taught at U.C. Berkeley and Swarthmore College. He’s received multiple national grants to fund his research and has been quoted in Forbes, Financial Times, and U.S. News. He is the author of “How Algorithms Create and Prevent Fake News: Exploring the Impacts of Social Media, Deepfakes, GPT-3, and More,” about which Nobel Laureate and former Chief Economist at the World Bank Paul Romer said “There is no better guide to the strategies and stakes of this battle for the future.”

 

M.S. Data Science at Harvard University, Z by HP Global Data Science Ambassador

Max Urbany

As Max progresses through his Master’s Program, he is particularly interested in intelligent digital accessibility design, along with the ethical analysis of existing predictive models. His passion for creating quality user-centered tools drives him to understand as much as he can about end users while leveraging what data can reveal.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

VP, Enterprise AI / Data Science Solutions at Future Tech

Dan Chaney

Dan Chaney is the VP, Enterprise AI / Data Science Solutions, for Future Tech Enterprise, Inc., an award-winning global IT solutions provider. He oversees all sales, marketing, and technical activities focused on Future Tech’s comprehensive range of AI and data science workstation solutions. Prior to joining Future Tech, Dan spent 20 years at Northrop Grumman, most recently serving as the company’s Enterprise Director of IT Solution Architecture & Engineering. Dan earned his bachelor’s and master’s degrees in communication and computer science from the University of Kentucky. Dan is a Certified Information Systems Security Professional (CISSP) and adjunct instructor for the University of Louisville’s cybersecurity workforce program sponsored by the National Centers of Academic Excellence in Cybersecurity.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

North America Business Development Manager, Z by HP

Kristin Hempstead

Kristin has been with HP for 11 years and is currently the North America business development manager for HP’s data science and artificial intelligence solutions focusing on federal, education, and public sector customers. She has an MBA from University in South Florida with a specialization in Finance and MIS and a BS in Agriculture from the University of Georgia.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

Research Staff Member and ManagerIBM

Kush R. Varshney Ph.D.

Dr. Varshney is a distinguished research staff member and manager with IBM Research at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the machine learning group in the Foundations of Trustworthy AI department. He was a visiting scientist at IBM Research – Africa, Nairobi, Kenya in 2019. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He conducts academic research on the theory and methods of trustworthy machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences and the 2019 Computing Community Consortium / Schmidt Futures Computer Science for Social Good White Paper Competition. He is currently writing a book entitled ‘Trustworthy Machine Learning’ with Manning Publications. He is a senior member of the IEEE and a member of the Partnership on AI’s Safety-Critical AI expert group.

A Unified View of Trustworthy AI with the 360 Toolkits(Talk)

Senior Manager Data SciencesPublicis Sapient

Sharmistha Chatterjee

Sharmistha Chatterjee is a Data Science Evangelist with 16+ years of professional experience in the field of Machine Learning (AI research and productionizing scalable solutions) and Cloud applications. She has worked in both Fortune 500 companies, as well as in very early-stage startups. She is currently working as a Senior Manager of Data Sciences at Publicis Sapient where she leads the digital transformation of clients across industry verticals. She is an active blogger, an international speaker at various tech conferences, and a 2X Google Developer Expert in Machine Learning and Google Cloud. She is also the Hackernoon Tech award winner for 2020, been listed as 40 under 40 Data Scientist by AIM and & 21 tech trailblazers 2021 by Google. She is involved in mentoring startups in Google Startup for Accelerators Program. She has also completed Business in AI from the London School of Business recently.

Need of Adaptive Ethical ML Models in Post Pandemic Era(Talk)

Senior Data ScientistPublicis Sapient

Juhi Pandey

Juhi Pandey is an Artificial Intelligence and Machine Learning Evangelist, a Speaker, and a Mentor. She has nearly 11 years of experience, statistical, and architectural experience in different domains like Life Science, Marketing, Finance, and Supply Chain Management. She has rich experience in building and scaling AI and Machine Learning businesses. She is currently working as a Senior Data Scientist at Publicis Sapient where she is part of the core data science team, working on various Machine Learning, Deep Learning, Natural Language Processing, and Artificial intelligence engagements by applying state of the art techniques in this space. She is Azure Data Science Certified and Certified Business Analysis Professional (CBAP). She Participated in International Conference for Engineering 2021-Talked about Anomaly Detection She holds a bachelor’s degree in the subject of Computer Science. She’s an active blogger. She engages in technical reading, blogging, answering technical queries, and mentoring budding Data Scientists in her leisure time.

