- talks - lists.cs.princeton.edu

Paul Ellenbogen will present his Pre-FPO, "Long range familial searches of genetic databases: New techniques and policy implications" on Tuesday, September 17, 2019 in CS 402 at 2:30pm
by Nicki Mahler 10 Sep '19

10 Sep '19

Paul Ellenbogen will present his Pre-FPO, "Long range familial searches of genetic databases: New techniques and policy implications" on Tuesday, September 17, 2019 in CS 402 at 2:30pm. The members of his committee are as follows: Arvind Narayanan (adviser), Ed Felten, Jonathan Mayer, Mona Singh, and Olga Troyanskaya. Everyone is invited to attend his talk. The talk title and abstract follow below. "Long range familial searches of genetic databases: New techniques and policy implications" Abstract: Consumer genetics databases hold dense genotypes of millions of people, and the number is growing quickly. In 2018, law enforcement agencies began using such databases to identify anonymous DNA via long-range familial searches. I show that this technique is far more powerful if combined with a genealogical database of the type collected by online ancestry services. I present a “genealogical triangulation” algorithm and study its effectiveness on simulated datasets. I show that for over 50% of targets, their anonymous DNA can be identified (matched to the correct individual or same-sex sibling) when the genetic database includes just 1% of the population. I also show the effectiveness of “snowball identification'” in which a successful identification adds to the genetic genealogical database, increasing the identification accuracy for future instances. I next turn to the policy implications of recent advances in familial searches, including the novel technique that I developed. I compare commercial databases to existing law enforcement databases. The existence of commercial databases and new familial searching requires new regulations. I argue for warrant requirements for law enforcement use of familial search against commercial databases. I also argue that websites that allow for user uploads must cryptographically verify the authenticity of those uploads to prevent re identification attacks.

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Colloquium Speaker Roxana Geambasu: Tuesday, Sept. 17
by Emily C. Lawrence 10 Sep '19

10 Sep '19

Colloquium Speaker Roxana Geambasu from Columbia University Tuesday, September 17 - 12:30pm Computer Science - Room 105 Host: Amit Levy [ https://www.cs.princeton.edu/events/25871 | https://www.cs.princeton.edu/events/25871 ] Security and Privacy Guarantees in Machine Learning with Differential Privacy Machine learning (ML) is becoming a critical foundation for how we construct the code driving our applications, cars, and life-changing financial decisions. Yet, it is often brittle and unstable, making decisions that are hard to understand and can be exploited. As one example, tiny changes to an input can cause dramatic changes in predictions; this results in decisions that surprise, appear unfair, or enable attack vectors such as adversarial examples. As another example, models trained on users' data have been shown to encode not only general trends from large datasets but also very specific, personal information from these datasets, such as social security numbers and credit card numbers from emails; this threatens to expose users' secrets through ML predictions or parameters. Over the years, researchers have proposed various approaches to address these rather distinct security, privacy, and transparency challenges. Most of the work has been best effort, which is insufficient if ML is to become a rigorous basis for how we construct our code. This talk positions differential privacy (DP) -- a theory developed by the privacy community -- as a versatile foundation for building into ML much-needed guarantees of not only privacy but also of security, stability, and transparency. As supporting evidence, I first present PixelDP , a scalable certified defense against adversarial examples that leverages DP theory to guarantee a level of robustness against this attack. I then present Sage, a DP ML platform that bounds the leakage of personal secrets through ML models while addressing some of the most pressing challenges of DP, such as the "running out of privacy budget" problem. Both PixelDP and Sage are designed from a pragmatic systems perspective and illustrate that DP theory is powerful but requires adaptation to achieve practical guarantees for ML workloads. Bio: Roxana Geambasu is an Associate Professor of Computer Science at Columbia University and a member of Columbia's Data Sciences Institute. She joined Columbia in Fall 2011 after finishing her Ph.D. at the University of Washington. For her work in cloud and mobile data privacy, she received: an Alfred P. Sloan Faculty Fellowship, an NSF CAREER award, a Microsoft Research Faculty Fellowship, several Google Faculty awards, a "Brilliant 10" Popular Science nomination, the Honorable Mention for the 2013 inaugural Dennis M. Ritchie Doctoral Dissertation Award, a William Chan Dissertation Award, two best paper awards at top systems conferences, and the first Google Ph.D. Fellowship in Cloud Computing.

