CIS Seminars & Events

Fall 2017 Colloquium Series

Unless otherwise noted, our lectures are held weekly on Tuesday or Thursday from 3:00 p.m. to 4:15 p.m. in Wu and Chen Auditorium, Levine Hall.

September 7th

Hovav Shacham
Department of Computer Science and Engineering
University of California, San Diego
"Trusted Browsers for Uncertain Times"

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Abstract

The Web is the most important platform for human communication, including communication by human-rights activists, reporters, dissidents, and others at risk of reprisal for their speech. Modern browsers are designed to keep their users’ computers from being compromised in “browse-by” attacks, they have no comparable architectural protections for users’ privacy. In this talk, I will show how the unintended interactions of browser features with underlying system components can be used — and are used — by malicious sites to track and identify Web users and to learn information shared by those users with other sites, in violation of the browser’s intended compartmentalization guarantees. Two decades of experience show that vendors’ ad-hoc efforts to close each hole as it is identified are unlikely to produce a browser that keeps its users’ secrets. Drawing on the trusted systems literature of the 1980s, I present a principled browser design that provably reduces the capacity of all timing channels that expose secret information.

Bio
Hovav Shacham is a Professor of Computer Science and Engineering at the University of California, San Diego. His research interests are in applied cryptography, systems security, privacy-enhancing technologies, and technology policy. Shacham was a student at Stanford and a postdoctoral fellow at the Weizmann Institute. He took part in California’s 2007 “Top-to-Bottom” voting systems review and served on the advisory committee for California’s 2011–13 post-election risk-limiting audit pilot program. His work has been cited by the Federal Trade Commission, the National Highway Traffic Safety Administration, and the RAND Corporation.

September 12th

Michael Franklin
Department of Computer Science
University of Chicago
"Big Data Software: What’s Next?"

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Abstract

The Big Data revolution has been enabled by a wealth of innovation in software platforms for data storage, analytics, and machine learning. The design of Big Data platforms such as Hadoop and Spark focused on scalability, fault-tolerance and performance. As these and other systems increasingly become part of the mainstream, the next set of challenges are becoming clearer. Requirements for performance are changing as workloads and hardware evolve. But more fundamentally, other issues are moving to the forefront. These include ease of use for a wide range of users, security, concerns about privacy and potential bias, and the perennial problems of data quality and integration from heterogeneous sources. In this talk, I will give an overview of how we got here, with an emphasis on the development of the Apache Spark system. I will then focus on these emerging issues with an eye towards where the academic research community can most effectively engage.

Bio
Michael Franklin is the Liew Family Chair of Computer Science at the University of Chicago where he also serves as senior advisor to the provost on computation and data science. Previously he was at UC Berkeley where he was the Thomas M. Siebel Professor of Computer Science and Chair of the Computer Science Division. He co-founded Berkeley’s AMPLab, a leading academic big data analytics research center, and served as an executive committee member for the Berkeley Institute for Data Science, a campus-wide initiative to advance data science environments. Michael is a Fellow of ACM, a two-time recipient of the ACM SIGMOD “Test of Time” award, and the Outstanding Advisor award from the Berkeley CS Graduate
Student Association.

 

September 21st

Special CIS Lecture
Gaurav Chakravorty
Co-Founder, CIO
qplum
"The next ten years: Deep Learning in Trading"

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Abstract

Gaurav will talk about some job trends in financial services to start and cover the transformational impact of A.I. and Deep Learning in making trading a scientific process. Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading and investment management fits this paradigm perfectly. It is a social science and not a pure science, and we are generating petabytes of data everyday making it tough to learn from. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or making chatbots. This focus on learning a hierarchical set of concepts is truly making investing a scientific process, a utility.

Bio
Gaurav Chakravorty is a UPenn alumnus (MSE CIS '05) and co-founder and CIO at qplum. Qplum is an asset management firm that offers A.I. based trading strategies. Gaurav has been one of the early pioneers in machine learning based high-frequency trading. He built the most profitable algo trading group at Tower Research from 2005-2010 and was the youngest partner in the firm. Gaurav's strategies have made more than $1.4bln to-date. He believes in the potential of using Deep Learning to reduce fees and make investing a science that is universally accessible.

