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ORFE Department Colloquium: TODAY at 4:30 PM: Dimitris Bertsimas, MIT
by Emily C. Lawrence 23 Sep '25

23 Sep '25

Today’s ORFE Department Colloquium Date & Time: Tuesday, September 23, 2025 at 4:30 PM Location: 104 - Computer Science Speaker: [ https://orfe.princeton.edu/events/2025/dimitris-bertsimas-mit | Dimitris Bertsimas, MIT ] Title: A vision of Customized and Adaptive Online Education using Gen-AI Abstract: I present a vision for online education that adapts the context, the duration, the language and the depth of a lecture to the learner. This is a vision of personalized education that, in my opinion, will revolutionize education and will change universities in the years to come. I outline the current efforts in these directions within Open Learning at MIT. * Please join us for a meet and greet with some light refreshments from 4:00pm – 4:30pm in the Sherrerd Hall Atrium before the talk.

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Barak Nehoran will present his FPO "Separations of Fundamental Quantum Properties and Their Role in Shaping Cryptography Beyond Classical Limits" on October 9, 2025 at 12:30pm in CS 402
by Gradinfo 22 Sep '25

22 Sep '25

Barak Nehoran will present his FPO "Separations of Fundamental Quantum Properties and Their Role in Shaping Cryptography Beyond Classical Limits" on October 9, 2025 at 12:30pm in CS 402 Examiners: Ran Raz (adviser), Mark Zhandry (adviser), and Alex Lombardi Readers: Gillat Kol and Sarang Gopalakrishnan All are welcome to attend. Please see abstract below. Quantum computers will break much of the cryptography that secures the internet, but they will also enable entirely new cryptographic frameworks with security based on the principles of quantum mechanics. Quantum mechanics prevents certain information processing tasks: unlike classical data, quantum information cannot generally be recognized, cannot be written down on paper (formally called “telegraphed”), and cannot be copied (formally, “cloned”). Some quantum information classicizes, or resembles its classical counterpart, by allowing one or more of these tasks, while other information remains fully quantum and resists all such classicizing tasks. We argue that differences between such classicizing tasks, especially cases when one task is efficient but another is not, are essential for enabling quantum cryptography with no classical analog. The earliest quantum cryptography explored was quantum money, which is quantum information that can be recognized efficiently but not cloned. Wiesner first defined it in 1968, with a public-key version introduced by Bennett, Brassard, Breidbart, and Wiesner in 1982. Many proposals for public-key quantum money have appeared over the past 43 years, yet proving the security of any of them has remained challenging. We use new tools from the mathematics of group representations and group actions to give the first secure construction of public-key quantum money that does not rely on obfuscation, and the first to also offer collision resistance. We also introduce quantum fire, which describes quantum information that can be cloned efficiently but not telegraphed. Like fire, it can be spread indefinitely from a single source, but only as long as it is kept alive. We provide a secure construction of quantum fire in the black-box setting and give a framework for a potential construction in the plain model. We further show that classicizing tasks can also characterize complexity classes involving efficient quantum verification, offering a new perspective on the fundamental question of the relative power of classical and quantum proofs.

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ORFE Colloquia: Tuesday, Sept 23, at 4:30PM: Dimitris Bertsimas, MIT
by Emily C. Lawrence 18 Sep '25

18 Sep '25

Next Week’s ORFE Department Colloquium Date & Time: Tuesday, September 23, 2025 at 4:30 PM Location: 104 - Computer Science Speaker: [ https://orfe.princeton.edu/events/2025/dimitris-bertsimas-mit | Dimitris Bertsimas, MIT ] Title: A vision of Customized and Adaptive Online Education using Gen-Ai Abstract: I present a vision for online education that adapts the context, the duration, the language and the depth of a lecture to the learner. This is a vision of personalized education that, in my opinion, will revolutionize education and will change universities in the years to come. I outline the current efforts in these directions within Open Learning at MIT.

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DeCenter Book Talk: Beyond Banks: Technology, Regulation, and the Future of Money
by Emily C. Lawrence 16 Sep '25

