Faculty Advisors: Vasant Dhar, Kyunghyun Cho
About: Arda is a PhD student at NYU Center for Data Science, advised by Sumit Chopra and Daniel Sodickson. He obtained his B.S., summa cum laude in Electrical Engineering, at Bilkent University with a full scholarship and Academic Excellence Award. After finishing his B.S., he did a summer internship with Erdal Arikan. He spent one year at EPFL’s Laboratory for Information in Networked Systems, working with Michael Gastpar.
Before joining NYU, he worked in the telecom industry for three years as a research engineer, and he recently finished his M.S. at Bilkent University, supervised by Orhan Arikan.
Outside of research, he enjoys playing the guitar, free-diving, and wine tasting.
About: Lucius is a PhD student at the NYU Center for Data Science working with Professors Julia Stoyanovich and Joshua Loftus. His current research interests center around the intersection between responsible data science and statistical theory and methodology e.g. in areas such as machine learning fairness and causal inference. Prior to joining CDS, he worked as a researcher at Pacific Northwest National Laboratory in their Applied Statistics and Computational Modeling group. Lucius holds a B.Sc. in Data Science from Harvey Mudd College.
About: Angelica Chen is a PhD student at the NYU Center for Data Science, advised by Kyunghyun Cho and Sam Bowman. She is broadly interested in natural language understanding and model robustness and generalization. Prior to NYU, Angie worked on neural semantic parsing research at Google NYC and graduated with high honors from Princeton University with a Bachelor’s degree in Computer Science. While at Princeton she worked with Professor Sebastian Seung on her undergraduate thesis, titled “Prediction of depression and suicidality from social media activity using deep neural networks.” Outside of her research, Angie enjoys running and baking pastries.
Anthony Guanxun Chen
Faculty Advisors: Rob Fergus and Cristina Savin
About: Guy is a PhD student at the NYU Center for Data Science, advised by Professor Brenden Lake. Guy’s interests center around the intersection between human cognition and machine learning, and particularly, what can we learn from studying humans to allow us to design wiser machine learning systems. Before coming to NYU, Guy served six years in the Israel Defense Forces, including roles as an instructor and a team leader. He then graduated (summa cum laude) with a B.Sc. in Computational Sciences from the Minerva Schools, a global undergraduate program based in San Francisco. He previously had the honor to collaborate with Professor Yael Niv (Princeton) and Professor Michael C. Mozer (Google Research / CU Boulder). Outside of his studies, Guy loves to play ultimate frisbee and is developing an appreciation for pour-over coffee.
About: Katrina is a PhD student at the NYU Center for Data Science, advised by Yann LeCun. Her research interests lie in self-supervised learning and deep learning theory. Katrina earned her Master’s degree in Data Science from CDS during which she collaborated with Kyunghyun Cho. Prior to starting her PhD, she worked as a research engineer at eBay NYC. Katrina holds a Bachelor’s degree in Mathematics from Harvard University.
About: Yunzhen is a PhD student at the Center for Data Science working with Prof. Julia Kempe. He is generally interested in improving machine learning beyond benchmark datasets and providing methodology guidance for the practical side. Prior to NYU, Yunzhen graduated from Peking University (Bachelor’s degree) with background in applied math, statistics, and data science. Outside of school, Yunzhen is an outdoor lover in mountain climbing, trekking, and snowboarding. The highest snowberg he has climbed is the Luodui Mount (6010m) in Tibet, China.
Diego Herrero Quevedo
About: Diego is a PhD student at the NYU Center for Data Science, co-advised by Prof. Carlos Fernandez-Granda and Prof. Rajesh Ranganath. His research interests revolve around probabilistic deep learning applied to healthcare. Before joining NYU, Diego obtained a B.S. in Data Science and Engineering from Universidad Carlos III de Madrid. He further earned an M.S. in Machine Learning for Health while working as a research assistant at the same university.
