- talks - lists.cs.princeton.edu

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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Yuhan Liu will present her General Exam "Understanding and Building Decentralized Sociotechnical Systems" on Wednesday, May 8, 2024 at 8:00 AM in CS 402 and via zoom.
by CS Grad Department 06 May '24

06 May '24

Yuhan Liu will present her General Exam "Understanding and Building Decentralized Sociotechnical Systems" on Wednesday, May 8, 2024 at 8:00 AM in CS 402 and via zoom. Zoom link: https://princeton.zoom.us/j/4652800681 Committee Members: Andrés Monroy-Hernández (advisor), Parastoo Abtahi, Janet Vertesi Abstract: Since the 19th century, the impact of technological advancements on labor has been recognized as a double-edged sword. The rise of scientific management, also known as Taylorism, increased production efficiency but also led to worker exploitation. Today, the evolution of algorithm-mediated labor reflects this dynamic, with the arena shifting from factories to different mobile applications and the source of stress evolving from timers on the assembly lines to AI-powered algorithms. The past decade has witnessed the rise of the gig economy, alongside significant challenges. For example, monopolistic food delivery platforms are the sole authority in deciding restaurant commissions, customer prices, and worker wages, causing imbalances of power. The black-boxed AI algorithms result in significant information asymmetry and the gig economy’s zero-sum nature leads to value misalignments between platforms and other stakeholders. My work aims to explore and develop decentralized solutions, drawing inspiration from the labor movement a hundred years ago and the recent decentralization trends in social media. To understand existing alternatives to mainstream gig platforms, I led a mixed-method study investigating the widespread but underresearched socio-technical phenomena: indie food delivery platforms. We collected data on 495 indie platforms and detailed survey responses from 29 platforms. We analyzed these platforms to understand why they were created, how they operate, what technologies they use, and what challenges they face. Furthermore, to dig deeper into the recent trend of migration to decentralized social media, I led a qualitative study on understanding how the nature of decentralization in systems like Mastodon, is associated with novel uses of the technology. Building on the insights from independent food delivery platforms and decentralized social media, I’m now working on designing a protocol for the decentralized food delivery network: Open Deli Protocol. In my future research, we plan to implement and deploy the decentralized food delivery system with the support of our industrial collaborators. The system will improve the user experience and agency of delivery workers and enable indie food delivery platforms to operate in a way that aligns with their values. Reading List: https://docs.google.com/document/d/1vSxL9e_Z6_UfVuahVfzG0sJoZ_BdKC7mGyAHYwm… 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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DeCenter seminar: Ben Fisch, May 8th at 4:30pm
by Emily C. Lawrence 06 May '24

06 May '24

The DeCenter is delighted to extend an invitation to a seminar featuring Ben Fisch on May 8th at 4:30pm . The seminar will be held in the Computer Science Building room 105 , followed by a light reception. Talk Title: How Cryptographic Proofs are Transforming the Decentralized Web Abstract: Ethereum, along with a growing number of decentralized virtual machines, hosts thousands of applications (dapps) that are updated synchronously and secured by a common consensus protocol. These characteristics have made these platforms particularly suitable for financial use cases, where users move funds in and out of dapps for trading, lending, payments, liquidity provision, and more. Users of Ethereum can interact with many dapps at once, such as receiving a loan and executing a trade in a single transaction (aka flash loan), without needing to trust anyone beyond Ethereum’s consensus for the safety of their funds. This shared infrastructure across dapps, however, has several downsides. Ethereum’s fixed transaction throughput creates a scaling bottleneck. An activity spike in one application can cause congestion, raising transaction prices in all others. Dapps are also forced to use Ethereum’s limited instruction set. Operations (like hash functions) that may be cheaply executed on most modern machines can blow up in complexity post compilation to Ethereum instructions. Lastly, by controlling the ordering of transactions, Ethereum’s operators profit through frontrunning or arbitrage — behavior which dapp creators may either want to prevent entirely or capture value from on their own. Today, cryptographic proof systems are enabling dapps to address these issues by moving their transaction processing off Ethereum while still inheriting Ethereum’s security. These are called “Layer-2” applications. Dedicated dapp servers (called “rollup sequencers”) collect, order, and execute large batches of transactions on their own, periodically posting only the resulting state change and a cryptographic proof of its correctness to Ethereum for verification. While this may limit users’ ability to interact synchronously with multiple dapps (e.g., for a flash loan), further reductions in the latency and cost of cryptographic proofs will re-enable atomic cross-dapp transactions. This new paradigm has also led to new mechanism design challenges, such as how to optimize and share cross-dapp revenue, or how to outsource cryptographic proof generation and appropriately price transactions to cover these costs. This talk will be a general presentation of this evolution and discussion of open challenges. Bio: Ben Fisch is an assistant professor of computer science at Yale University and co-director of the Yale Applied Cryptography Lab. His research centers around cryptographic proofs and decentralized systems, but broadly spans topics within privacy and verifiability. Prior to Yale, he completed his PhD at Stanford University. His thesis work included Proofs of Replication and Verifiable Delay Functions, which were influential in the designs of real-world decentralized systems, including Filecoin, Chia, and Ethereum. More recently, he co-founded Espresso Systems, which is improving the decentralization, security, and interoperability of “Layer-2” applications.