16 Sep '25

On behalf of The Princeton Center for the Decentralization of Power Through Blockchain Technology ( [ https://decenter.princeton.edu/ | DeCente ] r) and the Griswold Center for Economic Policy Studies ( [ https://gceps.princeton.edu/ | GCEPS ] ) we are delighted to invite you to a book talk featuring [ https://www.lawschool.cornell.edu/faculty-research/faculty-directory/daniel… | Dan Awrey ] , Beth and Marc Goldberg Professor of Law at Cornell Law School and author of Beyond Banks: Technology, Regulation, and the Future of Money. The event will take place on Wednesday, October 8th, at 12:15 PM in the Computer Science Building, Room 104 . The discussion will be moderated by [ https://www.linkedin.com/in/carolyn-wilkins-b9417338/?originalSubdomain=ca | Carolyn Wilkins ] , Senior Advisor at the Griswold Center for Economic Policy Studies, Princeton University, and former Senior Deputy Governor of the Bank of Canada. Open to the Public and the Princeton University Community; Livestream available on [ https://mediacentrallive.princeton.edu/ | Media Central ] . For any questions, please email [ mailto:decenter@princeton.edu | decenter(a)princeton.edu ] . We look forward to your participation! Madelyn LaBuda Administrative and Events Coordinator DeCenter Engineering Quad, C215 Princeton University E: ml2512 [ mailto:email@princeton.edu | @princeton.edu ] M: 570-599-5338

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Princeton Robotics Seminar: Friday Sept 12
by Emily C. Lawrence 11 Sep '25

11 Sep '25

Hi everyone, We're excited to kick-off the Princeton Robotics Seminar for this term with [ https://brianichter.com/ | Dr. Brian Ichter ] from Physical Intelligence. Speaker: Dr. Brian Ichter Date: 09/12/2025 Time: 11:00 AM EST Location: Bowen 222 Event Page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] We are looking forward to seeing you there! Please stay tuned for more details. Title: Scaling Robotic Learning Abstract : This talk will cover lessons learned scaling up robotic learning, beyond just increasing amounts of data and size of models. I'll first describe model architectures and types of data that enable generalizable and capable robotic models. I'll then describe organizational choices and how they enable scaling up learning. Finally, I'll end with a discussion of research directions I believe enable further scaling of robotic learning. Best Regards, Robotics Seminar Committee

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ECE Korhammer Seminar Series: Jason Cong, TODAY @ 4:30PM, B205, EQuad
by Emily C. Lawrence 09 Sep '25

09 Sep '25

ECE KORHAMMER SEMINAR SERIES Speaker: Jason Cong, UCLA Computer Science Department Title: Scalable Quantum Compilation Day: Wednesday, September 10, 2025 Time: 4:30 PM Location: B205 Engineering Quadrangle Hosts: Jeffrey Thompson and David Wentzlaff Abstract: Quantum compilation includes two major steps: quantum layout synthesis (QLS), which determines the space and time of each gate execution, and quantum circuit synthesis (QCS), which aims at obtaining most efficient circuits for execution. In this talk, I shall first present that our recent work on scalable QLS using multi-level optimization, graph edge coloring, and graph matching for both superconducting and neural atom based quantum systems. Then, I shall present our recent progress on QCS, including both initial state preparation with the shortest sequence of two-qubit gates, and T-gate minimization using the don’t-care conditions. Bio: Jason Cong is the Volgenau Chair for Engineering Excellence Professor at the UCLA Computer Science Department (and a former department chair), with joint appointment from the Electrical and Computer Engineering Department. He is the director of Center for Domain-Specific Computing (CDSC) and the director of VLSI Architecture, Synthesis, and Technology (VAST) Laboratory. Dr. Cong’s research interests include novel architectures and compilation for customizable computing, synthesis of VLSI circuits and systems, and quantum computing. He has over 500 publications in these areas, including 20 best paper awards, and 5 papers in the FPGA and Reconfigurable Computing Hall of Fame. He and his former students co-founded AutoESL, which developed the most widely used high-level synthesis tool for FPGAs (renamed to Vivado HLS and Vitis HLS after Xilinx’s acquisition). He is member of the National Academy of Engineering, the American Academy of Arts and Sciences, and a Fellow of ACM, IEEE, and the National Academy of Inventors. He is recipient of the SIA University Research Award, the EDAA Achievement Award, the IEEE Robert N. Noyce Medal, the Phil Kaufman Award, and most recently, the ACM Chuck Thacker Breakthrough Award “for fundamental contributions to the design and automation of field-programmable systems and customizable computing.” This seminar is supported with funds from the Korhammer Lecture Series.

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Abhishek Singh will present his FPO "Physics-inspired Computation for Next-Generation Wireless Systems: from Theory to Practice" on Tuesday, August 26, 2025 in Friend 108.
by Gradinfo 22 Aug '25

