- talks - lists.cs.princeton.edu

Tamjeed Azad will present his General Exam "Exploring and modeling intercellular interactions and microenvironments." on Friday, May 3, 2024 at 3:00 PM in Icahn 280.
by CS Grad Department 30 Apr '24

30 Apr '24

Tamjeed Azad will present his General Exam "Exploring and modeling intercellular interactions and microenvironments." on Friday, May 3, 2024 at 3:00 PM in Icahn 280. Committee Members: Yuri Pritykin (advisor), Ben Raphael, Mona Singh Abstract: https://docs.google.com/document/d/1B-6aCqVqQEK5OErdrKlFO-jXmnf69iZZiyd7Zqu… Reading List: https://docs.google.com/document/d/17gQJZYG3smNJZ74ayQhUwP38kYdPTXiFLMlcyTP… 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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Isabel Armour-Garb will present her General Exam "Leveraging Protein Language Models for Protein Characterization" on Friday, May 3, 2024 at 10:00 AM in Icahn 280 .
by CS Grad Department 30 Apr '24

30 Apr '24

Isabel Armour-Garb will present her General Exam "Leveraging Protein Language Models for Protein Characterization" on Friday, May 3, 2024 at 10:00 AM in Icahn 280 Committee Members: Mona Singh (advisor), Olga Troyanskaya, Yuri Pritykin Abstract: Genomes are sequenced much faster than experimental efforts to functionally characterize the proteins they contain. Computational methods based on sequence similarity are the first line of attack for annotating sequences with protein function. However, no functional annotations exist for over 1.1 billion protein sequences, and thousands of human proteins lack sufficient characterization. To understand protein function, it is important to identify functional sites (e.g. amino acids involved in crucial interactions such as catalysis or post-translational modifications), which are highly conserved across homologs. However, for proteins with few known homologs, current methods cannot predict evolutionary conservation, because state-of-the-art methods rely on the alignment of homologs. Current methods align these homologs in a multiple sequence alignment to find evolutionarily conserved positions. Protein language models (PLMs) are advanced deep learning natural language processing models specifically designed to understand and generate protein-related texts and sequences. These models represent each amino acid within a protein sequence as a high-dimensional vector, effectively capturing complex information about the amino acids and their sequence context, and providing a way to encode and process the inherent features of the protein as an embedding for computational analysis. First, we introduce vcMSA, a new method for more accurate multiple sequence alignment using PLMs. Then, leveraging PLMs, we introduce a new method for accurately predicting functional sites given a single protein sequence, which allows us to better characterize proteins of unknown function regardless of the presence or absence of known homologs. Reading List: https://docs.google.com/document/d/1thOoMwpz7H1JrgQKYH9Va-Xf5ZyEQnJV-11VeNB… 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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Qianfan Zhang will present his General Exam "Communication Separations for Truthful Auctions: Breaking the Two-Player Barrier" on May 7, 2024 at 1pm in Friend 005.
by Nicki Mahler 30 Apr '24

30 Apr '24

Qianfan Zhang will present his General Exam " Communication Separations for Truthful Auctions: Breaking the Two-Player Barrier" on May 7, 2024 at 1pm in Friend 005. Committee Members: Matt Weinberg (adviser), Mark Braverman, and Gillat Kol Abstract follows below. Combinatorial auctions are a paradigmatic problem at the intersection of Economics and Computation. However, it is still quite poorly understood how well one can approximate the optimal social welfare via truthful mechanisms. In most settings, it remains unknown whether there should be any separation between truthful mechanisms and non-truthful protocols at all. In this work, we study the communication complexity of truthful combinatorial auctions, and in particular the case where valuations are either subadditive or single-minded, which we denote with SubAdd∪SingleM. We show that for three bidders with valuations in SubAdd∪SingleM, any deterministic truthful mechanism which achieves at least a 0.366-approximation requires exp(m) communication. In contrast, a natural extension of [Feige09] yields a non-truthful poly(m)-communication protocol that achieves a 1/2-approximation, demonstrating a gap between the power of truthful mechanisms and non-truthful protocols for this problem. Our approach follows the taxation complexity framework laid out in [Dobzinski16], but applies this framework in a setting not encompassed by the techniques used in past work. In particular, the only successful prior application of this framework uses a reduction to simultaneous protocols which only applies for two bidders [AKSW20], whereas our three-player lower bounds are stronger than what can possibly arise from a two-player construction (since a trivial truthful auction guarantees a 1/2-approximation for two players). Abstract & Reading List: [ https://docs.google.com/document/d/1YfmRXSLmvQbUx2u1Q9IEeK5d9Y6ymQVTLaEEO5o… | https://docs.google.com/document/d/1YfmRXSLmvQbUx2u1Q9IEeK5d9Y6ymQVTLaEEO5o… ]

