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Previous AI Research PhD Awards


Abhinav Verma

Rice University

Abhinav Verma is a PhD student in the Computer Science Department at Rice University, where he is advised by Professor Swarat Chaudhuri.

Abhinav’s research lies at the intersection of machine learning and program synthesis. His work focuses on “programmatically interpretable learning”: solving learning tasks using models that can be expressed as descriptive programs in a high-level domain-specific language. Such programmatic models have several benefits, including being human interpretable and amenable to formal certification by scalable symbolic methods. The generation methods for programmatic models also provide a mechanism for systematically using domain knowledge for reducing the variance of the learner.

Abhinav received a MS degree in Mathematics from the University of Oregon. He is passionate about inclusivity in STEM, and is leading a graduate student out-reach program that engages with K-12 students from traditionally underrepresented demographics.

Anuj Mahajan

University of Oxford

Anuj Mahajan is a PhD student in machine learning at the Dept. of Computer Science, University of Oxford where he is working with Prof. Shimon Whiteson.

Anuj’s research is focused on single and multi-agent reinforcement learning. His work uses ideas from optimisation theory, probabilistic inference and learning theory to create algorithms for autonomous agents with principled theoretical foundations. Anuj also wants to use these methods for equipping agents with the ability to capture aspects like symmetry, compositionality and reasoning.

Anuj is interested in creating AI that can be deployed for solving real world tasks, he is especially keen on creating startups based on recent advances in machine learning.

Aravind Rajeswaran

University of Washington

Aravind Rajeswaran is a PhD student at the University of Washington with Profs. Sham Kakade and Emo Todorov. He is interested in the mathematical foundations and applications of deep learning for prediction and decision making.

Aravind is currently focusing on enabling robots to autonomously and efficiently acquire a vast repertoire of skills. This requires leveraging prior related experience to generalize and transfer skills, as well as efficient targeted optimization to invent new skills when necessary. Thus, Aravind draws upon reinforcement learning, model-based control, meta learning, and robotic simulators.

Aravind has received a best thesis award for his undergraduate research work at IIT Madras in 2015 and a best paper award from SIMPAR 2018.

Ashwin Kaylan

Georgia Institute of Technology

Ashwin Kalyan is a PhD student at Georgia Institute of Technology advised by Prof. Dhruv Batra.

Ashwin is interested in developing machine learning solutions for producing diverse outputs, developing fast and accurate reasoning systems and modeling human preferences — all essential components of effective assistive technology.

During his PhD, he has interned at Microsoft Research and IBM. Previously, Ashwin obtained his Bachelor's degree from National Institute of Technology Karnataka. He is also a professional violinist specializing in Karnatic Classical Music.

Cong Xie

University of Illinois Urbana Champaign

Cong Xie is a fourth-year PhD student in the Computer Science department at University of Illinois Urbana Champaign (US), under the supervision of Professor Oluwasanmi Koyejo and Professor Indranil Gupta.

Cong's research interests lie in the intersection of distributed systems and machine learning, most recently focusing on secure and communication-efficient distributed machine learning. He designs and develops algorithms that improve the security of distributed machine-learning systems, and reduce the communication overhead for large-scale learning tasks.

Cong received his bachelor's degree and master's degree from Shanghai Jiao Tong University (China), majoring in Computer Science and Engineering. In his spare time, Cong enjoys playing badminton and reading.

Juan D. Correa

Columbia University

Juan is a PhD student in Computer Science at Columbia University, working with Elias Bareinboim on Causal Inference.

Juan’s research is concerned with the identification of causal and probabilistic quantities from a combination of observational and experimental probability distributions, plagued with selection bias and originating from multiple environments (external validity), different from the actual target domain.

Juan earned an MSc in computer science from Purdue University and a BSc at Universidad Autonoma de Manizales in Colombia. His work has been published and presented in venues such as AAAI, UAI, ICML and IJCAI.

Kush Bhatia

University of California, Berkeley

Kush Bhatia is a PhD student in the EECS Department at the University of California, Berkeley, where he is advised by Peter Bartlett and Anca Dragan.

