[Cs-affiliates] Remaining department visits - Friday 3/24 (tomorrow), Monday 3/27, Wednesday 3/29

Devits, Christopher R cdevits at bu.edu
Thu Mar 23 14:03:24 EDT 2017

Dear Faculty and Students,

A reminder that tomorrow we have Han Liu visiting us. The talk will be at 11:00AM in Hariri and the graduate student meeting will be at 4:00 in the Hariri Fishbowl conference room.

His talk details are below, as well as the details for our remaining talks this semester.



Presenter: Han Liu
Seminar Date + time: Friday March 24, 11:00AM
Location: Hariri Institute Seminar Room

Title: Blessing of Massive Scale
Abstract: This talk introduces a phenomenon that significant statistical benefit may be obtained by increasing the problem scale.  To illustrate this phenomenon, I introduce two case studies. The first one shows the benefit of “large sample size” using a semiparametric graph estimation problem. The second one shows the benefit of “large dimensionality” using a semiparametric spatial graph estimation problem. The procedures for both problems are very suitable for distributed computation, thus are enabled by modern computational architecture.

Bio: Han Liu is an Assistant Professor in the Department of Operations Research and Financial Engineering at Princeton University, where he leads the Statistical Machine Learning (SMiLe) Laboratory. He received the joint PhD in Statistics and Machine Learning from the Machine Learning Department at the Carnegie Mellon University (under the co-supervision of Larry Wasserman and John Lafferty). He has broad research interests, ranging from modern data science to artificial general intelligence. Specifically, his theoretical research includes combinatorial inference, statistical optimization, and computational lower bounds. His applied research includes brain science, genomics, and computational finance. Han Liu is the recipient of many prestigious research awards including the Tweedie New Researcher Award (from the Institute of Mathematical Statistics), the Noether Young Scholar Award (from the American Statistical Association), the NSF CAREER Award (from the Division of Mathematical Sciences), and the Howard B Wentz Award (from Princeton SEAS), and has received numerous best paper awards including the Best Paper Prize in Continuous Optimization in the 5th ICCOPT and the Best Overall Paper Award honorable mention in the 26th ICML. He was also invited as a keynote speaker in the 2016 INFORMS Optimization Society Conference.

Presenter: Trinabh Gupta
Seminar Date + time: Monday March 27, 11:00AM
Location: Hariri Institute Seminar Room

Title: Toward practical and private online services
Abstract: The designs of today's common online services (social networks, media streaming, messaging, email, etc.) are in conflict with privacy. Indeed, there have been many incidents (hacks, accidental disclosures, etc.) where private information has leaked.

My research aims to build systems that provide strong privacy guarantees and are practical (that is, have functionality and costs comparable to that of the status quo). In the talk, I will describe the challenges in building such systems and how I address them. As an example, Popcorn is a Netflix-like media delivery system that provably hides (even from the content distributor) which movie a user is watching, is otherwise consistent with the prevailing commercial regime (copyrights, etc.), and achieves plausibly deployable performance (the per-request dollar cost is 3.87 times that of a non-private system).

Bio: Trinabh Gupta is a PhD candidate at The University of Texas at Austin. He is also a visiting academic in NYU's systems group. His research interests are in systems, security, and privacy, and he has worked on privacy-preserving online services, and failure detection in distributed systems. His advisors are Lorenzo Alvisi and Michael Walfish. Prior to being a PhD student he was a computer science undergraduate at Indian Institute of Technology Delhi (IITD).

Presenter: Emily Whiting
Seminar Date + time: Wednesday March 29, 11:00AM
Location: Hariri Institute Seminar Room

Abstract: With the advent of rapid prototyping technologies such as desktop 3D printers, it is now simple to produce physical realizations of complex 3D models. Yet today's tools for creating digital content are largely unaware of the fundamental laws that govern how structures behave in the real world. 3D modeling software typically gives no indication of gravity, support or other properties of mechanics. My research aims to establish a field of Mechanics-Aware Geometry Processing: a cross-pollination of digital geometry processing and rapid prototyping, leveraging techniques in constrained optimization, numerical methods, and geometric computing. For example, our recent work in architectural geometry reduces labor and material in assembly of self-supporting block structures, where optimal construction sequences are found by solving a combinatorial problem. On the same theme, we developed techniques that optimize the internal structure of 3D printed objects to achieve design goals including static equilibrium, rotational inertia, buoyancy, and acoustics of wind instruments. This work has been featured in numerous media sources including TEDx, MIT Technology Review, and Make magazine.

Website: http://www.cs.dartmouth.edu/~emily/

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