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Allison Chen will present her General Exam "Understanding Visual Processing In Vision Language Models" on Thursday, May 2, 2024 at 1:30 PM in CS 302 and via zoom.
by CS Grad Department 29 Apr '24

29 Apr '24

Allison Chen will present her General Exam "Understanding Visual Processing In Vision Language Models" on Thursday, May 2, 2024 at 1:30 PM in CS 302 and via zoom. Zoom link: https://princeton.zoom.us/j/99275578452 Committee Members: Olga Russakovsky (advisor), Tom Griffiths, Szymon Rusinkiewicz Abstract: Applications in modern platforms are complex and thus hard to reason about their security. For instance, modern web applications have largely become cloud-based due to the flexibility and cost efficiency brought by the cloud. However, writing secure web applications is challenging as developers need to devise sufficient security measures to ensure intended policies are enforced while applications are cross-components and rapidly changing during development. To address this problem, we propose Faasten, which is a secure cloud architecture and implementation that enforces end-user data policies and supports general cloud applications with negligible performance overheads. Faasten uses decentralized information flow control (DIFC) and leverages Function-as-a-Service (FaaS) programming models to provide a safe, general, and self-encapsulating cloud interface for application developers. Additionally, another challenge that remains unsolved is timing channels. Faasten is unable to close timing channels in cloud platforms because such channels are a result of resource sharing which is out of control of Faasten. Timing channels are critical not only to cloud platforms but also in critical infrastructure. Existing defenses and mitigations are ad-hoc and often come with large performance penalties. With Leak Avoidant Resource Provisioners (LARPS), we argue that a large class of timing channels can be closed by carefully controlling information flow through resource provisioning decisions. In addition, we require a minimal set of hardware primitives to ensure LARP operations themselves do not impose additional timing channels. Reading List: https://docs.google.com/document/d/1-sXQFoc8srt-hFcFyCmfxvFQlbJPCJA1vCuWKNs… 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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ECE/COS Seminar: Luca Carloni, Thursday, May 2 at 12:30pm
by Donna Ghilino 29 Apr '24

29 Apr '24

ECE/COS Seminar Speaker: Luca Carloni, Columbia University Date: Thursday, May 2,2024 Time: 12:30pm Location: CS 105 Host: Margaret Martonosi & Sharad Malik Event page: https://www.cs.princeton.edu/events/26654 Title: Collaborative System-on-Chip Design with Open-Source Hardware Platforms Abstract: Open-source hardware holds a promise of sustaining the progress of the semiconductor industry in the age of heterogeneous computing. It can enable design reuse, foster design collaboration, and support workforce development. ESP is an open-source research platform for system-on-chip (SoC) design that combines a modular architecture and an agile design methodology. The ESP architecture simplifies the design and prototyping of heterogeneous chips with multiple RISC-V processor cores and dozens of loosely-coupled accelerators, all interconnected with a scalable network-on-chip. The ESP methodology promotes system-level design while accommodating different specification languages and design flows. ESP's capabilities have allowed a small team of mostly graduate students to realize two SoCs of growing complexity, each in the span of just a few months. Conceived as a heterogeneous system integration platform and developed through years of teaching at Columbia University, ESP is intrinsically suited to advance collaborative engineering across the open-source hardware community. Bio: Luca Carloni is professor and chair of Computer Science at Columbia University in the City of New York. He holds a Laurea Degree Summa cum Laude in Electronics Engineering from the University of Bologna, Italy, and the MS and PhD degrees in Electrical Engineering and Computer Sciences from the University of California, Berkeley. His research interests include heterogeneous computing, system-on-chip platforms, embedded systems, and open-source hardware. He co-authored over one hundred and eighty refereed papers. Luca received the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, and the ONR Young Investigator Award. He is an IEEE Fellow.

