- talks - lists.cs.princeton.edu

Pawel F. Przytycki will present his pre-FPO Thursday February 15th, 2018 at 11am in LSI room 253.
by Nicki Gotsis 08 Feb '18

08 Feb '18

Pawel F. Przytycki will present his pre-FPO Thursday February 15th, 2018 at 11am in LSI room 253. The members of his committee are are Mona Singh (Advisor), Olga Troyanskaya, Ben Raphael, Josh Akey (Ecology & Evolutionary Biology) , and Yibin Kang (Mol Bio). All are welcome to attend. Abstract and title follow below. Algorithms for deciphering cancer genomes: from differential mutation to differential allele specific expression Large-scale cancer genome sequencing consortia have provided a huge influx of somatic mutation data across large cohorts of patients. Understanding how these observed genetic alterations give rise to specific cancer phenotypes represents a major aim of cancer genomics. A common approach is to test which genes and genomic locations tend to accumulate more changes across tumors. However, using mutation frequency alone is not sufficient due to gene-specific characteristics such as length, replication timing, and expression, which all play a role in any given gene’s propensity for acquiring mutations. In this talk I will present two methods for utilizing natural variation as a background for understanding cancer. First, I will introduce differential mutation analysis, a framework for uncovering cancer genes that compares the mutational profiles of genes across cancer genomes with natural germline variation across healthy individuals. DiffMut, a fast and simple approach for differential mutational analysis outperforms considerably more sophisticated approaches at discovering cancer genes across 24 cancer types by utilizing natural variation data from the 1000 Genomes Project. Second, I will present a method for detecting functional noncoding somatic mutations using allele specific expression. While much of the focus of previous cancer research has been on coding mutations, the vast majority of mutations occur in noncoding regions. The changes caused by these mutations can be difficult to detect and to date only a few driver mutations have been identified. When noncoding mutations lead to regulatory changes, the effect is often allele specific because the mutation only occurs in one copy of the individual's DNA. However, in the case somatic mutations, changes only occur in cancer cells which only form a fraction of the tumor sample. Furthermore, many genes already display allele specific expression in a normal state. The method I have developed links noncoding mutations to allele specific expression by looking at both allele expression in the tumor and a matched normal using a series of models with increasing sensitivity based on different assumptions of tumor heterogeneity and differential expression. I validate these models on simulations and show that they are able to detect allele specific expression resulting from nonsense mutations. When applied to noncoding mutations in breast tumors, the models are able to uncover mutations upstream of genes that appear to exhibit differential allele specific expression.

1 0

Fwd: NEXT: Joint PACM-CSML Colloquium: FEB 12/David Blei
by Jennifer Rexford 06 Feb '18

06 Feb '18

> > From: "Gina M. Holland" <gholland(a)Princeton.EDU> > Subject: NEXT: Joint PACM-CSML Colloquium: FEB 12/David Blei > Date: February 6, 2018 at 12:40:29 PM EST > To: "pacmdistribution(a)math.princeton.edu" <pacmdistribution(a)math.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, February 12, 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. > > ================= > >

1 0

The Art and Science of PIXAR, tonight at 7pm
by Emily Lawrence 06 Feb '18

06 Feb '18

The Art and Science of PIXAR Tuesday, February 6, 2018 - 7:00pm Andlinger Center, Maeder Hall Host: Council on Science and Technology Danielle Feinberg, Pixar's Director of Photography for Lighting, uses math, science and code to bring wonder to the big screen. Go behind the scenes of Finding Nemo, Toy Story 3, Brave, WALL-E, Coco and more, to discover how Pixar interweaves art and science to create fantastical worlds where the things you imagine can become real. https://cst.princeton.edu/news/evnin-lecture-2018

