- talks - lists.cs.princeton.edu

Eric Xue will present his General Exam "Settling the competition complexity of additive buyers over independent items" on May 15th, 2024 at 1:30pm in Friend 005.
by Gradinfo 10 May '24

10 May '24

Eric Xue will present his General Exam "Settling the competition complexity of additive buyers over independent items" on May 15th, 2024 at 1:30pm in Friend 005. Committee members: Mark Braverman ( adviser), Matt Weinberg, and Huacheng Yu Abstract: Settling the competition complexity of additive buyers over independent items Committee: Mark Braverman, Matt Weinberg, Huacheng Yu Abstract: Auction design studies how to sell items to bidders in a way that incentives truthful participation. The goal of revenue maximization—as its name suggests—is to extract as much revenue as possible while doing so. In single-dimensional settings, e.g., selling a single item to multiple bidders, the seminal work of Myerson completely characterizes the revenue optimal auction, showing that it is structurally quite simple. In stark contrast, the revenue optimal auction for selling multiple items remains elusive, and for the settings that the literature understands, it has been shown that the revenue optimal auction can be extremely complex and deeply unintuitive. To circumvent this barrier, the literature has adopted two main approaches. The first is to approximate the revenue of the optimal auction via a simple auction, such as selling each item separately or posting a price on each item. This line of work has been fruitful, but there are times in practice when approximate revenue guarantees are not enough or it is costly to know the prior—the distribution from which bidders come—so that an auction which achieves these approximations can be designed. The resource augmentation approach addresses these concerns: this approach seeks to sell items in a prior-independent way but guarantee as much revenue as the optimal auction for the original set of bidders by recruiting sufficiently many additional bidders so that economic competition itself drives up revenue. For a given auction environment, the minimum number of bidders needed to do so is termed its competition complexity. We show that the competition complexity of selling m independent items to n additive bidders when m < n is precisely \Theta(\sqrt{nm}). More specifically, prior work demonstrated an O(\sqrt{nm}) upper bound on the competition complexity for this setting. We prove a matching \Omega(\sqrt{nm}) lower bound by explicitly constructing a Bayesian incentive compatible auction for selling m items to n additive bidders with values drawn iid from the Equal Revenue distribution truncated at \sqrt{nm} that obtains at least as much revenue as optimally selling each item separately to n + \Omega(\sqrt{nm}) bidders. Abstract & Reading List: [ https://docs.google.com/document/d/1VQ9_0ffiQGxtun3CaG5tdDbH_A15IX_XLx-JVXA… | https://docs.google.com/document/d/1VQ9_0ffiQGxtun3CaG5tdDbH_A15IX_XLx-JVXA… ]

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Fengchen Gong will present her General Exam "Constrained Network Telemetry Imputation" on Wednesday, 5/15/24 at 2pm in CS 302.
by Gradinfo 10 May '24

10 May '24

Fengchen Gong will present her General Exam "Zooming Into Network Monitoring: Knowledge-Augmented Transformer for Imputing Fine-Grained Telemetry" on Wednesday, 5/15/24 at 2pm in CS 302 Zoom link: https://princeton.zoom.us/j/98048355515 The members of the committee are as follows: Maria Apostolaki (adviser), Aarti Gupta, Amit Levy Link to Abstract: [ https://docs.google.com/document/d/1pYSfiFvnMXJ3_EJqEBbiC8PETvjnXHnoKrg-sXM… | https://docs.google.com/document/d/1pYSfiFvnMXJ3_EJqEBbiC8PETvjnXHnoKrg-sXM… ] Link to Reading List: https://docs.google.com/document/d/1pYSfiFvnMXJ3_EJqEBbiC8PETvjnXHnoKrg-sXM…

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Zhongtian He will present the General Exam "On the Network Coding Conjecture" on 5/13/24 at 1:30pm in CS 401.
by Nicki Mahler 09 May '24

09 May '24

Zhongtian He will present the General Exam "On the Network Coding Conjecture" on 5/13/24 at 1:30pm in CS 401. Committee Members: Mark Braverman(adviser). Huacheng Yu, and Ran Raz Abstract and Reading list can be found here: [ https://docs.google.com/document/d/11OJHqPsG2Su3-Kv-cASw1h7kPu34W2RxRXkPhh8… | https://docs.google.com/document/d/11OJHqPsG2Su3-Kv-cASw1h7kPu34W2RxRXkPhh8… ] While it's recognized that the utilization of network coding can greatly enhance the throughput of directed networks, an enduring open problem remains as to whether coding provides any advantage over the multicommodity flow (MCF) rate in undirected networks. It was conjectured by Li and Li in 2004 that the answer is ‘no’. We will present our recent progress on the conjecture.

