Speakers

Karl L. Sangwon Medical Student, New York University | OLab

Karl is a Medical Student at NYU Grossman School of Medicine, who has an interest in Artificial Intelligence research. He's currently working on using Stable Diffusion model for augmenting medical data and predicting future course of MRI images of Brain Metastases post-treatment, as well as improving our understanding of cerebrovascular flow in STA-MCA bypass using FLOW800 metrics. His main passion lies in augmenting modern medicine using advanced technologies, to create a more 21-century-like ‘cyberpunk’ medical paradigm that fits my vision.

Yuntian Deng Ph.D. Student, Harvard University

Yuntian's research aims at enabling ubiquitous long-form text generation, where anyone can produce a document on any topic with just a few clicks. To achieve this goal, my research focuses on the following three directions, utilizing advances in deep learning combined with probabilistic modeling: long form coherence, transparent generation, and efficient systems. He also works on open-source projects such as OpenNMT, Im2LaTeX, LaTeX2Im, and Steganography to make his research efforts more readily available for developers and researchers.

Carlos Fernandez-Granda Associate Professor, New York University

Carlos focuses on the design and analysis of data-science methodology. Currently, the main focus of his group is machine learning, and its application to medicine, climate science and scientific imaging.

Margarita Boyarskaya Sr. Research Associate, JP Morgan AI Research Lab

Margarita Boyarskaya is a Senior Research Associate at JPMorgan AI Research lab, where she is a member of the XAI group. She works on developing methods for fair and explainable AI, focusing on algorithmic recourse and applications in consumer finance.Margarita did her doctoral studies at NYU Stern dept. of Technology, Operations, and Statistics, where she was a recipient of the Fubon fellowship. Previously, she collaborated with the FATE lab at Microsoft Research and co-developed a Breakthrough Tech Machine Learning course at Cornell Tech. Margarita's interests include AI regulation and governance.

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).

Claudia Skok-Gibbs PhD Student, New York University

Claudia is a 2nd year phd student at NYU Center for Data Science. Her research interest is exploring scalable and adaptable machine learning models for gene regulatory network inference. She is interested in applying these methods to extract information from single-cell genomics and chromatin structure to make useful predictions to inform wet lab biologists, drug discovery and targeted therapies. Outside of work, she loves running, baking, photography and adventures!

Zahra Kadkhodaies PhD Student, New York University

Zahra Kadkhodaie is a PhD student in Center for Data Science (CDS) at NYU. Before joining the PhD program in 2020, she received two Master’s degrees from NYU, in Data Science and Psychology. Prior to that, she received a Bachelor’s degree in Physics from K.N.Toosi University in Tehran, Iran. Her main research interest is understanding and utilizing priors of images embedded in deep neural network generative models. Particularly, she has worked on locality, scale and generalization properties of priors implicit in denoiser neural networks as well as using these priors to solve inverse problems in an unsupervised fashion.

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.

Angelica Chen PhD Student, New York University

Angelica is a PhD student at NYU Center for Data Science in the Machine Learning for Language group, advised by Sam Bowman and Kyunghyun Cho. I’m broadly interested in deep learning for natural language understanding, code generation, model robustness, and improved evaluation metrics for NLU models. I have also previously worked as a student researcher at Google Research (Jun-Dec. 2021) on streaming models for disfluency detection and at Google Brain (Jun. 2022-Mar. 2023) on LMs for neural architecture search (NAS).

Yanis Bahroun Research Fellow, Flatiron Institute

Yanis is a Research Fellow at Flatiron Institute, part of both the Center for Computational Neuroscience and the Center for Computational Mathematics. My research interest is on developing biologically plausible learning algorithms that can map onto the brain. Based on anatomical and physiological neuroscience data, I have developed algorithms that model brain computation and solve machine learning tasks, such as transformation learning, which led to various NeurIPS publications.

Bing Yan PhD Student, New York University

Bing is a first-year Data Science PhD student co-advised by Kyunghyun Cho and Joan Bruna. She holds a PhD in chemsitry from MIT.

Suraj Subramanian Machine Learning Advocate, Meta

Suraj is a machine learning advocate at Meta. He loves working with data and his primary interests include Data Engineering, Machine Learning, Backend and Cloud Development. During his free time, Suraj like to read about finance, crypto and browse memes.