2017 GRAD FELLOWSHIP

Research:  Abi's research focuses on the development of deep learning techniques for natural language understanding and generation tasks, such as translation and summarization. She's also interested in improving the interpretability of deep learning, and aims to devise representations of text that can be better understood by humans, while retaining the expressive power and flexibility of deep learning. Research Link

Bio: Abi is a second-year PhD student in the Computer Science department at Stanford University, where she works with Christopher Manning in the Natural Language Processing Group. She holds a BA and MMath in pure mathematics from Cambridge University, and has interned at Microsoft Research and Google Brain. Outside of research, Abi volunteers with outreach organizations that offer Computer Science tutoring and mentoring to high school girls.

Research:  Adams' research lies between large scale optimization and deep learning, two important and closely related directions in modern artificial intelligence and machine learning. He is fascinated in inventing efficient optimization algorithms for general machine learning tasks in a distributed manner, providing theoretical convergence guarantee, and building novel deep neural network models to speed up several applications, such as natural language processing. Research Link

Bio: Adams Wei Yu is a PhD candidate in Machine Learning Department, School of Computer Science at Carnegie Mellon University, advised by Jaime Carbonell and Alex Smola. His research lies in large scale optimization, deep learning, statistical machine learning and their applications. He has interned with the Google Brain team and MSR machine learning group. He received his B.S. in Math from Beihang University and his M.Phil in CS from the University of Hong Kong.

Research:  Awni’s research focuses on developing Machine Learning algorithms and applying this technology to high impact problem areas. He is especially interested in time-series data and sequence modeling and has spent several years working on applications in speech recognition and audio processing. Other areas that he is interested in include spoken and written language understanding and computer vision. Recently he has been investigating applications for this technology in healthcare and medicine.

Bio: Awni is a PhD candidate in Stanford’s Computer Science Department. He specializes in Machine Learning algorithms research and applications. From 2014-2016, he worked as a Research Scientist in Baidu Research’s Silicon Valley AI Lab, acting as a technical leader of their Deep Speech and Deep Speech 2 speech systems for both English and Mandarin. Awni received his B.A. in Mathematics from Dartmouth College.

Research:  Given the fundamental importance of memory consistency models, the overarching goal of Caroline's research is to facilitate their verification, particularly in heterogeneous systems. Modern computer systems employ increasing amounts of heterogeneity and specialization to achieve performance scaling at manageable power and thermal levels. Reaping the benefits of these heterogeneous and parallel systems necessitates memory consistency models which define behavior as fundamental as what value should be returned when software loads from memory. In many systems GPUs must communicate with CPUs, with accelerators for machine learning and other specialized functions, and even with on-chip micro-controllers that facilitate data movement and other aspects of GPU functionality. Each processing element may have been designed with different memory ordering expectations, and composing them together is complex and error-prone. Research Link

Bio: Caroline Trippel is a Ph.D candidate in the Computer Science department at Princeton University. She is currently in the fourth year of her Ph.D program and is advised by Professor Margaret Martonosi. Her specialization is broadly Computer Architecture and more specifically memory consistency model verification and design in heterogeneous systems. She received her B.S. in Computer Engineering from Purdue University in May 2013, her M.A in Computer Science from Princeton University in September 2015, and was a 2016 NVIDIA Graduate Fellowship Finalist.

Research: Deepak's research goal is to build intelligent systems that can learn with minimal human supervision by bootstrapping their own experience. This involves learning general (deep) visual representations of the environment using self-supervision and leveraging them to develop self-sustaining agents that could learn to act via their interactions with the real world. Research Link

Bio: Deepak is a third-year PhD student in the department of Electrical Engineering and Computer Science at the University of California Berkeley, working with Prof. Trevor Darrell and Prof. Alexei Efros. His research interests lie at the intersection of machine learning, computer vision and robotics. Before joining UC Berkeley, he finished his Bachelors with a Gold Medal in Computer Science and Engineering from IIT Kanpur.

