ICME Xpo Research Symposium 2017
This event was for Stanford faculty, staff and students, and external partners of ICME.
ICME's annual research symposium, Xpo, was on Friday, May 19, 2017 and started at 1:00pm.
This event featured an up-close and inside look at current research and future plans for ICME students and faculty, Xpo was a unique opportunity to see how computational mathematics, data science, scientific computing, and related fields are applied across a wide range of domain areas.
ICME Xpo featured a series of faculty vision talks followed by a poster session and tours of the ICME HANA Immersive Visualization Environment (HIVE), with opportunities to connect with ICME faculty, staff and students, alumni, and partners from industry and laboratories.
Some featured research:
- Daniel Zhengyu Huang's work on developing a high-fidelity multidisciplinary computational framework with the Farhat Research Group
- Shruti Bhargava and Neel Rakholia's work on deep learning for stance detection in news
- Victor Minden's work on fast algorithms for problems in scientific computing with Professor Lexing Ying
- Lan Huong Nguyen's work on methods for analyzing biological data with Professor Susan Holmes
ICME Xpo was held inside Huang Engineering Center, Mackenzie Room (Huang 300).
1:00-1:15 ICME Welcome by Director, Margot Gerritsen and Co-Director, Gianluca Iaccarino (click here to view Margot's welcome presentation)
Margot Gerritsen is the Senior Associate Dean for Educational Initatives, Associate Professor of Energy Resources Engineering and, by courtesy, of Mechanical Engineering and of Civil and Environmental Engineering. Margot has been the Director of ICME since 2010. She received her Ph.D. in Scientific Computing and Computational Mathematics at Stanford in 1997. After five years as faculty member at the University of Auckland, she returned to Stanford in 2001. Her primary appointment is in Energy Resources Engineering. Margot specializes in computational modeling of fluid flow processes, with emphasis on reservoir simulation. She teaches several of the ICME core and service courses in numerical analysis and linear algebra, as well as courses in renewable energy and reservoir simulation.
Aaron Sidford is an Assistant Professor of Management Science and Engineering and, by courtesy, of Computer Science at Stanford University. Aaron received his PhD from the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology where he was advised by Professor Jonathan Kelner.
Aaron's research interests lie broadly in the optimization, the theory of computation, and the design and analysis of algorithms. He is particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures.
1:30-1:45 Prof. Ali Mani, Assistant Professor of Mechanical Engineering
Ali Mani is an Assistant Professor at the Flow Physics and Computational Engineering and the Mechanics and Computation groups in the Mechanical Engineering Department at Stanford University. He received his Bachelor of Science in mechanical engineering from Sharif University of Technology in 2002, and his Master of Science in mechanical engineering from Stanford University in 2004, followed by his Ph.D in 2009. He worked as an engineering research associate at Stanford and a senior postdoctoral associate at MIT's department of chemical engineering before joining Stanford as a faculty.
The Mani Research Lab is broadly defined around multiphysics problems in fluids and transport engineering, commonly involving phenomena such as: interfaces, shocks, electrohydrodynamics, turbulence, and micro/nano-scale engineering. Our work contributes to the understanding of these problems primarily through theoretical tools such as large-scale computation and techniques of applied mathematics. Numerical simulations enable quantitative visualization of the detailed physical processes which can be difficult to detect experimentally. They also provide insight for the development of reduced-order models. The ultimate goal in each problem is to provide a simple representation of the essential physics (ideally ODE-level) which would naturally induce insight into design, optimization and control. While these efforts at core rely on mathematical techniques such as asymptotic methods or statistical analysis, close interaction with experiments is crucial in identification of practical bottlenecks and validation of the theoretical assumptions.
1:45-2:00 Ken Jung, Research Engineer at the School of Medicine
Ken Jung, Research Engineer at the School of Medicine, Stanford - Transfer Learning with Electronic Health Record Data
Ken Jung is a Research Engineer at Stanford University in the Center for Biomedical Informatic Research. His primary focus is predictive models of clinical outcomes using data from Electronic Health Records. He has worked as a software engineer at Thinking Machines and as a computational biologist at Genentech prior to returning to Stanford University, where he received his PhD under the supervision of Nigam Shah in 2015.