- talks - lists.cs.princeton.edu

Sotirios Apostolakis will present his FPO "A Sensible Approach to Speculative Automatic Parallelization" on Wednesday, December 9, 2020 at 2pm via Zoom.
by Nicki Mahler 07 Dec '20

07 Dec '20

Sotirios Apostolakis will present his FPO "A Sensible Approach to Speculative Automatic Parallelization" on Wednesday, December 9, 2020 at 2pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/97706523775 | https://princeton.zoom.us/j/97706523775 ] The members of his committee are as follows: Examiners: Davis August (adviser), Brian Kernighan, and Andrew Appel; Readers: Zachary Kincaid and Simone Campanoni (Northwestern University) A copy of his thesis is available upon request. Please email ngotsis(a)cs.princeton if you would like a copy of the thesis. Everyone is invited to attend the talk. Abstract: The promise of automatic parallelization, freeing programmers from the error-prone and time-consuming process of making efficient use of parallel processing resources, remains unrealized. For decades, the imprecision of memory analysis limited the applicability of automatic parallelization. The introduction of speculation to automatic parallelization overcame these applicability limitations but caused profitability problems due to high communication and bookkeeping costs for speculation validation and commit. This dissertation shifts the focus from applicability to profitability by making speculative automatic parallelization more efficient. Unlike current approaches that perform analysis and transformations independently and in sequence, the proposed system integrates, without loss of modularity, memory analysis, speculative techniques, and enabling transformations. Specifically, this dissertation involves three main contributions. First, it introduces a novel speculation-aware collaborative analysis framework (SCAF) that minimizes the need for high-overhead speculation. Second, it proposes a new parallelizing-compiler design that enables careful planning. Third, it presents new efficient speculative privatization transformations that avoid privatization overheads of prior work. SCAF learns of available speculative information via profiling, computes its full impact on memory dependence analysis, and makes this resulting information available for all code optimizations. SCAF is modular (adding new analysis modules is easy) and collaborative (modules cooperate to produce a result more precise than the confluence of all individual results). Relative to the best prior speculation-aware dependence analysis technique, SCAF dramatically reduces the need for expensive-to-validate memory speculation in the hot loops of all 16 evaluated C/C++ SPEC benchmarks. The introduction of planning in the compiler enables the selection of parallelizationenabling transformations based on their cost and overall impacts. The benefit is twofold. First, the compiler only applies a minimal-cost set of enabling transformations. Second, this decision-making process makes the addition of new efficient speculative enablers possible, including the proposed privatization transformations. This dissertation integrates these contributions into a fully-automatic speculativeDOALL parallelization framework for commodity hardware. By reducing speculative parallelization overheads in ways not possible with prior parallelization systems, this work obtains higher overall program speedup (23.0⇥ for 12 general-purpose C/C++ programs running on a 28-core shared-memory machine) than Privateer (11.5⇥), the prior automatic speculative-DOALL system with the highest applicability.

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UPDATED LINK: Cliff Liu will present his Pre FPO "Sublinear Time Algorithms for Graph Problems" on Thursday, December 3, 2020 at 2pm via Zoom
by Nicki Mahler 03 Dec '20