1 0

Colloquium Speaker-Pedro Lopes, Monday, Feb 12
by Emily Lawrence 05 Feb '18

05 Feb '18

CS Department Colloquium Speaker Pedro Lopes, Hasso Plattner Institute Monday, February 12, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. Adam Finkelstein "Interactive Systems based on Electrical Muscle Stimulation" How can interactive devices connect with users in the most immediate and intimate way? This question has driven interactive computing for decades. If we think back to the early days of computing, user and device were quite distant, often located in separate rooms. Then, in the '70s, personal computers "moved in" with users. In the '90s, mobile devices moved computing into users' pockets. More recently, wearables brought computing into constant physical contact with the user's skin. These transitions proved to be useful: moving closer to users and spending more time with them allowed devices to perceive more of the user, allowing devices to act more personal. The main question that drives my research is: what is the next logical step? How can computing devices become even more personal? Some researchers argue that the next generation of interactive devices will move past the user's skin, and be directly implanted inside the user's body. This has already happened in that we have pacemakers, insulin pumps, etc. However, I argue that what we see is not devices moving towards the inside of the user's body but towards the "interface" of the user's body they need to address in order to perform their function. This idea holds the key to more immediate and personal communication between device and user. The question is how to increase this immediacy? My approach is to create devices that intentionally borrow parts of the user's body for input and output, rather than adding more technology to the body. I call this concept "devices that overlap with the user's body". I'll demonstrate my work in which I explored one specific flavor of such devices, i.e., devices that borrow the user's muscles. In my research I create computing devices that interact with the user by reading and controlling muscle activity. My devices are based on medical-grade signal generators and electrodes attached to the user's skin that send electrical impulses to the user's muscles; these impulses then cause the user's muscles to contract. While electrical muscle stimulation (EMS) devices have been used to regenerate lost motor functions in rehabilitation medicine since the '60s, during my PhD I explored EMS as a means for creating interactive systems. My devices form two main categories: (1) Devices that allow users eyes-free access to information by means of their proprioceptive sense, such as a variable, a tool, or a plot. (2) Devices that increase immersion in virtual reality by simulating large forces, such as wind, physical impact, or walls and heavy objects. Bio: Pedro Lopes is a PhD Candidate at Prof. Baudisch's Human Computer Interaction Lab at the Hasso Plattner Institute, Germany. Pedro's work asks the question: what if interfaces would share part of our body? Pedro has materialized these ideas by creating interactive systems based on electrical muscle stimulation. These devices use part of the wearer's body for output, i.e., the computer can output by actuating the user's muscles with electrical impulses, causing it to move involuntarily. The wearer can sense the computer's activity on their own body by means of their sense of proprioception. Pedro's wearable systems have shown to (1) increase realism in VR, (2) provide a novel way to access information through proprioception, and (3) serve as a platform to experience and question the boundaries of our sense of agency. Pedro's work is published at top-tier conferences (ACM CHI &UIST) and demonstrated at venues such as ACM SIGGRAPH and IEEE Haptics. Pedro has received the ACM CHI Best Paper award for his work on Affordance++, Best Talk Awards and several nominations. As part of his research, Pedro has exhibited at Ars Electronica 2017, Science Gallery Dublin and World Economic Forum in San Francisco. His work also captured the interest of media, such as MIT Technology Review, NBC, Discovery Channel, NewScientist and Wired. (Learn more about Pedro's work here: plopes.org).

1 0

Shuran Song will present her Pre FPO on Tuesday, February 13, 2018 at 2:30pm in CS 302
by Nicki Gotsis 02 Feb '18