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Prof. Andrew Houck, EE, Tuesday, Sept 10 at Quadrangle Club
by Emily C. Lawrence 03 Sep '19

03 Sep '19

A Princeton Propeller Event at Quadrangle Club Please join us for cocktails & hors d’oeuvres… 6:00pm, Tuesday, September 10th, 2019 33 Prospect Ave., Quadrangle Club Speaker: Prof. Andrew Houck, Electrical Engineering “Decoding the Realm(s) of Quantum Computing: Tilting at Tackling the Computationally Infeasible” Quantum Computing is poised to revolutionize myriad fields: cryptography, chemistry, medicine, optimization and machine learning. In contrast to familiar transistors, with a state 0 or 1, the quantum analog of a transistor or bit of information, aka a qubit, can be in a “superposition” of 0 and 1 simultaneously (e.g., 0 + 1, 0 - 1); and multiple qubits can be entangled, i.e., their states depend on each other. These two principles, superposition and entanglement, could enable powerful new algorithms to solve problems far beyond the capacity of today’s digital computing. Like the aviation term, “holding patterns,” maneuvers designed to delay something already in flight, while keeping it within a specified distance --- Quantum Computing may in the next decade bring such exciting new achievements as: factoring large numbers; allowing for RSA decryption; calculating binding energies of complex molecules; and solving large-scale brute-force problems. Andrew Houck, BSE Princeton 2000, PhD Harvard 2005, will introduce us to this strange new world and describe how it’s likely to transform ours. REGISTRATION REQUIRED via [ http://propellers.princetonaaa.org/ | http://propellers.princetonaaa.org/ ]

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Marcela Melara will present her FPO, "Intra-Process Least Privilege and Isolation for Emerging Applications" on Thursday, 9/12/2019 at 1:30pm, in CS 402
by Nicki Mahler 29 Aug '19

29 Aug '19

Marcela Melara will present her FPO, " Intra-Process Least Privilege and Isolation for Emerging Applications" on Thursday, 9/12/2019 at 1:30pm in CS 402. The members of her committee are: Michael Freedman (adviser), Readers: Amit Levy and Mic Bowman (Intel Labs); Examiners: Wyatt Lloyd and Prateek Mittal (ELE) A copy of her thesis is available upon request. Everyone is invited to attend her talk. The abstract follows below: Third-party libraries reduce software development costs and e↵ort. Designed for flexible reuse, libraries implement a small set of features, allowing developers to build applications by combining libraries that provide the desired functionality. However, third-party code also poses a great risk: because the source code is rarely inspected or even accessible by the application developer, bugs or vulnerabilities that can leak sensitive data may go unnoticed. Yet, existing data protection tools are insucient because they do not enforce least privilege, restricting each library’s access to only those data it needs for its functionality. Prior academic proposals have addressed this issue with two main approaches: (1) running application components in separate processes for strong isolation, or (2) tracking individual data objects throughout the application to prevent unprivileged components from disclosing sensitive information. However, these approaches see limited real-world adoption because they introduce significant development overhead and integration complexity. This dissertation proposes intra-process least privilege, a design principle that facilitates enforcing least privilege for application developers by restricting access at the granularity of individual library functions, and strongly isolating data within a single process address space. We first present Pyronia, a privilege separation system for language runtimes that targets IoT device applications. To protect sensitive OS resources, Pyronia combines three access control techniques: system call interposition, stack inspection, and page table replication. Developers then specify data access rules only for directly imported third- party functions in a central policy. We next present Grin, a memory access control system for Intel SGX cloud applications. Intel SGX enables developers to run sensitive code inside an enclave, a hardware-protected memory region within an applications address space. However, in practice, developers often include untrusted third-party libraries in the enclave, giving them unfettered access to all inenclave data. Grin leverages Memory Protection Keys (MPK) to partition an enclave and assign per-compartment access rules. Developers declare sensitive data objects and access privileges for in-enclave functions. Grin then automatically confines these data objects in MPK compartments. Pyronia and Grin demonstrate the e↵ectiveness of our intra-process least privilege approach in today’s privacy-critical applications while easing integration e↵orts for developers

