- talks - lists.cs.princeton.edu

Reminder: Colloquium Speaker-Huacheng Yu, tomorrow at 12:30
by Emily C. Lawrence 07 May '18

07 May '18

Huacheng Yu from Harvard University Tuesday, May 8, 2018 - 12:30pm Computer Science - Room 105 Host: Ran Raz "Better understanding of efficient dynamic data structures" Abstract: Data structures have applications and connections to algorithm design, database systems, streaming algorithms and other areas of computer science. Understanding what efficient data structures can do (and what they cannot do) is crucial to these applications. In this talk, I will present my work in analyzing efficient data structures and proving what they cannot accomplish. I will focus on the recent development in building new connections between dynamic data structures and communication complexity, as well as a new approach to analyzing dynamic data structures with Boolean outputs and super-logarithmic time. Bio: Huacheng Yu is a postdoctoral researcher in the Theory of Computing group at Harvard University. He obtained his PhD from Stanford University in 2017 under the supervision of Ryan Williams and Omer Reingold . He also holds a Bachelor's degree from Tsinghua University (2012). His primary research interests are data structure lower bounds. He also works in algorithm design and communication complexity.

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May 11th Symposium - Frontiers in Quantitative Cancer Biology
by Emily Lawrence 02 May '18

02 May '18

Please join us on Friday, May 11th for the Frontiers in Quantitative Cancer Biology Symposium <https://lsi.princeton.edu/news-seminars/seminars/frontiers-quantitative-can cer-biology> . Talks will take place in the Icahn Laboratory Auditorium (Room 101) followed by a reception in the Icahn Atrium. Symposium Schedule: 9:00 - 9:30 a.m. Coffee and check-in (outside of Icahn 101) 9:30 - 10:45 a.m. Sean Morrison <https://cri.utsw.edu/scientists/sean-morrison-laboratory/> : "Metabolic regulation of melanoma metastasis" (With a short talk by Professor Josh Rabinowitz) 10:45 - 11:15 a.m. Break 11:15 a.m. - 12:30 p.m. Dana Pe <https://www.mskcc.org/research-areas/labs/dana-pe-er> 'er: "A single cell view into metastasis" (With a short talk by Professor Mona Singh) 12:30 - 2:00 p.m. Lunch break (independent, not included in symposium) 2:00 - 3:15 p.m. David Tuveson <http://tuvesonlab.labsites.cshl.edu/> : "Predictive Models for the Treatment of Pancreatic Cancer" (With a short talk by Professor Ben Raphael) 3:15 - 3:45 p.m. Break 3:45 - 4:45 p.m. Arlene Sharpe <https://sharpelab.hms.harvard.edu/> : "Biology of PD-1 checkpoint blockade" Reception to follow in the Icahn Laboratory Atrium No registration is required. For more information, contact Sara Thibeault at thibeault(a)princeton.edu <mailto:thibeault@princeton.edu> or 609-258-3930

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Colloquium Speaker-Huacheng Yu, Tuesday, May 8
by Emily Lawrence 02 May '18

02 May '18

Huacheng Yu from Harvard University Tuesday, May 8, 2018 - 12:30pm Computer Science - Room 105 Host: Ran Raz "Better understanding of efficient dynamic data structures" Abstract: Data structures have applications and connections to algorithm design, database systems, streaming algorithms and other areas of computer science. Understanding what efficient data structures can do (and what they cannot do) is crucial to these applications. In this talk, I will present my work in analyzing efficient data structures and proving what they cannot accomplish. I will focus on the recent development in building new connections between dynamic data structures and communication complexity, as well as a new approach to analyzing dynamic data structures with Boolean outputs and super-logarithmic time. Bio: Huacheng Yu is a postdoctoral researcher in the Theory of Computing group at Harvard University. He obtained his PhD from Stanford University in 2017 under the supervision of Ryan Williams and Omer Reingold. He also holds a Bachelor's degree from Tsinghua University (2012). His primary research interests are data structure lower bounds. He also works in algorithm design and communication complexity.

