Computer simulation of natural phenomena is used in areas as diverse as mechanical engineering, cellular biology, and special effects for movies. Phenomena of interest span a broad range materials and scales: e.g., fire, water, and solid objects on the macroscopic scale of everyday experience, and mitosis on scale of a single biological cell. In this talk, I’ll describe some of the physics, mathematics and algorithms used to simulate natural phenomena for computer graphics and the biological sciences.
Bio: Tamar Shinar is the Amrik Singh Poonian Assistant Professor of Computer Science at the University of California, Riverside. She received the B.S. in mathematics from UIUC and the Ph.D. from Stanford in Scientific Computing and Computational Mathematics.
One of the principal challenges of medicine is to find robust biomarkers, or predictors, of patient response to therapies and disease outcome, in order to better assign personalized treatments to patients. While the “Big Data” revolution in biology has enabled clinicians to measure millions of candidate biomarkers in patients, our ability to find robust predictors of treatment efficacy and disease outcome has lagged, in large part due to heterogeneity in patient cohorts, data types and measurement instruments. Fortunately, the field of machine learning has made tremendous progress in terms of developing mixture models that efficiently handle different types of heterogeneity.
In this talk, I will discuss how probabilistic topic models, originally designed to solve problems in information retrieval and nature language processing, are readily adapted to address problems in data heterogeneity in the clinic, and illustrate how they have been used to improve our ability to predict outcome of cancer patients.
Gerald Quon is an assistant professor in the Department of Molecular and Cellular Biology at UC Davis. He obtained his B. Math in Computer Science at the University of Waterloo, his Ph.D. in Computer Science from the University of Toronto, and completed postdoctoral research training at MIT.
Concurrent programming with shared memory can lead to a variety of concurrency bugs such as deadlocks and data races. How can we find such bugs? I will survey the vast literature on solutions to this problem, and then I will present a new approach that does even better. Our approach combines concolic execution and constraint solving into a new technique that drives an execution towards a concurrency bug candidate. In 4.5 million lines of Java, our tool found substantially more real concurrency bugs than many previous techniques combined. Joint work with Mahdi Eslamimehr; presented at PPOPP 2014 and FSE 2014.
Bio: Jens Palsberg is a Professor of Computer Science at University of California, Los Angeles (UCLA). His research interests span the areas of compilers, embedded systems, programming languages, software engineering, and information security. He is the editor-in-chief of ACM Transactions of Programming Languages and Systems, and a former conference program chair of ACM Symposium on Principles of Programming Languages (POPL). In 2012 he received the ACM SIGPLAN Distinguished Service Award.
In this talk, I will tell the story of our work with some truly remarkable undergraduate students at Rutgers-Camden, who despite many odds have achieved success that is unprecedented for the Camden campus. I will discuss the various challenges that we faced and some ideas that have worked very well (and some that have not) for us. We will also discuss how we have been applying some of these ideas in our work with high school students and students at other institutions.
Bio: Rajiv Gandhi is Associate Professor of Computer Science at Rutgers, Camden. He works in the design and analysis of algorithms, focusing on approximation algorithms for NP-hard problems. He has mentored many undergraduate students at Rutgers, Camden and elsewhere.