Quick links

CS Department Colloquium Series

Colloquium Speaker

Date and Time
Tuesday, February 11, 2020 - 12:30pm to 1:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
CS Department Colloquium Series
Speaker
Host
Elad Hazan

Talk info TBA

New Challenges in Question Answering: Natural Questions and Going Beyond Word Matching

Date and Time
Tuesday, December 3, 2019 - 1:30pm to 2:50pm
Location
Computer Science Large Auditorium (Room 104)
Type
CS Department Colloquium Series
Speaker
Host
Karthik Narasimhan

Recently, learned deep models have surpassed human performance on a number of question answering benchmarks such as SQuAD. However, these models resort to simple word matching and answer typing heuristics, and they are easily fooled. In this talk, I will present two different lines of work that aim to take us beyond the current status quo in question answering, and push us towards more robust representations of semantics, pragmatics, and knowledge.

First, I will present Natural Questions (NQ), a new question answering benchmark from Google. NQ contains real user questions, which require an understanding of the questioner's intent. NQ also requires systems to read entire Wikipedia pages to decide whether they fully answer a question. This is much harder than finding an answer given the knowledge that one is present. I will convince you that the question 'when was the last time a hurricane hit Massachusetts?' is under-specified with many reasonable answers, and I will tell you how we developed robust evaluation metrics to deal with this ambiguity.

In the second part of the talk I will present a complementary method of challenging today's question answering systems by removing access to evidence documents at inference time. Instead of building joint representations of questions and documents, we perform ahead of inference time reading and retrieve answers via fast maximum inner product search. I will show that this leads to large gains in accuracy and speed when finding answers in very large corpora. I will also show some preliminary results that show how our methods can be used to aggregate information from multiple diverse documents.

To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Putting together the threads of conversational AI

Date and Time
Tuesday, November 26, 2019 - 1:30pm to 2:50pm
Location
Computer Science Large Auditorium (Room 104)
Type
CS Department Colloquium Series
Speaker
Host
Danqi Chen

Jason Weston
Maybe we don't have enough threads yet to knit together the whole, but let's try anyway! We present our view of what is necessary for conversational AI, and the pieces we have worked on so far to get there. In particular: software (ParlAI, a unified platform for dialogue research), various neural architectures for memory, reasoning, retrieval and generation, and interactive learning, tasks for employing personality (Persona-Chat), knowledge (Wizard of Wikipedia) and perception (Image-Chat), evaluation studies & techniques (Acute-Eval), and a recent competition (ConvAI2). We'll try not to be too pessimistic (!), but we'll also discuss how far we still have to go.

Bio: Jason Weston is a research scientist at Facebook, NY and a Visiting Research Professor at NYU. He earned his PhD in machine learning at Royal Holloway, University of London and at AT&T Research in Red Bank, NJ (advisors: Alex Gammerman, Volodya Vovk and Vladimir Vapnik) in 2000. From 2000 to 2001, he was a researcher at Biowulf technologies. From 2002 to 2003 he was a research scientist at the Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. From 2003 to 2009 he was a research staff member at NEC Labs America, Princeton. From 2009 to 2014 he was a research scientist at Google, NY. His interests lie in statistical machine learning, with a focus on reasoning, memory, perception, interaction and communication. Jason has published over 100 papers, including best paper awards at ICML and ECML, and a Test of Time Award for his work "A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning", ICML 2008 (with Ronan Collobert). He was part of the YouTube team that won a National Academy of Television Arts & Sciences Emmy Award for Technology and Engineering for Personalized Recommendation Engines for Video Discovery. He was listed as the 16th most influential machine learning scholar at AMiner and one of the top 50 authors in Computer Science in Science.

To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Leveraging Allelic Heterogeneity in Association Studies

Date and Time
Friday, November 22, 2019 - 12:30pm to 1:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
CS Department Colloquium Series
Host
Ben Raphael

Eleazar Eskin
Over 100,000 variants have been implicated in human traits through genome-wide association studies (GWAS).  Virtually all reported GWAS identify associations by measuring the correlation between a single variant and a phenotype of interest. Recently, several studies reported that at many risk loci, there may exist multiple causal variants, a phenomenon referred to as allelic heterogeneity. Naturally, for a locus with multiple causal variants with small effect sizes, the standard association test is underpowered to detect the associations. Alternatively, an approach considering effects of multiple variants simultaneously may increase statistical power. Counterintuitively, most approaches that consider multiple variants in association studies find fewer associations than the single SNP association test.  This is due to the fact that most multiple variant methods assume a structure of allelic heterogeneity which is very different from what is observed in genetic studies.  In this work, we propose a new statistical method, Model-based Association test Reflecting causal Status (MARS), that tries to find an association between variants in risk loci and a phenotype, considering multiple variants at each locus. One of the main advantages of MARS is that it only requires the existing summary statistics to detect associated risk loci. Thus, MARS is applicable to any association study with summary statistics, even though individual level data is not available for the study. Utilizing extensive simulated data sets, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while robustly controls the type I error. Applied to data of 44 tissues provided by the Genotype- Tissue Expression (GTEx) consortium, we show that MARS identifies more eGenes compared to previous approaches in most of the tissues; e.g. MARS identified 16% more eGenes than the ones reported by the GTEx consortium. Moreover, applied to Northern Finland Birth Cohort (NFBC) data, we demonstrate that MARS effectively identifies association loci with improved power (56% of more loci found by MARS) in GWAS studies compared to the standard association test.