Need of Adaptive Ethical ML Models in Post Pandemic Era(Talk)

PhD studentStanford Machine Learning Group

Jeremy Irvin

Jeremy is a PhD candidate at Stanford University advised by Professor Andrew Ng. Jeremy is interested in developing machine learning tools for climate change and medicine. His current research is focused on developing machine learning approaches using remote sensing data for mapping energy and transportation infrastructure, with an emphasis on identifying sources of methane emissions globally.

Mapping for Climate Change with Deep Learning on Remotely Sensed Imagery(Talk)

Data Science ManagerGreenhouse Software

Mona Khalil

Mona is a Data Science Manager at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology, and are passionate about contributing to the ethical use of data science methodology in the public and private sector.

Leveling Up Your Organization’s Capacity for Data-informed Decisions(Talk)

SQL for Data Science(Training)

Senior Data ScientistFINRA

Alex Eftimiades

Alex is a data scientist at FINRA. He applies machine learning and statistics to identify anomalous and suspicious trading and has helped to develop model validation procedures and tools. Alex originally studied physics and is passionate about applying math to solve real world problems. He previously worked as a data engineer and as a software engineer.

Introducing Model Validation Toolkit(Talk)

Software Development Engineer In TestFINRA

Matt Gillett

Matthew Gillett is an Associate Director at FINRA who manages a team of Software Development Engineers in Test (SDET) across multiple projects. In addition to his primary focus in software development and assurance engineering, he also has an interest in various other technology topics such as big data processing, machine learning, and blockchain.

Introducing Model Validation Toolkit(Talk)

 

Student ResearcherAcademy for Mathematics, Science, and Engineering

Thomas Y Chen

Thomas Y. Chen is a student researcher in machine learning from New Jersey who is passionate about computer vision, artificial intelligence, and data science applications for Earth science. Recently, he developed an interpretable AI model to detect and assess infrastructure damage from satellite imagery. He is a member of the Research Data Alliance and serves on the U.S. Technology Policy Committee of the Association for Computing Machinery (ACM USTPC).

Deploying AI for Climate Adaptation: A Spotlight on Disaster Management(Talk)

Principal Technical ArchitectSAS

Edoardo Riva

Edoardo Riva has more than 20 years of experience enabling clients, partners, and colleagues in the areas of software architecture, integration and deployment. Over the last 10 years, he has been a featured presenter at industry and technology conferences. He has co-authored and delivered workshops globally on everything new and at the forefront of technology including distributed computing, high-performance analytics, in-database processing, workload management, and, most recently cloud computing. From Microsoft Azure to Red Hat OpenShift, he shares his experience running analytic workloads on different platforms, either on-prem or in the cloud. Edoardo holds a bachelor’s in computer engineering from Politecnico di Milano.

Automating Deployment using GitOps: SAS Viya on Red Hat OpenShift(Demo Talk)

Machine Learning Researcher Oracle

Adam Pocock, PhD

Adam Pocock is a Machine Learning researcher at Oracle Labs. He’s the lead developer of the Tribuo machine learning library, and maintains several other machine learning libraries on the JVM including TensorFlow-Java and ONNX Runtime’s Java API. Adam’s research has covered several areas of ML & applications, from work on scaling up and parallelizing Bayesian inference, to building multilingual NLP systems. He holds a PhD in Computer Science from the University of Manchester where his research focused on the theoretical underpinnings of feature selection algorithms.

Building Provenance and Reproducibility into ML Systems(Demo Talks)

Co-founder & Senior Data Scientistbnh.ai

Patrick Hall

Patrick Hall is principal scientist at BNH.AI, where he advises Fortune 500 clients on matters of AI risk and conducts research on AI risk management in support of NIST’s efforts on trustworthy AI and technical AI standards. He also serves as visiting faculty in the Department of Decision Sciences at The George Washington School of Business, teaching classes on data ethics, machine learning, and the responsible use thereof.
Prior to co-founding BNH, Patrick led H2O.ai’s efforts in responsible AI, resulting in one of the world’s first commercial solutions for explainable and fair machine learning. He also held global customer-facing roles and R&D research roles at SAS Institute. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
Patrick’s technical work has been profiled in Fortune, Wired, InfoWorld, TechCrunch and others. An ardent writer himself, Patrick has contributed pieces to outlets like McKinsey.com, O’Reilly Ideas,  Thompson-Reuters Regulatory Intelligence, and he is the lead author for the forthcoming book, Machine Learning for High Risk Applications.