About: Wanli is a PhD student at the NYU Center for Data Science working with Professor Shuyang Ling. His research interest lies in the interdisciplinary area of mathematics and data science such as optimal transport, random matrix, theoretical deep learning, etc. He is currently working with Professor Shuyang Ling on orthogonal group synchronization. He also held an M.S. degree in applied mathematics from Columbia University.
About: Michael is a PhD student at the NYU Center for Data Science working with Brenden Lake and Tal Linzen. Before joining NYU, he received a BSE in Computer Science at Princeton University and spent a year at Yobi Ventures as its first full-time engineer. Michael is interested in representation learning and learning dynamics in humans and machines. His work is supported by an NSF Graduate Research Fellowship.
About: Chris is a PhD student at the NYU Center for Data Science, currently jointly advised by Professors Brian McFee and David Hogg. Currently, his research interests lie in audio signal processing in urban environments, multi-source methods in recommender systems, and Bayesian modeling methods. Prior to joining CDS, Chris worked as a research scientist under David Hogg and Kyle Cranmer, utilizing Gaussian Processes for modeling solar flares, and low-count dark matter detection experiments, and as an adjunct instructor for several undergraduate courses. Chris holds a B.S. in physics with minors in computer science and mathematics.
About: Daniel is a PhD student under the supervision of Kyunghyun Cho. Daniel was a research scientist at HHMI, Janelia Research Campus under the advisory of Dr. Kristin Branson. He has worked on building an artificial fly. They focus on the problem of modeling behavior of an organism as a whole, in particular locomotion and social behavior of Drosophila melanogaster. Previously, he was a researcher at the University of Montreal, MILA machine learning institute under the advisory of Prof. Yoshua Bengio and Dr. Roland Memisevic. He also received his Masters degree from the University of Guelph’s School of Engineering department under the advisory of Graham W. Taylor. His research interests during MILA and UoGuelph were postulating deep generative models and analyzing unsupervised representation learning models under the view of a dynamical system or probabilistic perspective. He received his undergraduate degree from the University of Toronto in 2012. He also completed a specialist program in Computer Science with a specialization in Artificial Intelligence Specialist and pursued Mathematics and its application specialist program. He is also CEO and founder of AIFounded.inc, and CTO and co-founder of Coinscious.inc
About: Lavender Jiang is a PhD student, medical fellow co-advised by Eric Oermann and Kyunghyun Cho at the OLAB. She is interested in representation learning and its application to healthcare.
Lavender earned her bachelor’s degree in Electrical and Computer Engineering and Mathematical Sciences from Carnegie Mellon University. She worked on graph signal processing, EEG signal processing, and sensor fusion for robotics.
About: Zahra Kadkhodaie is a PhD student of Data Science at NYU advised by Prof. Eero Simoncelli, and Prof. Carlos Fernandez Granda. Her research focuses on understanding and improving deep neural networks by analyzing and imposing mathematical symmetries on the architecture. More recently, she has worked on extracting and utilizing the prior embedded in a trained deep neural network denoiser for solving other computer vision problems without further training. Zahra completed her B.Sc. in Solid State Physics at K.N. Toosi University in Tehran, Iran and her M.Sc in Data Science and Psychology at NYU.
About: Sanyam is pursuing a PhD in Data Science, advised by Andrew Gordon Wilson, and is interested in scaling up probabilistic inference and advancing reinforcement learning. Before this, he was at Uber AI Labs, working on approximate inference with Thang Bui. He has completed his Masters thesis on how to leverage communication for efficient Monte Carlo sampling, advised by Joan Bruna in the CILVR lab. In his previous life, as an undergraduate at IIT Hyderabad, he co-founded StoryXpress where we automated video creation for businesses.
