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.
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.
About: Tymor was a PhD student at the NYU Center for Data Science. Before coming to NYU, he received a master’s degree in Biomedical Data Science from Stanford and a bachelor’s degree in Economics from MIT. His research interests was at the intersection of machine learning, biology, and healthcare. As a passion project, he co-founded and helps run Fermat’s Library, a platform, and community for sharing and annotating academic papers as well as interesting science.
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.
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.
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.
About: Aakash was a PhD student at NYU Center for Data Science. He was interested in solving problems in the healthcare domain using machine learning. He worked with Prof. and CDS Interim Director Carlos Fernandez-Granda and Prof. Heidi Schambra on activity recognition task in stroke patients and with Prof. Narges Razavian and Prof. Yvonne Lui in the field of medical imaging and deep learning. Prior to joining the PhD program, he obtained a Master’s degree from NYU CDS where he worked on domain adaptation and medical image segmentation under the guidance of Prof. Carlos and Prof. Narges. He also has an MBA from the Indian Institute of Management Bangalore where he was on the Dean’s Merit List, which is awarded to the top 5% students. Post-MBA, Aakash worked as a management consultant for more than a year. He also holds a bachelor’s in Chemical Engineering from the Institute of Chemical Technology, Mumbai. In his leisure time, he loves to play cricket and read books.
About: Aishwarya was a PhD student at the NYU Center for Data Science, advised by Yann LeCun and Kyunghyun Cho. Her research interests were in the area of commonsense reasoning and self-supervised learning. She was previously advised by Andrew McCallum at UMass Amherst, where she earned her Master’s degree in Computer Science. Prior to joining CDS, Aishwarya worked at Oracle’s Machine Learning Research Group in Burlington, MA. Aishwarya holds a Bachelor’s degree in Electronics and Communication Engineering from Manipal University in India.
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.
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.
About: Angela was a PhD student at the NYU Center for Data Science and a DeepMind Fellow. She was currently working with Profs. Richard Bonneau and Joshua Tucker as part of the Social Media and Political Participation lab. Before joining NYU, Angela was at the University of Rochester, where she earned a B.S. in data science, a B.A. in political science, and a math minor. She also spent two summers as an intern in MIT Lincoln Lab’s human language technology group. Her research interests include online radicalization and political dynamics on social networks as well as natural language processing and network analysis.
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.
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.
Irina Morales Espejo
About: Irina was a PhD student in Data Science and also a DeepMind fellow. She was interested in applications of data science to the logistics of cities as well as in physics and theoretical machine learning. During her time at CDS, she initiated research with Kyle Cranmer. Irina graduated from Mathematics and Physics at Universitat Autonoma de Barcelona. During her studies, she collaborated in two research projects. The first one in Complex Systems trying to model the pairwise interactions of letters in texts. The second one was in experimental particle physics, which led to a summer internship at CERN with an analysis focused on the DMttbar signal. From her undergraduate merits, she was awarded a Fulbright Scholarship by the Spanish Fulbright Commission to pursue doctoral studies in the USA. She also obtained an MSc in Mathematical & Theoretical Physics at the University of Oxford.
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.
About: Swapneel Mehta was a PhD student at the NYU Center for Data Science working with Richard Bonneau, Jonathan Nagler, and Joshua Tucker at the Center for Social Media and Politics with interests in simulation-based inference and causality. He used probabilistic programming to conduct social network analysis, with a focus on understanding misinformation spread and control in a simulated training environment that he worked to develop. In the past, he has worked with recommender systems at Adobe Research, graph neural networks and particle physics at CERN, and Fortune 500 startups in cybersecurity and IoT. He has also mentored students doing research and software development as part of a student organization he co-founded, called Unicode.
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.
About: Yiqiu (Artie) Shen was a PhD student at the NYU Center for Data Science, co-advised by Prof. Kyunghyun Cho and Prof. Krzysztof J. Geras. His research interests primarily lie in Artificial Intelligence for healthcare and deep learning for medical image analysis. Prior to joining NYU, he was a software engineer at Two Sigma Investments where he maintained a platform that extracts trading signals from market sentiment. He earned a Bachelor’s degree in computer science from Rice University.
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.
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.
About: Nan was a PhD student at the NYU Center for Data Science, co-advised by Prof. Kyunghyun Cho and Prof. Krzysztof J. Geras. She was interested in data science with application in healthcare and now working on developing deep learning algorithms for medical imaging.
Before joining NYU, she graduated from the School of Gifted Young, University of Science and Technology of China, and received her B.S in Statistics and B.A. in Business Administration. In her undergraduate thesis, she built neurofeedback protocols with predictive models to help reducing cue-reacted nicotine craving.