- talks - lists.cs.princeton.edu

Reminder: Colloquium Speaker Umesh Vazirani, tomorrow at 12:30pm
by Emily C. Lawrence 18 May '21

18 May '21

Colloquium Speaker Speaker: Umesh Vazirani, University of California at Berkeley Title: Theoretical Reflections on Quantum Supremacy Date: Wednesday, May 19 Time: 12:30pm ET Hosts: Sanjeev Arora & Andrew Houck Event page: [ https://www.cs.princeton.edu/events/26082 | https://www.cs.princeton.edu/events/26082 ] Register for Zoom webinar here: [ https://princeton.zoom.us/webinar/register/WN_6zJv9owaS3WOuss4Gg7_7A | https://princeton.zoom.us/webinar/register/WN_6zJv9owaS3WOuss4Gg7_7A ] Abstract: The recent demonstration of quantum supremacy by Google is a first step towards the era of small to medium scale quantum computers. In this talk I will explain what the experiment accomplished and the theoretical work it is based on, as well as what it did not accomplish and the many theoretical and practical challenges that remain. I will also describe recent breakthroughs in the design of protocols for the testing and benchmarking of quantum computers, a task that has deep computational and philosophical implications. Specifically, this leads to protocols for scalable and verifiable quantum supremacy, certifiable quantum random generation and verification of quantum computation. Bio: Umesh Vazirani is Strauch Distinguished Professor of Computer Science at UC Berkeley, and Director of the Berkeley Quantum Computing Center (BQIC). His 1993 paper with Ethan Bernstein laid the foundations of quantum complexity theory, and his research has touched on many facets of quantum computation, including Quantum Algorithms, Quantum Hamiltonian Complexity and Interactive classical testing of quantum devices. Vazirani is co-inventor of the Bid Scaling algorithm for the AdWords auction which is widely used by Internet search companies, and co-winner of the Fulkerson Prize for the ARV graph partitioning algorithm. He is member of the NAS, and co-author of two books: An Introduction to Computational Learning Theory (MIT Press) and Algorithms (McGraw-Hill).

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Reminder: ECE and CS Seminar - Song Han, MIT: tomorrow at 10:30am
by Emily C. Lawrence 18 May '21

18 May '21

CS & ECE SEMINAR Speaker: Song Han, MIT Title: Putting AI on a Diet: TinyML and Efficient Deep Learning Day: Wednesday, May 19, 2021 Time: 10:30 am Zoom registration: [ https://princeton.zoom.us/meeting/register/tJ0tcu2tpzkqHtP8kEDbsDJLGM25atnx… | https://princeton.zoom.us/meeting/register/tJ0tcu2tpzkqHtP8kEDbsDJLGM25atnx… ] Hosts: Kai Li and Naveen Verma Abstract: Today’s AI is too big. Deep neural networks demand extraordinary levels of data and computation, and therefore power, for training and inference. This severely limits the practical deployment of AI in edge devices. We aim to improve the efficiency of neural network design. First, I’ll present MCUNet [1] that brings deep learning to IoT devices. MCUNet is a framework that jointly designs the efficient neural architecture (TinyNAS) and the light-weight inference engine (TinyEngine), enabling ImageNet-scale inference on micro-controllers that have only 1MB of Flash. Next I will introduce Once-for-All Network [2], an efficient neural architecture search approach, that can elastically grow and shrink the model capacity according to the target hardware resource and latency constraints. From inference to training, I’ll present TinyTL [3] that enables tiny transfer learning on-device, reducing the memory footprint by 7-13x. Finally, I will describe data-efficient GAN training techniques [4] that can generate photo-realistic images using only 100 images, which used to require tens of thousands of images. We hope such TinyML techniques can make AI greener, faster, more efficient and more sustainable. [1] MCUNet: Tiny Deep Learning on IoT Devices, (NeurIPS’20 spotlight) [2] Once-for-All: Train One Network and Specialize it for Efficient Deployment (ICLR’19) [3] Tiny Transfer Learning: Reduce Memory, not Parameters for Efficient On-Device Learning (NeurIPS’20) [4] Differentiable Augmentation for Data-Efficient GAN Training (NeurIPS’20) Bio: Song Han is an assistant professor at MIT’s EECS. He received his PhD degree from Stanford University. His research focuses on efficient deep learning computing. He proposed “deep compression” technique that can reduce neural network size by an order of magnitude without losing accuracy, and the hardware implementation “efficient inference engine” that first exploited pruning and weight sparsity in deep learning accelerators. His team’s work on hardware-aware neural architecture search that bring deep learning to IoT devices was highlighted by MIT News, Wired, Qualcomm News, VentureBeat, IEEE Spectrum, integrated in PyTorch and AutoGluon, and received many low-power computer vision contest awards in flagship AI conferences (CVPR’19, ICCV’19 and NeurIPS’19). Song received Best Paper awards at ICLR’16 and FPGA’17, Amazon Machine Learning Research Award, SONY Faculty Award, Facebook Faculty Award, NVIDIA Academic Partnership Award. Song was named “35 Innovators Under 35” by MIT Technology Review for his contribution on “deep compression” technique that “lets powerful artificial intelligence (AI) programs run more efficiently on low-power mobile devices.” Song received the NSF CAREER Award for “efficient algorithms and hardware for accelerated machine learning” and the IEEE “AIs 10 to Watch: The Future of AI” award.