Adam (Akhilesh) Khakhar
About: Adam is a PhD student at NYU Center for Data Science, advised by Yanjun Han and Qi Lei. He is interested in deep learning and its intersection with information theory. Adam received his Bachelor’s degree in Statistics from The Wharton School and Master’s degree in Computer Science from the School of Engineering and Applied Sciences at the University of Pennsylvania. He was a visiting scholar in statistical machine learning at Stanford University.
About: Falaah is a PhD student at the NYU Center for Data Science, working with Prof Julia Stoyanovich on the ‘fairness’ and ‘robustness’ of algorithmic systems. An engineer by training and an artist by nature, Falaah creates scientific comic books to bridge together scholarship from different disciplines, and to disseminate the nuances of her research in a way that is more accessible to the general public — She runs the ‘Data, Responsibly’ comic series with Prof Julia Stoyanovich at NYU’s Center for Responsible AI, and the ‘Superheroes of Deep Learning’ comic series with Prof Zack Lipton (CMU). Falaah holds an undergraduate degree in Electronics and Communication Engineering (with a minor in Mathematics) from Shiv Nadar University, India, and has industry experience in building machine learning models for access management and security at Dell EMC.
About: Polina is a PhD student at the NYU Center for Data Science and a DeepMind fellow supervised by Professor Andrew Wilson. Her current research focuses on probabilistic machine learning, Bayesian deep learning and uncertainty estimation. She is interested in building robust machine learning models and understanding when they can be trusted in making decisions, which is important for many sensitive applications. She obtained her Bachelor’s degree in Computer Science at the Higher School of Economics in Moscow. During her undergrad, she worked at Bayesian Methods Research group with professor Dmitry Vetrov, our research focused on variational inference and regularization for deep neural networks. Just before starting my PhD, she did a summer internship at EPFL in Machine Learning and Optimization Lab, where she worked on zero-order optimization for low precision neural networks supervised by professors Martin Jaggi and Dan Alistarh. She also had a chance to experience industry at Google as a software engineering intern in Munich and Seattle offices, where she worked on the backend, distributed systems and algorithm parallelization for internal tools. Before coming to NYU, she completed one year of a PhD program in Operations Research and Information Engineering at Cornell University where she started working with Professor Andrew Wilson on low-precision training of neural networks, Bayesian deep learning and normalizing flows; after her first year of a PhD, their research lab transferred to New York University.
About: Yilun is a PhD student at NYU Center for Data Science, advised by Professor Andrew Gordon Wilson. He is interested in probabilistic machine learning, representation learning, and more broadly how models generalize and learn. Prior to starting his PhD, Yilun worked on manifold geometry and efficient-encoding inspired self-supervised learning at Flatiron Institute, Simons Foundation. He graduated magna cum laude with high honors from NYU with a BA in Mathematics. Outside of research, he enjoys playing ping pong, ultimate frisbee, basketball, and reading about philosophy, politics, and economics.
About: Kangning is a PhD student at the NYU Center for Data Science, co-advised by Prof. Carlos Fernandez-Granda, Prof. Kyunghyun Cho and Prof. Krzysztof J. Geras. He is interested in machine learning, computer vision and their application for healthcare. Prior to joining NYU, Kangning pursued a Master’s degree in data science from the department of computer science at ETH Zurich, where his master thesis was on unsupervised video-to-video translation, under the supervision of Dr. Radu. Timofte, Dr. Shuhang Gu and Prof. Luc Van Gool at Computer Vision Laboratory. Before that, he also had project experience in medical imaging super-resolution and semantic segmentation. He earned a Bachelor’s degree from Tsinghua University in electrical engineering with a minor in Finance.
About: Sanae is a PhD student at the NYU Center for Data Science and a DeepMind fellow, advised by Professor Andrew Wilson. She is currently interested in understanding and quantifying the generalization of deep learning models. More broadly, her research interests include the mathematics of deep learning, probabilistic generative models, Bayesian learning and statistical learning theory.
