- talks - lists.cs.princeton.edu

Xinyi Fan will present her pre-FPO on Tuesday, October 10, 2017 at 2pm in CS 401.
by Nicki Gotsis 09 Oct '17

09 Oct '17

Xinyi Fan will present her pre-FPO on Tuesday (October 10) 2-4pm in CS 401. Advisor: Szymon Rusinkiewicz FPO examiners: Adam Finkelstein, Peter Ramadge (ELE) Thesis readers: Tom Funkhouser, Yuxin Chen (ELE) Title: View and Path Planning for Large-Scale 3D Acquisition Abstract: Demand for high-volume 3D scanning of real objects is rapidly growing in a wide range of applications, including online retailing, quality-control for manufacturing, stop motion capturing for 3D animation, as well as archaeological documentation and reconstruction. Although mature technologies exist for high-fidelity 3D model acquisition, deploying them at scale continues to require non-trivial manual labor. In this talk, I will present a system that allows non-expert users to scan large numbers of physical objects within a reasonable amount of time, and also with greater ease. I will describe the design of a structured-light scanner and positioning system, which are optimized for acquisition of relatively small objects, such as fragments of archaeological artifacts. I will also discuss novel view- and path-planning algorithms that enable automatic 3D acquisition, and demonstrate the ability of our prototype to safely, robustly, and automatically acquire 3D geometry and color models for large collections of small objects, such as toy soldiers and fragments of ancient artifacts.

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Tengyu Ma will present his FPO, "Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding" on Thursday, 10/12/2017 at 3pm, in CS 402.
by Nicki Gotsis 05 Oct '17

05 Oct '17

Tengyu Ma will present his FPO, "Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding" on Thursday, 10/12/2017 at 3pm, in CS 402. The members of his committee are: Sanjeev Arora (adviser); Readers: Rong GE (Duke), Sanjeev Arora, and David Steurer (ETH Zurich); Nonreaders: Sanjeev Arora, Elad Hazan, and Mark Braverman. A copy of his thesis, is available in Room 310. Everyone is invited to attend his talk. The talk abstract follows below: Non-convex optimization is ubiquitous in modern machine learning: recent breakthroughs in deep learning require optimizing non-convex training objective functions; problems that admit accurate convex relaxation can often be solved more efficiently with non-convex formulations. However, the theoretical understanding of optimizing non-convex functions remained rather limited to the intractability result of optimizing worst case non-convex functions. Can we extend the algorithmic frontier by effi- ciently optimizing a family of interesting non-convex functions? Can we successfully apply non-convex optimization to machine learning problems with provable guarantees? How do we interpret the complicated models in machine learning that demand non-convex optimizers? Towards addressing these questions, in this thesis, we theoretically studied various machine learning models including sparse coding, topic models, and matrix completion, linear dynamical systems, and word embeddings. We first consider how to find a coarse solution to serve as a good starting point for local improvement algorithms such as stochastic gradient descent. We propose ef- ficient methods for sparse coding and topic inference with better provable guarantees. Second, we propose a framework for analyzing local improvement algorithms with a good starting point. We apply it successfully to the sparse coding problem and demonstrate its advantage over other related frameworks. Then, we consider minimizing a family of non-convex functions that local improvement algorithms can succeed efficiently from random or arbitrary initialization — the family of functions of which all local minima are also global. The challenge, in turn, becomes proving the objective function belongs to this class. We establish such type of results for the natural learning objectives for matrix completion and learning linear dynamical systems. iii Finally, we make steps towards interpreting the non-linear models that require non-convex training algorithms. We reflect on the principles of word embeddings in natural language processing. We give a generative model for the texts, using which we explain why different non-convex formulations such as word2vec and GloVe can learn similar word embeddings with the surprising performance — analogous words have embeddings with similar differences.

