May 9th - 11th, 2023
Machine Learning Safety & Security
Learn the latest models, advancements, and trends from the top practitioners behind two of data science’s hottest topics
FOCUS AREA OVERVIEW
Pause for a moment to realize the number of machine learning models trained on crowdsourced data from social media and other web sources, and realize how easy it is to poison training data. This is one of the many treats raised when accessing machine learning safety Driven by concerns around foundational models, autonomous systems, and large-scale models, ML Safety is quickly becoming a key topic encompassing many areas of AI and ML. Adversarial attacks, backdoor model vulnerabilities, real-world deployment tail risks, risk monitoring, and boosting model defenses are a few of the topics that fall under the Machine Learning Safety umbrella.
ODSC East is one of the first applied data science and machine learning conferences to address this fast-trending topic.
TOPICS YOU'LL LEARN
Transparency & Explainability in Machine Learning
Differential Privacy & Federated Learning
Cybersecurity and Machine Learning
Idenifying Bias in Machine Learning
Data Privacy and Confidentiality
Safe Machine Learning & Deep Learning
Safe Autonomous Systems Control
Ethical and Legal Consequences of Unsafe Machine Learning
Engineering Safety in Machine Learning
Identifying & Fixing Vulnerabilities in the Machine Learning
Realiabilty in Critical Machine Learning Systems
Security Risks in Machine Learning and Deep Learning
Data & Poisoning Attacks in Machine Learning
Identifying Backdoor Attacks on Machine Learning
Deep learning and Adversarial Attacks
Adverserial Attacks on Autonomous Systems
Understanding Transfer Learning Attacks
Using Machine Learning to Detect Malicious Activity
Some of Our Past Machine Learning Safety & Security Speakers
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)
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.
Ankur Taly, PhD
Ankur Taly is a Staff Research Scientist at Google, where he carries out research in Machine Learning and Explainable AI. Previously, he served as the Head of Data Science at Fiddler labs, where he was responsible for developing, productionizing, and evangelizing core explainable AI technology. Ankur is most well-known for his contribution to developing and applying Integrated Gradients— a new interpretability algorithm for deep networks. His research in this area has resulted in publications at top-tier machine learning conferences and prestigious journals like the American Academy of Ophthalmology (AAO) and Proceedings of the National Academy of Sciences (PNAS). Besides explainable AI, Ankur has a broad research background and has published 30+ papers in areas including computer security, programming languages, formal verification, and machine learning. He has served on several academic conference program committees, and instructed short courses at summer schools and conferences. Ankur earned his PhD in computer science from Stanford University in 2012 and a BTech in Computer Science from IIT Bombay in 2007.
Evaluating, Interpreting and Monitoring Machine Learning Models(Talk)
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)
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)
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)
Balaji Lakshminarayanan, PhD
Balaji is currently a Staff Research Scientist at Google Brain working on Machine Learning and its applications. Previously, he was a research scientist at DeepMind for 4.5+ years. Before that, he received a PhD in machine learning from Gatsby Unit, UCL supervised by Yee Whye Teh. His research interests are in scalable, probabilistic machine learning. More recently, he has focused on: – Uncertainty and out-of-distribution robustness in deep learning – Deep generative models including generative adversarial networks (GANs), normalizing flows and variational auto-encoders (VAEs) – Applying probabilistic deep learning ideas to solve challenging real-world problems.
Practical Tutorial on Uncertainty and Out-of-distribution Robustness in Deep Learning(Tutorial)
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)
Sagar Samtani, PhD
Dr. Sagar Samtani is an Assistant Professor and Grant Thornton Scholar in the Department of Operations and Decision Technologies at Indiana University. Dr. Samtani graduated with his Ph.D. from the AI Lab from University of Arizona. Dr. Samtani’s research interests are in AI for Cybersecurity, developing deep learning approaches for cyber threat intelligence, vulnerability assessment, open-source software, AI risk management, and Dark Web analytics. He has received funding from NSF’s SaTC, CICI, and SFS programs and has published over 40 peer-reviewed articles in leading information systems, machine learning, and cybersecurity venues. He is deeply involved with industry, serving on the Board of Directors for the DEFCON AI Village and Executive Advisory Council for the CompTIA ISAO.
Ville Tuulos
Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of an upcoming book, Effective Data Science Infrastructure, published by Manning.
Human-Friendly, Production-Ready Data Science with Metaflow(Talk)
Dan Hendrycks
Dan Hendrycks is a PhD candidate at UC Berkeley, advised by Jacob Steinhardt and Dawn Song. His research aims to disentangle and concretize the components necessary for safe AI. His research is supported by the NSF GRFP and the Open Philanthropy AI Fellowship. Dan has helped contribute the GELU activation function, the default activation in most Transformers including BERT, GPT, and Vision Transformers.
Unsolved ML Safety Problems(Talk)
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)
Kevin Hu
Kevin Hu is co-founder and CEO of Metaplane, a data observability company based in Boston focused on helping every team find and fix data quality problems with as little setup as possible. Metaplane is backed by leading investors including Y Combinator and the founders of Okta, HubSpot, and Lookout, and is used across high-growth teams and large enterprises.
Kevin has over a decade of experience working in data. Most recently, he researched the intersection of machine learning and data science at MIT, where he collaborated with Fortune 500 companies while earning his PhD, SM, and SB. His research has been published in top computer science venues like ACM CHI, KDD, and SIGMOD, and featured in the New York Times, Wired, and The Economist.
The Origins, Purpose, and Practice of Data Observability(Talk)
Data Observability in 10 Minutes(Demo Talk)
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)
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)
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)
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)
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)
Andras Zsom, PhD
Andras Zsom is an assistant Professor of the Practice of Data Science and Director of Industry and Research Engagement at Brown University, Providence, RI. He works with high-level academic administrators to tackle predictive modeling problems, he collaborates with faculty members on data-intensive research projects, and he was the instructor of a data science course offered to the data science master students at Brown.
Introduction to Interpretability in Machine Learning(Workshop)
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)
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)
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.
Patrick Hall
A Tutorial on Contemporary Machine Learning Risk Management(Tutorial)
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)
Speakers Coming Soon
You Will Meet
Top speakers and practitioners in Machine Learning Safety
Data Scientists, Machine Learning Engineers, and AI Experts interested in risk in AI
Business professionals who want to understand safe machine learning
Core contributors in the fields of Machine Learning and Deep Learning
Software Developers focused on building safe machine learning and deep learning
Technologist seeking to better understand AI and machine learning risks and vulnerabilities
CEOs, CTOs, CIOs and other c-suite decision makers
Data Science Enthusiasts
Why Attend?
Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models and advanced trends
Expand your network and connect with like-minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career
Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning
Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and its coming impact on the domains of business, society, healthcare, finance, manufacturing, and more
ODSC EAST 2023 | May 9th-11th
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