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Christopher Branner-Augmon will present his General Exam "Multidispatch Strict Serializability" on May 13, 2024 at 10am in CS 302.
by Gradinfo 09 May '24

09 May '24

Christopher Branner-Augmon will present his General Exam "Multidispatch Strict Serializability" on May 13, 2024 at 10am in CS 302. The members of his committee are as follows: Amit Levy (adviser), Wyatt Lloyd (adviser), and Mae Milano All are welcome to attend. Link to Abstract & Reading List: [ https://docs.google.com/document/d/17tl_RVhAsbMn5ybUWr26wDSpEvQwHNtwBhbRpze… | https://docs.google.com/document/d/17tl_RVhAsbMn5ybUWr26wDSpEvQwHNtwBhbRpze… ] [ https://docs.google.com/document/d/17Am6ooY9jXDuhJ_es8aiggFagC2aSMjN9P28fEp… | https://docs.google.com/document/d/17Am6ooY9jXDuhJ_es8aiggFagC2aSMjN9P28fEp… ]

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Bhargav Reddy Godala will present his FPO "Criticality-Aware Front-end" on Tuesday, 5/14/2024 at 11am in CS 402.
by Gradinfo 09 May '24

09 May '24

Bhargav Reddy Godala will present his FPO "Criticality-Aware Front-end" on Tuesday, 5/14/2024 at 11am in CS 402. The members of his committee are as follows: Examiners: David August (adviser), Margaret Martonosi, and David Wentzlaff Readers: Gilles A. Pokam (Intel), Svilen Kanev (Google), and David August Please see abstract below. All are welcome to attend. Code footprints continue to grow faster than instruction caches, putting additional pressure on existing front-end structures. Even with aggressive front-ends with fetch-directed instruction prefetching (FDIP), modern processors experience signifcant front-end stalls. Due to the end of Moore’s Law, increasing cache sizes raises critical path latency, with modest returns for scaling instruction cache sizes. This dissertation aims to address front-end bottlenecks by making two key observations. In FDIP-enabled processors, cache misses have unequal costs, and a small fraction of critical instruction cache lines contribute to most of front-end stalls. EMISSARY, the pioneering cost-aware replacement policy tailored for the L1 Instruction Cache (L1I), defes conventional wisdom by presenting a groundbreaking approach. Unlike traditional replacements, EMISSARY demonstrates performance enhancements even amidst increased instruction cache misses. However, EMISSARY proves to be less efective when applied to datacenter workloads characterized by large code footprints. This is due to datacenter workloads having more critical lines greater than the capacity of L1I. This dissertation frst presents improved EMISSARY-L2, the frst criticality-aware cache replacement family of policies specifcally designed for datacenter workloads. Observing that modern architectures entirely tolerate many instruction cache misses, EMISSARY-L2 resists evicting those cache lines whose misses cause costly decode starvations from L2. In the context of a modern FDIP-enabled processor, EMISSARY-L2 delivers an impressive 3.24% geomean speedup (up to 23.7%) and a geomean energy savings of 2.1% (up to 17.7%) when evaluated on datacenter workloads. This speedup is 21.6% of the speedup obtained by an unrealizable L2 cache with a zero-cycle miss latency for all capacity and confict instruction misses. This dissertation then proposes Priority Directed Instruction Prefetching (PDIP), a novel cost-ware instruction prefetching technique that complements FDIP by issuing iii prefetches for targets along the resteer path where FDIP stalls occur. PDIP identifes these targets and associates them with a trigger for future prefetch. When paired with EMISSARY-L2, PDIP achieves a geomean IPC speedup of 3.7% across a set of datacenter workloads using a budget of only 43.5KB. PDIP achieves 62% of the ideal prefetching performance.