03 Dec '20

Please use this updated link: [ https://princeton.zoom.us/j/8983320648?pwd=QWdPL3BuWGt2Y0s0RE9oUS9HYy8yZz09 | https://princeton.zoom.us/j/8983320648?pwd=QWdPL3BuWGt2Y0s0RE9oUS9HYy8yZz09 ] -------------- Cliff Liu will present his Pre FPO "Sublinear Time Algorithms for Graph Problems" on Thursday, December 3, 2020 at 2pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/95875996152 | https://princeton.zoom.us/j/95875996152 ] The members of his committee are as follows: Examiners : Robert Tarjan (advisor), Matt Weinberg, Sepehr Assadi (Rutgers) Readers : Bernard Chazelle, Omri Weinstein (Columbia) Abstract: With the rapid growth of the internet, many optimization problems in recent big-data applications often exceed the capacity of traditional computing platforms. My goal is to lay the theoretical foundation of big-data analysis by designing algorithms on modern computing platforms to solve large-scale optimization problems under various resource constraints, with an emphasis on time efficiency and simplicity. The datasets in most applications are originated from or can be modeled as graphs, and this thesis focuses on solving graph problems. Usually, the graph is too large to store in a single memory, resulting in a very slow random access to the data. To capture this, one important computational model is streaming: we want to solve the problem using very limited space while minimizing the number of passes of scanning the entire data. Another framework to solve large-scale graph problems is to store the graph in a distributed manner and use the computational power of the distributed processors, which is called parallel computation. These two areas continue to be very active partly due to the success of the cloud computing platforms from Google, Microsoft, Amazon, and so on. We obtain faster and simpler algorithms in both streaming and parallel computation models for graph problems such as connectivity, bipartite matching, and maxflow. In particular, we solved three open problems. The first one is that given a graph of n vertices and m edges, does there exist a semi-streaming (using nearly n space) algorithm that computes the maximum weight bipartite matching in o(n) passes? We answer this question positively by giving an algorithm that runs in square root m passes. The algorithm relies on space-efficient versions of the interior point method, Laplacian system solving, and the generalized isolation lemma, all of which are not known before this work. The second problem is to break the square root n depth for computing maxflow in parallel. The square root n depth barrier exists for digraph reachability, matching, and other fundamental problems. Our solution is approximate, but only runs in n^{1/3} depth for unit-capacity graphs. The third problem is to give an o(log n) depth parallel algorithm for connectivity on a PRAM (parallel random access machine). All previous PRAM algorithms use at least log n depth. Our algorithm runs in O(log d + loglog n) depth where d is the diameter, which breaks the log n barrier for small-diameter graphs. Before our work, there is only an MPC (massively parallel computing, a model that is much stronger than PRAM) algorithm and it is very complicated. Our algorithm is simpler and more practical, albeit in a much weaker model. We also design several extremely simple algorithms for connectivity and bipartite matching. For the connectivity problem, we give several elegant algorithms (about five lines of code) in the MPC model, with a state-of-the-art running time of O(log n) for undirected graph. For the bipartite matching problem, we give a very simple streaming algorithm based on auctions that approximates the maximum matching to (1-eps) in O(eps^{-2}) passes and O(n) space, reducing the number of passes and space from previous work, which also has applications on faster MPC algorithms for matching.

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Yatin Manerkar will present his FPO "Progressive Automated Formal Verification of Memory Consistency in Parallel Processors" on Monday, December 7, 2020 at 1pm via Zoom
by Nicki Mahler 30 Nov '20