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Borislav Hristov will present his FPO, "Deciphering Disease Genomes in a Network Context" on Thursday, 8/29/2019 at 11am in CS 402
by Nicki Mahler 21 Aug '19

21 Aug '19

Borislav Hristov will present his FPO, "Deciphering Disease Genomes in a Network Context" on Thursday, 8/29/2019 at 11am in CS 402. The members of his committee are as follows: Mona Singh (Adviser); Examiners: Ben Raphael and Bernard Chazelle; Readers: Olga Troyanskaya and Barbara Engelhardt. A copy of his thesis, is available upon request. Everyone is invited to attend his talk. The abstract follows below: Despite the incredible influx of sequencing data pinpointing the gene variants responsible for the development of heterogeneous diseases remains a particularly hard task because the same phenotypic outcome (disease) can result from a myriad of combinations of different alterations across the genome. A promising avenue is to consider genome alterations within the context of pathways because different alterations within any of several genes comprising the same pathway can have similar consequences with respect to disease development. Large-scale biological networks provide a helpful proxy for biological pathway knowledge as genes that participate in the same pathway tend to interact with each other and form modules within the larger network. In this dissertation, I introduce two novel methods that further our ability to computationally highlight potential disease-causing genes by examining disease genomes in the context of biological networks. First, in Chapter 2, I present a novel network-based approach which tackles cancer mutational heterogeneity by utilizing per-individual mutational profiles. I provide an intuitive formulation relying on balancing the size of a connected subgraph within the larger network with covering many patients. I describe a machine learning-like schema for selecting the value of the single required parameter and both an integer linear programming framework and a fast heuristic for optimizing the objective function. I demonstrate the outstanding performance of my method in identifying cancer-relevant genes, especially those mutated at very low rates. Next, in Chapter 3, I propose a general computational framework that uses prior knowledge of disease-associated genes to guide a network-based search for novel ones based upon newly acquired information. I use a graph di↵usion kernel to spread the signal from the set of already known disease genes and then use it to bias a random walk originating from the newly implicated genes to move closer to the known ones. I demonstrate that integrating the two types of information is better than using either one of them alone. I show, in the context of cancer, that my method readily outperforms other network-based methods. Finally, I apply my approach to several complex diseases, thereby demonstrating its versatility in a broad range of settings.

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IEEE SSCS Distinguished Lecture Colloquium at Princeton - August 22, 2019 - E-Quad B205 - Prof. Zhihua Wang, Tsinghua University
by Emily Lawrence 20 Aug '19

