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March 22, 2019, 12:00 PM
Ashay Rane: Broad-Based Side-Channel Defenses for Modern Microarchitectures

[Friday, March 22, 2019 at 12:00 PM in Wegmans 1400] Abstract: Private or confidential information is used in several applications, including not just cryptographic implementations but also machine-learning algorithms, databases, and parsers. However, even after using techniques like encryption, authentication, and isolation, it is difficult to maintain the privacy or confidentiality of such information due to so-called side channels, using which attackers can infer sensitive information by monitoring program execution. Various side channels such as execution time, power consumption, exceptions, or micro-architectural components such as caches and branch predictors have been used to steal intellectual property, financial information, and sensitive document contents. In this talk, I will present a solution for closing a broad class of side channels in a diverse set of applications running on modern microprocessors. Compared to prior solutions, which close an isolated number of side channels, our solution closes digital side channels (such as the cache, address trace, and branch predictor side channels) which carry information over discrete bits. Our solution also extends the capabilities of non-digital side-channel defenses, specifically power channel defenses, to a broad class of applications running on modern microprocessors. Finally, our solution is customizable, since it permits the defense to be tailored to the threat model, the program, and the microarchitecture. More broadly, I will briefly highlight how techniques from program analysis and program verification can be useful in the area of hardware design for improving assurance, performance, and energy efficiency. Bio: Ashay Rane is a PhD student at the University of Texas at Austin, where he is advised by Professor Calvin Lin and Professor Mohit Tiwari. His current research is in the area of side-channel defenses, while his prior research included topics in high-performance computing.

March 25, 2019, 12:00 PM
Snigdha Chaturvedi : Structured Approaches to Natural Language Understanding

[Monday, March 25, 2019 at 12:00 PM in Wegmans 1400] Abstract:
Despite recent advancements in Natural Language Processing, computers today cannot understand text in the ways that humans can. My research aims at creating computational methods that not only read but also understand text. To accomplish this, I develop machine-learning methods that incorporate linguistic cues as well as the context in which they appear to understand language. In this talk, I will discuss two specific applications of language understanding that focus on comprehension of narratives: (i) Choosing correct endings to stories, and (ii) Automatically generating narratives. I will also discuss my ongoing and future work on applications of language understanding in domains like education, digital humanities and mental health care.

Snigdha Chaturvedi is an Assistant Professor in the department of Computer Science and Engineering at the University of California, Santa Cruz. She specializes in the field of Natural Language Processing with an emphasis on developing methods for natural language understanding. Her research has been recognized with the IBM Ph.D. Fellowship (twice), a best paper award at NAACL, and first prize at ACM student research competition held at Grace Hopper Conference. Previously, she was a postdoctoral fellow at University of Illinois, Urbana Champaign, and University of Pennsylvania working with Professor Dan Roth. She earned her Ph.D. in Computer Science at University of Maryland, College Park in 2016 (advisor: Dr. Hal Daume III) and Bachelors of Technology from Indian Institute of Technology, Kanpur in 2009. She was also a Blue Scholar at IBM Research, India from 2009 to 2011.

March 29, 2019, 12:00 PM
Fatemeh Nargesian: Data Enrichment for Data Science

[Friday, March 29, 2019 at 12:00 PM in Wegmans 1400] Abstract:
Data Science is built on the power of data processing and data preparation. In this talk, I discuss the challenges of data preparation for end-to-end data science. Particularly, I talk about data enrichment via discovery -- the problem of discovering and integrating the right data from data lakes to solve a given data science problem. I introduce two paradigms of data discovery. In the first paradigm, the query is a dataset and a data scientist is interested in interactively finding datasets in data lakes that can be integrated with the query. I introduce a probabilistic framework for searching top-k unionable tables and discuss the need for distribution-aware techniques for data discovery. In the second paradigm, search does not start with a query, instead, it is data-driven. I will talk about data lake organization problem of building a directory structure that enables users to most efficiently navigate data lakes. I will present a navigation model of how users interact with a directory structure and introduce a scalable local search algorithm for optimizing data lake organizations.

Fatemeh Nargesian is a PhD candidate in the Data Curation Group of the Department of Computer Science at University of Toronto. Her primary research interests are in the data management challenges of end-to-end data science. A paper she co-authored on data discovery was accorded the Best Demonstration Award at VLDB 2017. While at University of Toronto, Fatemeh was a joint Research intern at IBM Research-NY. Prior to University of Toronto, she worked on clinical data management at the Clinical Informatics Research Group at McGill University, and received M.Sc. degrees in Computer Science at University of Ottawa and Artificial Intelligence at Sharif University of Technology.

April 3, 2019, 12:00 PM
Berkay Celik: Automated IoT Safety and Security Analysis

[Wednesday, April 03, 2019 at 12:00 PM in Gavett 206] Abstract:
The introduction of Internet of Things (IoT) devices that integrate online processes and services with the physical world has had profound effects on society. Yet, while IoT systems have been widely embraced by consumers and industry alike, safety and security failures have raised questions about the risks of embracing IoT-augmented lives. These failures range from compromised baby monitors to vehicle crashes and monetary theft. As with traditional security problems, many of these failures are a consequence of software bugs, user error, poor configuration, or faulty design. In this talk, we will examine new classes of failures: Interactions within the physical domain that lead to unsafe or insecure environments. I will then demonstrate how to model the interactions between devices within physical spaces through source code analysis and formally verify via model checking not only the correct operation of one device, but the joint behavior of all of the devices in an environment. Using these techniques, we successfully identify threats to safety and security, and enforce the correct operation of IoT devices and environments in physical spaces. In so doing, we create a richer model of IoT safety and security, and provide consumers, developers, and industry with systems that mitigate threats to IoT in practice.

Berkay Celik is a PhD candidate in Computer Science and Engineering at the Pennsylvania State University, where he is advised by Professor Patrick McDaniel. Berkay has researched a variety of security topics, including machine learning systems, network security, and privacy enhancing technologies. His dissertation is in the area of Internet of Things (IoT), particularly the construction of systems that ensure safety, security, and privacy in IoT implementations through program analysis. He received his B.Sc in Computer Science from Naval Academy (Istanbul) and his M.S. in Computer Science and Engineering with a minor in Computational Science from Pennsylvania State University. He expects to earn his PhD in the Spring of 2019. Berkay has had several internships in industry, including at VMware and Vencore Labs.