30 Nov '20

Yatin Manerkar will present his FPO "Progressive Automated Formal Verification of Memory Consistency in Parallel Processors" on Monday, December 7, 2020 at 1pm via Zoom. Zoom link: https://princeton.zoom.us/j/99678767959 The members of his committee are as follows: Examiners: Margaret Martonosi (adviser), Sharad Malik, and Zachary Kincaid; Readers: Margaret Martonosi, Aarti Gupta, and Daniel Lustig (NVIDIA) A copy of his thesis is available upon request. Please email ngotsis(a)cs.princeton if you would like a copy of the thesis. Everyone is invited to attend the talk. Abstract follows below: In recent years, single-threaded hardware performance has stagnated due to transistor-level limitations stemming from the end of Moore’s Law and Dennard scaling. Instead, today’s designs improve performance through heterogeneous parallelism: the use of multiple distinct processing elements on a chip, many of which are specialised to run specific workloads. The processing elements in such architectures often communicate and synchronise with each other via loads and stores to shared memory. Memory consistency models (MCMs) specify the ordering rules for such loads and stores. MCM verification is thus critical to parallel system correctness, but is notoriously hard to conduct and requires examining a vast number of scenarios. Verification using formal methods can provide strong correctness guarantees based on mathematical proofs, and is an excellent fit for MCM verification. This dissertation makes several contributions to automated formal hardware MCM verification, bringing such techniques much closer to being able to handle real-world architectures. Firstly, my RTLCheck work enables the automatic linkage of formal models of design orderings to RTL processor implementations. This linkage helps push the correctness guarantees of design-time formal verification down to taped-out chips. The linkage doubles as a method for verifying microarchitectural model soundness against RTL. Secondly, my RealityCheck work enables scalable automated formal MCM verification of hardware designs by leveraging their structural modularity. It also facilitates the modular specification of design orderings by the various teams designing a processor. Thirdly, my PipeProof work enables automated all-program hardware MCM verification. A processor must respect its MCM for all possible programs, and PipeProof enables designers to prove such results automatically. This dissertation also proposes Progressive Automated Formal Verification, a novel generic verification flow. Progressive verification emphasises the use of automated formal verification at multiple points in system development—starting at early-stage design—and the linkage of the various verification methods to each other. Progressive verification has multiple benefits, including the earlier detection of bugs, reduced verification overhead, and reduced development time. The combination of PipeProof, RealityCheck, and RTLCheck enables the progressive verification of MCM properties in parallel processors, and serves as a reference point for the development of future progressive verification flows.

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Cliff Liu will present his Pre FPO "Sublinear Time Algorithms for Graph Problems" on Thursday, December 3, 2020 at 2pm via Zoom
by Nicki Mahler 26 Nov '20

26 Nov '20

Cliff Liu will present his Pre FPO "Sublinear Time Algorithms for Graph Problems" on Thursday, December 3, 2020 at 2pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/95875996152 | https://princeton.zoom.us/j/95875996152 ] The members of his committee are as follows: Examiners : Robert Tarjan (advisor), Matt Weinberg, Sepehr Assadi (Rutgers) Readers : Bernard Chazelle, Omri Weinstein (Columbia) Abstract: With the rapid growth of the internet, many optimization problems in recent big-data applications often exceed the capacity of traditional computing platforms. My goal is to lay the theoretical foundation of big-data analysis by designing algorithms on modern computing platforms to solve large-scale optimization problems under various resource constraints, with an emphasis on time efficiency and simplicity. The datasets in most applications are originated from or can be modeled as graphs, and this thesis focuses on solving graph problems. Usually, the graph is too large to store in a single memory, resulting in a very slow random access to the data. To capture this, one important computational model is streaming: we want to solve the problem using very limited space while minimizing the number of passes of scanning the entire data. Another framework to solve large-scale graph problems is to store the graph in a distributed manner and use the computational power of the distributed processors, which is called parallel computation. These two areas continue to be very active partly due to the success of the cloud computing platforms from Google, Microsoft, Amazon, and so on. We obtain faster and simpler algorithms in both streaming and parallel computation models for graph problems such as connectivity, bipartite matching, and maxflow. In particular, we solved three open problems. The first one is that given a graph of n vertices and m edges, does there exist a semi-streaming (using nearly n space) algorithm that computes the maximum weight bipartite matching in o(n) passes? We answer this question positively by giving an algorithm that runs in square root m passes. The algorithm relies on space-efficient versions of the interior point method, Laplacian system solving, and the generalized isolation lemma, all of which are not known before this work. The second problem is to break the square root n depth for computing maxflow in parallel. The square root n depth barrier exists for digraph reachability, matching, and other fundamental problems. Our solution is approximate, but only runs in n^{1/3} depth for unit-capacity graphs. The third problem is to give an o(log n) depth parallel algorithm for connectivity on a PRAM (parallel random access machine). All previous PRAM algorithms use at least log n depth. Our algorithm runs in O(log d + loglog n) depth where d is the diameter, which breaks the log n barrier for small-diameter graphs. Before our work, there is only an MPC (massively parallel computing, a model that is much stronger than PRAM) algorithm and it is very complicated. Our algorithm is simpler and more practical, albeit in a much weaker model. We also design several extremely simple algorithms for connectivity and bipartite matching. For the connectivity problem, we give several elegant algorithms (about five lines of code) in the MPC model, with a state-of-the-art running time of O(log n) for undirected graph. For the bipartite matching problem, we give a very simple streaming algorithm based on auctions that approximates the maximum matching to (1-eps) in O(eps^{-2}) passes and O(n) space, reducing the number of passes and space from previous work, which also has applications on faster MPC algorithms for matching.

