Bob Darnell seminar – Next Generation Clinical Genomics: The other 98%
Thursday, March 8th, 2018
3/1 3pm Bass 305
NIMH VIRTUAL WORKSHOP:
SOLVING COMPUTATIONAL CHALLENGES IN GENOMICS AND NEUROSCIENCE VIA PARALLEL & QUANTUM COMPUTING
March 28, 2018
9:00 am – 1:00 pm EST
Goal of the workshop
This virtual workshop aims to highlight core computational problems faced by genetics and the subdomains of neuroscience that parallel or quantum computing can address. By bringing together experts in quantum and parallel computing with experts in genetics and neuroscience, we hope to start a dialogue between academic and industry partners working in this area with the focus on algorithm optimization and development. This virtual workshop will be the forum and the nexus to find convergence between cross-disciplinary fields that are operating mostly independently – 1) genomics and neuroscience, and 2) AI/machine learning and 3) quantum computing. The goal is to identify key avenues for computation optimization via parallel and quantum algorithms. This workshop will facilitate the use of state-of-art computational technologies for addressing core bottlenecks in genomics and neuroscience.
This workshop will cover the following topics with 5 minutes break following each topic discussion:
*NOTE: Some speakers are yet to be confirmed and/or subject to change.
9:00 – 9:10 am:Opening Remarks – Thomas Lehner, Geetha Senthil, Susan
Wright, National Institute of Mental Health, Office of Genomics Research Coordination
Chairs: Alan Anticevic, Ph.D., Yale University and Alan Aspuru-Guzik, Ph.D., Harvard University
Topic 1: Computational Challenges in Genetics and Neuroscience
This session is to highlight where computational challenges/bottlenecks exist at the level of scaling (data and computational features) and computational speedup.
9:10 – 9:25 am: Presentation 1: Genetics and functional genomics
Michael McConnell, Ph.D., University of Virginia, Michael Gandal, M.D., Ph.D., University of California, Los Angeles
9:25 – 9:40 am: Presentation 2: Neurophysiology (processing data, extracting, analysis)
Potential speakers: Mike Halassa, M.D., Ph.D., Massachusetts Institute of Technology
9:40 – 9:55 am: Presentation 3: Neuroimaging
Potential speakers: Alan Anticevic, Ph.D., Yale University, Stephen Smith, Oxford
9:55 – 10:10 am: Presentation 4: Quantitative deep phenotypic analysis
Potential speakers: Andrey Rzhetsky, Ph.D., University of Chicago, Justin Baker, M.D., Ph.D., Massachusetts General Hospital, Jukka-Pekka Onnela, M.Sc., Ph.D., Harvard University
10:10 – 10:25 am: Presentation 5: Computational modeling
Suggested topic: Spiking and neural models and ion channel modelling – spiking network simulation
Speakers: John Murray, Ph.D., Yale University, Michael Hines, Ph.D., Yale University
10:25 – 10:30 am: Break
Topic 2: AI, machine deep learning and parallel computing
This session is to discuss application of state-of-the-art classical parallel computing algorithm applications for machine learning, simulation, & optimization of analysis with ‘big’ data.
10:30 – 10:45 am: Presentation 1: Overview of machine learning via classical and parallel computing technologies
Potential speakers: Guillermo Sapiro, M.Sc., Ph.D., Duke University
10:45 – 11:00 am: Presentation 2: Deep Learning for AI applications – e.g. DeepMind
Potential speakers: Tim Lillicrap, Ph.D., DeepMind
11:00 – 11:15 am: Presentation 3: Parallel processing & GPUs
Suggested topic: Nvidia parallel processing & GPU capabilities for efficient high-performance applications
Potential speakers: Alan will reach out to his contact at Nvidia
11:15 – 11:20 am: Break
Chairs: Aram Harrow, Ph.D., Massachusetts Institute of Technology, and John Murray, Ph.D.,
Topic 3: Quantum Algorithms for Accelerated Computation: Opportunities and Challenges
This session will discuss the current state of quantum hardware and algorithms. What kind of advantages (either in terms of speed or solution quality) can be obtained by using quantum machine learning? How close are existing or proposed near-term hardware platforms to being able to implement these algorithms?