Before joining NYU, Sanae obtained a master’s degree in applied mathematics from Polytechnique Montreal. She worked with Professors Andrea Lodi and Dominique Orban to design stochastic first- and second-order algorithms with compelling theoretical and empirical properties for machine learning and large-scale optimization. She also holds an engineering degree in applied mathematics from CentraleSupélec in France.
About: Taro is a PhD student at the NYU Center of Data Science advised by Kyunghyun Cho and Krzysztof Geras. He is interested in robustness and explainability in deep learning, and is researching these topics in the domain of medical images. He holds an M.S. in Artificial Intelligence from the University of Edinburgh where he worked with Amos Storkey, and a B.A. in Mathematics from Northwestern University.
Uriel Martinez Leon
Uriel is a PhD student at the NYU Center for Data Science, advised by Jonathan Niles-Weed. He specializes in the intersection of geometry, statistics, and optimization, specifically optimal transport. Before joining CDS, Uriel served as a Summer Geometry Initiative Fellow and was actively involved in UNAM’s Applied Geometry Lab. He holds a bachelor’s degree in Applied Mathematics from ITAM and has master’s studies in Mathematics from UNAM.
About: Will is a PhD student at the NYU Center for Data Science. Will’s research interests lie at the intersection of language and computation—spanning NLP, linguistics, and formal language theory. In particular, his recent work investigates the mysterious success of deep learning in NLP from a theoretical perspective. He is interested in how AI systems represent the compositional structure and meaning of language, and how we can improve current representations to enable deeper language understanding. Before coming to NYU, Will was a predoctoral researcher at the Allen Institute for AI and obtained his B.S. from Yale.
About: Nikita is a PhD student at the NYU Center for Data Science, where she is advised by Sam Bowman. Broadly, her research is in machine learning and natural language processing. Her publications can be found on her personal website. Prior to joining NYU, Nikita worked in R&D for a few years. She has a BA in Physics from the University of Chicago.
About: Vishakh is a PhD student at the NYU Center for Data Science being advised by Prof. He He. His current interests lie in exploring constrained/creative text generation and bias in NLP models. Prior to joining the PhD program, he earned a Master’s degree in Computer Science at NYU while working as an RA at the Center for Social Media and Politics.
About: Jacob Pfau is a PhD student at the NYU Center for Data Science, working in the NYU Alignment Research Group supervised by Sam Bowman and He He. Jacob’s research is motivated towards ensuring language models continue to be safely usable as they scale. As of 2022, he is working on empirically demonstrating failures of language models to generalize honestly. Jacob also thinks about formalizing incentives towards agency and deception in language model pre-training and fine-tuning. Previously, Jacob completed a masters in philosophy at the University of Edinburgh, a year of machine learning masters at the Ecole Polytechnique in France, and a bachelors in mathematics at Amherst College. Between years at university, he worked on mis-generalization in reinforcement learning and interpretability for medical imaging.
About: Jason is a PhD student at the NYU Center for Data Science, advised by Sam Bowman, Kyunghyun Cho, and Krzysztof Geras. His research interests include natural language processing and understanding, deep learning for medical imaging, and interpretability for deep learning models. Prior to NYU, Jason earned his Bachelor’s degree from the University of Chicago, majoring in Mathematics, Economics, and Statistics, and worked as a Quantitative Researcher at AQR Capital Management.
About: Aram is a PhD student at the NYU Center for Data Science, supervised by Jonathan Niles-Weed. His research interests lie at the intersection of optimization theory, computational and statistical optimal transport, and problems in deep learning.
Prior to joining CDS, he completed a bachelor’s degree in Honors Applied Mathematics and a master’s degree in Mathematics and Statistics, both at McGill University. During his MSc, he worked on problems in convex analysis, mathematical programs with vanishing constraints, and adversarial attacks for deep neural networks. For more information about past and present research interests, see his personal website.
