Speakers

Emily Denton Senior Research Scientist, Google

Emily Denton is a Senior Research Scientist at Google

Sabrina Mielke PhD Candidate, Johns Hopkins University

Cinjon Resnick PhD Candidate, New York University

Elena Sizikova Moore Sloan Faculty Fellow, New York University

Savannah Thais Postdoctoral Associate, Princeton University

Nan Wu PhD Candidate, New York University

Nan Wu is a Ph.D. candidate at NYU Center for Data Science, working with Professor Krzysztof Geras and Professor Kyunghyun Cho. She is one of the recipients of Google Ph.D. Fellowship 2020 on Machine Perception, Speech Technology, and Computer Vision. Her research focuses on helping deep neural networks to overcome difficulties when learning from multiple input modalities, with application on medical imaging, clinical notes understanding and text-to-speech. She was on the executive board of the NYU Women in Data Science group (2020-2021). Before joining NYU, she studied Statistics and Business Administration at University of Science and Technology of China, School of Gifted Young.

Lily Zhang PhD Candidate, New York University

Naomi Saphra Postdoctoral Researcher, New York University

Tai-Danae Bradley Research Mathematician | Visiting Research Professor of Mathematics, Alphabet | The Master's University

Tai-Danae Bradley is a research mathematician at Sandbox@Alphabet and a visiting research professor of mathematics at The Master's University. She is also creator of the mathematics blog Math3ma, a former cohost of the PBS YouTube channel "Infinite Series," and coauthor of the book Topology: A Categorical Approach. She received a PhD in mathematics from the CUNY Graduate Center, and her research interests include category theory, quantum physics, and machine intelligence.

Yash Deshpande Machine Learning Engineer, Fresenius Medical Care North America

Yash is an experienced data scientist with an interest in actionable ML and natural language processing; he has worked on problems such as consumption forecasting, text classification, and multi-modal representation learning. As an ML engineer, his focus is on creating efficient ML pipelines - specifically, ensuring that organizations can convert data science research into robust, actionable insights. He does this by helping to build, host, and monitor ML models at scale.

Miles Cranmer PhD Candidate, Princeton University

Miles is a PhD Candidate in Astrophysical Sciences at Princeton University, advised by David Spergel and Shirley Ho. He recently finished a research scientist internship at DeepMind, working under Peter Battaglia. He works on developing new machine learning algorithms for different problems in physics and astrophysics, with a research emphasis on "opening the black box" and distilling physical insight from trained neural networks.

Navita Goyal PhD Student in Computer Science, University of Maryland College Park

Navita is a first year Ph.D. student at University of Maryland College Park, advised by Prof. Hal Daumé III. Her research interests lie at the intersection of causal inference and NLP. Before starting her doctoral studies, Navita was a Research Associate at Adobe Research Labs in India where she worked on problems in natural language generation with a focus on interplay between text and user engagement/activity. She completed her Integrated Masters in Applied Mathematics from Indian Institute of Technology Roorkee.

Kush Varshney Distinguished Research Staff Member, IBM Research

Kush 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.

Sam Bowman Professor of Linguistics, Data Science & Computer Science, New York University

Sam Bowman is an Assistant Professor of Data Science, Linguistics, and Computer Science at NYU, where he leads research groups working on data collection and evaluation for artificial neural network models as well as the use of artificial neural network models as scientific tools in linguistics. He earned a PhD at Stanford, and has been connected to John Hopkins University, Google, ASAPP, and Anthropic.