 

September 28th

John Hughes
Computing Science Department
Chalmers University of Technology

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October 3rd

Salvatore Stolfo
Department of Computer Scienc
Columbia University

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October 12th

Arnab Nandi
Department of Computer Science and Engineering
Ohio State University

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October 17th

Juliana Freire
Department of Computer Science and Engineering
New York University

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October 26th

Avrim Blum
School of Computer Science
Carnegie Mellon University

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November 2nd

Kunle Olukotun
Departments of Electrical Engineering and Computer Science
Stanford University

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November 7th

Ehsan Hoque
Computer Science Department
University of Rochester
"When can a computer improve your social skills?"

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Abstract
Many people fear automation. They may see it as a potential job killer. They may also be concerned about what can be automated. Could we train a computer to teach us human skills? Should we? Artificial intelligence, when designed properly, can help people improve important social and cognitive skills. My research group has shown how automated systems can develop skills that improve performance in job interviews, public speaking, negotiations, working as part of a team, producing vowels during music
training, end-of-life communication between oncologists and cancer patients, and even routine social
interactions for people with Asperger’s syndrome. In this talk, I will offer insights gained from our exploration of several questions: How are humans able to improve important social and cognitive skills with a computer? What aspect of the feedback helps the most? How to design experiments to ensure that the skills generalize?

Bio
M. Ehsan Hoque is an assistant professor of computer science at the University of Rochester, where he leads the Rochester Human-Computer Interaction, or ROC HCI, Group. His group’s research focuses on understanding and modeling unwritten rules of human communication with applications in business communication, health, and assessment technologies. Ehsan received his Ph.D. from the MIT Media Lab in 2013 where his dissertation work was highlighted by the MIT Museum as one of the most unconventional inventions at MIT. Ehsan and his group’s work has received a Best Paper Award at Ubiquitous Computing (UbiComp 2013), Best Paper Honorable Mentions in Automated Face and Gesture Recognition (FG 2011) and Intelligent Virtual Agents (IVA 2006). Ehsan has received MIT TR35 Award(2016), World Technology Award (2016), and Google Faculty Award (2014, 2016). In 2017, Science News recognized him as one of 10 early to mid-career scientists to watch (the SN 10). Follow the group’s work on Twitter at @rochci.

 

November 9th

Konrad Kording
Department of Neuroscience, Department of Bioengineering
UPenn PIK Professor
"Rethinking the role of machine learning in (neuro)science"

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Abstract

The goal of much of computational biology is to numerically describe data from a system, but also to find ways of fixing it and to understand a system’s objectives, algorithms, and mechanisms. Here we will argue that, regardless the objective, machine learning should be a central contribution to progress in every flavor of biomedical science. Machine learning can typically better describe the data. In doing so it can also provide a benchmark for any other way of describing the data. Using examples from neuroscience we discuss how better performance matters for decoding models and how having a benchmark affects encoding models. Similar issues matter in medicine. As biomedical science evolves, machine learning is morphing into a critical tool across the full spectrum of scientific questions.

Bio
Konrad Kording is a Professor at the University of Pennsylvania in the Departments of Bioengineering and Neuroscience. He received his PhD in Physics from ETH Zurich and did postdoctoral training at ETH Zurich, UCL London, and MIT focusing on statistical approaches to the brain and cognition. He is using data science towards understanding the brain, medicine, and scientists themselves.

November 16th

Grace Hopper Lecture Series
Deborah Estrin
Department of Computer Science
Cornell Tech
"In pursuit of digital biomarkers"

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Abstract
Social networks, search engines, mobile apps, IoT vendors, online entertainment, and e-commerce sites have lead the way in using an individual’s digital traces to tailor service offerings, improve system performance, and target advertisements. A growing community of researchers are looking to these same data sources to create digital biomarkers for use in personalized health and wellness applications. This talk will discuss motivation and progress.

Bio
Deborah Estrin (PhD, MIT; BS, UCB) is a Professor of Computer Science at Cornell Tech in New York City where she founded the Jacobs Institute’s Health Tech Hub. She is also a co-founder of the non-profit startup, Open mHealth. Her current focus is on mobile health and small data, leveraging the pervasiveness of mobile devices and digital interactions for health and life management (TEDMED http://smalldata.io). Previously, Estrin was the founding director of the NSF-funded Science and Technology Center for Embedded Networked Sensing (CENS) at UCLA (2002-12). Estrin is an elected member of the American Academy of Arts and Sciences (2007) and National Academy of Engineering (2009). She was awarded honorary doctorates from EPFL and Uppsala.

 

November 30th

Aditya Parameswaran
Computer Science Department
University of Illinois at Urbana-Champaign

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December 7th

Saul Gorn Lecture Series
Shafi Goldwasser
Electrical Engineering and Computer Science
MIT

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