22 Aug '25

Abhishek Singh will present his FPO "Physics-inspired Computation for Next-Generation Wireless Systems: from Theory to Practice" on Tuesday, August 26, 2025 in Friend 108. The members of his committee are as follows: Examiners: Kyle Jamieson (adviser), Wyatt Lloyd, and Yasaman Ghasempour Readers: Margaret Martonosi and Peter McMahon (Cornell University) Title: Physics-inspired Computation for Next-Generation Wireless Systems: from Theory to Practice Wireless communication technologies have become an integral part of daily life. Whether it is video conferencing for work meetings or high-quality video streaming for leisure, there is an ever-increasing demand for high-speed cellular internet. Although significant strides have been made in this direction with the onset of 5G, despite several decades of research, several key tasks like Multiple-Input-Multiple-Output (MIMO) signal detection and Precoding are still performed sub-optimally by real-world deployments. The optimal methods for these functionalities are known to be “NP-Hard”, and therefore practical deployments resort to using polynomial-complexity approximations and suffer a loss in performance. Advancements in MIMO detection and precoding can significantly boost the physical layer throughput, which would lead to a drastic improvement in the cellular data-rates. The last decade has seen an emergence of “Physics-inspired” computation as an alternative to conventional computing to solve NP-Hard problems. Physics-inspired computation involves using the dynamics of a physical system to compute the optimal solution to an NP-Hard optimization problem. Typically, such systems are designed to solve the Ising optimization problem, and other problems need to be converted to the Ising problem. These methodologies span a wide range of technologies, ranging from quantum/optical systems to purely algorithmic methods. This thesis tackles MIMO detection and precoding using physics-inspired computation. In Part-I , we develop the theoretical framework required to utilize physics-inspired computation for MIMO detection and precoding functionalities in state-of-the-art wireless communication systems. We show that our techniques can significantly outperform the existing “conventional-computing” based methods used by real-world systems. In Part-II, we bridge the gap between theory and practice; we design and implement Mavis: a GPU-based, highly scalable, physics-inspired MIMO detector and precoder, which can meet the processing throughput and latency requirements of modern 5G systems. We demonstrate that Mavis can meet the processing requirements of 5G systems with large bandwidths (100 MHz) and provide significant throughput improvements.

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Javed Aman will present his FPO "Towards Scalable Inference of Protein-Protein Interfaces: Reasoning with structural, functional, and sequence data" on Monday, August 25, 2025 at 3:00 PM in Jadwin 111 and Zoom.
by Gradinfo 22 Aug '25

22 Aug '25

Javed Aman will present his FPO "Towards Scalable Inference of Protein-Protein Interfaces: Reasoning with structural, functional, and sequence data" on Monday, August 25, 2025 at 3:00 PM in Jadwin 111 and Zoom. The members of Javed’s committee are as follows: Examiners: Mona Singh (Adviser), Olga Russakovsky, Stanislav Shvartsman, Martin Wühr Readers: Olga Troyanskaya, Yuri Pritykin Zoom link: https://princeton.zoom.us/j/92015552762?pwd=gWpKajumfdBGLHIEgMQhbVaUinmrkM.1 Meeting ID: 920 1555 2762 Passcode: 590232 Everyone is invited to attend his talk. Abstract follows below: Protein–protein interactions (PPIs) are central to virtually all biological processes. Understanding how these interactions are mediated, including identifying which residues form the interface, is critical for studying disease mechanisms and designing targeted therapeutics. However, identifying and characterizing PPI interface residues at the proteome scale remains a major challenge due to limitations in experimental coverage and throughput. This dissertation presents three computational approaches for inferring or annotating protein–protein interfaces across different biological and data scales. The first, protein-protein interacDome (PPI-DOME), uses structural data from X-ray crystallography and cryoelectron microscopy to build domain–domain interaction profiles. These profiles identify which positions within domains mediate interactions and enable efficient screening of millions of candidate interactions in seconds, providing a scalable alternative to structure-based computational modeling methods. The second project, Kinase activity and inference dashboard (KINAID), focuses on identifying transient interactions in phosphorylation cascades. It combines orthology mapping, experimentally-derived position weight matrices capturing the specificities of human kinases, and experimentally observed phosphosites to annotate kinase–substrate relationships across over 2,000 kinases from ten model organisms. KINAID enables rapid analysis of high-throughput phosphoproteomic data. The third project, Comparable embeddings from sequence networks (CESN), addresses the need for high-quality, sequence-level representations, or embeddings, suitable for downstream machine learning models. CESN leverages amino acid embeddings generated by protein language models and learns representations that preserve amino acid-level detail across the entire sequence, overcoming the limitations of standard pooling strategies such as mean or CLS token pooling. The CESN approach provides a foundation for machine learning models that predict protein properties or interactions between pairs of proteins. Together, the methods introduced in this thesis contribute to scalable, interface-centric reasoning by leveraging structural information, functional information, and representation learning. They o er complementary strategies for advancing our understanding of how protein interfaces govern molecular interaction, function, and regulation across diverse biological systems.

