Applied Data Science Invited Speakers
Deepak Agarwal
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
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.
Jason Weston
Research Scientist at Meta AI, USA, and a Visiting Research Professor at NYU
Bio: Jason Weston is a research scientist at Meta AI, USA and a Visiting Research Professor at NYU. He earned his PhD in machine learning at Royal Holloway, University of London and at AT&T Research in Red Bank, NJ (advisors: Alex Gammerman, Volodya Vovk and Vladimir Vapnik) in 2000. From 2000 to 2001, he was a researcher at Biowulf technologies. From 2002 to 2003 he was a research scientist at the Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. From 2003 to 2009 he was a research staff member at NEC Labs America, Princeton. From 2009 to 2014 he was a research scientist at Google, NY. Jason’s publications include best paper awards at ICML and ECML, and a Test of Time Award for his work “A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning”, ICML 2008 (with Ronan Collobert). He was part of the YouTube team that won a National Academy of Television Arts & Sciences Emmy Award for Technology and Engineering for Personalized Recommendation Engines for Video Discovery. Some of his notable work influencing the field of NLP includes the “NLP from scratch” work starting in 2008 which introduced pretraining and fine-tuning of language models, Memory Networks in 2014-2015 which introduced multi-layer attention pre-Transformers, DrQA in 2017 which introduced RAG-like methods, BlenderBot 1-3 and other LLM dialogue research pre-chatGPT in 2018-2022, and more recently work like Self-Rewarding LLMs for self-improvement.