2018 GRAD FELLOWSHIP

Research: Abhishek’s research focuses on the development of deep learning techniques that leverage a large amount of unsupervised image data for 3D scene understanding. He is also interested in developing computational imaging techniques for capturing and developing novel visualizations of scenes for photography, virtual and augmented reality applications. Research Link

Bio: Abhishek is a Ph.D. candidate in the Electrical Engineering department at University of California, Santa Barbara. He is part of MIRAGE Lab where he is advised by Professor Pradeep Sen. His research interests lie at the intersection of machine learning, computer vision and computer graphics. He received his bachelor’s degree from National Institute of Technology, Trichy, India.

Research:  Adam's research goal is to accelerate deep reinforcement learning.  A foundational element of this work is to adapt algorithms to better utilize modern computing hardware--chiefly, by developing parallelized techniques using GPU acceleration--to dramatically reduce experiment turnaround times.  This will enable further research into improved algorithms and neural network architectures, which in turn may enable learning ever more challenging tasks.

Bio: Adam is a Ph.D. candidate in computer science at UC Berkeley, where he is advised by Professor Pieter Abbeel.  Adam received B.S. and M.S. degrees in physics from the U.S. Air Force Academy (Class of 2008) and U.C. Berkeley, respectively.  Subsequently, he developed space communication technologies at the Air Force Research Lab in Albuquerque, NM and served as a liaison at the Advanced Research Projects Agency--Energy in Washington, D.C.  Adam returned to graduate school and joined the deep learning community in 2015.

Research: 
Aishwarya's research goal is to develop AI systems that can “see” (i.e., understand the contents of an image: who, what, where, doing what?) and “talk” (i.e., communicate the understanding to humans in free-form natural language).  Research Link

Bio: Xiao Aishwarya Agrawal is a fourth year Ph.D. student in the School of Interactive Computing at Georgia Tech, advised by Dhruv Batra. Her research interests lie at the intersection of computer vision, machine learning and natural language processing. The Visual Question Answering (VQA) work by Aishwarya and her colleagues has witnessed tremendous interest in a short period of time (2 years). Aishwarya led the organization of the first VQA challenge and workshop at CVPR 2016 and co-organized the second VQA challenge and workshop at CVPR 2017. As a reviewer, she has served on the program committee of various conferences (CVPR, ICCV, ECCV, NIPS, ICLR) and a journal (IJCV). She has received Outstanding Reviewer awards at NIPS 2017 and CVPR 2017. Aishwarya received her bachelor's degree in Electrical Engineering with a minor in Computer Science and Engineering from Indian Institute of Technology (IIT) Gandhinagar in 2014, where she was awarded the institute silver medal for her academic performance. Aishwarya has held internship positions at Microsoft Research and Allen Institute for Artificial Intelligence.

Research:  Ana’s research spans several areas of visual computing, and her work lies at the intersection of perception and image processing. In the past, she has worked in the fields of computational imaging, and material perception. Currently, her main focus is to apply fundamental knowledge on human attentional behavior in order to overcome some of the current challenges of virtual reality (VR) video. Given the complex nature of human attentional interactions, an understanding of users’ perception and reactions is crucial both for the generation of compelling VR experiences, and for improving the VR visualization pipeline. Research Link

Bio: Ana is a fourth year PhD candidate in the Computer Science department at Universidad de Zaragoza (Spain). She is mentored by Prof. Diego Gutierrez and Prof. Belen Masia. During the course of her PhD, she has interned with the Adobe Creative Technologies Lab, the Stanford Computational Imaging Group, and the Max-Planck Computer Graphics Group.

Research:  Andy’s research is to develop learning algorithms that enable robots to intelligently interact with the physical world and improve themselves over time. In particular, he is interested in building reactive perception-driven systems that can autonomously acquire the sensorimotor skills necessary to execute complex tasks in dynamic and unstructured environments. Research Link

Bio: Andy Zeng is a third year PhD student in the Computer Science department at Princeton University, where he works on artificial intelligence. His research interests lie at the intersection of robotics, computer vision, and machine learning. He is a part of the Princeton Vision and Robotics Group, advised by Prof. Thomas Funkhouser. Before that, Andy graduated from UC Berkeley with a Bachelors double major in Computer Science and Applied Mathematics. He was perception team lead for Team MIT-Princeton, winning 1st place (stow task) at the worldwide Amazon Robotics Challenge 2017.