02 Feb '18

Shuran Song will present her Pre FPO on Tuesday, February 13,2018at 2:30pm in CS 302. The members of her committee are as follows: Thomas Funkhouser (Adviser), Olga Russakovsky, Szymon Rusinkiewicz, and Adam Finkelstein, and Alberto Rodriguez (MIT). Title: Seeing the Unseen: Data-Driven 3D Scene Understanding for Robot Vision Abstract: Intelligent robots require advanced vision capabilities to perceive and interact with the real physical world. While computer vision has made great strides in recent years, its predominant paradigm still focuses on analyzing image pixels to infer 2D output representations (bounding boxes, segmentation, etc.), which remain far from sufficient for real-world robotics applications. In this talk, I will advocate the use of complete 3D scene representations that enable intelligent systems to not only recognize what is seen (e.g. Am I looking at a chair?), but also predict contextual information about the complete 3D environment beyond visible surfaces (e.g. What could be behind the table? Where should I look to find an exit?). As examples, I will present a line of my recent works that demonstrate the power of these representations through amodal 3D object detection (Sliding Shape and Deep Sliding Shapes), analyzing and synthesizing 3D scenes (Semantic Scene Completion), and predicting semantic and 3D structure outside the image field of view (Im2Pano3D). Finally, I will discuss some ongoing efforts on how these 3D scene representations can further enable and benefit from real-world robotic interactions (in the Amazon Robotic Challenge), shifting the way we view computer vision problems from the perspective of a passive observer to that of an active explorer. Bio: Shuran Song is a fifth-year Ph.D. candidate in Computer Science at Princeton University, advised by Thomas Funkhouser. Before that, she received a BEng. in Computer Science from HKUST in 2013 with the highest honor. During her Ph.D., she spent time working at Microsoft Research and Google Daydream. Her research interests lie at the intersection of computer vision, computer graphics, and robotics. She was awarded the Facebook Fellowship in 2014, Siebel Scholar in 2016, Wallace Fellowship in 2017, and Princeton SEAS Award for Excellence in 2017. She was named Princeton Innovation’s 25 Under 25, and was part of the MIT-Princeton team for the Amazon Robotics Challenge, winning the 3rd place in 2016 and 1st place (stow task) in 2017.

1 1

4:30pm talk today on information-theoretic approaches to neural network compression, clustering, and concept learning
by Jennifer Rexford 29 Jan '18

29 Jan '18

Title: Information-Theoretic Approaches to Neural Network Compression, Clustering, and Concept Learning Speaker: Lav Varshney (University of Illinois at Urbana-Champaign) Location: B205 Engneering Quadrangle Date/Time: Monday, January 29, 2018 - 4:30pm Abstract: Deep neural networks have shown incredible performance for inference tasks in a variety of domains, but unfortunately are often enormous cloud-based structures, require significant training data, and are hard for people to interpret. To work towards addressing these challenges, we present three lines of information-theoretic investigation. We discuss optimal quantization of synaptic weights and universal lossless compressed representations of feedforward neural networks, taking inferential purpose into account. The basic insight yielding considerable rate reduction is a kind of permutation invariance to the labeling of nodes. We also discuss optimal energy allocation in specialized hardware implementations of neural networks. In considering unsupervised learning, we present asymptotically-optimal algorithms for universal clustering and joint registration/clustering that involve optimization of novel empirical multivariate information measures. Finally we present algorithms for hierarchical, human-interpretable concept learning that iteratively optimize empirical information measures. Such concept learning may act in support of computational creativity. Bio: Lav Varshney is an assistant professor of electrical and computer engineering, computer science, and neuroscience at the University of Illinois at Urbana-Champaign. He is also leading curriculum initiatives for the new B.S. degree in Innovation, Leadership, and Engineering Entrepreneurship in the College of Engineering. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University in 2004. He received the S.M., E.E., and Ph.D. degrees, all in electrical engineering and computer science, from the Massachusetts Institute of Technology, in 2006, 2008, and 2010, where his theses received the E. A. Guillemin Thesis Award and the J.-A. Kong Award Honorable Mention. He was a research staff member at the IBM Thomas J. Watson Research Center from 2010 until 2013, where he led the design and development of the Chef Watson computational creativity system. His research interests include information and coding theory; data science; limits of nanoscale, social, and neural computing; human decision making and collective intelligence; and creativity. Dr. Varshney is a founding member of the IEEE Special Interest Group on Big Data in Signal Processing and served on the Shannon Centenary Committee of the IEEE Information Theory Society. He also serves on the advisory board of the AI XPRIZE. He received the IBM Faculty Award in 2014 and was a finalist for the Bell Labs Prize in 2014 and 2016. He and his students have won several best paper awards, including a 2015 Data for Good Exchange Paper Award. His work appears in the anthology, The Best Writing on Mathematics 2014, and he was selected to present at the 2017 World Science Festival. He appears on the List of Teachers Ranked as Excellent and has been named a Center for Advanced Study Fellow at the University of Illinois.