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Rajesh Ranganath will present his FPO, "Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications" on Friday, 10/6/2017 at 11am in CS 105.
by Nicki Gotsis 02 Oct '17

02 Oct '17

Rajesh Ranganath will present his FPO, "Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications" on Friday, 10/6/2017 at 11:00 in CS 105. The members of his committee are as follows: David Blei (Adviser); Examiners: Sanjeev Arora, Barbara Engelhardt, and David Blei; Non Examiners: David Blei and Peter Orbanz (Columbia University, Department of Statistics) A copy of his thesis, is available in Room 310. Everyone is invited to attend his talk. The talk abstract follows below. Probabilistic generative models are robust to noise, uncover unseen patterns, and make predictions about the future. These models have been used successfully to solve problems in neuroscience, astrophysics, genetics, and medicine. The main computational challenge is computing the hidden structure given the data—posterior inference. For most models of interest, computing the posterior distribution requires approximations like variational inference. Variational inference transforms posterior inference into optimization. Classically, this optimization problem was feasible to deploy in only a small fraction of models. This thesis develops black box variational inference. Black box variational inference is a variational inference algorithm that is easy to deploy on a broad class of models and has already found use in models for neuroscience and health care. It makes new kinds of models possible, ones that were too unruly for previous inference methods. One set of models we develop is deep exponential families. Deep exponential families uncover new kinds of hidden pattens while being predictive of future data. Many existing models are deep exponential families. Black box variational inference makes it possible for us to quickly study a broad range of deep exponential families with minimal added eort for each new type of deep exponential family. The ideas around black box variational inference also facilitate new kinds of variational methods. First, we develop hierarchical variational models. Hierarchical variational models improve the approximation quality of variational inference by building higher-fidelity approximations from coarser ones. We show that they help with inference in deep exponential families. Second, we introduce operator variational inference. Operator variational inference delves into the possible distance measures that can be used for the variational optimization problem. We show that this formulation categorizes various variational inference methods and enables variational approximations without tractable densities. By developing black box variational inference, we have opened doors to new models, better posterior approximations, and new varieties of variational inference algorithms.

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Katie Anna Wolf will present her FPO, "Methods for Personalized Design of Data Sonification" on Wednesday, 8/30/2017 at 10am in CS 402
by Nicki Gotsis 23 Aug '17

23 Aug '17

Katie Anna Wolf will present her FPO, "Methods for Personalized Design of Data Sonification" on Wednesday, 8/30/2017 at 10:00am in CS 402. The members of her committee are: Advisers: Adam Finkelstein and Rebecca Fiebrink (Goldsmiths, University of London); Readers: Michael Nees (Lafayette College), Adam Finkelstein, and Rebecca Fiebrink (Goldsmiths, University of London); Examiners: Adam Finkelstein, Marshini Chetty, and Daniel Trueman (Music). A copy of her thesis is available in Room 310. Everyone is invited to attend her talk. Attached is the talk abstract.

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Stephen Beard will present his Pre FPO "Containment Architecture with Verified Output: A Model for Practical Trust in Real Systems" in CS 302 on Monday, August 14th at 1pm.
by Nicki Gotsis 08 Aug '17

08 Aug '17

Stephen Beard will present his Pre FPO "Containment Architecture with Verified Output: A Model for Practical Trust in Real Systems" in CS 302 on Monday, August 14th at 1pm. The members of his committee are as follows: David August (advisor) Andrew Appel (reader) Jothy Rosenberg (Dover Microsystems) (reader) Aarti Gupta (non-reader) Arvind Narayanan (non-reader) Everyone is invited to attend his talk. The talk abstract follows below: Abstract: All layers of today’s computing systems, from hardware to software, are vulnerable to attack. Market and economic pressure generally result in companies focusing on performance and features, leaving security and correctness as post deployment concerns. Thus, systems must frequently be patched or augmented after deployment to fix security vulnerabilities. While this non-virtuous exploit-patch-exploit cycle is not the most secure, it is practical enough for companies to use. Formal methods in both software and hardware can give true guarantees as to the security they provide. Ideally, modern systems would be comprised of entirely formally verified and secure components. Unfortunately, such methods have not seen widespread adoption for a number of reasons, such as difficulty in scaling, lack of tools, and high skill requirements. Additionally, the economics involved in securing and replacing every component in all systems, both new and currently deployed, are a major factor in clean slate solutions being unrealistic. Thus, a practical solution should rely on a few, simple components and should be adoptable incrementally. As the first implementation of the Containment Architecture with Verified Output (CAVO) model developed at Princeton, TrustGuard showed how a simple trusted Sentry could protect against malicious or buggy hardware components to ensure integrity of external communications. This was accomplished by ensuring the correct execution of signed software, with support from a modified CPU and system architecture. However, TrustGuard’s practicality was limited due to its reliance on modified host hardware and its requirement to trust entire application stacks, including the operating system. The work presented in this dissertation seeks to make the CAVO model a practical solution for ensuring the integrity of data communicated externally in an untrusted commodity system. This work extends CAVO in two ways. The first extension is to make the Sentry compatible with a wide range of devices without requiring hardware modifications to the host system, thus increasing its flexibility and ease of integration into existing environments. The second extension is to give developers the option to use trusted code to verify the execution of untrusted code, thus reducing the size of the trusted code base. This is similar to the way that the small, trusted Sentry can ensure correctness of execution of a large amount of complex, untrusted hardware.