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Colloquium Speaker: Ben Lee, TODAY at 4:30pm
by Emily C. Lawrence 01 May '18

01 May '18

Ben Lee from Duke University Tuesday, May 1, 2018 at 4:30 p.m. Maeder Hall auditorium Title: Datacenters and Energy Efficiency: A Game-Theoretic Perspective Abstract: Sharing datacenter hardware improves energy efficiency, but whether strategic users participate in consolidated systems depends on management policies. Users who dislike allocations may refuse to participate and deploy private, less-efficient systems. We rethink systems management, drawing on game theory to model strategic behavior and incentivize participation. We illustrate this perspective for two fundamental challenges in datacenters. For power delivery, we design sprinting games to produce equilibria in which users selfishly draw power for performance boosts yet avoid oversubscribing the shared supply. For resource allocation, we use Cobb-Douglas utility functions to produce fair allocations that incentivize users to share cache and memory. These solutions provide foundations for rigorously managing systems shared by strategic, competitive participants. Biography: Benjamin Lee is an Associate Professor of Electrical and Computer Engineering at Duke University. He received his B.S. from the University of California at Berkeley (2004), his Ph.D. from Harvard University (2008), and his post-doctorate from Stanford University (2010). He has held visiting positions at Microsoft Research, Intel Labs, and Lawrence Livermore National Lab. Dr. Lee’s research interests include computer architecture, energy efficiency, and security / privacy. He pursues these interests by building interdisciplinary links to statistical inference and algorithmic economics. His research has been recognized by IEEE Micro Top Picks (4x), Communications of the ACM Research Highlights (3x), as well as paper honors from the ASPLOS, HPCA, MICRO, and SC conferences. He received the NSF Computing Innovation Fellowship, NSF CAREER Award, and Google Faculty Research Award.

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Reminder: Ben Lee, tomorrow at 4:30pm
by Emily C. Lawrence 30 Apr '18

30 Apr '18

Ben Lee from Duke University Tuesday, May 1, 2018 at 4:30 p.m. Maeder Hall auditorium Title: Datacenters and Energy Efficiency: A Game-Theoretic Perspective Abstract: Sharing datacenter hardware improves energy efficiency, but whether strategic users participate in consolidated systems depends on management policies. Users who dislike allocations may refuse to participate and deploy private, less-efficient systems. We rethink systems management, drawing on game theory to model strategic behavior and incentivize participation. We illustrate this perspective for two fundamental challenges in datacenters. For power delivery, we design sprinting games to produce equilibria in which users selfishly draw power for performance boosts yet avoid oversubscribing the shared supply. For resource allocation, we use Cobb-Douglas utility functions to produce fair allocations that incentivize users to share cache and memory. These solutions provide foundations for rigorously managing systems shared by strategic, competitive participants. Biography: Benjamin Lee is an Associate Professor of Electrical and Computer Engineering at Duke University. He received his B.S. from the University of California at Berkeley (2004), his Ph.D. from Harvard University (2008), and his post-doctorate from Stanford University (2010). He has held visiting positions at Microsoft Research, Intel Labs, and Lawrence Livermore National Lab. Dr. Lee’s research interests include computer architecture, energy efficiency, and security / privacy. He pursues these interests by building interdisciplinary links to statistical inference and algorithmic economics. His research has been recognized by IEEE Micro Top Picks (4x), Communications of the ACM Research Highlights (3x), as well as paper honors from the ASPLOS, HPCA, MICRO, and SC conferences. He received the NSF Computing Innovation Fellowship, NSF CAREER Award, and Google Faculty Research Award.

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Colloquium Speaker-Bo Wang, TODAY at 12:30
by Emily C. Lawrence 27 Apr '18

