2021 Carver Mead New Adventures Fund Symposium
Carver Mead New Adventures Fund Symposium
Thursday, April 29, 2021
00:00 - Adam Wierman, Welcome
3:58 - Erik Winfree, Pattern Recognition in the Nucleation Kinetics of Molecular Self-Assembly
27:45 - Katie Bouman and Yisong Yue, Learning to Better Capture and Understand Images
46:54 - Azita Emami, Arrhythmia Detection Enabled by Co-Design of Analog Hardware and AI Algorithms
1:07:55 - Q&A
1:20:56 - Carver Mead, Closing Remarks
Schedule
| 12:00—12:05 |
Welcome ADAM WIERMAN Professor of Computing and Mathematical Sciences;Director, Information Science and Technology |
| 12:05—12:25 |
Pattern Recognition in the Nucleation Kinetics of Molecular Self-Assembly ERIK WINFREE Professor of Computer Science, Computation and Neural Systems,and Bioengineering |
| 12:25—12:45 |
Learning to Better Capture and Understand Images KATIE BOUMAN Assistant Professor of Computing and Mathematical Sciences,Electrical Engineering and Astronomy; Rosenberg Scholar and YISONG YUE Professor of Computing and Mathematical Sciences |
| 12:45—1:05 |
Arrhythmia Detection Enabled by Co-Design of Analog Hardwareand AI Algorithms AZITA EMAMI Andrew and Peggy Cherng Professor of Electrical Engineering andMedical Engineering; Investigator, Heritage Medical Research Institute; Executive Officer for Electrical Engineering |
| 1:05—1:20 | Q & A |
| 1:20—1:30 |
Closing Remarks CARVER MEAD Gordon and Betty Moore Professor of Engineering and Applied, Science, Emeritus |
Speakers
Katie Bouman is an assistant professor in the Computing and Mathematical Sciences Department at the Caltech. Before joining Caltech, she was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics. She received her PhD in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT in EECS. Before coming to MIT, she received her bachelor's degree in electrical engineering from the University of Michigan. The focus of her research is on using emerging computational methods to push the boundaries of interdisciplinary imaging.
Azita Emami's research interests include integrated circuits and systems, integrated photonics, high-speed data communication, and wearable and implantable devices for neural recording, neural stimulation, sensing, and drug delivery. Professor Emami joined Caltech in 2007 and served as deputy chair of the Division of Engineering and Applied Science from 2015 to 2018. She received her MS and PhD in electrical engineering from Stanford University and her BS from Sharif University of Technology in Tehran, Iran. Before joining Caltech, she worked at IBM's Thomas J. Watson Research Center and was an assistant professor of electrical engineering at Columbia University. She is currently associate editor for the IEEE Journal of Solid-State Circuits (JSSC) and a lecturer for the IEEE Solid-State Circuits Society Distinguished Lecturer Program.
Carver Mead is known for electron tunneling, semiconductor interface energies, the first working MESFET, scaling of Very-Large-Scale-Integrated-circuit (VLSI) technology, structured VLSI design, the first VLSI design course, physics of computation, neuromorphic VLSI systems, and collective electrodynamics. He pioneered the silicon foundry concept and the fabless semiconductor business model. He holds BS, MS, and PhD degrees in electrical engineering from Caltech, as well as honorary doctorates from USC and the University of Lund, Sweden. He has been a member of the Caltech faculty since 1958, and the Gordon and Betty Moore Professor of Engineering and Applied Science, Emeritus, since 1999. His honors and awards include: National Medal of Technology, BBVA Frontiers of Knowledge Award, NAE Founder's Award, IEEE John von Neumann Medal, Walter Wriston Public Policy Award, ACM Allen Newell Award, IEEE Centennial Medal and Lemelson-MIT Prize. He is a member of the National Academy of Science, the National Academy of Engineering, and Fellow of IEEE, Computer History Museum and National Academy of Inventors among others.
Adam Wierman is a professor of computing and mathematical sciences, where he currently serves as executive officer. He is the director of the Information Science and Technology Initiative. He completed his PhD in computer science at Carnegie Mellon University. His research focuses on developing mathematical tools for stochastic processes, optimization, machine learning, and game theory, and applying these techniques to the design of distributed systems, such as data centers and the smart grid. He is a recipient of the ACM Sigmetrics Rising Star award and the IEEE Communications Society William R. Bennett prize.
Erik Winfree graduated with a B.S. in Mathematics, with a specialization in Computer Science, from the University of Chicago in 1991. Under the supervision of John Hopfield — and with considerable input from Len Adleman, Ned Seeman, Paul Rothemund, and Sam Roweis — he earned his Ph.D. in Computation & Neural Systems from Caltech in 1998. After postdoctoral research at Princeton in Stan Leibler's group and a period as a visiting scientist in Tom Knight's group at MIT, he joined the faculty at Caltech in 2000, where he is now Professor of Computer Science, Computation & Neural Systems and Bioengineering. Winfree's theoretical and experimental research examines chemistry as an information technology, making use of DNA nanotechnology and cell-free synthetic biology to develop a systematic approach to molecular programming with a sound theoretical foundation. Winfree currently leads the DNA and Natural Algorithms group at Caltech, and headed the Molecular Programming Project (an NSF Expedition in Computing) from 2008 to 2019. He was inducted as a Fellow of the AAAS (2015), is the recipient of the Feynman Prize for Nanotechnology (2006), the NSF PECASE/CAREER Award (2001), the ONR Young Investigators Award (2001), a MacArthur Fellowship (2000), the Rozenberg Tulip Prize in DNA Computing (2000), and appeared in MIT Technology Review's first TR100 list of "top young innovators" (1999).
Yisong Yue is a professor of Computing and Mathematical Sciences at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong's research interests are centered around machine learning, and in particular getting theory to work in practice. To that end, his research agenda spans both fundamental and applied pursuits. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, experiment design for science, protein engineering, program synthesis, learning-accelerated optimization, robotics, and adaptive planning and allocation problems.