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Danny Huang on Cryptocurrencies, July 25 @ 12:30p
by Nick Feamster 25 Jul '17

25 Jul '17

All, Danny Huang will visit us from UCSD on July 25. He’ll give a talk on Cryptocurrencies (abstract below). If you’d like to chat with him, please sign up here: https://goo.gl/nhni3p -Nick ————— Date: July 25, 2017 Time: 12:30 p Place: Sherrerd Hall 306 (Conference Room) Title: Crypto-currencies: New opportunities for criminals, investors, and human-trafficking fighters. Abstract: When Satoshi Nakamoto designed Bitcoin, he had intended it as an “electronic cash system.” However, it is difficult to treat Bitcoin as a form of cash or currency, especially given its price volatility. Still, Bitcoin, as well as thousands of other similar crypto-currencies, allows for the exchange of value in a decentralized and partially anonymous manner. These properties have created opportunities for novel use cases, far beyond the original design goals of Satoshi Nakamoto. As I will discuss in the talk, these use cases span from being unethical to nobel, and from profit-driven to promoting social justice. In particular, three classes of users will be our focus today: (1) criminals who monetize stolen CPU cycles and files with Bitcoin; (2) investors who speculate in crypto-currencies; and (3) human-trafficking fighters who take advantage of a timing side channel in Bitcoin. ——— About the speaker: Danny Y. Huang is a PhD student at UC San Diego. He is generally interested in the economics of security/privacy. He is graduating in August. Details: https://sysnet.ucsd.edu/~dhuang

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Rafael Mendes de Oliveira will present his FPO, "Polynomial Identity Testing: Derandomization Results and Applications to Algebraic Computation" on Friday, July 21, 2017 at 1pm in Friend Center 401.
by Nicki Gotsis 18 Jul '17

18 Jul '17

Rafael Mendes de Oliveira will present his FPO, "Polynomial Identity Testing: Derandomization Results and Applications to Algebraic Computation" on Friday, July 21, 2017 at 1pm in Friend Center 401. The members of his committee are as follows: Nonreaders: Zeev Dvir (adviser), Gillat Kol, Mark Braverman, and Ran Raz; Readers: Zeev Dvir (adviser) and Gillat Kol A copy of his thesis is available in Room 310. Everyone is invited to attend his talk. The talk abstract follows below: The polynomial identity testing problem (PIT) is a fundamental problem in Complexity Theory, as it is one of the few problems for which there exists a polynomial time randomized algorithm, but no deterministic sub-exponential time algorithm has been discovered. Moreover, many fundamental algorithmic problems can be reduced to particular instances of the PIT problem, which makes its derandomization even more interesting, as it may yield fast deterministic parallel algorithms for such algorithmic problems. The aim of this thesis is to provide new results and new approaches to solving the PIT problem. In the first part of this thesis (Chapter 3), we focus on the PIT problem in the commutative setting. In the first half of this chapter, we provide new deterministic algorithms for multiliinear circuits of low depth and for regular multilinear formulas. In the second half we provide a new application of PIT to the problem of testing equivalences of two polynomials under shifts of the inputs. In the second part of this thesis (Chapter 4), we focus on structural results for factors of commutative polynomials whose degree in each variable is bounded by a constant. We prove that if such a polynomial can be computed by a low-depth circuit of polynomial size, then its factors can also be computed by low-depth circuits of polynomial size. We hope that such structural results provide additional insights in tackling the PIT problem for bounded depth circuits. In the final part of this thesis (Chapter 5), we introduce the setting of non-commutative computation with inversion gates and study the rational identity testing (RIT) problem. We prove the first deterministic polynomial time algorithm for the RIT problem for general non-commutative formulas. Our proof goes via a reduction to the operator scaling problem, which we also discuss in this chapter, and is an analytic approach to the PIT problem, unlike all previous approaches, which are of a purely algebraic nature.