Kianté Brantley PhD in Computer Science, University of Maryland College Park

Kianté Brantley is a Ph.D. candidate in computer science advised by Professor Hal Daumé III. Brantley designs algorithms that efficiently integrate domain knowledge into sequential decision-making problems. He is most excited about imitation learning and interactive learning—or, more broadly, settings that involve a feedback loop between a machine learning agent and the input the machine learning agent sees. He has published five first-author conference papers and co-authored three more. He won second place for his talk at the Natural Language, Dialog and Speech Symposium, a leading machine learning conference. Brantley recently received a prestigious Computing Innovation Fellowship, which will support him for two years as a postdoc at Cornell University. He will study theoretical and practical aspects of learning-to-rank recommendation system problems with Professor Thorsten Joachims. The outcome of their study will be new methodologies with theoretical guarantees and practical benefits for sequential decision-making in recommendation systems. As a Ph.D. student, Brantley was awarded the competitive Microsoft Research Dissertation Grant, the Association for Computing Machinery’s Special Interest Group on High-Performance Computing/Intel Computational and Data Science Fellowship, the National Science Foundation Louis Stokes Alliances for Minority Participation Bridge to the Doctorate Program Fellowship, the UMD Ann G. Wylie Dissertation Fellowship and the UMD Graduate School’s Dean’s Fellowship. Over the past four summers, he interned for Microsoft Research. Before coming to UMD in 2016, Brantley attended the University of Maryland, Baltimore County where he earned his bachelor’s degree and master's degree (advised by Tim Oates) in computer science. He also worked as a developer for the U.S. Department of Defense from 2010 to 2017. In his free time, Brantley enjoys playing sports; his favorite sport at the moment is powerlifting.

Andrea Jones-Rooy Professor and Director of Undergraduate Studies, New York University

Andrea Jones-Rooy, Ph.D. (they/them) is the Director of Undergraduate Studies at the NYU Center for Data Science, where they also developed and teach their popular flagship course, Data Science for Everyone, as well as advanced courses on natural language processing. Before coming to NYU, they founded the interdisciplinary major in quantitative social science at NYU Shanghai, taught at Carnegie Mellon University, and earned a Ph.D. in political science with a focus on complex systems at the University of Michigan, Ann Arbor. Prof. Jones-Rooy is also a research consultant, coach, and keynote speaker for Global Fortune 500s and tech companies on how to more thoughtfully use data science to more objectively and efficiently measure talent and performance. In their spare time, they are an internationally touring standup comedian and circus performer, host of the podcast Majoring in Everything (available everywhere!), and host of the WORLD'S GREATEST one-person comedy- and circus-accented data science show, the Data Science Spectacular (premiering at Caveat in NYC and livestreaming on March 1).

Sarah Shugars Faculty Fellow, New York University

Dr. Sarah Shugars is a computational social scientist who develops new methods in natural language processing, network analysis, and machine learning in order to better understand how people talk and reason about political issues. They are currently a Faculty Fellow at NYU’s Center for Data Science (CDS) and a Research Fellow at George Washington University’s School of Media & Public Affairs.

Ren Yi Software Engineer, Google

Ren is a software engineer at Google working on federated learning. She recently completed her PhD in computer science from NYU co-advised by Dr. Rich Bonneau and Dr. Kyunghyun Cho, where she developed machine learning methods for data-limited problems in Genomics. Prior to her journey in computer science, she was a biology major in undergrad, where she spent a year working on zebrafish lateral line embryonic development. Outside of research, she likes growing plants from seeds, going on rocky hikes, and trying not to injure herself too badly when snowboarding.

Yury Zemlyanskiy PhD student, University of Southern California (Fomerly Research Scientist at Facebook)

Yury is a fifth-year Computer Science Ph.D. student at the University of Southern California, advised by professor Fei Sha. Before joining graduate school, he worked on Neural Machine Translation at the Applied Machine Learning team from Facebook. Yury got his specialist degree in Computer Science and Math from St. Petersburg State University with professor Alexander Vakhitov as the thesis advisor. Yury is interested broadly in Machine Learning and Natural Language Processing, focusing on text and entity representations, memory augmented models and reasoning over text.

Kyunghyun Cho Associate Professor of Computer Science and Data Science | Senior Director of Frontier Research, New York University | Prescient Design

Kyunghyun is an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines & Brains. He is also a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving PhD and MSc degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

,