Research:  Daniel performs HPC physics simulations on petascale supercomputers including Blue Waters to generate datasets used to train AI algorithms which he is developing for time-series signal processing, using deep neural networks that exploit deep-learning-optimized GPUs, to enable real-time analysis of highly noisy Big Data from the LIGO detectors and telescopes such as LSST for multimessenger astrophysics. Research Link 

Bio: Daniel is a 3rd year PhD student in Astronomy and a Computational Science and Engineering Fellow at the University of Illinois at Urbana-Champaign. He obtained his Bachelor's degree in Engineering Physics, with Honors, from IIT Bombay. He is a Research Assistant in the Gravity Group at the National Center for Supercomputing Applications (NCSA). He is also a member of the LIGO, NANOGrav, and Dark Energy Survey (DES) collaborations, and an LSST Data Science Fellow.

Research: Huizi's research focuses on software and hardware co-design for efficient deep learning. He is interested in algorithms to compress and then accelerate deep neural networks on general purpose processors or custom hardware. Recently he is interested in deep learning for video, which involves algorithm design to exploit temporal locality in video and efficiently execute the model on hardware. Research Link 

Bio: Huizi is a PhD candidate in Stanford's Electrical Engineering department. He received B.E. in Electronic Engineering and B.S. in Mathematics from Tsinghua University.

Research: Philippe's research lies at the intersection between Machine Learning and High-Performance Computing. He focuses on the design and implementation of new programming models for tensor computations, with an emphasis on the use of machine-learned compiler heuristics capable of accomodating a wide variety of hardware architecture and problem characteristics. He is also interested in the applications of his research to scientific computing, and spends part of his time working on large-scale volumetric segmentation for connectomics. Research Link

Bio: is a fifth-year Ph.D. student in Computer Science. at Harvard University, and is co-advised by Professors David Cox and H.T. Kung. He received his Bachelor's degree in Information Technology from Telecom Sudparis, France and a Master's in Computer Science from National Chiao Tung University, Taiwan. He developed optimized BLAS kernels as an intern at NVIDIA, AMD and Intel, and was a 2017 NVIDIA Graduate Fellowship Finalist.

Research: Xun's research focuses on synthesizing and manipulating natural images with deep learning techniques. His recent works include developing novel architectures of deep generative models, and applying them to computer vision tasks such as image-to-image translation, style transfer, and video synthesis. He is also interested in leveraging state-of-the-art generative models to reduce supervision of discriminative tasks. Research Link 

Bio: Xun Huang is a second-year Ph.D. student at Cornell Tech and the Department of Computer Science at Cornell University, advised by Professor Serge Belongie. His research interests lie at the intersection of machine learning and computer vision. Before coming to Cornell, he received his B.S. in Computer Science from Beihang University, China.

Research: Zhilin Yang aims at empowering AI with the ability to learn natural language like human, especially the ability to learn without a large, static labeled dataset. Towards this goal, he designed deep learning algorithms and models that learn by modeling priors from unlabeled data, by generative modeling, by transferring latent structure, and by interacting with humans in an environment. Research Link 

Bio: Zhilin Yang is a third-year PhD student at Carnegie Mellon University, advised by Ruslan Salakhutdinov and William Cohen. He works on deep unsupervised learning and its applications in natural language understanding. In the past two years, he published 11 papers (9 first-author) in ICLR, NIPS, ICML, and other top AI venues. He had research experience at Facebook AI Research, and received a B.E. in computer science with top1 GPA from Tsinghua University.

Research: William's research is focused on constructing deep learning models for investigating the intersections between histopathology and genomics for diagnostics and hypothesis creation in cancer biology. He is constructing convolutional neural network-based classifiers for biopsy slide images can allow for rapid and accurate determination of cancerous vs. benign tissue, cell morphology, genomic status, or patient subtype. He is particularly interested in the generalizability of these models across different cancer and tissue types. Research Link 

Bio: William is a second year graduate student advised by Isaac Kohane in the Department of Biomedical Informatics at Harvard Medical School. He recieved his Masters in Chemistry from the University of Oxford. His research involves application of deep learning and big data techniques towards cancer biology and precision medicine.