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Kaiqu Liang will present his General Exam "Towards human-centered AI safety" on Monday, May 13, 2024 at 1:00 PM in CS 105.
by CS Grad Department 08 May '24

08 May '24

Kaiqu Liang will present his General Exam "Towards human-centered AI safety" on Monday, May 13, 2024 at 1:00 PM in CS 105. Committee Members: Jaime Fernández Fisac (advisor), Tom Griffiths, Benjamin Eysenbach Abstract: In the domain of human-AI interactions, ensuring the safety of AI systems is crucial, particularly as they are deployed in high-stakes scenarios. This extends beyond the conventional focus on aligning AI objectives with human values to include a deep understanding and respect for human needs as hard constraints within the AI's decision-making process. Our research aims to develop safety-critical human-AI systems to prevent violations of critical human needs proactively. To achieve this, my PhD research actively pursues three critical dimensions: developing safe representations, enhancing human-AI alignment, and advancing safety-aware cognition. In my early Ph.D. research, I took the first step in safety-aware cognition, which quantifies safety risks and uncertainty in the domain of the robot task execution with a Large language models (LLMs) planner. We introduced introspective planning, a novel method enabling robots to proactively identify and seek clarification on uncertainties, effectively minimizing risk without fine-tuning. Through rigorous testing, we demonstrated that introspective planning outperforms existing LLM-based methods in both success rate and safety, showing significant promise for enhancing human-AI interactions in safety-critical settings. This approach not only addresses the direct issue of ensuring compliance and safety in robot task execution but also contributes to the broader objective of developing AI systems that prioritize human needs and safety as foundational principles. Reading List: https://docs.google.com/document/d/14BHiLF8wsrXU2YwErddQ6RD2LKNNIIQeajkHkkg… 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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NextG Seminar: Alberto Valdes-Garcia, Tuesday May 14 at 12:30pm
by Emily C. Lawrence 07 May '24

07 May '24

Speaker: Alberto Valdes-Garcia, IBM T. J. Watson Research Center Title: 3D millimeter-wave imaging and sensing with Si-based phased arrays, edge computing, and AI Day: May 14, 2024 Time: 12:30pm Location: B205, EQuad Hosts: Yasaman Ghasempour and Kaushik Sengupta Event Page: [ https://ece.princeton.edu/events/3d-millimeter-wave-imaging-and-sensing-si-… | https://ece.princeton.edu/events/3d-millimeter-wave-imaging-and-sensing-si-… ] Title : 3D millimeter-wave imaging and sensing with Si-based phased arrays, edge computing, and AI Abstract: The use of millimeter-wave frequencies for 5G networks has been a primary contributor for transitioning Si-based phased array technology from R&D to real-world deployments. While the commercial use of millimeter-wave sensing so far has been dominated by low-cost, compact MIMO radars for automotive and industrial applications, the on-going wide deployment and advancement of Si-based phased arrays opens a new horizon of opportunities for sensing and event recognition. This talk will first cover the fundamentals and KPIs of 3D radar systems using phased arrays including associated key circuit design and packaging design techniques. Examples of such 3D radar systems at 28-GHz, 60-GHz and 94-GHz will be provided. Next, the presentation will describe how the full potential of such systems can be realized through synergistic co-design with algorithms and edge computing assets. Key examples of emerging applications based on these vertically integrated antennas-to-software/AI systems will be provided including multi-spectral imaging, 5G mmWave joint sensing and communications, and AI-based recognition of human gestures and concealed objects. Bio: Alberto Valdes-Garcia is currently a Principal Research Scientist and Manager of the RF Circuits and Systems Group at the IBM T. J. Watson Research Center. In his current role, he leads a multi-disciplinary team that investigates and develops technologies that bridge the gap between antennas and edge-compute-based AI, enabling new millimeter-wave systems and applications for imaging and communications. Dr. Valdes-Garcia received the Ph.D. degree in Electrical Engineering from Texas A&M University in 2006. He holds >140 issued US patents and has authored >140 peer-reviewed publications. Recent awards include the 2017 Lewis Winner Award for Outstanding Paper, presented by the IEEE International Solid-State Circuits Conference, and the 2017 IEEE Journal of Solid-State Circuits Best Paper Award. In 2013, he was selected by the National Academy of Engineering for its Frontiers of Engineering Symposium. He currently serves in the Inaugural Editorial Board of the IEEE Journal of Microwaves and was the Chair of the IEEE MTT-S Microwave and Millimeter-Wave Integrated Circuits Committee in 2020-2021. Dr. Valdes-Garcia is a Fellow of IEEE, was inducted into the IBM Academy of Technology in 2015, and was recognized as an IBM Master Inventor in 2016, 2019, and 2022.

