People
Meet Barot
Contact: meetbarot@nyu.edu
About: Meet was a PhD student at the Center for Data Science working with Richard Bonneau and Kyunghyun Cho. He graduated with his B.A. in Chemistry and Computer Science from NYU in 2016, and worked in the Center for Computational Biology at the Flatiron Institute on protein function prediction methods until starting his PhD in September 2018. His research interests included deep learning, network science, protein function prediction, and complex systems. In his spare time, he enjoys gymnastics, martial arts and meditation. More information on Meet’s research can be found on his website.
Zhouhan Chen
Contact: zc1245@nyu.edu
About: Zhouhan was a Ph.D. student at NYU Center for Data Science. His research lay at the intersection of social network analysis, cyber security, and machine learning. His focuses were on discovering and understanding the spread of misinformation across multiple platforms. Zhouhan was advised by Professor Richard Bonneau, Professor Juliana Freire, and Professor Joshua Tucker. Before coming to NYU, Zhouhan earned his Bachelor’s and Master’s degree in Computer Science at Rice University. Outside of school, Zhouhan is a longtime marathon runner and a certified personal trainer.
Xintian Han
About: Xintian was a Ph.D. student at the NYU Center for Data Science, advised by Prof. Rajesh Ranganath. Xintian’s main research focus was machine learning for healthcare. His broad research interests were in neural sequence modeling, adversarial examples, graphs, generative models and semi-supervised learning. Xintian has a BS in statistics from Peking University.
Phu Mon Htut
About: Phu was a Ph.D. student at the NYU Center for Data Science. She was also a member of the Machine Learning for Language (ML²) group where she is working with two amazing professors, Sam Bowman and Kyunghyun Cho. Phu was broadly interested in Machine Learning, Natural Language Processing, and Information Retrieval. She earned her Bachelor’s degree in Computer Science from Nanyang Technological University in Singapore. Phu also worked as a research engineer for a couple of years in Singapore before joining CDS.
Leslie Huang
Contact: lesliehuang@nyu.edu
About: Leslie was a Ph.D. student in Data Science at the NYU Center for Data Science, where she was advised by Arthur Spirling. Her research was focused on NLP applications and text-as-data in political science. Leslie has a BA, summa cum laude, from Columbia University, and an MA from NYU, where she was a Ph.D. student in political science prior to joining Data Science.
Vladimir Kobzar
Vladimir was a PhD student at the NYU Center for Data Science where he was a part of the Math and Data Group. His graduate work was advised by Robert Kohn and Carlos Fernandez-Granda, and was supported in part by the Moore-Sloan Data Science Environment at New York University.
He completed an MS in Mathematics at the Courant Institute under the advisement of Afonso Bandeira, as well as a JD and LLM at the NYU School of Law.
His research area is mathematical and statistical foundations of data science and machine learning. I focus on their intersection with differential equations, control theory and optimization, and on applications to online learning, medical and molecular imaging, computer vision, robotics, finance and regulatory policy.
Sheng Liu
Contact: shengliu@nyu.edu
About: Sheng Liu was a Ph.D. student at the Center for Data Science, NYU, co-advised by Professor Carlos Fernandez-Granda, Professor Jonathan Niles-Weed, and Professor Narges Razavian. His research interests were in the general area of machine learning, particularly in robust deep learning with imperfect datasets (corrupted data, limited supervision, small dataset, etc.) as well as their medical applications such as Alzheimer’s automatic detection. He was a member of the Math and Data (MAD) group at NYU where he worked on inverse problems and optimization. Out of school, he loves tennis, scuba diving, surfing and most water sports.
Wesley Maddox
Contact: wjm363@nyu.edu
About: Wesley was a Ph.D. student advised by Andrew Gordon Wilson. His interests were in statistical machine learning, bayesian deep learning, Gaussian processes, and generative models, with a specific focus on developing new methods to incorporate and utilize uncertainty in machine learning models. Prior to NYU, he spent two years as a Ph.D. student in Statistics at Cornell University and did a master’s in statistics and a bachelor’s degree in systems biology at Case Western Reserve University. In Summer 2019, he interned at Amazon in Cambridge, UK. He is an NSF Graduate Research Fellow, received in 2017.
Omar Mahmood
Contact: onm217@nyu.edu
About: Omar was a Ph.D. student at the NYU Center for Data Science (CDS), working with Kyunghyun Cho and Richard Bonneau. His research projects focused on graphical neural networks and biological network inference. Prior to CDS, Omar pursued a master’s degree in machine learning at the University of Cambridge, where his dissertation project was on deep generative models for molecule design and optimization, under the supervision of José Miguel Hernández-Lobato. Before this, Omar worked as a software engineer at a natural language processing startup, where he was responsible for successfully pitching the company’s product to a major US financial institution, and executing and coordinating the ensuing project. He also holds a Bachelor of Science, magna cum laude, in applied physics from Columbia University.
Harvineet Singh
Contact: hs3673@nyu.edu
About: Harvineet was a Ph.D. student in Data Science at New York University. His research interests included statistical modeling of structured data such as sequences and graphs, with applications in digital health and social sciences. Prior to joining NYU, he was a research engineer at Adobe Research in India, where he worked on methods for survival analysis, interactive recommendations, and sequence prediction. He has an Integrated Master’s degree from the Indian Institute of Technology Delhi in Mathematics and Computing. His Master’s thesis work, advised by Prof. Amitabha Bagchi and Prof. Parag Singla, explored graph representation learning techniques for social network data. He did two summer internships at Adobe Research, focusing on customer behavior prediction. Also, he was a visiting researcher at BME, Hungary. His other interests include traveling, playing basketball, and listening to music.
Sreyas Mohan
About: Sreyas was a Ph.D. student at the Center for Data Science, NYU working under the joint guidance of Prof. Eero P Simoncelli and Prof. Carlos Fernandez-Granda. He was also a part of Math and Data Group. He graduated in July 2017 with a Bachelor’s in Electrical Engineering from the Indian Institute of Technology (IIT) Madras where his undergraduate thesis was supervised by Prof. Kaushik Mitra. Sreyas spent the summer of 2017 at the Institute of Science and Technology (IST), Austria working with Prof. Gasper Tkacik on deep learning models for a particular neuroscience application and the summer before that at the Simons Center for Data Analysis, NYC working with Dr. Dmitri Chklovskii in the intersection of unsupervised learning and computational neuroscience.
Sam Stanton
Contact: ss13641@nyu.edu
About: Sam was a Ph.D. student in the NYU Center for Data Science and a NDSEG Fellow (class of 2018), working with Professor Andrew Wilson. His research focused on the incorporation of probabilistic state transition models in reinforcement learning algorithms. Sam holds a Master’s degree in Operations Research from Cornell University, where he started working with Professor Wilson as a first-year Ph.D. student. Sam transferred from the Cornell doctoral program to continue his research agenda at NYU with his advisor. Prior to his studies at Cornell, Sam earned a Bachelor’s degree in Mathematics from the University of Colorado Denver, graduating summa cum laude. In addition to his dissertation research, Sam is interested in modern art and philosophy, especially epistemology and ethics. Outside of research, Sam enjoys volleyball, rock climbing, surfing, and snowboarding.