Bio: Dr. Eleazar Eskin serves as the inaugural chair for the UCLA Department of Computational Medicine. Fascinated by the intersection of computer science and biology, Dr. Eskin is researching and developing computational methods for the analysis of genetic variations in human disease. There are millions of variants in the genome and identifying the variants involved in disease often requires tens of thousands of patient samples. In order to analyze these tremendously large datasets, Dr. Eskin and his team are solving challenging computational problems and developing new computational techniques. He received his PhD in computer science from Columbia University. A recipient of the Alfred P. Sloan Foundation Research Fellowship, Dr. Eskin’s work is supported by the National Science Foundation and the National Institutes of Health.

Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Network Level IoT Security

Date and Time
Friday, November 22, 2019 - 11:00am to 12:00pm
Location
Computer Science Small Auditorium (Room 105)
Type
CS Department Colloquium Series
Host
Jennifer Rexford

David Hay
Computer networks have undergone and continue to experience a major transformation, whereby billions of low-cost devices are being connected to the network to provide additional functionality and better user experience. Unlike traditional network devices, these devices, collectively known as the ``Internet of Things'' (IoT), typically have very limited computational, memory, and power resources. These IoT devices became a major security concerns, both due to human factors and to technical challenges in deploying security mechanisms on devices with low resources. The number and diversity of IoT devices creates a huge attack surface that is often exploited by attackers to launch large-scale attacks, sometimes exploiting well-known vulnerabilities.

This talk will highlight the security concerns of IoT devices from a networking perspective and explore how to secure IoT devices using whitelists, in which communication between a device and an endpoint is prohibited unless that endpoint appears in the corresponding whitelist.  Finally, we will discuss deployment options for such a solution (namely, within the internet gateway, as virtual network function within the ISP network, or a combination of the two). 

Bio: David Hay is an Associate Professor with the Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel. He received the B.A. (summa cum laude) and Ph.D. degrees in computer science from the Technion—Israel Institute of Technology, Haifa, Israel, in 2001 and 2007, respectively. In addition, he was with IBM Haifa Research Labs, Haifa, Israel; Cisco Systems, San Jose, CA, USA; the Electronic Department, Politecnico di Torino, Turin, Italy; and the Electrical Engineering Department with Columbia University, New York, NY, USA. In 2010, he co-founded (with Prof. Brembler-Barr) the DEEPNESS lab, focusing on deep packet inspection in next-generation network devices. He has served as a technical program committee member of numerous networking conferences, and since 2018 serves as en editor of ACM/IEEE Transactions on Networking. His research interests are in computer networks—in particular, network algorithmics, packet classification, deep packet inspection, network survivability and resilience, software-defined networking, network-function virtualization, and various aspects of network security. 

To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Hardness of Approximation: From the PCP Theorem to the 2-to-2 Games Theorem

Date and Time
Monday, December 9, 2019 - 12:30pm to 1:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
CS Department Colloquium Series
Host
Ran Raz

Subhash Khot
Computer scientists firmly believe that no algorithm can efficiently compute optimal solutions to a class of problems called NP-hard problems. For many NP-hard problems, even computing an approximately optimal solution remains NP-hard. This phenomenon, known as the hardness of approximation, has numerous connections to proof checking, optimization, combinatorics, discrete Fourier analysis, and geometry.

The talk will provide an overview of these connections. It will address why graph coloring is a computationally hard problem, how it is possible to check a proof without even looking at it, why computer scientists love the majority vote, and whether a shape exists that looks spherical as well as cubical. It will explain how all this fits into a 30-year research program starting with the foundational Probabilistically Checkable Proofs (PCP) Theorem and leading to the recent 2-to-2 Games Theorem.