20 Aug '19

Sent on behalf of Prof. Kaushik Sengupta **Refreshments at 1:00 pm and a chance to talk with speaker ** IEEE Princeton/Central Jersey Section - Distinguished Lecture Colloquium, August 2019 Speaker: Prof. Zhihua Wang, Tsinghua University Talk Title: Binaural Hearing Aid System and the Intelligent Acoustic Signal Processing Date: Thursday, August 22, 2019 Time: 1:30 - 2:30 p.m. Location: Princeton University, Department of Electrical Engineering, Engineering Quadrangle, Room B205 Abstract: Nowadays, the hearing aid technology is facing a new horizon based on advanced digital signal processing, wireless communication and artificial intelligence. In this lecture, new methodology with a systematic solution covering both the auditory periphery and the cognitive system is given. The up to date and rapidly evolves technologies for the binaural hearing aid system are well addressed. The multi-channel wide dynamic range compression, active noise reduction, self-adaptable directivity, acoustic scene analysis, and the wireless linking with other audio or communication systems are presented. The key technologies including the ultra-low power chip design, the advanced digital signal processing (DSP), and the wireless system integration and connectivity are discussed. The micromechanics and high performance electro-acoustics, the miniaturized antenna, the user interface, and the fitting system development are also the important aspects for the hearing aid technologies are shown in this lecture. A smart binaural hearing aid technology, which simultaneously processes the acoustic signals from the four microphones in both ears are indicated which utilizing the computing power from the smart phone to finish the advanced binaural DSP algorithms. Bio: Zhihua Wang (M'99-SM'04-F'17) received the B.S., M.S., and Ph.D. degrees in Electronic Engineering in 1983, 1985 and 1990, respectively, from Tsinghua University, Beijing, China, where he has served as full professor and Deputy Director of the Institute of Microelectronics since 1997 and 2000. He was a visiting scholar at CMU (1992-1993) and KU Leuven (1993-1994), and was a visiting professor at HKUST (2014.9-2015.3). His current research mainly focuses on CMOS RFIC and biomedical applications, involving RFID, PLL, low-power wireless transceivers, and smart clinic equipment combined with leading edge RFIC and digital image processing techniques. He has co-authored 12 books/chapters, over 183 (480) papers in international journals (conferences), over 244 (29) papers in Chinese journals (conferences) and holds 123 Chinese and 8 US patents. Prof. Wang has served as the chairman of IEEE SSCS Beijing Chapter (1999-2009), an AdCom Member of the IEEE SSCS (2016-2019), a technology program committee member of the IEEE ISSCC (2005-2011), a steering committee member of the IEEE A-SSCC (2005-), the technical program chair for A-SSCC 2013, a guest editor for IEEE JSSC Special Issues (2006.12, 2009.12 and 2014.11), an associate editor of IEEE Trans on CAS-I, CAS-II and IEEE Trans on BioCAS, and other administrative/expert committee positions in China's national science and technology projects. Contact Info: Nagi Naganathan, <mailto:nagisub@gmail.com> nagisub(a)gmail.com or Kaushik Sengupta, <mailto:kaushiks@princeton.edu> kaushiks(a)princeton.edu

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Mihir Kulkarni will present his general exam "Training models for NLP tasks on novel domains using transfer learning" on Wednesday, August 7, 2019 at 3pm in Friend 006
by Nicki Mahler 01 Aug '19

01 Aug '19

Mihir Kulkarni will present his general exam "Training models for NLP tasks on novel domains using transfer learning" on Wednesday, August 7, 2019 at 3pm in Friend 006. The members of his committee are as follows: Karthik Narasimhan (advisor), Sanjeev Arora, and Olga Russakovsky. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Abstract: Recent advances in machine learning for NLP have shown that there are great benefits to unsupervised pre-training followed by finetuning to specific tasks (BERT, GPT): in particular, BERT was able to obtain state of the art results on 11 NLP tasks using this method. However, having these models perform well on domains that are different from the one where they were trained is still a challenge. In our work, we explore methods to transfer representations learnt by BERT to different models for improved performance on NLP tasks in a different domain-- medical papers. We generate a dataset from medical reviews obtainable on the Cochrane Database of Systematic Reviews, to train models to learn representations on technical documents that match representations learnt by BERT on plain text summaries of those documents. We experiment with different methods to perform section-wise matching between documents to create our parallel corpus, both for performing the matching and transferring representations. Performance on the domain transfer task is reported on both intrinsic and extrinsic evaluation metrics including named entity recognition on the EBM-NLP and sentence similarity on the MED-STS datasets. Reading list: 1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016. 2. Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. 3. Peters, Matthew E. and Neumann, Mark and Iyyer, Mohit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke: Deep contextualized word representations, Proc. of NAACL, 2018. 4. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I: Attention is all you need. In Advances in neural information processing systems, 2017. 5. Jiaqi Mu, Suma Bhat, Pramod Viswanath: All-but-the-Top: Simple and effective Postprocessing for Word Representations, International Conference on Learning Representations, 2018. 6. Tan, Chuanqi & Sun, Fuchun & Kong, Tao & Zhang, Wenchang & Yang, Chao & Liu, Chunfang: A Survey on Deep Transfer Learning: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4–7, 2018, Proceedings, Part III. 10.1007/978-3-030-01424-7_27. 7. Sinno Jialin Pan and Qiang Yang and Wei Fan and Sinno Jialin Pan: A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, 2010. 8. Yifan Peng and Shankai Yan and Zhiyong Lu, Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets, Proceedings of the 2019 Workshop on Biomedical Natural Language Processing (BioNLP 2019), 2019. 9.Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, Iain J. Marshall, Ani Nenkova, Byron C. Wallace: A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018. 10. Yanshan Wang, Naveed Afzal, Sunyang Fu, Liwei Wang, Feichen Shen, Majid Rastegar-Mojarad, Hongfang Liu: MedSTS: a resource for clinical semantic textual similarity, Language Resources and Evaluation, 2018.

