February 2018 - talks - lists.cs.princeton.edu

Colloquium Speaker-Elena Glassman, Monday, Feb 26
by Emily Lawrence 20 Feb '18

20 Feb '18

CS Department Colloquium Speaker Elena Glassman, University of California, Berkeley Monday, February 26, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. Adam Finkelstein "Interactive systems for code and data demography " Programming-the means by which we tell computers what to do-has changed a lot over time. Programming today means programming alongside hundreds of fellow students, thousands of fellow professional software engineers at a particular company, or millions of fellow developers in the open-source community sharing their code online. In this talk, I will describe several interactive systems I have built that exploit the structure within large volumes of peer-produced code to help individual programmers learn how to write more correct, readable code. These systems are made possible by code demography, which I define as statistics, algorithms, and visualizations that help people comprehend and interact with population-level structure and trends in large code corpora. The key to my approach is designing or inferring abstractions that capture critical features and abstract away variation that is irrelevant to the user. Code demography can reveal strategically diverse sets of aligned code examples which, according to theories of human concept learning, help people learn, i.e., construct mental abstractions that generalize well. I will focus this talk on two families of systems that use program analysis, program synthesis, and visualization to either power active data-driven teaching in large programming classrooms or passive knowledge sharing within developer communities. Some of these systems have been integrated into UC Berkeley's largest introductory programming class, which regularly enrolls over 1500 students. I will conclude with my vision for how the techniques of code demography can be generalized to more types of messy, structured, complex data corpora in order to help data scientists and enable new data-driven programming paradigms. Bio: Elena Glassman is an EECS postdoctoral researcher at UC Berkeley, in the Berkeley Institute of Design, funded by the NSF ExCAPE Expeditions in Computer Augmented Program Engineering grant and the Moore/Sloan Data Science Fellowship from the UC Berkeley Institute for Data Science (BIDS). She earned her PhD in EECS at the MIT CS & AI Lab in August 2016, where she created scalable systems that analyze, visualize, and provide insight into the code of thousands of programming students. She has been a summer research intern at both Google and Microsoft Research, working on systems that help people teach and learn. She recently joined the program committees of ACM CHI, ACM Learning at Scale, and two SPLASH workshops on programming usability. She was awarded the 2003 Intel Foundation Young Scientist Award, both the NSF and NDSEG graduate fellowships, the MIT EECS Oral Master's Thesis Presentation Award, a Best of CHI Honorable Mention, and the MIT Amar Bose Teaching Fellowship for innovation in teaching methods. Prior to entering the field of human-computer interaction (HCI), she earned her MEng in the MIT CSAIL Robot Locomotion Group and was a visiting researcher at Stanford in the Stanford Biomimetics and Dextrous Manipulation Lab.

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Steven Goldfeder will present his Pre FPO on Monday, February 26, 2018 at 3PM in CS 302.
by Nicki Gotsis 20 Feb '18

20 Feb '18

Steven Goldfeder will present his Pre FPO on Monday, February 26, 2018 at 3PM in CS 302. The members of his committee are: Arvind Narayanan (adviser), Ed Felten, Mark Zhandry, and Matt Weinberg, Rosario Gennaro All are welcome to join. Abstract and title follow below. Increasing scalability and privacy for cryptocurrencies via off-chain protocols Abstract: The limits of Bitcoin’s scripting language motivate the need for off-blockchain protocols that extend the functionality of Bitcoin scripts. These protocols are run out-of-band by the transacting parties, but they are constructed in a manner that cryptographically binds them to on-chain scripts. Even with the advent of Ethereum, which provides a much richer scripting language, there are still privacy and scalability benefits to running off-chain protocols, even when on-chain analogs exist. I present off-chain protocols for a variety of applications as well as a general framework for off-chain smart contracts. An important tool for constructing off-chain protocols is threshold-signatures, a primitive that enables distributing the signing power of a given public key into n shares, such that at least k shares are required to produce a signature from that key. I show how to construct threshold signatures that are compatible with Bitcoin, and use this primitive to build off-chain protocols for privacy-preserving access control and escrow services. Turning to more advanced smart contracts, I show off-chain protocols for the fair exchange of digital goods and services for payment. Finally, we present a private and scalable smart contract system which enables arbitrary smart contracts that are enforced via off-chain protocols.