1 0

Muhammad Shahbaz will present his PRe FPO on Friday, March 2, 2018 at 2pm in CS 302.
by Nicki Gotsis 23 Jan '18

23 Jan '18

Muhammad Shahbaz will present his PRe FPO on Friday, March 2, 2018 at 2pm in CS 302. Committee Members: Nick Feamster (advisor), Jen Rexford (reader), Ben Pfaff (VMware), Mike Freedman and Wyatt Lloyd (non-readers) Time: Friday, March 2nd from 2 to 4 pm ET Title: Programmable Services on Software Switches Abstract: Software switches, such as Open vSwitch (OVS), play a critical role in modern data centers: with few exceptions, every packet that passes to or from a virtual machine (VM) passes through a software switch. These switches frequently need upgrading and customization to support new encapsulation headers, improved troubleshooting and debugging features, more sophisticated routing such as source-controlled routing, and virtual-to-physical gateway and middlebox-like functions such as load balancing, address virtualization, and encryption. Today, modifying these switches requires both intimate knowledge of the switch codebase (including kernel code) and extensive expertise in network protocol design, making the bar for customizing these software switches prohibitively high. Changing how a software switch forwards packets should not require intimate knowledge of its implementation. Instead, it should be possible to specify how packets are processed and transmitted in a high-level domain-specific language (DSL), such as P4, and compiling the resulting program to run on a software switch. This decoupling of forwarding behavior from underlying switch code would enable protocol designers to develop proprietary customizations without having to track the evolution of the (larger and more complex) underlying software switch codebase. This thesis addresses these challenges by (1) providing a programmable software switch whose behavior is customizable using a high-level DSL, and (2) demonstrating the benefits of such switches by building a new network service. Together these contributions show how programmable switches provide greater flexibility to network administrators and protocol designers, and to allow them to innovate and improve their systems. The first part of the dissertation presents the design and implementation of PISCES, a programmable, protocol-independent software switch. PISCES allows a protocol designer to specify a software switch’s custom packet-processing behavior in a high-level, domain-specific language (in our case, P4); a compiler then produces source code for the underlying target software switch (in our case, OVS). Our evaluation shows that PISCES programs are on average about 40 times shorter than equivalent changes to OVS source code and—with appropriate compiler optimizations—incur a forwarding performance (i.e., throughput) overhead of only about 2%. The second part demonstrates the benefits of these programmable switches by building a new network service, Baseerat, that requires custom modifications to switch forwarding behavior. Baseerat adds support for native multicast in data center networks using both software (e.g., PISCES) and hardware (e.g., Barefoot’s Tofino) programmable switches. In Baseerat, a PISCES switch encodes multicast routing information inside packets, and intermediate switches act on this information to route packets to recipients. Baseerat obviates the need for maintaining state per switch, allowing it to scale to many multicast groups. Our experiments show that for a data center with more than 25,000 hosts, Baseerat can support one million groups while utilizing a packet header size of 300 bytes and requiring only 1,000 multicast entries in leaf switches, with a traffic overhead of about 1%.

