ODSC WEST 2023

Afrah Shafquat Ph.D

Afrah Shafquat is a Sr. Data Scientist II at Medidata, a Dassault Systemès company where she leads synthetic data solutions in clinical trials. At Medidata, her work focuses on innovative solutions to generate synthetic data, synthetic data evaluation (fidelity and privacy metrics), and new use cases for synthetic data. She has a Ph.D. in Computational Biology from Cornell University and an S.B. in Biological Engineering from Massachusetts Institute of Technology.

Andrew Wilson

Andrew is a Solutions Engineer, enabling customers, prospects, and partners to get the most out of Seldon technology. Andrew has spent the last 10 years in technical, client-facing roles with a focus on data, machine learning, and architecture. He loves transforming a complex and ambiguous topic into a clear path to drive value within an organization. Outside of work, you might see Andrew riding his bike around the streets of London or snowboarding somewhere in The Alps.

Avinash Sooriyarachchi

Avinash Sooriyarachchi is a Solutions Architect at Databricks. His current work involves working with large Retail and Consumer Packaged Goods organizations across the United States and enabling them to build Machine Learning based systems. His specific interests include streaming machine learning systems and building applications leveraging foundation models. Avi holds a Master’s degree in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania.

Brian Drake

Brian Drake is the Federal Chief Technology Officer of Accrete.AI Government. He is responsible for the application of Accrete’s commercial Artificial Intelligence (AI) solutions to the public sector. Prior to joining Accrete.AI, Drake was an Intelligence Officer within the Defense Intelligence Agency where he led the briefing team for the Secretary of Defense, Deputy Secretary, the Undersecretary for Policy, and other “E Ring” principals. He also managed over $20M worth of projects as the agency’s first Director of Artificial Intelligence. In 2021, he was given the Federal 100 award by Federal Computer Week for his program’s achievements and work on the agency’s AI strategy. As an analyst, he led multiple teams countering threats from state and non-state actors across technology, counterintelligence, and counternarcotics topics. Before his government career, he was a Manager with Deloitte Consulting and a management consultant with Toffler Associates specializing in strategic planning, business development, collaboration consulting, technology, and innovation for commercial and government clients. He also worked as a military platforms and policy analyst for Systems Planning and Analysis and as a nuclear weapons programs analyst for DynCorp. In his personal time, Mr. Drake serves as the President and Chief Executive Officer of the Defense Intelligence Memorial Foundation; a scholarship fund for the children of Defense Intelligence Officers killed in the line of duty. He holds a Bachelor of Arts from Mercer University and a Masters from Georgetown University.

Catherine Havasi

Catherine is a technology strategist, artificial intelligence researcher, and entrepreneur. She co-founded the Common Sense Computing Initiative (ConceptNet), the first crowdsourced project for artificial intelligence and the largest open knowledge graph for language understanding. ConceptNet has played a role in thousands of AI projects for nearly 20 years. She was honored as one of Fast Company’s 100 Most Creative People in Business. She continues as a visiting scientist at the MIT Media Lab, where she works on computational creativity.

Chandra Khatri

Best known for developing state-of-the-art AI products such as the world’s first fully autonomous Conversational AI technology, the Alexa Prize (ChatGPT-like voice experience for Alexa users 5 years before ChatGPT), and Truth Checker AI, the first and currently only model to detect hallucinations generated by language models such as GPT-4.

Clive Cox

Clive is CTO of Seldon. He has worked for many years in the MLOps field creating the core range of Seldon products starting with Seldon Core. He has contributed to many open source projects in the field including Kubeflow and has presented at many industry conferences. His PhD research was in Natural Language Processing and he worked for some early speech recognition pioneers.

Daniel Voigt Godoy

Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping many students advance their careers. He writes regularly for Towards Data Science. He is also the main contributor of two python packages: HandySpark and DeepReplay.

David Danks Ph.D

David Danks is Professor of Data Science & Philosophy and affiliate faculty in Computer Science & Engineering at University of California, San Diego. His research interests range widely across philosophy, cognitive science, and machine learning, including their intersection. Danks has examined the ethical, psychological, and policy issues around AI and robotics across multiple sectors, including transportation, healthcare, privacy, and security. He has also done significant research in computational cognitive science and developed multiple novel causal discovery algorithms for complex types of observational and experimental data. Danks is the recipient of a James S. McDonnell Foundation Scholar Award, as well as an Andrew Carnegie Fellowship. He currently serves on multiple advisory boards, including the National AI Advisory Committee.

Dillon Bostwick

Dillon Bostwick has spent the last 5 years as a Solutions Architect at Databricks, where he has an extensive background in working with data engineers, data scientists, and business stakeholders to productionize data and machine learning projects. He is also active in developing new field projects intended to accelerate the management of data infrastructure.

Eli Chen

Eli is CTO and Co-Founder at Credo AI. He has led teams building secure and scalable software at companies like Netflix and Twitter. Eli has a passion for unraveling how things work and debugging hard problems. Whether it’s using cryptography to secure software systems or designing distributed system architecture, he is always excited to learn and tackle new challenges. Eli graduated with an Electrical Engineering and Computer Science degree from U.C. Berkeley.

Elizabeth Adams

Elizabeth M. Adams is a Responsible AI influencer recognized as one of Forbes “15 AI Ethics Leaders Showing The World The Way Of The Future.” For over two decades, she has studied the science of business and technology influences on society while leading large-scale technology initiatives for Fortune 500 companies and various government organizations. As a scholar-practitioner and a Fractional Chief AI Culture Officer (CAICO), Elizabeth has developed her expertise working with technical and non-technical leaders, creating alliances with leaders that translate theory into results. Elizabeth is pursuing an executive doctoral degree at Pepperdine University with a research focus on Leadership of Responsible AI™.