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PPH Lunch seminar: Nir Eyal, Friday 5/10 at 12 pm @ 252 Nassau St
by Emily C. Lawrence 07 May '24

07 May '24

Please join the Princeton Precision Health Initiative for the next talk in our lunch time seminar series this Friday, May 10, at 12:00 pm, in our new space at 252 Nassau Street. Nir Eyal, the founding director of the Rutgers Center for Population-level Bioethics in the Rutgers University School of Health will present a talk titled, “The ethics of human challenge trials for emerging disease outbreaks”. Lunch will be provided. Please note that getting to the seminar space currently requires that you climb a set of stairs. If an accommodation is needed, please contact PPH in advance at: [ mailto:princetonPPH@princeton.edu | princetonPPH(a)princeton.edu ] The PPH Team ************************************ Princeton Precision Health [ http://pph.princeton.edu/ | pph.princeton.edu ] [ mailto:princetonpph@princeton.edu | princetonpph(a)princeton.edu ]

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Dmitry Paramonov will present his FPO "Handling Data Too Large To Handle: On Multi-pass Streaming and Interactive Coding" on May 7th, 2024 at 11am in Friend 006.
by Gradinfo 07 May '24

07 May '24

Dmitry Paramonov will present his FPO "Handling Data Too Large To Handle: On Multi-pass Streaming and Interactive Coding" on May 7th, 2024 at 11am in Friend 006. His committee is as follows: Examiners: Gillat Kol (adviser), Ran Raz, Mark Braverman Readers: Huacheng Yu and Matt Weinberg Title: Handling Data Too Large To Handle: On Multi-pass Streaming and Interactive Coding Abstract: Over the last decades, the world has become increasingly more information-centric and massive amounts of data, potentially distributed between many different sources, are being processed all the time. In this thesis, I consider two mechanisms for coping with big data and the distributed nature of timely tasks. In Part I, I showcase my work on the streaming setting, where the input to the algorithm is given as a stream of elements. The algorithm’s goal is to compute a value that depends on the stream while only utilizing memory that is much smaller than the entire stream. My work in this field focuses on proving that various fundamental graph problems essentially require the streaming algorithm to store the entire graph, even if it is allowed to make several passes through the given stream of edges. In Part II, I consider error-correcting codes for distributed, interactive settings. Classical error- correcting codes assume that a sender who has all the information wishes to send it to a receiver over a noisy channel. However, in many modern, big data applications, the information is distributed amongst many parties that communicate back-and-forth to compute a value that depends on all their inputs. My work examines the noise resilience of various such settings. For some models, we can design error-correcting protocols that allow the encoding of every noiseless protocol by a noise-resilient protocol with low overhead, whereas for other models, it can be shown that this task is impossible. While these two topics appear greatly unrelated, and almost orthogonal to one another, the tools used to prove results in both turn out to be remarkably similar, with many standard problems and information theory lemmas being a critical part of both.

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Chenyue Cai will present her General Exam "Geometry and Directional Aware Deep Detail Transfer from a Single 3D Model." on Thursday, May 9, 2024 at 3:30 PM in CS 301 and via zoom.
by CS Grad Department 06 May '24

06 May '24

Chenyue Cai will present her General Exam " Geometry and Directional Aware Deep Detail Transfer from a Single 3D Model. " on Thursday, May 9, 2024 at 3:30 PM in CS 301 and via zoom. Zoom link: https://princeton.zoom.us/j/95829239477 Committee Members: Szymon Rusinkiewicz (advisor), Felix Heide , Adam Finklestein Abstract: As the demand for 3D content creation is on the rise, it is still notoriously time-consuming for artists to create high quality, detailed 3D models. One significant challenge lies in the intricate detailing of 3D shapes. Traditional synthesis methods generate details procedurally, patch by patch, and then blend them with the underlying geometry or texture. More recently, 3D texture generation methods using pre-trained image generation have shown promise, but they often require a large training set and struggle to produce high-quality details in the generated assets due to view inconsistency, occlusion and distortion. In this talk, I will introduce my work in transferring details from one single high quality model to arbitrary shapes. I will talk about the algorithm that aims to tackle the two main challenges in texture synthesis on 3D shape: first is the need for the transfer to respond to local geometry, second is to make the texture anisotropic and directional aligned. The algorithm first uses a geometric feature extraction module to develop a probabilistic matching of source and target surface. Then, it generates paired directional fields which will later guide the synthesis process. Lastly, the algorithm directly synthesizes texture on surface by mapping geometric features to the learned texture space through a deep network guided by the directional field. I will also discuss the design choices and challenges inherent in this approach, as well as its potential applications and future developments. Reading List: https://docs.google.com/document/d/1HyS0tGw6ZmqgJrrpb6VJ5oKkZZXnTBrLI5ENFlg… 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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