Research: Fereshteh's research is focused towards making autonomous robot controllers using deep reinforcement learning and vision. Her goal is to take advantage of advances in 3D graphics simulations and deep learning to train robust and generalizable controllers that can be transferred to the real world. In the past, she has worked on various problems in visual understanding and recognition. Research Link

Bio: Fereshteh Sadeghi is a Computer Science PhD candidate at University of Washington and a visiting student at University of California, Berkeley. She is a member of Berkeley AI Research (BAIR) Lab as well as Graphics and Imaging Laboratory (GRAIL) at UW. Fereshteh is advised by Prof. Sergey Levine and her area of research is at the intersection of robotics, computer vision and machine learning. Her focus is on developing new deep reinforcement learning methods to learn robot controllers with high generalization capability that can perform tasks in diverse real-world settings.

Research: Ling-Qi's research is mainly focused on physically-based rendering, especially at real world complexity, while exploiting GPU power to make it fast until ultimately real-time. He derives physically-correct appearance models from material details, to introduce new phenomena that could not be resolved before, including glints and animal fur rendering techniques which have been adopted by the movie and video game industries. He is also devoted to applying sampling and reconstruction theory together with light transport techniques to make real-time ray tracing feasible in the near future. Research Link

Bio: Ling-Qi is a fourth year Ph.D. candidate in the Department of Electrical Engineering and Computer Sciences at University of California, Berkeley, advised by Ravi Ramamoorthi at University of California, San Diego. He received his bachelor degree in Computer Science from Tsinghua University in China in 2013, under the supervision of Kun Xu and Shi-Min Hu. His research interest is physically-based rendering, including appearance modeling, volumetric scattering, light transport algorithms and sampling & reconstruction theory.

Research: Robert's research interests lie at the intersection of computational displays and human physiology with a specific focus on virtual and augmented reality systems. He has recently worked on relieving the vergence-accommodation conflict in current VR and AR displays via non-traditional methods such as monovision and accommodation-invariant displays. He has also worked on creating computationally efficient cinematic VR capture systems for live streaming of stereo omni-directional VR content. Research Link

Bio: Robert is a 3rd year PhD candidate in the Electrical Engineering department at Stanford University. He is mentored by Professor Gordon Wetzstein in the Stanford Computational Imaging Group. He received his Bachelor's degree from the ECE department at the University of Toronto in 2014, and his Master's degree from the EE department at Stanford University in 2016.

Research: Robin's research aims to use GPUs to understand how drugs bind to their protein targets. She develops methods that combine all-atom molecular dynamics simulation with machine learning to computationally observe ligand-protein binding. She is interested in finding out how small differences in molecules can lead to large changes in cell signaling. Research Link

Bio: Robin is a third year PhD candidate in the Biophysics program at Stanford University, where she is advised by Ron Dror. She is interested in both developing and applying new methods for molecular dynamics simulation. She holds a B.S. in Bioinformatics from UC San Diego.

Research: Xiaolong’s research focuses on exploiting redundancy in visual data to train visual representations. Specifically, he focuses on automatically extracting supervisory signals from RGBD and video data where human labels are hard to come by.  In these domains, his work utilizes the signals from depth and temporal information respectively to design auxiliary tasks to train convolutional neural network based RGB, RGBD and RGBT (RGB+Time) representations. Research Link

Bio: Xiaolong is a third-year Ph.D. student in the Robotics Institute, Carnegie Mellon University, advised by Professor Abhinav Gupta. His research interests are computer vision and machine learning. He holds a M.S. in Computer Science from Sun Yat-sen University and B.E. in Software Engineering from South China Agricultural University.

Research: In her research Anna seeks to develop deep learning algorithms that synergistically combine multiple types of genetic and epigenetic data to develop accurate models of gene regulation. She is building neural networks that can identify the specific change, or variant, in the DNA code that is responsible for errors in gene regulation and ultimately the onset of disease. She is primarily focusing on developing such models to identify pathogenic variants implicated in colorectal cancer, with plans to extend the model to other types of diseases. Research Link

Bio: Anna is a second year Ph.D student in the department of Biomedical Informatics at Stanford University, working with Prof. Anshul Kundaje and Prof. Euan Ashley. Her research interests include developing algorithms that utilize deep learning and data mining approaches to derive medically-relevant conclusions from multi-layer omics data . Prior to her graduate studies at Stanford, Anna was a bioinformatician at MIT Lincoln Laboratory, where she developed algorithms to characterize microbiome metagenomic datasets as well as algorithms to predict kinship and biological ancestry from genetic variant data. She holds degrees in Computer Science (M.Eng, B.S) and Biological Engineering (B.S)  from the Massachusetts Institute of Technology.