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Forward Fest - Data Science and Artificial Intelligence, this Friday Nov 20 at 8pm
by Emily C. Lawrence 20 Nov '20

20 Nov '20

[ https://forwardthinking.princeton.edu/festival | Forward Fest - Data Science and Artificial Intelligence ] Date: Friday, November 20, 2020 Time: 8:00-9:15 pm (ET) [ https://forwardthinking.princeton.edu/festival | https://forwardthinking.princeton.edu/festival ] As the pace of data creation and collection continues to accelerate, professors in a variety of disciplines talk about both the power and potential perils of artificial intelligence and what limitations and safeguards we need to take into account moving forward. Join Matthew Salganik, Elad Hazan *06, and Mona Singh as they participate in the Forward Fest series discussion examining Data Science and Artificial Intelligence. Brad Smith '81, President of Microsoft, will serve as moderator. All Forward Fest programming will be live-streamed on [ https://forwardthinking.princeton.edu/festival | this page ] and on the [ https://www.youtube.com/princeton | University’s YouTube channel ] . No registration or login information is required. If you want to engage in Q&A, submit your questions in advance to [ mailto:forwardfest@princeton.edu | forwardfest(a)princeton.edu ] or in real-time in the chat on YouTube. And be sure to include #PrincetonForward in all your social posts to keep the conversation going!

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NSF / WINLAB Workshop on Reconfigurable Radio Environments
by Emily C. Lawrence 13 Nov '20

13 Nov '20

[ https://www.cosmos-lab.org/nsf-winlab-workshop-on-reconfigurable-radio-envi… | NSF / WINLAB Workshop on Reconfigurable Radio Environments ] Date: TODAY - Friday, November 13, 2020 Time: 12:00 – 2:30pm Eastern Smart or reconfigurable radio environments have attracted considerable attention for next-generation wireless networks. In these networks, objects in the wireless environment are enhanced with passive or active reconfigurable antennas and reflectors or coatings of (meta)materials that can be electrically controlled to manipulate the scattering patterns of radio waves. These intelligent surfaces, combined with suitable control and sensing algorithms, offer the potential for increased coverage and data rates along with new sensing capabilities. Is this technology hype or real? This workshop brings together leading experts in the field to discuss the capabilities of intelligent reconfigurable radio environments and their impact on future wireless networks. The workshop is part of an effort linked to the National Center for Wireless Spectrum Research (SII-Center) Panelists Marco di Renzo, CNRS Research Director – CentraleSupelec, Paris-Saclay University Andrea Alu, CUNY Advanced Science Research Center Emil Bjornson, Linköping University Kyle Jamieson, Princeton University Mark Rodwell, UCSB Sergei Tretyakov, Aalto University Moderators Yasaman Ghasempour, Princeton Harish Krishnaswamy, Columbia --- Join Zoom Meeting: [ https://rutgers.zoom.us/j/99267668463?pwd=V3lzdnNBSGJJV1QwOU5Lclp0MXZLUT09 | https://rutgers.zoom.us/j/99267668463?pwd=V3lzdnNBSGJJV1QwOU5Lclp0MXZLUT09 ] Join by SIP: 99267668463(a)zoomcrc.com Meeting ID: 992 6766 8463 Password: 857965 One tap mobile +13017158592,,99267668463# US (Washington D.C) +13126266799,,99267668463# US (Chicago) Join By Phone +1 301 715 8592 US (Washington D.C) +1 312 626 6799 US (Chicago) +1 646 558 8656 US (New York) +1 253 215 8782 US (Tacoma) +1 346 248 7799 US (Houston) +1 669 900 9128 US (San Jose) Meeting ID: 992 6766 8463

