The Siebel Scholars Foundation has named six Carnegie Mellon University graduate students to the 2016 class of Siebel Scholars, including one in the field of energy science, which is newly added to the program this year.
Matt Wytock was named as a scholar in energy science, while John Dickerson, Rohit Girdhar, Po-Yao Huang, Jeffrey Rzeszotarski and Xun Zheng were honored as exceptional students in computer science.
The Siebel Scholars program recognizes the most talented students at the world's leading graduate schools of business, bioengineering, computer science and, now, energy science.
"Expanding the program into energy science accelerates the development of innovative, collaborative research to address today's most pressing challenges," said Thomas M. Siebel, foundation chairman.
Scholars are chosen based on outstanding academic achievement and demonstrated leadership. On average, Siebel Scholars rank in the top 5 percent of their class, and many are within the top 1 percent. Each receives a $35,000 award for their final year of study.
Wytock is a Ph.D. candidate in the Machine Learning Department. His research focuses on the design and development of novel algorithms for large-scale optimization, with a focus on applications in electrical energy systems. He received a bachelor's degree in computer science, from the University of San Diego in 2005, and worked at Google Inc. from 2005 to 2010 with a final position as a senior staff software engineer. He is the first author of seven publications in the areas of machine learning, energy and control, and an author on six patents.
Dickerson is a Ph.D. candidate in the Computer Science Department, where he works in the Electronic Marketplaces Lab with his advisor, Tuomas Sandholm. His research centers on solving practical economic problems using computer science and stochastic optimization. He has worked extensively on theoretical and empirical approaches to kidney exchange, game-theoretic approaches to counter-terrorism and negotiation and computational advertising through Optimized Markets, a CMU spin-off company. With Sandholm, he created FutureMatch, a general framework for learning to match subject to human value judgments. FutureMatch won a 2014 HPCWire Supercomputing Award and now provides sensitivity analysis for matching policies at the United Network for Organ Sharing. Prior to CMU, he worked at IBM and at the National Security Agency. He won a 2012-2015 National Defense Science & Engineering Fellowship and a 2015-2017 Facebook Fellowship.
Girdhar is a master's student in the Robotics Institute, where he works in computer vision with Martial Hebert, Abhinav Gupta and Kris Kitani. His research attempts to use advancements in scene understanding to build better image retrieval systems. As a part of the DARPA Memex program to counter human trafficking, he has engineered large-scale image search systems to find links between online escort advertisements. Previously, he was a computer science undergraduate at IIIT-Hyderabad and interned with Facebook in 2013. Beyond research, he helps scientists become better public communicators as an officer of Public Communication for Researchers. He was selected as a National Social Entrepreneurship Forum Author of Change in 2012 and worked with MentorTogether, a non-profit helping connect underprivileged students with mentors.
Huang, a master's degree student in the Language Technologies Institute, has contributed to speech algorithm development in one of world's largest mobile IC houses, where he led a team that designed and patented speech enhancement and recognition algorithms. As a student, Huang has performed research on how to understand human behavior via computer vision and language techniques. He has also worked on an automatic asthma inhaler monitoring and coaching system, and has designed a system that enables computers to understand and answer reading comprehension tests. He participates in the CMU Informedia project, last year's winner of the large scale Internet video content analysis and retrieval competition organized by the National Institute of Standards and Technology.
Rzeszotarski is a Ph.D. student in the Human-Computer Interaction Institute as well as a co-founder and technical lead for a Pittsburgh data visualization startup, DataSquid. He holds a bachelor's degree in computer science from Carleton College and a master's in HCI from CMU. Rzeszotarski has authored or co-authored numerous papers at top HCI venues, garnering three best paper awards. He is a Carnegie Mellon Innovation Scholar for his work combining research and commercialization with DataSquid, and is formerly a Microsoft Research Graduate Fellow. He has worked at Google and Microsoft Research as a research intern, and his work has been featured publicly in venues such as TechCrunch and GigaOM.
Zheng is a master's student in the Machine Learning Department. His major research interests lie in fast Markov Chain Monte Carlo (MCMC) methods for Bayesian inference and scalable distributed machine learning. He has developed efficient MCMC algorithms for regularized Bayesian models and built a model-parallel update scheduler for large-scale machine learning. His works have been published in a number of premier conferences in machine learning. He is now working on a more general framework for distributed machine learning to scale up fast MCMC methods. Previously, he earned a bachelor's degree in software engineering at Beihang University. He was a research intern at the Artificial Intelligence group in Microsoft Research Asia, where he worked on a scalable system for deep learning.