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Aaron Blankstein will present his FPO, "Exploiting the Structure of Modern Web Applications" on Wednesday, 6/28/2017 at 11:00am in CS 402.
by Nicki Gotsis 21 Jun '17

21 Jun '17

Aaron Blankstein will present his FPO, "Exploiting the Structure of Modern Web Applications" on Wednesday, 6/28/2017 at 11:00am in CS 402. The members of his committee are as follows: Readers: Jennifer Rexford and Nick Feamster; Nonreaders: Robert Tarjan, Kyle Jamieson, and Michael Freedman (Adviser) A copy of the thesis is available in CS 310. All are welcome to attend. Abstract: In this thesis, I show how, in many respects, modern web applications are built with implicit structure, such that several classic problems in computer systems can be tackled in new ways, allowing today’s applications to readily reap security or performance benefits. I apply this to two separate contexts. In the first, I look at the problems of providing some automatic security to web applications by partitioning the server-side code of the application, isolating those partitions, learning access control policies for those partitions, and extending this isolation to front-end code with JavaScript sandboxing. In the second, I look at how modern web applications interact with application caches, and develop a family of cache eviction policies tailored to these needs by focusing on prioritizing individual items to capture how item request frequencies, associated costs, and expiration times affect cache performance.

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Fwd: Friday 6/2 : Erez Lieberman-Aiden “A 3D Code in the Human Genome”
by Nicole E. Wagenblast 25 May '17

25 May '17

Dear colleagues, Erez Lieberman-Aiden, inventor of the Hi-C method for three-dimensional DNA sequencing, will be visiting Princeton on June 2nd. He will be giving a talk in Icahn 101 at 11am on Friday, June 2nd on the development of Hi-C and its applications, including the construction of 3D maps in multiple cell types and the correlation of these loops with gene activation. Erez also has time in his schedule for some individual meetings throughout the day. If you are interested in meeting with him, please email Dawn Capizzi ( dcapizzi(a)exchange.Princeton.EDU ). Thanks, and hope to see you there! All the best, Sarah Kocher Save

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Friday at 11a - Hijacking Bitcoin: Routing Attacks on Cryptocurrencies
by Nick Feamster 24 May '17

24 May '17

Please let me know if you’d be interested in meeting Maria on Friday afternoon. Thanks! -Nick ————— Hijacking Bitcoin: Routing Attacks on Cryptocurrencies Maria Apostolaki, ETH Zürich 11a, Sherrerd Hall 306 Abstract As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. In this talk, we will look at the impact and practicality of Internet routing attacks on the cryptocurrency. We will show that the efficiency of routing manipulation and the significant centralization of Bitcoin in terms of mining and routing make two large-scale routing attacks possible today. The first attack consists in partitioning the Bitcoin network into two disjoint components of almost equal mining power by hijacking few (<100) BGP prefixes. The second attack consists in considerably delaying block propagation by interfering with few key Bitcoin messages in a stealth way. The potential damage to Bitcoin is worrying. Among others, such attacks could reduce miners’ revenue and render the network much more susceptible to double spending and selfish mining. Bio Maria Apostolaki is a second year PhD student in the Networked Systems Group at ETH Zürich, advised by Prof. Laurent Vanbever. Her research focus on Internet routing and its implication on performance and security. Prior to joining ETH, Maria earned her diploma in Electrical and Computer Engineering at the National Technical University of Athens, Greece.

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