Bio: Subhash Khot is a professor of computer science at the Courant Institute of Mathematical Sciences at New York University. His prior affiliations include Princeton University (PhD), Institute for Advanced Study (member), Georgia Tech (assistant professor) and University of Chicago (visiting professor). His research centers on computational complexity and its connections to geometry and analysis. He is a recipient of the National Science Foundation’s Alan T. Waterman Award, the International Mathematical Union’s Nevanlinna Prize, the Simons Investigator Award, a MacArthur Fellowship, and is a Fellow of the Royal Society. 

Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Representation, Modeling, and Optimization in Reinforcement Learning

Date and Time
Thursday, October 24, 2019 - 12:30pm to 1:30pm
Location
Friend Center Convocation Room
Type
CS Department Colloquium Series
Host
Sanjeev Arora

Sham Kakade
Reinforcement learning is now the dominant paradigm for how an agent learns to interact with the world. The approach has led to successes ranging across numerous domains, including game playing and robotics, and it holds much promise in new domains, from self-driving cars to interactive medical applications. 
Some of the central challenges are:
-Representational learning: does having a good representation of the environment permit efficient reinforcement learning?
-Modeling: should we explicitly build a model of our environment or, alternatively, should we directly learn how to act?
-Optimization: in practice, deployed algorithms often use local search heuristics. Can we provably understand when these approaches are effective and provide faster and more robust alternatives?
This talk will survey a number of results on these basic questions. Throughout, we will highlight the interplay of theory, algorithm design, and practice.

Bio: Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in the Department of Computer Science and the Department of Statistics at the University of Washington, and is a co-director for the Algorithmic Foundations of Data Science Institute. He works on the mathematical foundations of machine learning and AI. Sham's thesis helped in laying the foundations of the PAC-MDP framework for reinforcement learning. With his collaborators, his additional contributions include: one of the first provably efficient policy search methods, Conservative Policy Iteration, for reinforcement learning; developing the mathematical foundations for the widely used linear bandit models and the Gaussian process bandit models; the tensor and spectral methodologies for provable estimation of latent variable models (applicable to mixture of Gaussians, HMMs, and LDA); the first sharp analysis of the perturbed gradient descent algorithm, along with the design and analysis of numerous other convex and non-convex algorithms. He is the recipient of the IBM Goldberg best paper award (in 2007) for contributions to fast nearest neighbor search and the best paper, INFORMS Revenue Management and Pricing Section Prize (2014). He has been program chair for COLT 2011.

Sham was an undergraduate at Caltech, where he studied physics and worked under the guidance of John Preskill in quantum computing. He then completed his Ph.D. in computational neuroscience at the Gatsby Unit at University College London, under the supervision of Peter Dayan. He was a postdoc at the Dept. of Computer Science, University of Pennsylvania , where he broadened his studies to include computational game theory and economics from the guidance of Michael Kearns. Sham has been a Principal Research Scientist at Microsoft Research, New England, an associate professor at the Department of Statistics, Wharton, UPenn, and an assistant professor at the Toyota Technological Institute at Chicago.

Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Designing and deploying social computing systems inside and outside the lab

Date and Time
Monday, October 21, 2019 - 12:30pm to 1:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
CS Department Colloquium Series
Host
Adam Finkelstein

Andrés Monroy-Hernández
Social computing has permeated most aspects of our lives, from work to play. In recent years, however, social platforms have faced challenges ranging from labor concerns about crowd work to misinformation on social media. These and other challenges, along with the emergence of new technical platforms, create opportunities to re-imagine the future of the field. In this talk, I will discuss my research designing and deploying social computing systems to help people connect and collaborate to learn new skills, crowdsource news reporting, and delegate tasks to hybrid AI systems. First, I describe an online community I created to connect millions of young creators to learn computational thinking skills as they create, share, and remix games and animations on the web. Then I shift to how this work inspired my next set of systems to connect urban residents as they share news about their local communities. Finally, I will discuss a novel workplace productivity tool we created to enable information workers to delegate tasks to hybrid intelligence systems powered by humans and AI. I close by articulating the challenges and opportunities ahead for the field, and the ways my team is beginning to explore new systems that support more authentic and intimate connections, building on new technologies including wearables sensors and AR. 

Bio: Andrés Monroy-Hernández is a lead research scientist at Snap Inc, where he manages the human-computing interaction research team. He is also an affiliate professor at the University of Washington. His work focuses on the design and study of social computing technologies that help people connect and collaborate. Previously, he was at Microsoft Research for six years, where he led the research and development of Calendar.help, Microsoft’s first hybrid AI product. His research has received best paper awards at  CHI, CSCW, HCOMP, and ICWSM, and has been featured in The New York Times, CNN, Wired, BBC, and The Economist. Andrés was named one of the 35 Innovators under 35 by the MIT Technology Review magazine in Latin America, and one the most influential Latinos in Tech by CNET. Andrés holds master’s and Ph.D. from MIT, where he led the creation of the Scratch online community. He has a BS from Tec de Monterrey in México. More info at http://andresmh.com

Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Enabling Continuous Learning through Synaptic Plasticity in Hardware

Date and Time
Thursday, November 7, 2019 - 12:30pm to 1:30pm
Location
Engineering Quadrangle B205
Type
CS Department Colloquium Series
Host
Margaret Martonosi

Ever since modern computers were invented, the dream of creating artificial intelligence (AI) has captivated humanity. We are fortunate to live in an era when, thanks to deep learning (DL), computer programs have paralleled, and in many cases even surpassed human level accuracy in tasks like visual perception and speech synthesis. However, we are still far away from realizing general-purpose AI. The problem lies in the fact that the development of supervised learning based DL solutions today is mostly open loop.  A typical DL model is created by hand-tuning the deep neural network (DNN) topology by a team of experts over multiple iterations, followed by training over petabytes of labeled data. Once trained, the DNN provides high accuracy for the task at hand; if the task changes, however, the DNN model needs to be re-designed and re-trained before it can be deployed. A general-purpose AI system, in contrast, needs to have the ability to constantly interact with the environment and learn by adding and removing connections within the DNN autonomously, just like our brain does. This is known as synaptic plasticity.

In this talk we will present our research efforts towards enabling general-purpose AI leveraging plasticity in both the algorithm and hardware. First, we will present GeneSys (MICRO 2018), a HW-SW prototype of a closed loop learning system for continuously evolving the structure and weights of a DNN for the task at hand using genetic algorithms, providing 100-10000x higher performance and energy-efficiency over state-of-the-art embedded and desktop CPU and GPU systems. Next, we will present a DNN accelerator substrate called MAERI (ASPLOS 2018), built using light-weight, non-blocking, reconfigurable interconnects, that supports efficient mapping of regular and irregular DNNs with arbitrary dataflows, providing ~100% utilization of all compute units, resulting in 3X speedup and energy-efficiency over our prior work Eyeriss (ISSCC 2016). Finally, time permitting, we will describe our research in enabling rapid design-space exploration and prototyping of hardware accelerators using our dataflow DSL + cost-model called MAESTRO (MICRO 2019).

Bio: 
Tushar Krishna is an Assistant Professor in the School of Electrical and Computer Engineering at Georgia Tech. He also holds the ON Semiconductor Junior Professorship. He has a Ph.D. in Electrical Engineering and Computer Science from MIT (2014), a M.S.E in Electrical Engineering from Princeton University (2009), and a B.Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Delhi (2007). Before joining Georgia Tech in 2015, Dr. Krishna spent a year as a post-doctoral researcher at Intel, Massachusetts.

Dr. Krishna’s research spans computer architecture, interconnection networks, networks-on-chip (NoC) and deep learning accelerators - with a focus on optimizing data movement in modern computing systems. Three of his papers have been selected for IEEE Micro’s Top Picks from Computer Architecture, one more received an honorable mention, and two have won best paper awards. He received the National Science Foundation (NSF) CRII award in 2018, and both a Google Faculty Award and a Facebook Faculty Award in 2019. He also received the “Class of 1940 Course Survey Teaching Effectiveness” Award from Georgia Tech in 2018.

Painting in More Dimensions

Date and Time
Monday, October 14, 2019 - 4:30pm to 5:30pm
Location
James Stewart Theater, Lewis Center for the Arts, 185 Nassau St.
Type
CS Department Colloquium Series
Speaker
Host
Adam Finkelstein

Alexa Meade painting half her face.
Artist Alexa Meade paints on the human body and three-dimensional spaces, creating the illusion that our reality is a two-dimensional painting. She will discuss her artistic process and collaborations with mathematicians, magicians, and theoretical physicists.

Her groundbreaking work has been exhibited around the world at the Grand Palais in Paris, the United Nations in New York, the Smithsonian National Portrait Gallery in Washington, DC and Shibuya Crossing in Tokyo. Her solo show on Rodeo Drive in Beverly Hills was attended by forty-thousand people. She has created large scale interactive installations at Coachella, Cannes Lions, and Art Basel. Alexa has been commissioned by BMW, Sony, Adidas, and the San Francisco Symphony Orchestra. She painted pop star Ariana Grande for her iconic “God is a Woman” music video, which has about 250 million views.

Alexa has lectured at TED, Apple, and Stanford and accepted an invitation to the White House under President Obama. She has been honored with the "Disruptive Innovation Award" by the Tribeca Film Festival and has been Artist-in-Residence at both Google and the Perimeter Institute for Theoretical Physics. InStyle has named Alexa among their "Badass Women."

FREE & OPEN TO THE PUBLIC

Follow us: Facebook Twitter Linkedin