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Chaitanya Aluru will present his Pre FPO "Reconciliation based methods for Understanding Transcription Factor Evolution" on Thursday, May 27, 2021 at 3PM via Zoom.
by jfarquer＠cs.princeton.edu 17 May '21

17 May '21

Chaitanya Aluru will present his Pre FPO "Reconciliation based methods for Understanding Transcription Factor Evolution" on Thursday, May 27, 2021 at 3PM via Zoom. Zoom link: https://princeton.zoom.us/j/91687324537 Committee: Examiners: Mona Singh, Yuri Prytikin, Barbara Englehardt Readers: Bernard Chazelle, Ben Raphael All are welcome to attend. Title: Reconciliation based methods for Understanding Transcription Factor Evolution Abstract: Regulatory networks are thought to underlie the biological differences between organisms. However, the evolutionary mechanisms by which these networks evolve is still an active area of research. Proteins called transcription factors form a central component of regulatory networks and are thought to evolve through repeated duplications and losses of functional subunits known as domains. Previous work has captured single duplication events, but fails to capture tandem duplications of multiple domains, a key evolutionary event in certain transcription factor families. In this talk, I will discuss two parsimony frameworks I have designed to incorporate tandem duplications. I will show how to write these problems as constraint satisfaction problems and find optimal solutions using ILPs. Finally, I will show example protein families in which our methods have successfully uncovered ancestral duplication patterns, and discuss future large-scale studies of zinc finger genes, the largest family of transcription factors in humans.

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Electoral Innovation Lab Friday Forum: Gary King, Friday, May 21 at 1:30pm
by Emily C. Lawrence 14 May '21

14 May '21

The Electoral Innovation Lab at Princeton University presents a Friday Forum: Gary King, Ph.D. Albert J. Weatherhead III University Professor Department of Government Director, Institute for Quantitative Social Science Harvard University Friday, May 21 1:30 PM ET [ https://princeton.zoom.us/j/273691213 | https://princeton.zoom.us/j/273691213 ] “Scientific Measurement in Redistricting Research” We discuss the essential requirements for the measurement of any quantity of interest as applied to redistricting research. Most importantly, the quantity of interest must be defined separately from its measure, without which empirical estimates cannot evaluated or improved. Measurement without such a standard may be of use in describing the data are of no use for learning about an electoral system or understanding fundamental concepts in the field such as legislative compactness, partisan bias, electoral responsiveness, among others, all of which we will illustrate. Host: Electoral Innovation Lab / Princeton Gerrymandering Project

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Colloquium Speaker: Umesh Vazirani, Wednesday, May 19 at 12:30pm
by Emily C. Lawrence 12 May '21

12 May '21

Colloquium Speaker Speaker: Umesh Vazirani, University of California at Berkeley Title: Theoretical Reflections on Quantum Supremacy Date: Wednesday, May 19 Time: 12:30pm ET Hosts: Sanjeev Arora & Andrew Houck Event page: [ https://www.cs.princeton.edu/events/26082 | https://www.cs.princeton.edu/events/26082 ] Register for Zoom webinar here: [ https://princeton.zoom.us/webinar/register/WN_6zJv9owaS3WOuss4Gg7_7A | https://princeton.zoom.us/webinar/register/WN_6zJv9owaS3WOuss4Gg7_7A ] Abstract: The recent demonstration of quantum supremacy by Google is a first step towards the era of small to medium scale quantum computers. In this talk I will explain what the experiment accomplished and the theoretical work it is based on, as well as what it did not accomplish and the many theoretical and practical challenges that remain. I will also describe recent breakthroughs in the design of protocols for the testing and benchmarking of quantum computers, a task that has deep computational and philosophical implications. Specifically, this leads to protocols for scalable and verifiable quantum supremacy, certifiable quantum random generation and verification of quantum computation. Bio: Umesh Vazirani is Strauch Distinguished Professor of Computer Science at UC Berkeley, and Director of the Berkeley Quantum Computing Center (BQIC). His 1993 paper with Ethan Bernstein laid the foundations of quantum complexity theory, and his research has touched on many facets of quantum computation, including Quantum Algorithms, Quantum Hamiltonian Complexity and Interactive classical testing of quantum devices. Vazirani is co-inventor of the Bid Scaling algorithm for the AdWords auction which is widely used by Internet search companies, and co-winner of the Fulkerson Prize for the ARV graph partitioning algorithm. He is member of the NAS, and co-author of two books: An Introduction to Computational Learning Theory (MIT Press) and Algorithms (McGraw-Hill).