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EE and CS Seminar - Carole-Jean Wu, Facebook AI Research/ASU, Thursday Nov 12 at 12:30pm
by Emily C. Lawrence 12 Nov '20

12 Nov '20

COMPUTER SCIENCE AND ELECTRICAL ENGINEERING SEMINAR Speaker: Carole-Jean Wu , Facebook AI Research/ASU Title: [ https://ee.princeton.edu/events/deep-learning-its-not-all-about-recognizing… | Deep Learning: It’s Not All About Recognizing Cats and Dogs ] Day: Thursday, November 12, 2020 (ET) Time: 12:30 pm Zoom Link: Please register using [ https://princeton.zoom.us/meeting/register/tJcuduGtrTguGdTbUci5h2UCDH6zISdm… | this link ] . Hosts: Margaret Martonosi & David Wentzlaff Abstract: In this seminar, we will start this seminar by examining the underinvested deep learning personalization and recommendation systems in the overall research community. The training of state-of-the-art industry-scale personalized and recommendation models consumes the highest number of compute cycles among all deep learning use cases. For AI inference, recommendation and ranking consume even higher compute cycles of 80%. What do state-of-the-art industry-scale neural personalization and recommendation models look like? I will present recent advancement on the development of deep learning recommender systems, the implications on system and architectural design and parallelism opportunities across the machine learning system stack over a variety of platforms [HPCA-2020, ISCA-2020, HPCA-2021a]. I will conclude the talk with future directions on multi-scale system design and optimization to advance the field of AI [HPCA-2019, MICRO-2020, HPCA-2021b]. Bio: Carole-Jean Wu is a Research Scientist at Facebook AI Research. Her research focus lies in the domain of computer system architecture with particular emphasis on energy- and memory-efficient systems. Her recent research has pivoted into designing systems for machine learning execution at-scale, such as for personalized recommender systems and mobile deployment. Carole-Jean chairs the MLPerf Recommendation Benchmark Advisory Board and co-chairs MLPerf Inference. Carole-Jean holds tenure from ASU. She received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell. She is the recipient of the NSF CAREER Award, Facebook AI Infrastructure Mentorship Award, the IEEE Young Engineer of the Year Award, the Science Foundation Arizona Bisgrove Early Career Scholarship, and the Intel PhD Fellowship, among a number of Best Paper awards. She is a senior member of both ACM and IEEE. This seminar is supported by CS and EE Korhammer Lecture Series Funds

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Tuesday 12:30: CITP seminar, Vanessa Teague on 2 problems in election integrity
by Andrew Appel 09 Nov '20