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Brian Bullins will present his FPO, "Efficient Higher-Order Optimization for Machine Learning" on Tuesday, 7/23/2019 at 2pm in CS 402
by Nicki Mahler 22 Jul '19

22 Jul '19

Brian Bullins will present his FPO, "Efficient Higher-Order Optimization for Machine Learning" on Tuesday, 7/23/2019 at 2pm in CS 402. The members of his committee are: Elad Hazan (Adviser), Examiners: Mark Braverman and Sanjeev Arora; Readers: Yoram Singer and Yuxin Chen (EE). A copy of his thesis, is available upon request. Everyone is invited to attend his talk. The talk abstract follows below: In recent years, stochastic gradient descent (SGD) has taken center stage for training large-scale models in machine learning. Although some higher-order methods have improved iteration complexity in theory, the per-iteration costs render them unusable when faced with millions of parameters and training examples. In this thesis, I will present several works which enable higher-order optimization to be as scalable as first-order methods. The first method is a stochastic second-order algorithm for convex optimization, called LiSSA, which uses Hessian information to construct an unbiased Newton step in time linear in the problem dimension. To bypass the typical eciency barriers for second-order methods, we harness the ERM structure in standard machine learning tasks. While convex problems allow for global convergence, recent state-of-the-art models, such as deep neural networks, highlight the importance of developing a better understanding of non-convex guarantees. In order to handle this challenging setting, I will present FastCubic, a Hessian-based method which provably converges to first-order critical points faster than gradient descent, while additionally converging to second-order critical points. Finally, I will establish how to leverage even higher-order derivative information by means of the FastQuartic algorithm, which achieves faster convergence for both smooth and non-smooth convex optimization problems by combining ecient tensor methods with near-optimal higher-order acceleration.

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Ruth Dannenfelser will present her Pre FPO "From gene expression data to biomedical abstracts: data-driven approaches and systems to interrogate complex disease" on Tuesday, July 16, 2019 at 9am in CS 402.
by Nicki Mahler 11 Jul '19

