2016-17 marks the beginning of another outstanding year for UT Computer Science, with the addition of six new faculty in the fields of quantum computing, computer vision, natural language processing, and theory. This builds upon the very successful 2015-16 academic year, when UT Computer Science recruited four new assistant professors in systems and robotics, ensuring a vibrant future for computer science education and research at The University of Texas at Austin.

Ph.D. 2004, University of California, Berkeley

Quantum Computing
Computational Complexity

Scott Aaronson's research focuses on the capabilities and limits of quantum computers, and more generally on computational complexity and its relation to physics. His recent interests include how to demonstrate a quantum computing speedup with the technologies of the near future (via proposals such as BosonSampling, which Aaronson introduced in 2011 with Alex Arkhipov); the largest possible quantum speedups over classical computing; the computational power of closed timelike curves; and the role of computational complexity in the black hole information paradox and the AdS/CFT correspondence. In addition to research, he writes a widely-read blog, and has written about quantum computing for Scientific American, the New York Times, and other popular venues. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press.

Aaronson received his bachelor's from Cornell University, and his Ph.D. from UC Berkeley under Umesh Vazirani. He also did postdoctoral fellowships at the Institute for Advanced Study in Princeton as well as at the University of Waterloo. Before coming to UT, he spent nine years as a professor in Electrical Engineering and Computer Science at MIT. He’s received the National Science Foundation’s Alan T. Waterman Award, the United States PECASE Award, the Department of Defense Vannevar Bush Faculty Fellowship, and MIT's Junior Bose Award for Excellence in Teaching.

Ph.D. 2016, University of California, Berkeley

Natural Language Processing
Machine Learning

Greg Durrett will be joining UT Computer Science as an Assistant Professor in Fall 2017.

Greg's research focuses on solving core natural language processing problems, all of which are fundamentally concerned with turning unstructured text into structured information.  This kind of text processing is a critical step for allowing computers to access all of the information that's available on the web.  To tackle this problem, Greg's work uses structured machine learning methods, especially joint models that integrate multiple approaches or address multiple tasks simultaneously.  Such models need to be sophisticated and high-capacity so they can make use of large datasets, yet also tailored to capture the key linguistic phenomena specific to each task.  Greg has studied a range of NLP problems including coreference resolution, entity linking, syntactic parsing, and document summarization.

Greg values building systems that can be deployed outside the laboratory.  He has previously worked on conversational dialogue systems at Semantic Machines and on machine translation at Google.  He also maintains several publicly available NLP tools that are byproducts of his research.

Prior to joining UT Austin, Greg completed his Ph.D. at UC Berkeley, where he was advised by Dan Klein.  During his graduate career, Greg received the Facebook Fellowship in Natural Language Processing and an NSF Graduate Research Fellowship.  His work on coreference resolution was a Best Paper finalist at EMNLP 2013.
 

Ph.D. 2012, Stanford University

Computer Graphics
Computer Vision
Machine Learning

Qixing Huang is an Assistant Professor and the director of Graphics & AI Lab in UT Computer Science. He obtained his PhD in Computer Science from Stanford University in 2012. From 2012 to 2014 he was a postdoctoral research scholar at Stanford University. Prior to joining UT, Qixing was a research assistant professor at Toyota Technological Institute at Chicago from 2014 to 2016.

Qixing’s research focuses on developing machine learning techniques (particularly deep learning) that leverage Big Data to solve core problems in processing geometric data (e.g., 1D GPS traces, 2D images, and 3D shapes). Qixing is also interested in statistical data analysis, compressive sensing, low-rank matrix recovery, and large-scale optimization, which provide theoretical foundation for much of his research. His research has been widely recognized in industry such as Google and Adobe, where he has worked as three summers. These internships resulted in three patents. His work on data-driven visual correspondences has received the best paper award at Symposium on Geometry Processing 2013.

His ongoing research activities lie at the intersection of Graphics and various fields of AI. Highlighted research projects include virtual content creation from text specifications, autonomous geometry reconstruction, and learning to generate synthetic data for training convolutional neural networks. He also continues to work at the intersection of Graphics and compressive sensing. The key factor made him choose UT among other schools is that his research can significantly benefit from as well as contribute to the already very strong AI lab at UT.

During spare-time, he enjoys playing volleyball and cooking.

Ph.D. 2013, University of California, Berkeley

Computational Neuroscience
Machine Learning
Computational Statistics

Alex Huth is currently a neuroscience postdoc in Dr. Jack Gallant's laboratory at UC Berkeley, where he does work in computational and experimental neuroscience using fMRI. His research is focused on how the many different areas in the human brain work together to perform complex tasks such as understanding natural language. He uses fMRI to measure brain responses while subjects do real-life tasks, such as listening to a story, and then uses those data to build computational models of how the brain functions. Alex was awarded a Burroughs Wellcome Career Award in 2016, which will support his research at UT. He will be joining the UT faculty in fall of 2017.

Alex earned his PhD in Dr. Jack Gallant's laboratory through the Helen Wills Neuroscience Institute at UC Berkeley. Before that, Alex earned both his bachelor's and master's degrees in computation and neural systems (CNS) at Caltech, where he worked with Dr. Christof Koch and Dr. Melissa Saenz.

 

Ph.D. 2014, Stanford University

Computer Vision
Machine Learning
Computer Graphics

Philipp  Krähenbühl is joing UT Computer Sciences as an assistant professor in Fall 2016. He received his Ph.D. in 2014 from Stanford University and then spent two years as a postdoctoral researcher at the University of California at Berkeley.

Philipp's research spans the fields of computer vision, machine learning and computer graphics, with a special focus on deep learning. The goal of Philipp's research is to teach computers to see the world as we humans do. Towards this goal, he is developping algorithms that learn rich visual representations from a minimal amount of human supervision. Philipp's research has been published at major computer vision and machine learning conferences, and received the NIPS best student paper award.

Ph.D. 2008, Weizmann Institute, Israel

Theoretical Computer Science

Dana Moshkovitz in an associate professor of Computer Science at UT Austin. Her research is in Theoretical Computer Science. Much of it focuses on the limitations of approximation algorithms and probabilistic checking of proofs.

Dana earned her Ph.D. at the Weizmann Institute in Israel. Her thesis co-won the Nessyahu Prize for best math Ph.D. thesis in Israel in 2009, and part of this work was awarded the FOCS 2008 Best paper.

Dana went on to spend two years at Princeton University and the Institute of Advanced Study before joining MIT as an assistant professor. Dana is the recipient of the Jerome Saltzer teaching award of MIT EECS.