1 0

Irene Lo, Jan 23rd (tomorrow) @ 12:30pm, Sherrerd 101
by Emily Lawrence 22 Jan '18

22 Jan '18

Irene Lo is giving a colloquium in ORFE on Tuesday January 23rd at 12:30pm in Sherrerd 101. She works broadly in mechanism design, with a focus on matching markets. Title and abstract are below if anyone is interested in attending. Thanks! -Matt Abstract: As market design theory increasingly shapes the design and operations of real-life marketplaces, it is important for designers to provide simple policy levers that practitioners can use to optimize platform objectives. In the school choice market, where scarce public school seats are assigned to students, a key operational issue is how to reassign seats that are vacated after an initial round of centralized assignment. Practical solutions to the reassignment problem must be simple to implement, truthful, efficient and fair while also alleviating costly student movement between schools. In this talk, I will propose and axiomatically justify a class of reassignment mechanisms, the Permuted Lottery Deferred Acceptance (PLDA) mechanisms. Our mechanisms generalize the commonly used Deferred Acceptance (DA) school choice mechanism to a two-round setting and retain its desirable incentive, fairness and efficiency properties. School choice systems typically run Deferred Acceptance with a lottery number assigned to each student to break ties in school priorities. I will show that under natural conditions on demand, correlating the tie-breaking lotteries across rounds preserves allocative welfare, and reversing the first-round lottery order minimizes reassignment among all PLDA mechanisms. Empirical investigations based on data from NYC high school admissions support our theoretical findings. This is based on joint work with Itai Feigenbaum, Yash Kanoria and Jay Sethuraman. Bio: Irene Lo is a 5th year Ph.D. student in the Industrial Engineering & Operations Research department at Columbia University, advised by Jay Sethuraman, Jacob Leshno and Yash Kanoria. Her main research interests are in operational and algorithmic issues in marketplace design, particularly in using theory to provide practical solutions for markets with social value. Her current focus is on operational challenges in school choice mechanisms. She is also interested in platform-based marketplaces, mechanism design for social good, graph theory, and games on networks. She graduated from Princeton University in 2013 with an A.B. in mathematics.

1 0

Colloquium Speaker Natalie Enright Jerger, TODAY at 11:00AM
by Emily C. Lawrence 18 Jan '18

18 Jan '18

Joint EE and CS Colloquium Speaker Prof. Natalie Enright Jerger, University of Toronto Thursday, January 18, 2018 - 11:00AM *please note, lunch will NOT be served at this talk* Computer Science - Room 105 Host: Prof. Margaret Martonosi “Architectural Techniques to Efficiently Handle Big Data Challenges” As Moore’s Law continues in the post-Dennard scaling era, architects and programmers must consider energy efficiency even more carefully as part of their designs. The energy cost of moving and storing data exceeds that of computing with that data. At the same time, we expect to see 100s of zettabytes of digital data in the next decade. This explosion of data that must be analyzes creates numerous challenges for current designs. In this talk, we will look at two techniques to address big data challenges facing computer architecture. One promising approach to boost both energy efficiency and performance is approximate computing. The approximate computing paradigm trades-off correctness for improvements in energy and/or performance by targeting key applications that do not require 100% accurate execution such as image processing and machine learning. We propose a microarchitectural technique, load value approximation that selectively predicts memory values in order to forego expensive accesses to the memory hierarchy. By predicting instead of moving data, we can save energy and improve the performance when a small amount of error is tolerable. In the second part of my talk, I will discuss the performance-cost trade-offs of interposer-based multi-chip, multi-core systems. Connecting multiple disparate chips via a silicon interposer allows us to tightly couple processors and memory within the same package for efficient data movement. I will briefly present our network solutions to realize these systems. Considering solutions that span technology, architecture and software opens up new opportunities to solve energy and performance challenges facing next generation systems. Bio: Natalie Enright Jerger is the Percy Edward Hart Professor of Electrical and Computer Engineering at the University of Toronto. Prior to joining the University of Toronto, she received her MSEE and PhD from the University of Wisconsin-Madison in 2004 and 2008, respectively. She received her Bachelor's degree from Purdue University in 2002. She is a recipient of the Ontario Ministry of Research and Innovation Early Researcher Award in 2012, the 2014 Ontario Professional Engineers Young Engineer Medal recipient and the 2015 Borg Early Career Award winner. She served as the program co-chair of the 7th Network-on-Chip Symposium and as the program chair of the 20th International Symposium on High Performance Computer Architecture. She is currently serving as the ACM SIGMICRO Vice Chair and an ACM SIGARCH Executive Committee member. Her current research explores on-chip networks, approximate computing, IoT architectures and machine learning acceleration. She is also passionate about increasing the representation of women in computing, particular in computer architecture. She currently chairs the organizing committee for the Women in Computer Architecture group (WICARCH). In 2017, she co-authored the second edition of the Computer Architecture Synthesis Lecture on On-Chip Networks with Li-Shiuan Peh and Tushar Krishna. Her research is supported by NSERC, Intel, CFI, AMD, Huawei and Qualcomm.

