About: Meet is 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 include deep learning, network science, protein function prediction, and complex systems. In his spare time, he enjoys gymnastics, martial arts and meditation.
About: Lucius is a Ph.D. 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 Ph.D. 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.
About: Zhouhan is a Ph.D. student at NYU Center for Data Science. His research lies at the intersection of social network analysis, cyber security, and machine learning. Currently he is focusing on discovering and understanding the spread of misinformation across multiple platforms. Zhouhan is 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: Guy is a Ph.D. student at the Center for Data Science, currently working with 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: Irina is a Ph.D. student in Data Science and also a DeepMind fellow. She is interested in applications of data science to the logistics of cities as well as in physics and theoretical machine learning. At the moment, she is initiating her 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: Katrina is a Ph.D. student at CDS 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 Ph.D., she worked as a research engineer at eBay NYC. Katrina holds a Bachelor’s degree in Mathematics from Harvard University.
About: William is a Ph.D. student, Deepmind Fellow and NSF Fellow co-advised by Kyunghyun Cho and Yann LeCun at the CILVR lab.
During his first year he created PyTorch Lightning while at NYU and Facebook AI Research (FAIR). His research focuses on developing unsupervised learning techniques inspired by neuroscience with applications to neuroscience, NLP and computer vision.
Before NYU he worked on neural decoding from the brain and retina at Columbia University’s Center For Theoretical Neuroscience supervised by Liam Paninski. He also co-founded AI startup NextGenVest (acquired) and spent time at Bonobos, Goldman Sachs and developed over 8 iOS apps. He started his career as a naval officer undergoing US Navy SEAL training (BUD/S class 277) where he was injured and finished his military service at NSW Special Reconnaissance Team One (SRT-1). He obtained a B.A (magna cum laude) from Columbia University in Computer Science and Statistics with a minor in Math.
About: Tymor is a Ph.D. student in Data Science 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 lie 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 is currently a Ph.D. student at the NYU Center for Data Science, advised by Prof. Rajesh Ranganath. Xintian’s main research focus is machine learning for healthcare. He also has broad research interests in neural sequence modeling, adversarial examples, graphs, generative models and semi-supervised learning. Xintian has a BS in statistics from Peking University.
About: Phu is a Ph.D. student at the NYU Center for Data Science. She is 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 is 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: Lavender Jiang is a first-year Ph.D. 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: Chris is a Ph.D. student at the Center for Data Science at NYU 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 first year Ph.D. 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: Aakash is a First-year Ph.D. student at NYU Center for Data Science. He is interested in solving problems in the healthcare domain using machine learning. Currently, he is working with Prof. 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 Ph.D. 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, he 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: 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: Aishwarya is a first year PhD student at CDS advised by Yann LeCun and Kyunghyun Cho. Her current research interests lie 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.
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.
About: Falaah is a Data Science PhD student 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: I am a first-year student Ph.D. student in Data Science and a DeepMind fellow supervised by Professor Andrew Wilson. My current research focuses on probabilistic machine learning, Bayesian deep learning and uncertainty estimation. I am interested in building robust machine learning models and understanding when they can be trusted in making decisions, which is important for many sensitive applications. I obtained my Bachelor’s degree in Computer Science at the Higher School of Economics in Moscow. During my undergrad, I 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 Ph.D., I did a summer internship at EPFL in Machine Learning and Optimization Lab, where I worked on zero-order optimization for low precision neural networks supervised by professors Martin Jaggi and Dan Alistarh. I also had a chance to experience industry at Google as a software engineering intern in Munich and Seattle offices, where I worked on the backend, distributed systems and algorithm parallelization for internal tools. Before coming to NYU, I completed one year of a Ph.D. program in Operations Research and Information Engineering at Cornell University where I started working with Professor Andrew Wilson on low-precision training of neural networks, Bayesian deep learning and normalizing flows; after my first year of a Ph.D., our research lab transferred to New York University.