About: Andres is a PhD student at the NYU Center for Data Science, currently being advised by Professor Andrew Wilson. He is interested in developing probabilistic models to extract meaningful insights from scientific data and also in increasing the applicability of these models for making decisions in high-stakes situations. To this end, he is working on designing high-fidelity and scalable inference algorithms, formulating principled approaches for incorporating domain knowledge and improving how uncertainty is quantified. Before starting his PhD, he spent a year collaborating with Professor John Cunningham at the Zuckerman Institute, immediately after finishing his master’s in Data Science at Columbia University. Prior to his graduate studies, he was a consultant at McKinsey & Company. Additionally, he holds both an undergraduate degree in Applied Mathematics and in Economics from ITAM.
Haresh Rengaraj Rajamohan
Faculty Advisors: Cem Deniz and Kyunghyun Cho
About: Danielle is a PhD student at the NYU Center for Data Science, working with Prof. Kyunghyun Cho. Her research interests include commonsense reasoning as well as text-aided sequential decision making. Before joining CDS she was at Facebook AI Research, New York and then Paris, working on a variety of reinforcement learning challenges including training agents for Starcraft and Minecraft. Danielle holds a Bachelor’s degree in Engineering Physics and Computer Science from Brown University.
Claudia N. Skok-Gibbs
About: Claudia is a PhD at the NYU Center for Data Science. My current research projects focus on developing algorithms to infer gene regulatory networks from genome-wide data. Prior to starting at CDS, she was a research analyst in Richard Bonneau’s Lab in the Simon’s Foundation’s Flatiron Institute. At the Flatiron institute she extended and adapted the network inference algorithm, the Inferelator, and applied this software to several novel single-cell datasets across different species. She holds a Bachelors of Science in Mathematics, with minors in Computer Science, Finance and English. Outside of her research, she enjoys training for marathons, photography, and painting.”
About: Vlad is a PhD student at the NYU Center for Data Science and is advised by Kyunghyun Cho, and Yann LeCun. He is interested in NLP, computer vision and self-driving cars. Vlad’s research interests were inspired by his internships at Nvidia, where he worked on self-driving cars. Apart from working at Nvidia in the Bay Area, Vlad has traveled to London and Zurich to do internships at Google and Jane Street. Between the internships, he received his undergraduate degree in computer science from the University of Warsaw in Poland.
About: Jingtong is a PhD student at the NYU Center for Data Science, working with Prof. Julia Kempe. His research interests are around machine learning, both in designing algorithms based on theoretical insights and empirical observation, and in its applications to sciences such as Physics. Before the PhD study, He obtained his Bachelor’s degree in Data Science and Big Data Technology at Yuanpei College, Peking University.
Faculty Advisors: Mengye Ren
About: Nikos is a PhD student at the NYU Center for Data Science, advised by Julia Kempe. He is interested in the theory of Deep Learning. He holds a Diploma in Electrical and Computer Engineering from the National Technical University of Athens. His undergraduate thesis was about sparsity in nonlinear spaces and it was supervised by Petros Maragos.
About: Artem is a PhD student at the NYU Center for Data Science, working with Julia Kempe. In 2019, he graduated from the University of Massachusetts Amherst with a B.S. in Mathematics and completed an undergraduate thesis in matroid theory with Professor Tom Braden. Before joining NYU, he completed research on predicting Riemann Zeta-function zeroes with neural networks in San Diego State University and worked on a computer vision project at the Institute for Pure and Applied Mathematics. His current interests lie in the optimization and mathematics of machine learning.”
About: Wentao is a PhD student at the NYU Center for Data Science, working with Professor Brenden Lake and Professor Tal Linzen. He is interested in the intersection of machine learning, human cognition and linguistics. Before joining NYU, he received a BS in Computer Science at Peking University and a MS in Computer Science at NYU.
About: Yanqi is a PhD student at the NYU Center for Data Science, advised by Krzysztof J. Geras. She is interested in computer vision and its application for healthcare.