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Mona Wang will present her FPO "Protecting the Network Traffic of One Billion People: Transport Security in the Global Mobile Ecosystem" on August 18, 2025 at 1pm in 306 Sherrerd Hall and Zoom.
by Gradinfo 18 Aug '25

18 Aug '25

Mona Wang will present her FPO "Protecting the Network Traffic of One Billion People: Transport Security in the Global Mobile Ecosystem" on August 18, 2025 at 1pm in 306 Sherrerd Hall and Zoom. Time: August 18 2025, 1PM Eastern time. Location: 306 Sherrerd Hall Zoom link: https://princeton.zoom.us/j/94818544623 Title: Protecting the Network Traffic of One Billion People: Transport Security in the Global Mobile Ecosystem Examiners: Jonathan Mayer (adviser), Prateek Mittal (adviser), and Arvind Narayanan. Readers: Andres Monroy-Hernandez and Roya Ensafi All are welcome to attend Abstract: TLS is the de-facto standard for encrypting network communications. Today, upwards of 80% of pages loaded on Firefox, Chrome, and Safari are encrypted with TLS. This might be the story for web, but what about mobile? Existing measurements of mobile network en- cryption fall short: they often focus on the Google Play ecosystem, which necessarily excludes mobile users in China, who comprise a massive portion of the global Internet. This thesis demonstrates that HTTPS is, in fact, not everywhere, and that a massive por- tion of mobile network communications remains poorly encrypted and accessible to systems of mass surveillance. These issues are particularly concentrated in mobile applications devel- oped in China, which have been overlooked by the global security community despite their massive popularity and influence. Three studies provide different perspectives that demonstrate both the (1) massive pop- ularity of proprietary network encryption protocols in top mobile applications, and (2) the insecurity of such homegrown protocols. First, I present our reverse-engineering of WeChat’ s proprietary transport encryption protocol and subsequent privacy analysis of the WeChat Mini Program ecosystem. Then, I review the network encryption used by popular Chinese keyboards to encrypt user keystrokes. Finally, I present a large-scale study of encryption pro- tocols used by thousands of popular mobile applications. We discovered severe vulnerabilities enabling network attackers to decrypt sensitive data in the vast majority of the proprietary encryption protocols we analyzed. Through the vulnera- bilities fixed en masse as a result of this work, this research has directly improved the network security of over one billion people.

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Javed Aman will present his FPO "Towards Scalable Inference of Protein-Protein Interfaces: Reasoning with structural, functional, and sequence data" on Monday, August 25, 2025 at 3:00 PM in Jadwin 111.
by CS Grad Department 15 Aug '25

15 Aug '25

Javed Aman will present his FPO " Towards Scalable Inference of Protein-Protein Interfaces: Reasoning with structural, functional, and sequence data " on Monday, August 25, 2025 at 3:00 PM in Jadwin 111. The members of Javed ’s committee are as follows: Examiners: Mona Singh (Adviser), Ellen Zhong , Stanislav Shvartsman , Martin Wühr Readers: Olga Troyanskaya , Yuri Pritykin Everyone is invited to attend his talk. Abstract follows below: Protein–protein interactions (PPIs) are central to virtually all biological processes. Understanding how these interactions are mediated, including identifying which residues form the interface, is critical for studying disease mechanisms and designing targeted therapeutics. However, identifying and characterizing PPI interface residues at the proteome scale remains a major challenge due to limitations in experimental coverage and throughput. This dissertation presents three computational approaches for inferring or annotating protein–protein interfaces across different biological and data scales. The first, protein-protein interacDome (PPI-DOME), uses structural data from X-ray crystallography and cryoelectron microscopy to build domain–domain interaction profiles. These profiles identify which positions within domains mediate interactions and enable efficient screening of millions of candidate interactions in seconds, providing a scalable alternative to structure-based computational modeling methods. The second project, Kinase activity and inference dashboard (KINAID), focuses on identifying transient interactions in phosphorylation cascades. It combines orthology mapping, experimentally-derived position weight matrices capturing the specificities of human kinases, and experimentally observed phosphosites to annotate kinase–substrate relationships across over 2,000 kinases from ten model organisms. KINAID enables rapid analysis of high-throughput phosphoproteomic data. The third project, Comparable embeddings from sequence networks (CESN), addresses the need for high-quality, sequence-level representations, or embeddings, suitable for downstream machine learning models. CESN leverages amino acid embeddings generated by protein language models and learns representations that preserve amino acid-level detail across the entire sequence, overcoming the limitations of standard pooling strategies such as mean or CLS token pooling. The CESN approach provides a foundation for machine learning models that predict protein properties or interactions between pairs of proteins. Together, the methods introduced in this thesis contribute to scalable, interface-centric reasoning by leveraging structural information, functional information, and representation learning. They o er complementary strategies for advancing our understanding of how protein interfaces govern molecular interaction, function, and regulation across diverse biological systems.

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