11:20 – 11:35 am: Presentation 1: Overview and primer: what is quantum computing good for?
Potential speakers: Alán Aspuru-Guzik, Ph.D., Harvard University
11:35 – 11:50 am: Presentation 2: Status and Prospects for Quantum Hardware
Potential speaker: Nicole Barberis, IBM
11:50 am – 12:05 pm: Presentation 3: Promising Quantum Computing Algorithms on the
Potential speakers: Ashley Montanaro, Ph.D., University of Bristol
12:05 – 12:20 pm: Presentation 4: Quantum Machine Learning and Optimization
Seth Lloyd, Massachusetts Institute of Technology
12:20 – 12:30 pm: Break
12:30 – 12:50 pm: Roundtable Discussion & Summary
Moderators: Stefan Bekiranov, University of Virginia & John Murray, Yale University
12:50 – 1:00 pm: Summary/Closing Remarks
Potential speakers: Alán Aspuru-Guzik, Harvard University, Alan Anticevic, Yale University
1:00 pm: Adjourn
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Monday, February 26
4:00 p.m., AKW 200 (coffee & cookies at 3:45)
Speaker: Danqi Chen, Stanford University
Title: Knowledge from Deep Understanding of Language
Host: Dragomir Radev
Almost all of humanity’s knowledge is now available online, but the vast majority of it is principally encoded in the form of human language explanations. In this talk, I explore novel neural network or deep learning approaches that open up increased opportunities for getting a deep understanding of natural language text. First, I show how distributed representations enabled the building of a smaller, faster, better dependency parser for finding the structure of human language sentences. Then I show how related neural technologies can be used to improve the construction of knowledge bases from text. However, maybe we don’t need this intermediate step and can directly gain knowledge and answer people’s questions from large textbases? In the third part, I explore doing this by looking at a simple but highly effective neural architecture for question answering.
Danqi Chen is a PhD student in Computer Science at Stanford
University, working with Christopher Manning on deep learning approaches to Natural Language Processing. Her research centers on how computers can achieve a deep understanding of human language and the information it contains. Danqi received Outstanding Paper Awards at ACL 2016 and EMNLP 2017, a Facebook Fellowship, a Microsoft Research Women’s Fellowship and an Outstanding Course Assistant Award from Stanford. She holds a B.E. with honors from Tsinghua University.
Tuesday, February 27, 2018
4:00 p.m., AKW 200 (coffee & cookies at 3:45)
Speaker: Kevin Fu, University of Michigan
Title: Analog Cybersecurity and Transduction Attacks
Host: Zhong Shao
Medical devices, autonomous vehicles, and the Internet of Things depend on the integrity and availability of trustworthy data from sensors to make safety-critical, automated decisions. How can such cyberphysical systems remain secure against an adversary using intentional interference to fool sensors? Building upon classic research in cryptographic fault injection and side channels, research in analog cybersecurity explores how to protect digital computer systems from physics-based attacks. Analog cybersecurity risks can bubble up into operating systems as bizarre, undefined behavior. For instance, transduction attacks exploit vulnerabilities in the physics of a sensor to manipulate its output. Transduction attacks using audible acoustic, ultrasonic, or radio interference can inject chosen signals into sensors found in devices ranging from fitbits to implantable medical devices to drones and smartphones.
Why do microprocessors blindly trust input from sensors, and what can be done to establish trust in unusual input channels in cyberphysical systems? Why are students taught to hold the digital abstraction as sacrosanct and unquestionable? Come to this talk to learn about undefined behavior in basic building blocks of computing. I will also suggest educational opportunities for embedded security and discuss how to design out analog cybersecurity risks by rethinking the computing stack from electrons to bits. This work brings some closure to my curiosity on why my cordless phone would ring whenever I executed certain memory operations on the video graphics chip of an Apple IIGS.
Kevin Fu is Associate Professor of EECS at the University of Michigan where he directs the Security and Privacy Research Group
(SPQR.eecs.umich.edu) and the Archimedes Center for Medical Device Security (secure-medicine.org). His research focuses on analog cybersecurity—how to model and defend against threats to the physics of computation and sensing. His embedded security research interests span from the physics of cybersecurity through the operating system to human factors. Past research projects include MEMS sensor security, pacemaker/defibrillator security, cryptographic file systems, web authentication, RFID security and privacy, wirelessly powered sensors, medical device safety, and public policy for information security & privacy.