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Colloquium Speaker-Marshini Chetty, TODAY at 12:30
by Emily C. Lawrence 20 Feb '18

20 Feb '18

CS Department Colloquium Speaker Marshini Chetty, Princeton University Tuesday, February 20, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. David Dobkin "Parting the Cloud: Empowering End-Users through Internet Transparency" Why is my Internet slow? Why have I run out of data again? Is my thermostat talking to strangers? Internet users face seemingly simple questions like these every time they connect to the Internet with increasingly significant consequences for inaccurate information. Yet, as networks, applications, and devices proliferate and become more complex, questions like these are getting harder to answer. I study, design, build, and evaluate technologies to help users answer these questions, by improving the “transparency” of the networks, applications, and networked devices used to get online. My research goal is to empower Internet users by providing them with accurate and real-time information about, and control over, Internet performance, costs, privacy, and security. By doing so, my work gives Internet users the power to make well-informed decisions and agency for protecting themselves against and holding others accountable for online malfeasance. My research informs Internet policy by providing evidence of what kinds of transparency Internet users require and respond to. In this talk, I describe why Internet transparency is so important and how challenging it is to provide transparency to end-users. I present three projects that make Internet costs, online privacy and security, and Internet constraints more transparent for end-users. These projects include a system for giving adult users information and control over home broadband data usage, a prototype for helping elementary school age children learn about and manage their own Internet safety, and a mixed method approach for understanding how Internet constraints affect end-users in developing countries. I conclude the talk with open questions for making the Internet more transparent for end-users. Bio: Marshini Chetty is a research scholar in the Department of Computer Science at Princeton University where she directs the Princeton Human-Computer Interaction Laboratory. She specializes in human-computer interaction, usable security, and ubiquitous computing. Marshini designs, implements, and evaluates technologies to help users manage different aspects of Internet use from performance, costs to privacy and security. She often works in resource-constrained settings and uses her work to help inform Internet policy. She has a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, USA and a Masters and Bachelors in Computer Science from University of Cape Town, South Africa. Prior to joining Princeton, Marshini was an assistant professor in the College of Information Studies at the University of Maryland, College Park. Her work on creating consumer-facing tools for monitoring Internet speeds won a CHI best paper award; her research has been funded by the National Science Foundation, the National Security Agency, Intel, Microsoft, and two Google Faculty Research Awards.

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Reminder: Colloquium Speaker-Marshini Chetty, Tomorrow at 12:30
by Emily C. Lawrence 19 Feb '18

19 Feb '18

CS Department Colloquium Speaker Marshini Chetty, Princeton University Tuesday, February 20, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. David Dobkin "Parting the Cloud: Empowering End-Users through Internet Transparency" Why is my Internet slow? Why have I run out of data again? Is my thermostat talking to strangers? Internet users face seemingly simple questions like these every time they connect to the Internet with increasingly significant consequences for inaccurate information. Yet, as networks, applications, and devices proliferate and become more complex, questions like these are getting harder to answer. I study, design, build, and evaluate technologies to help users answer these questions, by improving the “transparency” of the networks, applications, and networked devices used to get online. My research goal is to empower Internet users by providing them with accurate and real-time information about, and control over, Internet performance, costs, privacy, and security. By doing so, my work gives Internet users the power to make well-informed decisions and agency for protecting themselves against and holding others accountable for online malfeasance. My research informs Internet policy by providing evidence of what kinds of transparency Internet users require and respond to. In this talk, I describe why Internet transparency is so important and how challenging it is to provide transparency to end-users. I present three projects that make Internet costs, online privacy and security, and Internet constraints more transparent for end-users. These projects include a system for giving adult users information and control over home broadband data usage, a prototype for helping elementary school age children learn about and manage their own Internet safety, and a mixed method approach for understanding how Internet constraints affect end-users in developing countries. I conclude the talk with open questions for making the Internet more transparent for end-users. Bio: Marshini Chetty is a research scholar in the Department of Computer Science at Princeton University where she directs the Princeton Human-Computer Interaction Laboratory. She specializes in human-computer interaction, usable security, and ubiquitous computing. Marshini designs, implements, and evaluates technologies to help users manage different aspects of Internet use from performance, costs to privacy and security. She often works in resource-constrained settings and uses her work to help inform Internet policy. She has a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, USA and a Masters and Bachelors in Computer Science from University of Cape Town, South Africa. Prior to joining Princeton, Marshini was an assistant professor in the College of Information Studies at the University of Maryland, College Park. Her work on creating consumer-facing tools for monitoring Internet speeds won a CHI best paper award; her research has been funded by the National Science Foundation, the National Security Agency, Intel, Microsoft, and two Google Faculty Research Awards.