27 Apr '18

Bo Wang, Stanford University Friday, April 27, 2018 - 12:30pm Computer Science - Room 105 Host: Mona Singh "Machine Learning Algorithms for Exploiting Spectral Structures of Biological Networks" Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from populations to a single cell. How to extract and understand non-trivial topological features and structures inherent in the networks is critical to understanding interactions within complicated biological systems. In this talk, I will introduce recent developments of machine learning algorithms that exploit spectral structures of networks for a wide range of biological applications, ranging from single-cell analysis to function prediction on protein-protein interaction networks. Specifically, I will first present a new method named SIMLR, combining both low-rank spectral regularization and multiple-kernel learning, to construct cell networks for sparse noisy single-cell RNA- seq data. The learned cell networks will enable effective dimension reduction, clustering and visualization. Second, I will discuss a novel method, Network Enhancement (NE), that aims to de-noise complex networks such as protein-protein interaction networks without corrupting spectral structures of the networks, therefore improving function prediction accuracy. Last, I will also briefly introduce recent advances where deep convolutional neural network is applied on biological networks (e.g., drug-target network) as a first-order spectral approximation of network structures. Bio: Bo Wang is a recent PhD graduate in Computer Science at Stanford University, an IEEE and CVF Fellow and an NSF Graduate Research Fellow. His research focuses on machine learning (particularly deep learning) on many applications in computer vision (e.g., image segmentation, object detection) and computational biology (e.g., single-cell analysis, integrative cancer subtyping). Prior to Stanford, he received his master degree at University of Toronto, majoring in numerical analysis.

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CORRECTION - Xinyi Fan will present her FPO on Friday, May 4, 2018 at 1:00 pm in CS302
by Barbara A. Mooring 26 Apr '18

26 Apr '18

Good aftenoon, Xinyi Fan will present her FPO on Friday, May 4, 2018 at 1:00 pm in CS302 Title: View and Path Planning for Scaling 3D Acquisition to Many Objects Committee: Szymon Rusinkiewicz (advisor, COS) Adam Finkelstein (nonreader, COS) Peter Ramadge (nonreader, ELE) Tom Funkhouser (reader, COS) Yuxin Chen (reader, ELE) Abstract: Demand for high-volume 3D scanning of real objects is rapidly growing in a wide range of applications, including quality-control for manufacturing, online retailing, entertainment with virtual reality, as well as archaeological documentation and reconstruction. Fully realizing the potential of 3D acquisition requires scanning large numbers of objects with high quality and at reasonable cost. Although mature technologies exist for high-fidelity 3D model acquisition, deploying them at scale continues to require non-trivial manual labor. This dissertation focuses on studying practical 3D acquisition for large numbers of objects. The problem is challenging, because it is hard to automatically find a proper set of scanner views that can not only completely cover the surface of multiple objects with different shapes, but also capture high fidelity surface model of the objects. Furthermore, it is non-trivial to position a 3D scanner at each of the desired views accurately and efficiently. We propose a prototype system for multi-object 3D acquisition, which allows non-expert users to scan large numbers of physical objects within a reasonable amount of time, and with greater ease. Our system uses novel planning algorithms to control a structured-light scanner mounted on a calibrated motorized positioning system. We demonstrate the ability of our prototype to safely, robustly, and automatically acquire 3D models for large collections of small objects. We propose an objective function for automated view and path planning, taking into account both accuracy and efficiency of the scanning system. We analyze different approaches to optimize for the objective, and discuss their performance and practicality. In addition, we address the problem of surface inaccessibility to further refine our multi-object 3D acquisition system. We explore solutions for improvement from both the hardware and software ends. A copy of the thesis is on display in the F-wing of Equad rnoon, Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Xinyl Fan will present his FPO on Friday, May 4, 2018 at 1:00 pm in CS302
by Barbara A. Mooring 26 Apr '18

26 Apr '18

Xinyl Fan will present his FPO on Friday, May 4, 2018 at 1:00 pm in CS302 Title: View and Path Planning for Scaling 3D Acquisition to Many Objects Committee: Szymon Rusinkiewicz (advisor, COS) Adam Finkelstein (nonreader, COS) Peter Ramadge (nonreader, ELE) Tom Funkhouser (reader, COS) Yuxin Chen (reader, ELE) Abstract: Demand for high-volume 3D scanning of real objects is rapidly growing in a wide range of applications, including quality-control for manufacturing, online retailing, entertainment with virtual reality, as well as archaeological documentation and reconstruction. Fully realizing the potential of 3D acquisition requires scanning large numbers of objects with high quality and at reasonable cost. Although mature technologies exist for high-fidelity 3D model acquisition, deploying them at scale continues to require non-trivial manual labor. This dissertation focuses on studying practical 3D acquisition for large numbers of objects. The problem is challenging, because it is hard to automatically find a proper set of scanner views that can not only completely cover the surface of multiple objects with different shapes, but also capture high fidelity surface model of the objects. Furthermore, it is non-trivial to position a 3D scanner at each of the desired views accurately and efficiently. We propose a prototype system for multi-object 3D acquisition, which allows non-expert users to scan large numbers of physical objects within a reasonable amount of time, and with greater ease. Our system uses novel planning algorithms to control a structured-light scanner mounted on a calibrated motorized positioning system. We demonstrate the ability of our prototype to safely, robustly, and automatically acquire 3D models for large collections of small objects. We propose an objective function for automated view and path planning, taking into account both accuracy and efficiency of the scanning system. We analyze different approaches to optimize for the objective, and discuss their performance and practicality. In addition, we address the problem of surface inaccessibility to further refine our multi-object 3D acquisition system. We explore solutions for improvement from both the hardware and software ends. A copy of the thesis is on display in the F-wing of Equad Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Reminder: Colloquium Speaker-Bo Wang, tomorrow at 12:30
by Emily C. Lawrence 26 Apr '18