Kush's research interests broadly lie at the intersection of statistics and machine learning. Currently, he is interested in the design of AI systems whose objectives are well aligned with human values. A key challenge in the design of such systems is to infer underlying human preferences from observed decisions. His research focuses on designing algorithms for this task which are robust to misspecifications, for example, unmodeled biases in human decision making, as well as studying the statistical and computational properties of these procedures.

Kush received his bachelor's degree from Indian Institute of Technology Delhi, majoring in Computer Science and Engineering. After that, he spent a couple of years working at Microsoft Research India as a Research Fellow.

Leo de Castro

Massachusetts Institute of Technology

Leo de Castro is a currently pursuing a PhD at the Massachusetts Institute of Technology, advised by Vinod Vaikuntanathan.

Leo's current research is in the area of lattice cryptography, including the design and optimization of lattice cryptography primitives as well as constructing cryptographic protocols that make use of lattice primitives. In particular, his recent work has focused on the design and implementation of secure computation protocols, including protocols to securely evaluate AI algorithms.

Leo completed his BSc in computer science from MIT in 2018.

Naomi Ephraim

Cornell University

Naomi Ephraim is a Ph.D. candidate in Computer Science at Cornell University, advised by Professor Rafael Pass.

Naomi’s research interests are in cryptography and its connection to complexity theory. Her recent work is focused on the study of interactive proof systems and their applications to verifiable outsourcing of computation, and specifically on aspects related to the efficiency of these proof systems.

Naomi did her undergraduate studies at Johns Hopkins University, where she received a B.S. in Computer Science with a second major in Mathematics.

Shaojie Bai

Carnegie Mellon University

Shaojie Bai is a third-year PhD student in the Machine Learning Department of Carnegie Mellon University (CMU), working with J. Zico Kolter.

Shaojie's research direction focuses on integrating optimizations with deep learning algorithms, especially in the sequence domain (e.g., textual, time-series, etc.). His current research studies 1) the unification of different model families in sequence modeling; and 2) the scalability and representational capacity of implicit-depth models, which involves rethinking some of the current "deep" approaches. He is also interested in extending these algorithms to other applications, and has won the 1st place of a Kaggle competition on predicting molecular properties.

Shaojie received his BS in Computer Science and BS in Applied Mathematics from CMU in 2017, where he graduated with University Honor.

Xin Dong

Rutgers University – New Brunswick

Xin Dong is a PhD student in the Computer Science department at Rutgers University—New Brunswick, under the supervision of Professor Gerard de Melo at Deep Data Lab.

Xin’s research interests lie in Natural Language Processing with Deep Learning, most recently focusing on solving the training issues for low-resource languages. He proposes to integrate semi-supervised methods into pretrained multilingual representation models.

Previously, he received a master's degree from Rutgers University — New Brunswick and a bachelor's degree from Xidian University in Xi’an, China.

Yang Song

Stanford University

Yang Song is a fourth-year PhD student in Stanford AI Lab, working with Prof. Stefano Ermon.

Yang's main research interest lies in the intersection between robust machine learning and generative modeling. He aims to improve the robustness of machine learning models by understanding the distribution of anomalous inputs using generative models, and detecting/defending against them accordingly. In order to capture the distributions better, Yang also works on new model architectures and training methods for building more flexible and expressive generative models.

Yang obtained his Bachelor of Science degree in Mathematics and Physics from Tsinghua University. He enjoys playing the violin in his leisure time.

Mo Tiwari

Stanford University

Mo Tiwari is a fourth-year PhD student in Computer Science at Stanford University, where he is advised by Sebastian Thrun (funding advisor) and Chris Piech.

Mo's primary research interests include statistical machine learning and applications to healthcare. Previously, Mo has worked in other subfields of artificial intelligence, including reinforcement learning, graph deep learning, time series analysis, and broader fields such as particle physics, quantum computation, finance, cybersecurity, and virtual/augmented reality. Prior to his PhD, Mo spent several years in industry.

Mo holds a BS in Mathematics and Physics from Caltech, and an MS in Computer Science from Stanford. In his free time, he enjoys tennis, frisbee, running and video games.