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March 27, 2017, 12:00 PM
Ang Chen: Secure Diagnostics and Forensics with Network Provenance

[Monday, March 27, 2017 at 12:00 PM in Computer Studies Building, Room 209]

Abstract: Distributed systems are behind many important services that we use every day, such as online banking, social media, and video conferencing. However, in a large-scale distributed system, many things can go wrong: routers can be misconfigured, programs can be buggy, and computers can be compromised by an attacker. To investigate these problems, system administrators need to play the role of 'part-time detectives'. Their tasks would be much easier if there were a way for them to ask the system to explain certain events, such as 'Why was this particular route chosen?'.

My work leverages data provenance - a concept from the database community - to enable distributed systems to offer such explanations. At a high level, provenance tracks causality between network states and events, and produces a detailed, structured explanation of any event of interest. Such information can be a helpful starting point when investigating a variety of problems, ranging from benign misconfigurations to malicious attacks.

In this talk, I will present one technique in detail that can accurately pinpoint the root causes of problems by comparing the provenance of 'correct' and 'incorrect' events. I will then give an overview of my other work on network provenance, including an extension of provenance to repair network programs, a generalization of provenance to avoid collateral damage during repair, and an application of secure provenance to the Internet's data plane.

Bio: Ang Chen is a fifth-year Ph.D. student in the Department of Computer and Information Science at the University of Pennsylvania, advised by Professor Andreas Haeberlen. His research interests are distributed systems, networking, and security. Besides network provenance, he has also worked on systems and network security, including projects on detecting covert timing channels, mitigating attacks in cyber-physical systems, and defending against DDoS attacks.


March 30, 2017, 03:30 PM
Zhou Yu: Situated Intelligent Interactive Systems

[Thursday, March 30, 2017 at 3:30 PM in Meliora 203]

Goergen Institute for Data Science Presents: Zhou Yu

Abstract: Communication is an intricate dance, an ensemble of coordinated individual actions. Imagine a future where machines interact with us like humans, waking us up in the morning, navigating us to work, or discussing our daily schedules in a coordinated and natural manner. Current interactive systems being developed by Apple, Google, Microsoft, and Amazon attempt to reach this goal by combining a large set of single-task systems. However, products like Siri, Google Now, Cortana and Echo still follow pre-specified agendas that cannot transition between tasks smoothly, and track and adapt to different users naturally. My research draws on recent developments in speech and natural language processing, human-computer interaction, and machine learning to work towards the goal of developing situated intelligent interactive systems. These systems can coordinate with users to achieve effective and natural interactions. I have successfully applied the proposed concepts to various tasks, such as social conversation, job interview training and movie promotion. My team's proposal on engaging social conversation systems was selected to receive $100,000 from Amazon Inc. to compete in the Amazon Alexa Prize Challenge.

Bio: I am a graduating PhD student at the Language Technology Institute under School of Computer Science, Carnegie Mellon University, working with Prof. Alan W Black and Prof. Alexander I. Rudnicky. I interned with Prof. David Suendermann-Oeft in ETS San Francisco Office on cloud based mulitmodal dialog systems in 2015 summer and 2016 summer. I interned with Dan Bohus and Eric Horvitz in Microsoft Research on human-robot interaction in 2014 fall.

Prior to CMU, I received a B.S. in Computer Science and a B.A. in Linguistics from Zhejiang University in 2011. I worked with Prof. Xiaofei He and Prof. Deng Cai on Machine Learning and Computer Vision, and Prof. Yunhua Qu on Machine Translation.

Host: Henry Kautz, henry.kautz@rochester.edu


April 3, 2017, 12:00 PM
Keval Vora: TBA

[Monday, April 03, 2017 at 12:00 PM in Computer Studies Building, Room 209] TBA


April 10, 2017, 12:00 PM
Aasheesh Kolli: Architecting Persistent Memory Systems

[Monday, April 10, 2017 at 12:00 PM in Computer Studies Building, Room 209]

Abstract: Persistent Memory (PM) technologies (also known as Non-Volatile RAM, e.g., Intel’s 3D XPoint) offer the exciting possibility of disk-like durability with the performance of main memory. Persistent memory systems provide applications with direct access to storage media via processor load and store instructions rather than having to rely on performance sapping software intermediaries like the operating system, aiding the development of high-performance, recoverable software. For example, I envision storage software that provides the safety and correctness of a conventional database management system like PostgreSQL and the performance of an in-memory store like Redis. However, today’s computing systems have been optimized for block storage devices and cannot fully exploit the benefits of PMs. Designing efficient systems for this new storage paradigm requires a careful rethink of computer architectures, programming interfaces, and application software.

While maintaining recoverable data structures in main memory is the central appeal of persistent memories, current systems do not provide efficient mechanisms (if any) to do so. Ensuring the recoverability of these data structures requires constraining the order of PM writes, whereas current architectures are designed to reorder memory accesses, transparent to the programmer, for performance. In this talk, I will introduce recently proposed programming interfaces, called persistency models, that will allow programmers to express the required order of PM writes. Then, I will present my work on developing efficient hardware implementations to enforce the PM write order prescribed by persistency models and tailoring software for these new programming interfaces.

Bio: Aasheesh Kolli is a doctoral candidate in Computer Science and Engineering at the University of Michigan. He investigates application software, programming interfaces, and computer architectures in light of emerging persistent memory technologies. His work has resulted in multiple research papers, including a best paper nomination, at venues like the International Symposium on Microarchitecture (MICRO) and the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS).


April 12, 2017, 10:30 AM
Tim Kopp: Symmetry Exploitation for Inference in Relational Theories

[Wednesday, April 12, 2017 at 10:30 AM in 703 Computer Studies Building]

Relational theories are a powerful representational tool for expressing problems in a vast array of domains from planning and circuit verification to link prediction and social networks. Many systems, such as Markov Logic Networks, theorem provers, model counters, verifiers, and more, use relational theories to express their problem domains. Early algorithms for inference over relational theories relied on viewing the problem at the level of propositional atoms. However, a great deal of recent research has been on lifting these algorithms to exploit the structure that exists in these problems at higher levels. In this work, I give techniques for exploiting the symmetrical structure that is common in problems expressed as relational theories. I give formal definitions of classes of symmetries, algorithms for discovering and exploiting them, and empirical results that speak to the efficacy of these methods.

Reception to follow in the Graduate Student Lounge at 5:00pm


April 17, 2017, 12:00 PM
Dr. Reza Rawassizadeh: TBA

[Monday, April 17, 2017 at 12:00 PM in CSB 209]