Prior to joining the PhD program, she earned a Master’s degree at CDS. Before joining NYU, she worked as an algorithm engineer at Supremind Technology and also a data scientist at Deloitte in Shanghai. She obtained her Bachelor’s degree in Mathematics and Philosophy at Kenyon College.
About: Jiayang is a PhD student at the NYU Center for Data Science, advised by Mathieu Laurière and Shuyang Ling. She is broadly interested in theoretical machine learning and deep learning, with a particular emphasis on investigating the mathematical tools that facilitate their interpretation. Currently, she is also interested in the branches of mathematics such as stochastic analysis, high-dimensional probability/statistics, and random matrix theory with their applications in data science. Prior to joining NYU, Jiayang obtained a M.Sc. in Statistics from the University of British Columbia and a B.Sc. in Statistics from East China Normal University.
About: Boyang is a PhD student at the NYU Center for Data Science. Before joining CDS, Boyang majored in probability and statistics at the School of Mathematical Science, University of Science and Technology of China (USTC). She is broadly interested in the intersection of machine learning and healthcare. She has studied the association between body composition and mortality of lung cancer patients with Professor Junwei Lu at Harvard T. H. Chan School of Public Health. She has also worked on proteomics big data at Guomics, Westlake University. During her previous internship at ByteDance, she also had experience in learning to rank models and graph databases. In her spare time, she enjoys marathons, rock climbing, and cooking.
About: Jianyu is a PhD student at the NYU Center for Data Science, advised by Léon Bottou and Yann LeCun. His research interests lie in out-of-distribution generalization and large-scale optimization. Prior to NYU, Jianyu studied silence speech recognition from lips and tongue at Institut Langevin with Bruce Denby and physical opjects prediction at Facebook AI Research (FAIR) with Léon Bottou. Jianyu earned his Master’s degree in compute science from Tianjin University under the advisory of Françoise Fogelman Soulié.
About: Lily is a PhD student at the NYU Center for Data Science and a DeepMind Fellow, advised by Professors Rajesh Ranganath and Kyle Cranmer. Her research interests are in topics at the intersection of deep learning and probabilistic modeling, including generalization, uncertainty calibration, and out of distribution detection. She is also interested in machine learning applications for health and science.
Lily graduated from Harvard University with a B.A. in Statistics and minor in Computer Science and spent time at start ups Indico Data Solutions and Gamalon, working on deep learning and probabilistic machine learning-based solutions for text processing across industries.
Ziliang Samuel Zhong
About: Samuel is a PhD student at the NYU Center for Data Science (Shanghai-track), advised by Prof. Shuyang Ling. He is broadly interested in optimization, network analysis, and machine learning. Prior to CDS, Samuel received a Bachelor’s degree in Mathematics from NYU Shanghai. While at NYU Shanghai, he also worked with Prof. Ling on his undergraduate thesis: “Exact recovery for the stochastic co-block model”. Currently, he is working on finding an efficient algorithm for the Procrustes Matching Problem. Outside of work, he enjoys badminton, painting, and photography.
About: Yanli is a PhD student at the NYU Center for Data Science, working with Dr. Brenden Lake. Previously at NYU, she received her BA in Mathematics and Psychology in 2016 and an MS in Data Science in 2018. Before joining the Lake lab, she worked as a research assistant under the supervision of Dr. Wei Ji Ma at the Center for Neural Science and Department of Psychology where she built probabilistic models of visual decision-making tasks. She is broadly interested in incorporating insights from cognitive science into building AI systems that can efficiently and flexibly learn.
About: Weicheng Zhu is a PhD student at NYU Center for Data Science, co-advised by Professor Narges Razavian and Professor Carlos Fernandez-Granda. His research interests lie in machine learning for healthcare and model interpretability. His recent work includes learning graph representations from electronic health records and deep learning for medical imaging. Prior to the PhD study, he received B.S. in Honors Mathematics at NYU Shanghai and M.S. in Data Science at NYU CDS.