26 Apr '18

Bo Wang, Stanford University Friday, April 27, 2018 - 12:30pm Computer Science - Room 105 Host: Mona Singh "Machine Learning Algorithms for Exploiting Spectral Structures of Biological Networks" Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from populations to a single cell. How to extract and understand non-trivial topological features and structures inherent in the networks is critical to understanding interactions within complicated biological systems. In this talk, I will introduce recent developments of machine learning algorithms that exploit spectral structures of networks for a wide range of biological applications, ranging from single-cell analysis to function prediction on protein-protein interaction networks. Specifically, I will first present a new method named SIMLR, combining both low-rank spectral regularization and multiple-kernel learning, to construct cell networks for sparse noisy single-cell RNA- seq data. The learned cell networks will enable effective dimension reduction, clustering and visualization. Second, I will discuss a novel method, Network Enhancement (NE), that aims to de-noise complex networks such as protein-protein interaction networks without corrupting spectral structures of the networks, therefore improving function prediction accuracy. Last, I will also briefly introduce recent advances where deep convolutional neural network is applied on biological networks (e.g., drug-target network) as a first-order spectral approximation of network structures. Bio: Bo Wang is a recent PhD graduate in Computer Science at Stanford University, an IEEE and CVF Fellow and an NSF Graduate Research Fellow. His research focuses on machine learning (particularly deep learning) on many applications in computer vision (e.g., image segmentation, object detection) and computational biology (e.g., single-cell analysis, integrative cancer subtyping). Prior to Stanford, he received his master degree at University of Toronto, majoring in numerical analysis.

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Joint PACM-CSML Colloquium: APR 30/David Blei (Rescheduled)
by Jennifer Rexford 24 Apr '18

24 Apr '18

> From: "Gina M. Holland" <gholland(a)Princeton.EDU> > Subject: NEXT: Joint PACM-CSML Colloquium: APR 30/David Blei (Rescheduled) > Date: April 24, 2018 at 10:35:12 AM EDT > To: "pacmdistribution(a)math.princeton.edu" <pacmdistribution(a)math.princeton.edu> > Cc: "Andrea E. La Bella" <alabella(a)Princeton.EDU> > > The next colloquium will be a Joint PACM-CSML Colloquium given by David Blei, Columbia University > > Title and abstract are below. > > ================= > DATE/LOCATION: Monday, April 30, 2018, 4:00 – 5:00 PM / 214 Fine Hall > > SPEAKER: David Blei, Columbia University > > TITLE: Scaling and Generalizing Bayesian Inference > > ABSTRACT: A core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in probabilistic modeling, which frames all inference about unknown quantities as a calculation about a conditional distribution. In this talk I review and discuss variational inference (VI), a method that approximates probability distributions through optimization. VI has been used in myriad applications in machine learning. It tends to be faster than more traditional methods, such as Markov chain Monte Carlo sampling. > > I discuss our recent research on VI. I first describe stochastic variational inference, an approximate inference algorithm for handling massive data sets, and demonstrate its application to probabilistic topic models of millions of articles. Then I discuss black box variational inference, a generic algorithm for approximating the posterior. Black box inference easily applies to many models but requires minimal mathematical work to implement. I will demonstrate black box inference on deep exponential families---a method for Bayesian deep learning---and describe how it enables powerful tools for probabilistic programming. > > ================= > >

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