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Linpeng Tang will present his FPO, "Efficient Processing and Delivery of Multimedia Data" on Friday, 2/23/2018 at 1:30pm in CS 402
by Nicki Gotsis 15 Feb '18

15 Feb '18

Linpeng Tang will present his FPO, "Efficient Processing and Delivery of Multimedia Data" on Friday, 2/23/2018 at 1:30pm in CS 402. The members of this committee is as follows: Kai Li (Adviser); Examiners: Jennifer Rexford, Kyle Jamieson, and Kai Li; Readers: Wyatt Lloyd and Michael Freedman Everyone is invited to attend. His abstract follows below. The explosion of multimedia data on the Internet in recent years has greatly enriched people's online experience. However, it also poses a great challenge to analyze and process, and then deliver such content to the worldwide audience. This dissertation presents novel approaches to improve the overall efficiency of the stack by novel system designs. First, to improve the caching performance of the flash-memory caches for content delivery network, this thesis proposes RIPQ, a framework for efficient and advanced caching with flash memory. Traditional implementations of these algorithms generate random writes that are not appropriate for flash devices, decreasing the device's performance and lifespan. RIPQ overcomes this issue by aggregating small writes, colocating items with similar priorities, and perform lazy updates to achieve low overhead. Second, this thesis proposes Chess, which uses popularity prediction for higher quality video streaming. Although better encodings improve video streaming, they are also compute-intensive, and it's infeasible to encode all videos uploaded to Facebook with the highest quality codec. However, because the accesses to videos are highly skewed, we may obtain most of the benefit by only running the compute- intensive encoding on a small portion of popular videos. Chess meets this demand by designing an approximate but fast base predictor with the access history information, and using an online learning method to combine multiple such predictors as well as the social signals to boost accuracy. Lastly, this thesis investigates how to accelerate deep learning models on manycore CPUs. Deep learning is now widely used for analyzing multimedia data, but it's compute-intensive, which constitutes its major bottleneck. The manycore CPU, combining both high FLOPS and a flexible computing model, is a promising solution to this problem. However, existing frameworks are still mainly optimized for GPU, and don't run efficiently on this architecture. To overcome this issue, this thesis proposes Graphi, the first attempt to accelerate the execution of computation graphs for deep learning models on this architecture. Graphi determines the optimal parallel settings with a profiling step, runs concurrent operations with low contention, and further reduces execution makespan with critical-path first scheduling.

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Colloquium Speaker-Marshini Chetty, Tuesday, Feb 20
by Emily Lawrence 13 Feb '18

13 Feb '18

CS Department Colloquium Speaker Marshini Chetty, Princeton University Tuesday, February 20, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. David Dobkin "Parting the Cloud: Empowering End-Users through Internet Transparency" Why is my Internet slow? Why have I run out of data again? Is my thermostat talking to strangers? Internet users face seemingly simple questions like these every time they connect to the Internet with increasingly significant consequences for inaccurate information. Yet, as networks, applications, and devices proliferate and become more complex, questions like these are getting harder to answer. I study, design, build, and evaluate technologies to help users answer these questions, by improving the "transparency" of the networks, applications, and networked devices used to get online. My research goal is to empower Internet users by providing them with accurate and real-time information about, and control over, Internet performance, costs, privacy, and security. By doing so, my work gives Internet users the power to make well-informed decisions and agency for protecting themselves against and holding others accountable for online malfeasance. My research informs Internet policy by providing evidence of what kinds of transparency Internet users require and respond to. In this talk, I describe why Internet transparency is so important and how challenging it is to provide transparency to end-users. I present three projects that make Internet costs, online privacy and security, and Internet constraints more transparent for end-users. These projects include a system for giving adult users information and control over home broadband data usage, a prototype for helping elementary school age children learn about and manage their own Internet safety, and a mixed method approach for understanding how Internet constraints affect end-users in developing countries. I conclude the talk with open questions for making the Internet more transparent for end-users. Bio: Marshini Chetty is a research scholar in the Department of Computer Science at Princeton University where she directs the Princeton Human-Computer Interaction Laboratory. She specializes in human-computer interaction, usable security, and ubiquitous computing. Marshini designs, implements, and evaluates technologies to help users manage different aspects of Internet use from performance, costs to privacy and security. She often works in resource-constrained settings and uses her work to help inform Internet policy. She has a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, USA and a Masters and Bachelors in Computer Science from University of Cape Town, South Africa. Prior to joining Princeton, Marshini was an assistant professor in the College of Information Studies at the University of Maryland, College Park. Her work on creating consumer-facing tools for monitoring Internet speeds won a CHI best paper award; her research has been funded by the National Science Foundation, the National Security Agency, Intel, Microsoft, and two Google Faculty Research Awards.

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Colloquium Speaker-Pedro Lopes, TODAY at 12:30
by Emily C. Lawrence 12 Feb '18

12 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 or Wired. (Learn more about Pedro's work here: plopes.org).

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REMINDER Colloquium Speaker-Pedro Lopes, tomorrow at 12:30
by Emily C. Lawrence 11 Feb '18

11 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).

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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.

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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. > > ================= > >

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