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Wednesday, May 19, 2021 - ECE and CS Seminar - Song Han, MIT
by Emily C. Lawrence 12 May '21

12 May '21

CS & ECE SEMINAR Speaker: Song Han, MIT Title: Putting AI on a Diet: TinyML and Efficient Deep Learning Day: Wednesday, May 19, 2021 Time: 10:30 am Zoom registration: [ https://princeton.zoom.us/meeting/register/tJ0tcu2tpzkqHtP8kEDbsDJLGM25atnx… | https://princeton.zoom.us/meeting/register/tJ0tcu2tpzkqHtP8kEDbsDJLGM25atnx… ] Hosts: Kai Li and Naveen Verma Abstract: Today’s AI is too big. Deep neural networks demand extraordinary levels of data and computation, and therefore power, for training and inference. This severely limits the practical deployment of AI in edge devices. We aim to improve the efficiency of neural network design. First, I’ll present MCUNet [1] that brings deep learning to IoT devices. MCUNet is a framework that jointly designs the efficient neural architecture (TinyNAS) and the light-weight inference engine (TinyEngine), enabling ImageNet-scale inference on micro-controllers that have only 1MB of Flash. Next I will introduce Once-for-All Network [2], an efficient neural architecture search approach, that can elastically grow and shrink the model capacity according to the target hardware resource and latency constraints. From inference to training, I’ll present TinyTL [3] that enables tiny transfer learning on-device, reducing the memory footprint by 7-13x. Finally, I will describe data-efficient GAN training techniques [4] that can generate photo-realistic images using only 100 images, which used to require tens of thousands of images. We hope such TinyML techniques can make AI greener, faster, more efficient and more sustainable. [1] MCUNet: Tiny Deep Learning on IoT Devices, (NeurIPS’20 spotlight) [2] Once-for-All: Train One Network and Specialize it for Efficient Deployment (ICLR’19) [3] Tiny Transfer Learning: Reduce Memory, not Parameters for Efficient On-Device Learning (NeurIPS’20) [4] Differentiable Augmentation for Data-Efficient GAN Training (NeurIPS’20) Bio: Song Han is an assistant professor at MIT’s EECS. He received his PhD degree from Stanford University. His research focuses on efficient deep learning computing. He proposed “deep compression” technique that can reduce neural network size by an order of magnitude without losing accuracy, and the hardware implementation “efficient inference engine” that first exploited pruning and weight sparsity in deep learning accelerators. His team’s work on hardware-aware neural architecture search that bring deep learning to IoT devices was highlighted by MIT News, Wired, Qualcomm News, VentureBeat, IEEE Spectrum, integrated in PyTorch and AutoGluon, and received many low-power computer vision contest awards in flagship AI conferences (CVPR’19, ICCV’19 and NeurIPS’19). Song received Best Paper awards at ICLR’16 and FPGA’17, Amazon Machine Learning Research Award, SONY Faculty Award, Facebook Faculty Award, NVIDIA Academic Partnership Award. Song was named “35 Innovators Under 35” by MIT Technology Review for his contribution on “deep compression” technique that “lets powerful artificial intelligence (AI) programs run more efficiently on low-power mobile devices.” Song received the NSF CAREER Award for “efficient algorithms and hardware for accelerated machine learning” and the IEEE “AIs 10 to Watch: The Future of AI” award.

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Geoffrey Roeder will be presenting his General Exam "A Probabilistic Interpretation of Scale and Location Invariance' on Thursday, May 13, 2021 at 9AM via Zoom.
by jfarquer＠cs.princeton.edu 11 May '21

11 May '21

Geoffrey Roeder will be presenting his General Exam "A Probabilistic Interpretation of Scale and Location Invariance' on Thursday, May 13, 2021 at 9AM via Zoom. Zoom link: https://princeton.zoom.us/j/9638294920 The members of Geoffrey's committee are as follows: Ryan Adams (adviser), Jia Deng, Tom Griffiths Link to Abstract: https://docs.google.com/document/d/1mQwKDgKT2OljbYB1QlNKvpi1Je17J9IBHXUMskI3 Jlg/edit?usp=sharing Link to Reading List: https://docs.google.com/document/d/12EhDJNaMn2Kcxml3UNhZ8YdyvQ5w8_JEzwtssGZy NoQ/edit?usp=sharing Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Natalie Popescu will present her General Exam "Safer at Any Speed: Automatic Context-Aware Safety Enhancement for Rust " on May 11, 2021 at 10AM via Zoom.
by jfarquer＠cs.princeton.edu 10 May '21