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Jingyuan Chen will present the General Exam "Lumos: Provenance-guided Automatic Online Debugging" on 5/3/24 at 3pm in CS 105 and Zoom.
by Gradinfo 29 Apr '24

29 Apr '24

Jingyuan Chen will present the General Exam "Lumos: Provenance-guided Automatic Online Debugging" on 5/3/24 at 3pm in CS 105 and Zoom. The zoom link is: [ https://princeton.zoom.us/j/95830334391 | https://princeton.zoom.us/j/95830334391 ] The members of the committee are as follows: Amit Levy (adviser), Ravi Netravali, Mae Milano Abstract: [ https://docs.google.com/document/d/1_yVb8aS7wQNMpnLz0PLAFmemiwnIGNZ1W9tPe4A… | https://docs.google.com/document/d/1_yVb8aS7wQNMpnLz0PLAFmemiwnIGNZ1W9tPe4A… ]

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Aatmik Gupta will present his MSE talk “Selecting LLM Training Data using Subjective Qualities” on Monday, April 29, 2024 at 9:30am in Friend 125.
by Gradinfo 26 Apr '24

26 Apr '24

Aatmik Gupta will present his MSE talk “Selecting LLM Training Data using Subjective Qualities” on Monday, April 29, 2024 at 9:30am in Friend 125. The members of his committee are as follows: Danqi Chen (Adviser) and Karthik Narasimhan (reader) All ae welcome to attend. Please see abstract below. I will discuss a novel method for selecting pre-training data that focuses on capturing abstract text qualities that are intuitively recognized by humans. The focus is on four qualities: writing style, required expertise, facts & trivia, and educational value. We find that LLMs are better at comparing these qualities between texts rather than directly assessing a text's quality. We select data based on quality ratings to train language models from scratch, and discover the importance of balancing quality and diversity in data selection. Using quality ratings as selection criteria, our models outperform models trained on randomly selected data/data selected using other methods on perplexity and in-context learning task performance. Further analysis delves into the ratings' characteristics, biases, and broader implications, including how they suppress/promote texts associated with certain languages, regions, topics, or social roles. The talk concludes with a study of the offensiveness of selected texts using toxicity models.

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Zheng Shi will present her FPO "Task-Specific Computational Cameras" on Friday, May 3, 2024 at 3:00 PM in CS 302.
by CS Grad Department 26 Apr '24

26 Apr '24

Zheng Shi will present her FPO "Task-Specific Computational Cameras" on Friday, May 3, 2024 at 3:00 PM in CS 302. Location: CS 302 The members of Zheng’s committee are as follows: Examiners: Felix Heide (Adviser), Adam Finkelstein, Olga Russakovsky Readers: Ellen Zhong, Tian-Ming Fu A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend her talk. Abstract follows below: Machine vision, while fundamentally relying on images as inputs, has traditionally treated image acquisition and image processing as two separate tasks. However, traditional image acquisition systems are tuned for aesthetics, photos that please the human eye, and not computational tasks that requires beyond human vision. My research focuses on developing task-specific computational imaging systems to enable the capture of information that extends beyond the capabilities of standard RGB cameras, thereby enhancing the effectiveness of downstream machine vision applications. This thesis begins with combining multiple imaging modalities to facilitate training on unpaired real-world datasets, addressing the scarcity of supervised training data. We introduce ZeroScatter, a single-image descattering method capable of removing adverse weather effects from RGB captures. By integrating model-based, temporal, and multi-view cues, as well as information contained in gated imager captures, we offer indirect supervision for training on real-world adverse weather captures lacking ground truth. This approach significantly enhances generalizability on unseen data, surpassing methods trained exclusively on synthetic adverse weather data. Despite its great applicability, relying solely on conventional RGB image inputs limits available information, and requires the model to fill in gaps by generating plausible inferences based on learnt prior, such as when car window wiper obscure objects from the dash cameras. To bypass these constraints, we shift towards computational cameras, and design specialized flat optics to boost the capabilities of cameras for a range of applications. We first propose a computational monocular camera that optically cloaks unwanted near-camera obstructions. We learn a custom diffractive optical element (DOE) that performs depth-dependent optical encoding, scattering nearby occlusions while allowing paraxial wavefronts emanating from background objects to be focused. This allows us to computationally reconstruct unobstructed images without requiring captures different camera views or hallucinations. Lastly, we introduce a split-aperture 2-in-1 computational camera that combines application-specific optical modulation with conventional imaging into one system. This approach simplifies complex inverse problems faced by computational cameras, enhances reconstruction quality, and offers a real-time viewfinder experience; paving the way for the adoption of computational camera technology in consumer devices.

