Applied Data Science Invited Speakers
Deepak Agarwal
LinkedIn’s Chief AI Officer
Bio: Deepak Agarwal is LinkedIn’s Chief AI Officer focused on empowering the global workforce, delivering economic opportunity, and ensuring LinkedIn remains at the forefront of building with AI in a responsible and compliant way.
Prior to his current role, Deepak was the Chief AI Officer and VP of Consumer and Trust Engineering at Pinterest, where he delivered transformative AI innovations that redefined the member experience. Before that, he spent 8 years as LinkedIn’s VP of AI, leading key initiatives to infuse AI into the fabric of the company culture and closely integrate the technology into all facets of how the platform was built.
Deepak is also on the advisory board for VentureBeat Transform, has written a book – Statistical Methods for Recommender Systems – and published extensively at top-tier conferences. He has a PHD in Statistics from the University of Connecticut and outside of work, he enjoys trying new recipes and cooking for his family.
Jay Alammar
Director and Engineering Fellow at Cohere
Bio: Jay Alammar is co-author of Hands-On Large Language Models, published by O’Reilly Media, and Director and Engineering Fellow at Cohere (a pioneering provider of large language models as an API).
Through his popular AI/ML blog, Jay has helped millions of researchers and engineers visually understand machine learning tools and concepts from (e.g., The Illustrated Transformers, BERT, DeepSeek-R1, and others).
Natalie Glance
Chief Engineering Officer at Duolingo
Bio: Natalie is a lifelong learner and seasoned leader with extensive experience at startups and established companies. She’s currently the Chief Engineering Officer at Duolingo.
At Duolingo, Natalie ensures engineers can help set product direction and strategy. She’s championed a culture of extensive A/B testing and is excited about the ways generative AI can both build new features and accelerate content creation for these features. She oversees many of the efforts dedicated to scaling Duolingo’s technology to new courses, like Math, Music, and Chess.
Christina Ilvento
Apple
Bio: Christina Ilvento leads the privacy preserving measurement and machine learning team at Apple. She holds a PhD from Harvard University.
Marc Najork
Distinguished Research Scientist at Google DeepMind
Bio: Marc Najork is a Distinguished Research Scientist at Google DeepMind, working on new techniques to make it easier for people to obtain relevant and useful information when and where they need it. Marc is interested in using generative language models to answer questions directly, rather than referring users to relevant sources. Direct answers represent a major paradigm shift in Information Retrieval, affecting the user experience, the fundamental architecture of the retrieval system, and the economic foundation of commercial web search and the entire web content ecosystem. Prior to joining Google, Marc was a Principal Researcher at Microsoft Research, and a Research Scientist at Digital Equipment Corporation. He is an ACM Fellow, IEEE Fellow, AAAS Fellow and a SIGIR Academy member.
Nina Mishra
Principal Scientist at Amazon
Bio: Nina Mishra is a Principal Scientist at Amazon where her research interests include health AI, search algorithms and privacy. Most of her career has been in industry including Microsoft Research. Her work has product implications including machine learning-related algorithms in many services in Amazon’s cloud and in Microsoft’s search engine. Her academic experience includes Associate Professor at the University of Virginia and Acting Faculty at Stanford University. She is driven by the transformative potential of health AI to revolutionize the lives of millions worldwide.
Yaron Singer
Vice President, Engineering & AI at CISCO
Bio: Yaron Singer is VP of Engineering and AI at Cisco. He was previously CEO and co-founder of Robust Intelligence which was recently acquired by Cisco. Before Robust Intelligence, Yaron was a tenured Professor of Computer Science and Applied Mathematics at Harvard University, a researcher at Google AI and consulting researcher at Microsoft. He is the recipient of multiple awards, including the NSF CAREER award, the DARPA award, the Sloan Fellowship, the Facebook faculty award, the Google faculty award, the 2010 Facebook Graduate Fellowship, the 2009 Microsoft Research Ph.D. Fellowship, and best student paper award at the ACM Web Search and Data Mining conference. He is also an active investor in startups that work at the intersection of AI and security as well as an advisor to leading cybersecurity companies on AI.
Chris Wiggins
Columbia University, Chief Data Scientist at the New York Times
Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of Applied Physics and Applied Mathematics as well as the Department of Systems Biology, and is affiliated faculty in Statistics. He is a co-founder and co-organizer of hackNY (http://hackNY.org),
a nonprofit which since 2010 has organized once a semester student hackathons and the hackNY Fellows Program, a structured summer internship at NYC startups. Prior to joining the faculty at Columbia he was a Courant Instructor at NYU (1998-2001) and earned his PhD at Princeton University (1993-1998) in theoretical physics. He is a Fellow of the American Physical Society and is a recipient of Columbia’s Avanessians Diversity Award.
Books:
- “How Data Happened: A History from the Age of Reason to the Age of Algorithms” with Matthew L. Jones (Norton Press; March 21, 2023)
https://www.amazon.com/How-Data-Happened-History-Algorithms/dp/1324006730/ - “Data Science in Context: Foundations, Challenges, Opportunities” with Alfred Spector, Peter Norvig, and Jeanette Wing (Cambridge Press; Dec 31, 2022)
https://www.amazon.com/Data-Science-Context-Foundations-Opportunities/dp/1009272209/