- talks - lists.cs.princeton.edu

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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Molly Pan will present her FPO "Verifiable Traffic Control with Compact Data Structures in the Data Plane" on Friday, Aug 8, 2025 at 10am in Friend 125 and Zoom.
by Gradinfo 30 Jul '25

30 Jul '25

Molly Pan will present her FPO "Verifiable Traffic Control with Compact Data Structures in the Data Plane" on Friday, Aug 8, 2025 at 10am in Friend 125 and Zoom. Advisers: Jennifer Rexford and Andrew Appel Examiners: Jennifer Rexford (Adviser), David Walker, and Maria Apostolaki Readers: Hyojoon Kim (University of Virginia) and Andrew Appel Zoom: [ https://princeton.zoom.us/j/7979634003 | https://princeton.zoom.us/j/7979634003 ] All are welcome to attend. Abstract: As network traffic grows in volume and diversity, operators increasingly require real-time, in-network control over packets. Tasks such as rate-limiting large flows, identifying heavy downloaders, and dropping unsolicited packets demand stateful processing at line rate—capabilities absent in traditional fixed-function switches. The advent of programmable data planes has made such control feasible, allowing custom stateful logic to run directly in the network. However, programming traffic control remains challenging. Data planes impose strict memory constraints, making conventional data structures impractical. Instead, operators rely on approximate data structures like Bloom filters and hash tables, trading accuracy for efficiency. Selecting and configuring these structures requires expertise, as poor choices can lead to excessive errors or wasted resources. To address this, we developed Network Approximate Programming (NAP), a high-level language centered on a versatile approximate dictionary abstraction. This abstraction captures a broad range of compact data structures while allowing programmers to specify the types of errors an application can tolerate. The NAP compiler automatically selects and configures the appropriate structure to optimize hardware utilization and compiles to P4, the native programming language for data planes---significantly simplifying development. Ensuring correctness of approximate data structures in P4 poses additional challenges. While research has advanced new streaming algorithms, little attention has been paid to adapting them systematically to data-plane constraints and refreshing them at runtime. Moreover, these practical concerns may introduce subtle bugs. To address these concerns, we developed general synthesis and verification frameworks. We deployed an approximate sliding-window Bloom filter on Intel Tofino and verified its correctness, demonstrating how to build P4 data structures with correctness guarantees. Finally, P4 itself presents foundational challenges: its low-level design, ambiguous specification, and lack of formal semantics make stateful programming error-prone. To provide a rigorous foundation, we developed a formal semantics for P4 based on its two-phase evaluation model. Our mechanized formalization precisely defines language constructs, including stateful externs on Intel Tofino. By adhering to P4’s intended behavior, our work uncovered previously undocumented ambiguities and contributed improvements to the specification. Together, these efforts form a cohesive framework for enabling robust network control in programmable data planes.

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Kunal Mittal will present his FPO "Parallel Repetition of Multiplayer Games: New Bounds and Applications" on August 1, 2025 at 12pm is CS 302 and Zoom.
by Gradinfo 29 Jul '25

29 Jul '25

Kunal Mittal will present his FPO "Parallel Repetition of Multiplayer Games: New Bounds and Applications" on August 1, 2025 at 12pm is CS 302 and Zoom. The committee is as follows: Examiners: Ran Raz (adviser), Gillat Kol,Mark Braverman Readers: Pravesh Kothari and Zeev Dvir Zoom Link: [ https://princeton.zoom.us/j/98962124632?pwd=9Or6rUipHulgmrqsgRfqvIn24haJzh.1 | https://princeton.zoom.us/j/98962124632?pwd=9Or6rUipHulgmrqsgRfqvIn24haJzh.1 ] Meeting ID: 989 6212 4632 Passcode: 750823 All are welcome to attend. Abstract: This thesis studies the parallel repetition of multiplayer games. These games were first (implicitly) introduced in quantum mechanics, to study quantum entanglement with regard to local hidden-variable theories, in the context of the Einstein-Podolsky-Rosen (EPR) paradox. Later, multiplayer games were formally defined and extensively studied in complexity theory, in the framework of interactive proof systems and probabilistically checkable proofs (PCPs). Parallel repetition is a natural hardness-amplification technique used to decrease the value of multiplayer games while maintaining their single-round structure. Despite its apparent simplicity, analyzing the effects of parallel repetition on the value of a game remains a challenging problem, particularly for games involving three or more players. This work makes two main contributions: establishing stronger bounds on the value of parallel repetition for several classes of multiplayer games, and developing new connections to computational complexity and extremal combinatorics. In our first contribution, we substantially improve the best-known bounds on the value of parallel repetition for several classes of multiplayer games. In particular, we prove an inverse-polynomial bound for all 3-player games with 1-bit questions, as well as an inverse polynomial bound for a very large class of k-player games called playerwise-connected games. Furthermore, we prove exponential bounds for certain special 3-player games like the anticorrelation game. Prior to our work, nothing better than weak inverse-Ackermann bounds were known, for any of these games. Our second contribution shows that a sufficiently strong parallel repetition theorem for a special case of multiplayer games implies super-linear lower bounds for multi-tape Turing machines with advice, which is a long-standing open problem in complexity theory. Additionally, we demonstrate equivalences between several high-dimensional problems in extremal combinatorics and parallel repetition of multiplayer games over large answer alphabets.

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Zhijun Zhang will present his FPO "Efficient and Robust Communication for Massive Data: Sublinear Algorithms and Coding with Interaction" on Monday, 7/21/2025 at 10:00am in CS 302
by Gradinfo 15 Jul '25

15 Jul '25

Zhijun Zhang will present his FPO "Efficient and Robust Communication for Massive Data: Sublinear Algorithms and Coding with Interaction" on Monday, 7/21/2025 at 10:00am in CS 302. The members of his committee are as follows: Examiners: Gillat Kol (Adviser), Ran Raz, Mark Braverman Readers: Huacheng Yu, Pravesh Kothari All are welcome to attend. Abstract: The world has been witnessing a massive surge in data accumulation, which, combined with the distributed nature of the data, necessitates efficient and robust methods for information transmission. The study of communication addresses diverse aspects of such transmission between different parties, and has become a powerful tool across a wide range of seemingly unrelated research fields. In this dissertation, we first examine sublinear models, where algorithms for handling massive data are designed to minimize the usage of valuable computational resources. We study fundamental graph problems, such as MST, MIS, and Maximal Matching, for which celebrated multi-pass algorithms are known in streaming and distributed sketching settings. Using novel information theoretic techniques, we prove almost matching lower bounds for any number of passes. Additionally, we explore how interaction can improve the robustness of communication. While the optimal noise resilience of one-way binary error correcting codes has long been known to be 1/4, we show how to achieve resilience beyond 1/4 by allowing a single short round of noisy communication from the receiver back to the sender. In the setting where the receiver’s feedback is noiseless, we characterize the resilience as a function of the number of feedback rounds.

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