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Yihan Wang will present his General Exam "Towards Robust and Scalable Dense Matching" on Wednesday, May 1, 2024 at 10:05 AM in CS 302 and via zoom.
by CS Grad Department 26 Apr '24

26 Apr '24

Yihan Wang will present his General Exam "Towards Robust and Scalable Dense Matching" on Wednesday, May 1, 2024 at 10:05 AM in CS 302 and via zoom. Zoom link: https://princeton.zoom.us/j/5278355359 Committee Members: Jia Deng (advisor), Olga Russakovsky, Szymon Rusinkiewicz Abstract: https://docs.google.com/document/d/1Y-0ToNeQ8dxBeBUvi0O9l4A0VoGzLd8T1DNyQnX… Reading List: https://docs.google.com/document/d/1Y-0ToNeQ8dxBeBUvi0O9l4A0VoGzLd8T1DNyQnX… 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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Feng Xia will present her MSE Thesis talk "Exploring Early Exiting Strategies for Deep Neural Networks" on Monday, April 29, 2024 at 3pm in PSH 311.
by Grad Info 26 Apr '24

26 Apr '24

Feng Xia will present her MSE Thesis talk "Exploring Early Exiting Strategies for Deep Neural Networks" on Monday, April 29, 2024 at 3pm in PSH 311. Advisor: Tom Griffiths; Reader: Matt Weinberg All are invited to attend. Please see abstract below. Abstract: Abstract: Deep neural networks (DNNs) are renowned for their accuracy across a spectrum of machine learning tasks but often suffer from prolonged inference times due to their depth. To address this, early exiting strategies have been proposed, allowing predictions to be made at intermediate layers, thus reducing inference time. This paper explores several methodologies to enhance early exit mechanisms within a single multi-exit DNN architecture on an image classification task. First, we improve the task transferability of traditional confidence measures by converting entropy to probabilities. Second, we observe that the total uncertainty used by conventional confidence measures does not consistently reflect true model uncertainty, especially for ambiguous images. To address this, we utilize a Dirichlet framework for neural networks, which assumes the network outputs a Dirichlet distribution of class probabilities rather than a point estimate. We introduce a novel training mechanism that includes both original training data and artificially created ambiguous data by blending training images. This approach allows more ambiguous data to exit early compared to the previous approach. Lastly, we propose two alternative perspectives to determine if an intermediate prediction is sufficient for an early exit. While our experiments focus on a specific neural network architecture and dataset, our methodologies are designed to be independent of architectures and tasks, rendering potential for wider applicability in deep neural networks. CS Grad Calendar Link: https://calendar.google.com/calendar/event?action=TEMPLATE <https://calendar.google.com/calendar/event?action=TEMPLATE&tmeid=NXJna3B2c2…> &tmeid=NXJna3B2c2xsMGU4ZGlyaTZxdHY4ZmZzYzYgYWNnMDc5YmxzbzRtczNza2tmZThwa2lyb2dAZw&tmsrc=acg079blso4ms3skkfe8pkirog%40group.calendar.google.com

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