Kevin was recognized as an IEEE Fellow, Sloan Research Fellow, MIT Technology Review TR35 Innovator of the Year, and recipient of a Fed100 Award and NSF CAREER Award. He received best paper awards from USENIX Security, IEEE S&P, and ACM SIGCOMM. He co-founded healthcare cybersecurity startup Virta Labs. Kevin has testified in the House and Senate on matters of information security and has written commissioned work on trustworthy medical device software for the National Academy of Medicine. He is a member the Computing Community Consortium Council, ACM Committee on Computers and Public Policy, and the USENIX Security Steering Committee. He advises the American Hospital Association and Heart Rhythm Society on matters of healthcare cybersecurity. Kevin previously served as program chair of USENIX Security, a member of the NIST Information Security and Privacy Advisory Board, a visiting scientist at the Food & Drug
Administration, and an advisor for Samsung’s Strategy and Innovation Center. Kevin received his B.S., M.Eng., and Ph.D. from MIT. He earned a certificate of artisanal bread making from the French Culinary Institute.
MB&B Dissertation Seminar (Flyer attached)
Speaker: Michael Lacy (Julien Berro, Advisor)
Title: “Single-molecule dynamics in clathrin-mediated endocytosis and membrane remodeling”
Date: Friday, March 2, 2018
Time & 2:00 pm
Place: 305 Bass
Tea at 1:45 pm
Lacy dissertation flyer.pdf
DEPARTMENT OF STATISTICS AND DATA SCIENCE SEMINAR
Date: Monday, February 26, 2018
Time: 4:15pm – 5:15pm
Place: 24 Hillhouse Avenue, Rm. 107
Seminar Speaker: Aaditya Ramdas
University of California, Berkeley, http://people.eecs.berkeley.edu/~aramdas/
Title: Interactive algorithms for multiple hypothesis testing
Abstract: Data science is at a crossroads. Each year, thousands of new data scientists are entering science and technology, after a broad training in a variety of fields. Modern data science is often exploratory in nature, with datasets being collected and dissected in an interactive manner. Classical guarantees that accompany many statistical methods are often invalidated by their non-standard interactive use, resulting in an underestimated risk of falsely discovering correlations or patterns. It is a pressing challenge to upgrade existing tools, or create new ones, that are robust to involving a human-in-the-loop. In this talk, I will describe two new advances that enable some amount of interactivity while testing multiple hypotheses, and control the resulting selection bias. I will first introduce a new framework, STAR, that uses partial masking to divide the available information into two parts, one for selecting a set of potential discoveries, and the other for inference on the selected set. I will then show that it is possible to flip the traditional roles of the algorithm and the scientist, allowing the scientist to make post-hoc decisions after seeing the realization of an algorithm on the data. The theoretical basis for both advances is founded in the theory of martingales : in the first, the user defines the martingale and associated filtration interactively, and in the second, we move from optional stopping to optional spotting by proving uniform concentration bounds on relevant martingales.
This talk will feature joint work with (alphabetically) Rina Barber, Jianbo Chen, Will Fithian, Kevin Jamieson, Michael Jordan, Eugene Katsevich, Lihua Lei, Max Rabinovich, Martin Wainwright, Fanny Yang and Tijana Zrnic. Bio : Aaditya Ramdas is a postdoctoral researcher in Statistics and EECS at UC Berkeley, advised by Michael Jordan and Martin Wainwright. He finished his PhD in Statistics and Machine Learning at CMU, advised by Larry Wasserman and Aarti Singh, winning the Best Thesis Award in Statistics. A lot of his research focuses on modern aspects of reproducibility in science and technology — involving statistical testing and false discovery rate control in static and dynamic settings.
4:00 p.m. Refreshments in Common Room, 24 Hillhouse Avenue
4:15p.m. – 5:15p.m. Seminar, Room 107, 24 Hillhouse Avenue
For more details and upcoming events visit our website at
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