1 0

Reminder: Colloquium Speaker Natalie Enright Jerger, tomorrow at 11:00AM
by Emily C. Lawrence 17 Jan '18

17 Jan '18

Joint EE and CS Colloquium Speaker Prof. Natalie Enright Jerger, University of Toronto Thursday, January 18, 2018 - 11:00AM *please note, lunch will NOT be served at this talk* Computer Science - Room 105 Host: Prof. Margaret Martonosi “Architectural Techniques to Efficiently Handle Big Data Challenges” As Moore’s Law continues in the post-Dennard scaling era, architects and programmers must consider energy efficiency even more carefully as part of their designs. The energy cost of moving and storing data exceeds that of computing with that data. At the same time, we expect to see 100s of zettabytes of digital data in the next decade. This explosion of data that must be analyzes creates numerous challenges for current designs. In this talk, we will look at two techniques to address big data challenges facing computer architecture. One promising approach to boost both energy efficiency and performance is approximate computing. The approximate computing paradigm trades-off correctness for improvements in energy and/or performance by targeting key applications that do not require 100% accurate execution such as image processing and machine learning. We propose a microarchitectural technique, load value approximation that selectively predicts memory values in order to forego expensive accesses to the memory hierarchy. By predicting instead of moving data, we can save energy and improve the performance when a small amount of error is tolerable. In the second part of my talk, I will discuss the performance-cost trade-offs of interposer-based multi-chip, multi-core systems. Connecting multiple disparate chips via a silicon interposer allows us to tightly couple processors and memory within the same package for efficient data movement. I will briefly present our network solutions to realize these systems. Considering solutions that span technology, architecture and software opens up new opportunities to solve energy and performance challenges facing next generation systems. Bio: Natalie Enright Jerger is the Percy Edward Hart Professor of Electrical and Computer Engineering at the University of Toronto. Prior to joining the University of Toronto, she received her MSEE and PhD from the University of Wisconsin-Madison in 2004 and 2008, respectively. She received her Bachelor's degree from Purdue University in 2002. She is a recipient of the Ontario Ministry of Research and Innovation Early Researcher Award in 2012, the 2014 Ontario Professional Engineers Young Engineer Medal recipient and the 2015 Borg Early Career Award winner. She served as the program co-chair of the 7th Network-on-Chip Symposium and as the program chair of the 20th International Symposium on High Performance Computer Architecture. She is currently serving as the ACM SIGMICRO Vice Chair and an ACM SIGARCH Executive Committee member. Her current research explores on-chip networks, approximate computing, IoT architectures and machine learning acceleration. She is also passionate about increasing the representation of women in computing, particular in computer architecture. She currently chairs the organizing committee for the Women in Computer Architecture group (WICARCH). In 2017, she co-authored the second edition of the Computer Architecture Synthesis Lecture on On-Chip Networks with Li-Shiuan Peh and Tushar Krishna. Her research is supported by NSERC, Intel, CFI, AMD, Huawei and Qualcomm.

1 0