About: Angela is a data science Ph.D. student and a DeepMind Fellow. She is 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: Sanae is a PhD student at CDS and a DeepMind fellow, advised by Professors Andrew Wilson and Julia Kempe. 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: Kangning is a first-year Ph.D. student at the 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: Sheng Liu is a second-year 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. He is interested in robust representation learning in computer vision, and its applications for healthcare. He is also a member of the Math and Data (MAD) group at NYU where he works on inverse problems and optimization. Out of school, he likes scuba diving, surfing, and singing.
About: Wesley is a Ph.D. student advised by Andrew Gordon Wilson. He is interested 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.
About: Omar is a Ph.D. student at the NYU Center for Data Science (CDS), working with Kyunghyun Cho and Richard Bonneau. His current research projects focus on graphical neural networks and biological network inference. Prior to starting at 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.
Taro is a first-year Ph.D. 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.
About: Swapneel is a Ph.D. student at the Center for Data Science jointly advised by Kyle Cranmer and Rajesh Ranganath. His interests lie in statistical learning and exploring the bridge between quantum physics and machine learning. In the past, he has worked on deep learning for high-energy physics with the CMS Experiment at CERN and for Fortune 50 clients at leading startups in the domains of cybersecurity and operational analytics. He holds a Bachelors degree in Computer Engineering from the University of Mumbai where he continues to mentor students undertaking open-source projects. In his spare time, he enjoys blogging, sketching, running, and cooking.
About: 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: Sreyas is 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 is 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: Nikita is a Ph.D. student at CDS, 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: Jason is a first-year Ph.D. Candidate 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 new PhD student at the NYU Center for Data Science with Jonathan Niles-Weed and Julia Kempe as first-year mentors. His research interests lie at the intersection of optimization theory, high-dimensional statistics, 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: “I’m a PhD student at NYU’s Center for Data Science, currently being advised by Professor Andrew Wilson. I’m 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, I’m working on designing high-fidelity and scalable inference algorithms, formulating principled approaches for incorporating domain knowledge and improving how uncertainty is quantified. Before starting my PhD, I spent a year collaborating with Professor John Cunningham at the Zuckerman Institute, immediately after finishing my master’s in Data Science at Columbia University. Prior to my graduate studies, I was a consultant at McKinsey & Company. Additionally, I hold both an undergraduate degree in Applied Mathematics and in Economics from ITAM.”
About: Yiqiu Shen is a Ph.D. student at the 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 is a Ph.D. student in Data Science at New York University. His research interests include 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.
Claudia N. Skok-Gibbs
About: “My current research projects focus on developing algorithms to infer gene regulatory networks from genome-wide data. Prior to starting at CDS, I was a research analyst in Richard Bonneau’s Lab in the Simon’s Foundation’s Flatiron Institute. At the Flatiron institute I extended and adapted the network inference algorithm, the Inferelator, and applied this software to several novel single-cell datasets across different species. I hold a Bachelors of Science in Mathematics, with minors in Computer Science, Finance and English. Outside of my research, I enjoy training for marathons, photography, and painting.”
About: Vlad is a 1-st year Ph.D. student who is very excited about working with Kyunghyun Cho, and Yann LeCun, and CDS in general. 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: Sam is a Ph.D. student in the NYU Center for Data Science and a NDSEG Fellow (class of 2018), working with Professor Andrew Wilson. His current research focuses on the incorporation of probabilistic state transition models in reinforcement learning algorithms. Model-based RL agents generalize from past experience very effectively, allowing the agent to evaluate policies with fewer environment interactions than their model-free counterparts. Improving the data-efficiency of RL agents is crucial for real-world applications in fields like robotics, logistics, and finance. 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. When he is not occupied with research, Sam enjoys volleyball, rock climbing, surfing, and snowboarding.
About: Nikos is a PhD student 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: “I am a first-year Ph.D. student working with Julia Kempe. In 2019, I 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, I 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. My current interests lie in the optimization and mathematics of machine learning.”
About: Nan is a Ph.D. student at the Center for Data Science, co-advised by Prof. Kyunghyun Cho and Prof. Krzysztof J. Geras. She is 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.
About: 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: Lily is a PhD student and 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 first-year Ph.D. 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 Ph.D. 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 first-year Ph.D. 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 Ph.D. study, he received B.S. in Honors Mathematics at NYU Shanghai and M.S. in Data Science at NYU CDS.