09 Nov '20

CITP seminar, Tuesday November 10 at 12:30 ( [ https://princeton.zoom.us/j/94881769712 | Zoom link ] ) Some Election Integrity Problems are Surprisingly Easy to Solve and Others are Very Very Hard by Vanessa Teague This talk will compare two recent advances in the understanding of election integrity. The Scytl/SwissPost e-voting solution was intended to provide complete verifiability for Swiss government elections. The talk will show failures in both individual verifiability and universal verifiability, based on mistaken implementations of cryptographic components. These failures allow for the construction of “proofs” of an accurate election outcome that pass verification though the votes have been manipulated. This shows that even well-regulated Internet voting systems that seem to provide some evidence of an accurate election outcome are still brittle in their assumptions and incentives, to the extent of making large-scale undetectable fraud relatively easy. Every time internet voting is reconsidered, we learn about a new aspect that makes it harder than we thought. On a more optimistic note, the talk will describe a recent pilot of Risk-Limiting Audits of Instant Runoff Votes (IRV) for the San Francisco DA’s race. Any election might be too close (or any count too error-prone) for confidence to be gained from a small sample, but IRV was once considered too complex and unstable for rigorous audits even when the margin was large. It will be shown that in practice that belief was mistaken: most real IRV elections can be analyzed and reduced to a series of well-defined assertions that can be tested efficiently! Joint work with Michelle Blom, Andrew Conway, Thomas Haines, Sarah Jamie-Lewis, Olivier Pereira, Philip Stark and Peter Stuckey. Bio: Vanessa Teague is the CEO of Thinking Cybersecurity and an associate professor (Adj.) in the Research School of Computer Science at the Australian National University. Her research focuses primarily on cryptographic methods for achieving security and privacy, particularly for issues of public interest such as election integrity and the protection of government data. She was part of the team (with Chris Culnane and Ben Rubinstein) who discovered the easy re-identification of doctors and patients in the Medicare/PBS open dataset released by the Australian Department of Health. She has co-designed numerous protocols for improved election integrity in e-voting systems, and co-discovered serious weaknesses in the cryptography of deployed e-voting systems in New South Wales, Western Australia and Switzerland. She lives and works on Wurundjeri land in Southeastern Australia (near Melbourne).

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Monday, November 9, 2020 at 4:30 pm - Virtual EE Seminar - Kaushik Chowdhury, Northeastern University
by Emily C. Lawrence 06 Nov '20

06 Nov '20

ELECTRICAL ENGINEERING SEMINAR Speaker: Kaushik Chowdhury , Northeastern University Title: Deep Convolutional Neural Networks for Device Identification Day: Monday, November 9, 2020 Time: 4:30 pm Zoom Link: Meeting link below Host: Prof. Kaushik Sengupta Abstract: Network densification is poised to enable the massive throughout jump expected in the era of 5G and beyond. In the first part of the talk, we identify the challenges of verifying identify of a particular emitter in a large pool of similar devices based on unique distortions in the signal, or ‘RF fingerprints’, as it passes through a given transmitter chain. We show how deep convolutional neural networks can uniquely identify a radio in a large signal dataset composed of over a hundred WiFi radios with accuracy close to 99%. For this, we use tools from machine learning, namely, data augmentation, attention networks and deep architectures that have proven to be successful in image processing and modify these methods to work in the RF-domain. In the second part of the talk, we show how intentional injection of distortions and carefully crafted FIR filters applied to the transmitter-side can help in enhanced classification. Finally, we discuss how to detect new devices not previously seen during training using observed statistical patterns. We conclude by showing a glimpse of other applications of RF fingerprinting, like 5G waveform detection in large-scale experimental platforms and identifying a specific UAV in a swarm. Bio: Kaushik Chowdhury is Professor and Faculty Fellow in the ECE department and Associate Director at the Institute for the Wireless IoT at Northeastern University, Boston. He was awarded the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2017, the DARPA Young Faculty Award in 2017, the Office of Naval Research Director of Research Early Career Award in 2016, and the NSF CAREER award in 2015. He has received best paper awards at several conferences that include, Infocom, Globecom, ICC (3x), SenSys, ICNC, and DySpan. He is presently a co-director of the Platforms for Advanced Wireless Research (PAWR) project office and the Colosseum RF emulator. His current research interests span applied machine learning to wireless systems, networked robotics, wireless charging and at-scale experimentation for emerging 5G and beyond networks. Join Zoom Meeting : [ https://princeton.zoom.us/j/99499632211?pwd=Qm4wWFFWSUFyMjJVQ3lseFh1TGNyUT09 | https://princeton.zoom.us/j/99499632211?pwd=Qm4wWFFWSUFyMjJVQ3lseFh1TGNyUT09 ]

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Engage 2020 sessions featuring SEAS dean and faculty
by Emily C. Lawrence 04 Nov '20