A Tutorial on Contemporary Machine Learning Risk Management(Tutorial)

Data ScientistGoogle Cloud AI Services

Aishwarya Srinivasan

Aishwarya is working as a Data Scientist in the Google Cloud AI Services team to build machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and AI Platform. Aishwarya was working as an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She is an advocate for open-source technologies; previously a developer advocate for PyTorch Lightning and a contributor to Scikit Learn. She holds a post-graduate in Data Science from Columbia University. She has worked with clients all across the globe and has traveled internationally to London, Dubai, Istanbul, and India to lead and work with them. She is very focused on expanding her horizons in the machine learning research community including her recent Patent Award won in 2018 for developing a Reinforcement Learning model for Machine Trading.

She is an ambassador for the Women in Data Science community, originating from Stanford University. She has a huge follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world.

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn: https://www.linkedin.com/in/aishwarya-srinivasan/

Responsible AI for Customer Product Organizations(Talk)

Senior Applied ScientistGretel.ai

Lipika Ramaswamy

Lipika Ramaswamy is a Senior Applied Scientist at Gretel.ai where she focuses on developing advanced synthetic data generation technologies that include privacy guarantees. Prior to Gretel.ai, she worked as a data scientist at LeapYear, a differential privacy software company. Lipika attended Bryn Mawr College for her undergrad, where she began her STEM career, and holds a Master’s in Data Science from Harvard University.

Democratizing Access to Data with Synthetic Data Generation(Demo Talk)

Open-source Tools for Synthetic Data On-Demand(Talk)

Co-founder and Head of Decision ScienceMoov AI

Olivier Blais

Olivier is co-founder and VP of decision science at Moov AI. He is the editor of the international ISO standard that defines the quality of artificial intelligence systems, where he leads a team of 50 AI professionals from around the world.

His cutting-edge AI and machine learning knowledge have led him to implement a data culture in various industries and support digital transformation projects in many companies such as Pratt & Whitney, Metro, Sharethrough, Merck, and Premier Tech.

He is a mentor for AI for Creative Destruction Labs and coaches several start-ups. As a speaker, his topics of choice are adopting and applying AI and responsible AI.

Olivier is the recipient of the prestigious “30 under 30” award (2019) and is co-author of a patent for an advanced algorithm that evaluates a borrower’s creditworthiness.

How to Deliver High-Quality ML Projects(Tutorial)


See all our talks and hands-on workshop and training sessions
See all sessions

You Will Meet

  • Top speakers and practitioners in Machine Learning and Deep Learning with an interest in Responsible AI

  • Data Scientists, AI experts Data Engineers, and Machine Learning Engineers

  • Decision-makers, team leads, and other influencers

  • Data Science and AI innovators looking to make an impact with responsible AI

  • CEOs, CTOs, CIOs and other c-suite executives seeking insights on responsbible AI

  • Software Developers focused on Machine Learning and Deep Learning

  • Industry leaders, business practitioners, and product developers seeking to understand trust and privacy in AI

  • Core contributors in the fields of Machine Learning and Deep Learning

  • Citizen data scientist and and Science Enthusiasts 

Why Attend

Accelerate and broaden your knowledge of key areas in Responsible AI

With numerous introductory level workshops, get hands-on experience to quickly build up your skills

Post-conference, get access to recorded talks online and learn from over 100+ high-quality recording sessions that let you review content at your own pace

Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science

Learn directly from world-class instructors who are the authors and contributors to many of the tools and languages used in data science today

Meet hiring companies, ranging from hot startups to Fortune 500, looking to hire professionals with data science skills at all levels

Get speaker insights and training in AI frameworks such as TensorFlow, MXNet, PyTorch, Spark, Storm, Drill, Keras, and other AI platforms

Connect with peers and top industry professionals at our many networking events to discover your next job, service, product, or startup.

Who should attend

The Responsible AI Track is where industry’s top creative minds gather to discuss and shape the most challenging social problems. Whether you are an expert, or just starting your journey, this is the conference for you.

  • Data scientists looking to build an understanding of ethical intelligent machines

  • Data scientists seeking to investigate and define potential adverse biases and effects, mitigation strategies, fairness objectives and validation of fairness

  • Anyone interested in understanding areas such as fairness, safety, privacy and transparency in artificial intelligence and data

  • Business professionals and industry experts looking to understand data science ethics in practice

  • Software engineers and technologists who need to develop algorithms to solve fundamental algorithmic fairness problems

  • CTO, CDS, and other managerial roles that require a bigger picture view of data science

  • Technologists in the field of AI Fairness and others looking to learn mitigation strategies, algorithmic advances, fairness objectives, and validation of fairness

  • Students and academics looking for more practical applied training in data science tools and techniques

ODSC EAST 2023 | May 9th-11th

First 100 Tickets | Register now

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