I don’t know how many of you have come across this blog, simply statistics https://simplystatistics.org, but he is one of the main contributors, and to say, he’s expert on batch effect and meta-analysis.
Title: The Library of Babel: On Trying to Read My Genome
Information and Abstract:
Applied Data Science Seminar. Not long ago, information about our DNA was virtually impossible to gain. Now, thanks to the falling cost of DNA sequencing and the growing power of bioinformatics, genetic information is undergoing a Gutenberg-scale explosion of popularity. Millions of people are paying for DNA tests from companies like 23andMe and Ancestry.com, and they are getting unprecedented amounts of information about their ancestry and hereditary diseases. For my latest book, “She Has Her Mother’s Laugh,” I got my genome sequenced and enlisted scientists at Yale and elsewhere to help me interpret it. In my talk, I’ll discuss the results of that exploration–at once enlightening and baffling
For more details and upcoming events visit our website at
http://statistics.yale.edu/ .
“Toward theoretical understanding of deep learning”
Speaker: Professor Sanjeev Arora
Princeton University & Institute for Advanced Study
Tomorrow – Wednesday, April 18, 2018, 12:00-1:00pm
Location: Yale Institute for Network Science, 17 Hillhouse Avenue, 3rd floor
Abstract: This talk will be a survey of ongoing efforts and recent results to develop better theoretical understanding of deep learning, from expressiveness to optimization to generalization theory. We will see the (limited) success that has been achieved and the open questions it leads to. (My expository articles appear at
www.offconvex.org (link is external))
Bio: Sanjeev Arora is Charles C. Fitzmorris Professor of Computer Science at Princeton University and Visiting Professor at the Institute for Advanced Study. He is an expert in theoretical computer science, especially theoretical ML. He has received the Packard Fellowship (1997), Simons Investigator Award (2012), Goedel Prize (2001 and 2010), ACM-Infosys Foundation Award in the Computing Sciences (now called the ACM prize) (2012), and the Fulkerson Prize in Discrete Math (2012).
Upcoming:
4/25/18: Adam Auton (23andme)
5/2/18: Andre Levchenko
APPLIED MATH/ANALYSIS SEMINAR
Speaker Mauro Maggioni, John Hopkins University
Date: Tuesday, April 10, 2018
Time: 3:45p.m. Refreshments (AKW, 1st Floor Break Area)
4:00p.m. Seminar (LOM 206)
Title: “Learning and Geometry for Stochastic Dynamical Systems in high dimensions”
Abstract:
We discuss geometry-based statistical learning techniques for performing model reduction and modeling of certain classes of stochastic high-dimensional dynamical systems. We consider two complementary settings. In the first one, we are given long
trajectories of a system, e.g. from molecular dynamics, and we estimate, in a robust fashion, an effective number of degrees of freedom of the system, which may vary in the state space of then system, and a local scale where the dynamics is well-approximated by a reduced dynamics with a small number of degrees of freedom. We then use these ideas to produce an approximation to the generator of the system and obtain, via eigenfunctions of an empirical Fokker-Planck equation (constructed from data), reaction coordinates for the system that capture the large time behavior of the dynamics. We present various examples from molecular dynamics illustrating these ideas.
In the second setting we only have access to a (large number of expensive) simulators that can return short paths of the stochastic system, and introduce a statistical learning framework for estimating local approximations to the system, that can be (automatically) pieced together to form a fast global reduced model for the system, called ATLAS. ATLAS is guaranteed to be accurate (in the sense of producing stochastic paths whose distribution is close to that of paths generated by the original system) not only at small time scales, but also at large time scales, under suitable assumptions on the dynamics. We discuss applications to homogenization of rough diffusions in low and high dimensions, as well as relatively simple systems with separations of time scales, and deterministic chaotic systems in high-dimensions, that are well-approximated by stochastic
diffusion-like equations.
Mauro Maggioni 4-10 flyer.pdf
Attached please find a seminar announcement for Nobel Laureate, W.E. Moerner on Wednesday, April 11, 2018.
Speaker: W.E. Moerner, Nobel Laureate
Title: “Single Molecules for 3D Super-Resolution Imaging and Single Particle Tracking in Cells: Methods and Applications:
Date: Wednesday, April 11, 2018
Time & Place: 3:30 PM, SCL 110
Host: Biophysics Training Grant Students
Admins Please Post
biostatistics / STATISTICS & DATA SCIENCE Joint SEMINAR
Date: Monday, April 9, 2018
Time: 4:15pm – 5:30pm
Place: Yale Institute for Network Science, 17 Hillhouse Avenue, 3rd Floor, Rm 328
Seminar Speaker: Carey E. Priebe
Department of Applied Mathematics & Statistics, Johns Hopkins University
Personal Website: https://www.ams.jhu.edu/~priebe/
Title: On Spectral Graph Clustering
Abstract: Clustering is a many-splendored thing. As the ill-defined cousin of classification, in which the observation to be classified X comes with a true but unobserved class label Y, clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering — clustering the vertices of a graph based on their spectral embedding — is all the rage, and recent theoretical results provide new understanding of the problem and solutions. In particular, we reset the field of spectral graph clustering, demonstrating that spectral graph clustering should not be thought of as kmeans clustering composed with Laplacian spectral embedding, but rather Gaussian mixture model (GMM) clustering composed with either Laplacian or Adjacency spectral embedding (LSE or ASE); in the context of the stochastic blockmodel (SBM), we use eigenvector CLTs & Chernoff analysis to show that (1) GMM dominates kmeans and (2) neither LSE nor ASE dominates, and we present an LSE vs ASE characterization in terms of affinity vs core-periphery SBMs. Along the way, we describe our recent asymptotic efficiency results, as well as an interesting twist on the eigenvector CLT when the block connectivity probability matrix is not positive semidefinite. (And, time permitting, we will touch on essential results using the matrix two-to-infinity norm.) We conclude with a ‘Two Truths’ LSE vs ASE spectral graph clustering result — necessarily including model selection for both embedding dimension & number of clusters — convincingly illustrated via an exciting new diffusion MRI connectome data set: different embedding methods yield different clustering results, with one (ASE) capturing gray matter/white matter separation and the other (LSE) capturing left hemisphere/right hemisphere characterization.
4:00 p.m. Pre-talk Refreshments
4:15 p.m. – 5:30 Seminar, Room 328, 17 Hillhouse Avenue
For more details and upcoming events visit our website at
http://statistics.yale.edu/ .
On Tuesday, March 6, 2018, Professor Daniel Kahne of Harvard University will be presenting The 2018 Treat B. Johnson Lecture in Organic Chemistry.
The title of his talk is “Molecular Machines That Build Membranes”.
You are welcomed to attend the lecture which will be held at The Sterling Chemistry Lab – 225 Prospect Street – in SCL 110.
If you cannot attend, you can watch by going to the following link:
https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=848c083b-b7cc-4b48-93ed-a897010fbd2e
KAHNE Poster.pdf
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.
Overview
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
Morning Session
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
Afternoon Session:
Chairs: Aram Harrow, Ph.D., Massachusetts Institute of Technology, and John Murray, Ph.D.,
Yale University
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
Horizon
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
NIMH Quantum Computing Virtual Workshop Agenda_02-15-2018.docx
CS Colloquium
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
Abstract:
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.
Bio:
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.
CS Colloquium
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
Abstract:
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.
Biography:
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.