11 Jul '19

Ruth Dannenfelser will present her Pre FPO "From gene expression data to biomedical abstracts: data-driven approaches and systems to interrogate complex disease" on Tuesday, July 16, 2019 at 9am in CS 402. The members of her committee are: Olga Troyanskaya (adviser), Mona Singh, Kai Li, Yibin Kang (Mol Bio), Wendell Lim (UCSF). Everyone is invited to attend her talk. The talk title and abstract follow below: Title: From gene expression data to biomedical abstracts: data-driven approaches and systems to interrogate complex disease Large-scale genomic studies now give more predictive power than ever, allowing us to profile the composition of tissues, study cellular functions, and understand organismal traits at an unprecedented level of detail. This is particularly important for studying heterogeneous diseases, such as cancer, where small patient-specific differences play critical roles in disease development and progression. Here we build off the wealth of publicly available data to examine the interplay between cancer and the immune system and then develop two query-based visualization systems that enable interactive data exploration for the biomedical community at large. The first part of my talk will present two perspectives on cancer and the immune system, starting with a semi-supervised approach for immune cell type quantification. Using derived immune markers we examine lymphocyte infiltration in breast cancer and find that estrogen receptor activity and genomic complexity are the key factors driving variation in lymphocytic infiltrate across individuals. In a broader scope, we leverage public gene expression data to further the development of targeted immunotherapeutics for solid tumors. Engineered T cell therapies have shown great promise for hematological cancers but have only found limited success in targeting solid tumors due to off target effects. Working closely with experimental collaborators we are developing a method to prioritize safer pairs of antigen targets that will help engineered T cells hone in on specific tumor targets minimizing damage to normal tissues. The second half will cover how we can extract unbiased signals from large collections of biomedical data in the form of scientific abstracts and large repositories of transcriptomics data. First, we show how we can obtain informative tissue-disease-gene relationships from abstracts and integrate them into a web system that presents different snapshots of curated interactions and adds tissue and disease annotations to gene lists from experimental assays (e.g. GWAS, differentially expressed genes, drug screens, etc). Secondly, we extend SEEK, a gene expression search engine that simultaneously returns coexpressed genes and relevant datasets where the genes are likely coregulated. Our extension enables significant performance improvements, expands the search space across the major model organisms, and provides a new cross-organism exploration interface to help facilitate translational research.

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Maciej Halber will present his FPO, "RGBD pipeline for Indoor Scene Reconstruction and Understanding" on Monday, July 15th, 2019 at 1pm in CS 402.
by Nicki Mahler 08 Jul '19

08 Jul '19

Maciej Halber will present his FPO,"RGBD pipeline for Indoor Scene Reconstruction and Understanding" on Monday, July 15th, 2019 at 1pm in CS 402. The members of his committee are Thomas Funkhouser (adviser); Readers: Szymon Rusinkiewicz and Matthias Niessner (The Technical University of Munich); Examiners: Thomas Funkhouser, Olga Russakovsky, and Adam Finkelstein. A copy of his thesis is available upon request. Everyone is invited to attend his talk. The talk abstract follows below: In this work, we consider the problem of reconstructing a 3D model from a sequence of color and depth frames. Generating such a model has many important applications, ranging from the entertainment industry to real estate. However, transforming the RGBD frames into high-quality 3D models is a challenging problem, especially if additional semantic information is required. In this document, we introduce three projects, which implement various stages of a robust RGBD processing pipeline. First, we consider the challenges arising during the RGBD data capture process. While the depth cameras are providing dense, per-pixel depth measurements, there is a non-trivial error associated with the resulting data. We discuss the depth generation problem and propose an error reduction technique based on estimating an image-space undistortion field. We describe the capture process of the data required for the generation of such an undistortion field. We showcase how correcting the depth measurements improves the reconstruction quality. Second, we address the problem of registering RGBD frames over a long video sequence into a globally consistent 3D model. We propose a “fine-to-coarse” global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of geometrical constraints, modeled as planar structures, at coarser scales. To test global registration algorithms, we provide a benchmark with 10,401 manually-clicked point correspondences in 25 scenes from the SUN3D dataset. We find that our fine-to-coarse algorithm registers long RGBD sequences better than previous methods. Last, we show how repeated scans of the same space can be used to establish associations between the different observations. Specifically, we consider a situation where 3D scans are acquired repeatedly at sparse time intervals. We develop an algorithm that analyzes these “rescans” and builds a temporal model of a scene with semantic instance information. The proposed algorithm operates inductively by using a temporal model resulting from past observations to infer instance segmentation of a new scan. The temporal model is continuously updated to reflect the changes that occur in the scene over time, providing object associations across time. The algorithm outperforms alternate approaches based on state-of-the-art networks for semantic instance segmentation.

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