10 May '21

Natalie Popescu will present her General Exam "Safer at Any Speed: Automatic Context-Aware Safety Enhancement for Rust" on Tuesday, May 11,2021 at 10AM via Zoom. Zoom Link: https://princeton.zoom.us/j/98143193169 The members of Natalie's committee are as follows: Amit Levy (adviser), David August, and Wyatt Lloyd Link to Abstract: https://docs.google.com/document/d/1NHaum5M8VjIEEDwh0cp9uW4bRqtNuW-x3qfvZSr0 Gl4/edit?usp=sharing Link to Reading List: https://docs.google.com/document/d/1IMJvWMohqUu3pcJZGJYZ-kXpG-TLbkY7UdX0OUIZ G1Y/edit?usp=sharing Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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ACEE seminar: Todd Brady, Intel Corp. May 13, 12:30pm ET
by Emily C. Lawrence 07 May '21

07 May '21

Andlinger Center for Energy and the Environment Title: The digital carbon footprint is growing, or is it? Speaker: Todd Brady, Intel Corp. Date: Thursday May 13 - 12:30pm ET [ https://acee.princeton.edu/events/highlight-seminar-series-todd-brady/ | https://acee.princeton.edu/events/highlight-seminar-series-todd-brady/ ] The intersection of digitalization and climate change, examined from the lens of the world’s largest seminconductor manufacturer, Intel Corporation. Brady will be introduced by Prof. Mine Chen. Abstract: There are two trends that are rapidly changing the world we live in – digitalization and climate change. At first glance, the two appear to be completely disparate trends. However upon further examination, there are correlations that are worth exploring. For one, digital technologies, devices and applications are ubiquitous in today’s world and growing. With such growth comes environmental impact. But what is that impact and is the growth significantly contributing to climate change or do efficiency improvements offset emissions? Digitalization has a clear environmental “footprint” that must be prioritized by information and communication technology (ICT) companies. At the same time, ICT has a “handprint” that has had a measurable benefit in reducing the growth of the world’s carbon emissions, with many more unrealized opportunities. The seminar will examine this topic from the lens of the world’s largest semiconductor manufacturer, Intel Corporation, and will discuss both the challenges ahead as well as the opportunities that digitalization presents. Bio: Todd Brady is the director of global public affairs and sustainability for Intel Corporation. In this role, he leads state and local government affairs, media and community relations, corporate volunteerism and sustainability at the company’s major manufacturing and office locations around the globe. In addition to overseeing regulatory and community engagement strategies in the U.S., China, India, Southeast Asia, Israel, Ireland and Latin America, Brady directs Intel’s global sustainability initiatives including climate, energy, water, green buildings and circular economy. During his 25 years at Intel, Brady has represented the company in numerous public forums and led industry-wide initiatives in many national and international committees. He has authored more than 20 papers in scientific journals and conference proceedings on a variety of sustainability topics. In 2009, he was named by Scientific American one of ten outstanding leaders involved in research, business or policy pursuits that have advanced science and technology. Brady holds a Bachelor of Science degree in chemical engineering from Brigham Young University and a Master of Science in environmental engineering from the University of Illinois at Urbana-Champaign.

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Anne Kohlbrenner will present her General Exam "Web Science: A Library for Browser-Based Studies " on Friday, May 14, 2021 at 10AM via Zoom.
by Judith A. Farquer 07 May '21

07 May '21

Anne Kohlbrenner will present her General Exam "Web Science: A Library for Browser-Based Studies " on Friday, May 14, 2021 at 10AM via Zoom. Zoom link: [ https://princeton.zoom.us/j/92885723015?pwd=MU5rdllDVlZrMUJSSFNwRFUza0o1UT09 | https://princeton.zoom.us/j/92885723015?pwd=MU5rdllDVlZrMUJSSFNwRFUza0o1UT09 ] The members of Anne’s committee are as follows: Jonathan Mayer (adviser), Jen Rexford, Matt Salganik Link to Abstract and Reading List: [ https://docs.google.com/document/d/1T24HsToWlyhWCvfWgcMzu5KaQmAsNdIF3vzBWbW… | https://docs.google.com/document/d/1T24HsToWlyhWCvfWgcMzu5KaQmAsNdIF3vzBWbW… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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