Emily Webber

Machine learning specialist solutions architect at Amazon Web Services (AWS). She’s been leading data science projects for many years, piloting the application of machine learning into such diverse areas as social media violence detection, computer vision, reinforcement learning, the IoT, drones, and robotic design. Previous data scientist at the Federal Reserve Bank of Chicago.

Eric Xing Ph.D

Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the Rutgers University, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI) and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group. He is a recipient of the National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University; he is a Fellow of AAAI, IEEE, and ASA.

Hagay Lupesko

Hagay is the VP of Engineering at MosaicML, where he focuses on making generative AI training and inference efficient, fast, and accessible. Prior to MosaicML, Hagay held AI engineering leadership roles at Meta, AWS, and GE Healthcare. He shipped products across various domains: from 3D medical imaging to deep learning systems.

Hao Zhang

Hao is currently an Assistant Professor at University of California San Diego. He is recently working on the Alpa project and the Sky project, aiming at democratizing large models like GPT-3. He is an Assistant Professor at Halıcıoğlu Data Science Institute and Department of Computer Science and Engineering (affiliate) at UC San Diego in Fall 2023.

He research is primarily focused on large-scale distributed ML in the joint context of ML and systems, concerning performance, usability, cost, and privacy. His work spans across distributed ML algorithms, large models, parallelisms, performance optimizations, system architectures, ML privacy, and AutoML, with applications in computer vision, natural language processing, and healthcare.

Julien Simon

Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.

Kfir Bar Ph.D

Kfir Bar is the Chief Scientist at Babel Street. He has worked many years in a wide range of natural language processing (NLP) disciplines, including statistical machine translation, named entity recognition, and digital-humanity applications. Before Babel Street, he worked for Intuview as CTO, supporting national security and counter-terrorism missions by deducing authorship, sentiment, intent, and other contextual information. In 2013, he co-founded Comprendi, which transforms big data into actionable marketing insights and served some large scale advertisers in different verticals, with great success. He is a lecturer at Tel Aviv University, COMAS and IDC, where he teach courses in computer science, digital humanities, machine learning and natural language processing. He holds a PhD in computer science from Tel Aviv University for a thesis on Semantics and Machine Translation, titled “Deriving Paraphrases for Highly Inflected Languages, with a Focus on Machine Translation.

Max Welling Ph.D

Prof. Max Welling is a Distinguished Scientist at Microsoft Research and research chair in Machine Learning at the University of Amsterdam. He is a fellow at the Canadian Institute for Advanced Research (CIFAR). His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft.

Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021.

Maya Ackerman

Dr. Maya Ackerman is a world renowned researcher in generative AI, CEO of musical AI startup WaveAI, and Associate Professor of Computer Science and Engineering at Santa Clara University. A pioneer in the space, Ackerman has been researching generative AI models for text, music and art since 2014. Maya was an early advocate for human-centered generative AI, bringing awareness to the power of AI to profoundly elevate human creativity. Dr. Ackerman has over 50 peer reviewed research publications and her research has been cited in over 1200 publications. Under Ackerman’s leadership, WaveAI is transforming how music is created in the age of AI. Their AI-powered lyrics assistant, LyricStudio, supports millions of artists and creators, and was used to compose #1 chart-topping hits.

Named “Woman of Influence” by the Silicon Valley Business Journal, interviews with Dr. Ackerman appear on NBC News, Forbes, Music Business Worldwide, New Scientist, NPR, Grammy.com, SiriusXM and international television stations across the globe. Dr. Ackerman is a member of the Steering Committee of the Association on Computational Creativity and has been on the program and reviewing committees of twenty different AI journals and conferences. A sought-after speaker, she appeared on many top stages, including the United Nations, IBM Research, Microsoft and Stanford University. Dr. Ackerman is the recipient of awards from the Association of Computational Creativity, Office of Naval Research, Natural Sciences and Engineering Research Council of Canada and more. She earned her PhD from the University of Waterloo, held postdoctoral fellowships at Caltech and UC San Diego.

Ozan Irsoy Ph.D

Sherif Akoush

Sherif is an MLOps engineer working on solving challenges at the intersection of systems and machine learning at scale. He also has interests in active learning systems that drive efficient data acquisition strategies. During his PhD studies I studied how to reduce the environmental footprint of computing in data centres; an area of research that he continues to investigate in the background. Outside of work, he is blessed with two wonderful kids that keep me very busy.

Stefano Ermon Ph.D

Stefano Ermon is an Associate Professor of Computer Science in the CS Department at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory, and a fellow of the Woods Institute for the Environment. His research is centered on techniques for probabilistic modeling of data and is motivated by applications in the emerging field of computational sustainability. He has won several awards, including Best Paper Awards (ICLR, AAAI, UAI and CP), a NSF Career Award, ONR and AFOSR Young Investigator Awards, Microsoft Research Fellowship, Sloan Fellowship, and the IJCAI Computers and Thought Award. Stefano earned his Ph.D. in Computer Science at Cornell University in 2015.

Tracy Ring

Tracy brings more than 20 years of experience working with clients across pharmaceuticals and medical devices.. Tracy was honored with a Top 25 Consultant Leadership award by Consulting Magazine for her work around diversity, equity, and inclusion. She is a founding member of Women Leaders in Data & Analytics (WLDA).

Yuval Fernbach

Yuval Fernbach is the Co-founder & CTO of Qwak, where he is focused on building next-generation ML Infrastructure for ML teams of various sizes. Before Qwak, Yuval was an ML Specialist at AWS , where he helped AWS Customers across EMEA with their ML challenges. Previous to that, he was the CTO of the IT department of the IDF (“Mamram”).