04 Nov '20

Engage 2020, Princeton’s first innovation and entrepreneurship conference, runs this week Nov. 4-6. There are over 50 sessions aimed at helping faculty, staff and postdocs, graduate students and undergraduates learn more about building the impact of their research through innovation and entrepreneurship. Here are just a small selection of sessions. A single registration gives you access to all three days of programming: Register for [ https://princetonengage2020.pathable.co/ | Engage 2020 ] . Engage 2020 is free and open to all, including people not affiliated with Princeton. Wednesday, November 4 Educator-Entrepreneur: A Personal Journey 12:00 p.m. to 1:00 p.m. Speaker: Andrea Goldsmith and Naveen Verma, Princeton University Series: Engage 2020 Thursday, November 5 Dean for Research Award for Distinguished Innovation: Honorary Lecture by Professor Robert Prud'homme 12:00 p.m. to 1:00 p.m. Speaker: Robert Prud’homme, Princeton University Series: Engage 2020 The Future of Collaboration and Inclusive Innovation: Microsoft + Princeton 2:00 p.m. to 3:30 p.m. Speaker: Jen Rexford, '91, Princeton University; Brad Smith, ’81 and President of Microsoft Series: Engage 2020 Celebrate Princeton Innovation: Technology Showcase 3:30 p.m. to 6 p.m. Speakers: Pablo Debenedetti, Rodney Priestley, Daniel Powell, co-founder of a startup based on the work of Warren Powell, Craig Arnold, Dalton Conley, Sujit Datta, Phil Efthimion from PPPL, Ning Lin, David MacMillan, Naveen Verma, Xiao Fan on behalf of Nieng Yan Series: Engage 2020 Friday, November 6 Princeton's Pandemic Research and Solutions: Spotlight on Several Princeton-Funded COVID-19 Research Projects 2:00 p.m. to 2:55 p.m. Speakers: Cliff Brangwynne, Kyle Jamieson, Natalie Cox, Princeton University; Nishith Prakash, Harvard University Series: Engage 2020 Overcome the Funding Hurdle: Funding My Clean Energy Startup 3:00 p.m. to 3:55 p.m. Speakers: Angelo Campus, ’16; Andrew Hsieh, ’10, *14; Stephanie Spiers, *14; Elizabeth Washburn Surti, ‘08 Series : Engage 2020 Engage 2020 Faculty and Alumni Startup Showcase 5:00 p.m. to 7:00 p.m. Speakers: Ed Felten and Harry Kalodner, *16, Offchain Labs, Inc.; Marcus Hultmark and Yuyang Fan, *17; Mark Esposito, *17; Princeton University; and other company co-founders Series : Engage 2020 Featured Tracks: Core Facilities Showcase (Wed 9:30 a.m. – 4:30 p.m.): Now in its second year, the Showcase sessions and virtual exhibit booths will highlight research services and equipment offered by academic institutions from around the Garden State. Featuring Craig Arnold, Nan Yao, Jared Toettcher, Nieng Yan and more Data Science in Oncology and Immunology (Thurs 10 a.m. – 4 p.m.): In this in-depth workshop, industry partners and academic researchers will explore areas of mutual interest and identify potential collaboration in the areas of biomedical data science, computational oncology and computational immunology. Featuring Ben Raphael, Barbara Engelhardt, Curtis Callan, Olga Troyanskaya, Mona Singh Entrepreneurship Track (Fri 9 a.m. – 7 p.m.): Webinars, skill-building workshops and Princeton-related startup showcases will offer entrepreneurs and those who work with them an abundance of thought-provoking opportunities. Catherine Zandonella | Communications Manager Office of the Dean for Research | Princeton University 91 Prospect Ave. | Princeton | NJ 08540 [ mailto:czandone@princeton.edu | czandone(a)princeton.edu ] | 609-258-0541 [ https://research.princeton.edu/ | research.princeton.edu ] [ http://www.facebook.com/princetonuniversityresearch | facebook ] [ http://www.twitter.com/princetonideas | @princetonideas ] [ http://discovery.princeton.edu/ | Discovery: Research at Princeton ]

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