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Xinran Liang will present her General Exam "Reducing Contextual Bias using Synthetic Augmentations from Generative Models" on Wednesday, May 8, 2024 at 3:00 PM in CS 302.
by CS Grad Department 03 May '24

03 May '24

Xinran Liang will present her General Exam "Reducing Contextual Bias using Synthetic Augmentations from Generative Models" on Wednesday, May 8, 2024 at 3:00 PM in CS 302. Committee Members: Olga Russakovsky (advisor), Szymon Rusinkiewicz, Jia Deng Abstract: Many vision datasets are known to contain contextual bias. For example, training images of “skateboard” very usually co-occurs with “person”, while in rare cases skateboards occur exclusively. Models trained on these biased datasets tend to leverage co-occurrence between objects and their contexts to improve recognition accuracy. Consequently these models often fail to generalize to scenarios where such co-occurrence patterns are absent. Previous works addressed this problem by enforcing models to decorrelate learned features of a category from its co-occurring context. In this project, with recent advances in generative models, we explore the possibility of using synthetic samples from generative models to reduce contextual bias. We generate useful variations of training data that do not contain typical co-occurring patterns and use them as augmentations in training downstream classifiers. We evaluate our framework on object classification tasks and show that it improves downstream performance compared to standard classifiers. We hope our work could demonstrate the effectiveness of using generated samples to improve limitations in vision datasets and learn better vision models, potentially overcoming the challenge of manually collecting and annotating curated data at scale. Reading List: https://docs.google.com/document/d/1YWZjPo272HKOp9CLd6v4yK1RzXejGr4mit12lcY… 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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Dillon Lue will present his General Exam "Multimodal Single Cell Genomics Datasets for Identifying Cell Types and their Function" on Wednesday, May 8, 2024 at 1:30 PM in Icahn 280.
by CS Grad Department 03 May '24

03 May '24

Dillon Lue will present his General Exam "Multimodal Single Cell Genomics Datasets for Identifying Cell Types and their Function" on Wednesday, May 8, 2024 at 1:30 PM in Icahn 280. Committee Members: Yuri Pritykin (advisor), Ben Raphael, Olga Troyanskaya Abstract: Recent advances in single-cell genomics enable the simultaneous measurement of RNA and another modality in the same cell, known as multimodal scRNA-seq. In this talk, I demonstrate how multimodal assays can enhance our understanding of T cell biology and inform scRNA-seq analysis. First, I will talk about the analysis of a multimodal scRNA-seq assay with a novel combination of modalities: barcoded lineage tracing, CRISPR perturbations, and RNA. This combination of modalities enables us to interrogate how CRISPR perturbations influence the transcriptional states in T cell clones. Next, I propose a new framework to benchmark scRNA-seq analysis by utilizing the second modality in multimodal datasets as ground truth. Due to the complex nature of scRNA-seq data, a multi-step procedure has emerged to map cells onto a low dimensional transcriptional manifold. This procedure has many user-defined choices that can influence biological results. Unlike existing benchmarks, this new framework introduces a ground truth for the context of continuous biological processes. Reading List: https://docs.google.com/document/d/12K-XVW5NfBQZ5MBXesUvJlqHBIoA9blOhI0CYHJ… 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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Karhan Kayan will present his General Exam "Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation" on Tuesday, May 7, 2024 at 3:30 PM in CS 301.
by CS Grad Department 02 May '24

02 May '24

Karhan Kayan will present his General Exam " Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation " on Tuesday, May 7, 2024 at 3:30 PM in CS 301 . Committee Members: Jia Deng (advisor), Szymon Rusinkiewicz , Adam Finkelstein Abstract: We introduce Infinigen Indoors, a Blender-based procedural generator of photorealistic indoor scenes. It builds upon the existing Infinigen system, which focuses on natural scenes, but expands its coverage to indoor scenes by introducing a diverse library of procedural indoor assets, including furniture, architecture elements, appliances, and other day-to-day objects. It also introduces a constraint-based arrangement system, which consists of a domain-specific language for expressing diverse constraints on scene composition, and a solver that generates scene compositions that maximally satisfy the constraints. We provide an export tool that allows the generated 3D objects and scenes to be directly used for training embodied agents in real-time simulators such as Omniverse and Unreal. Infinigen Indoors will be open-sourced under the BSD license. Reading List: https://docs.google.com/document/d/1MVaGLJx5293qyQZJN1PQLa1QJBHnfBV6TzzTBYJ… 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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Alexander Strzalkowski will present his FPO "Inferring the Biological Time of Single Cells using Supervised Dimensionality Reduction and Trees" on Monday, May 6, 2024 at 11:00 AM in CS 301.
by gradinfo＠cs.princeton.edu 02 May '24

02 May '24

Alexander Strzalkowski will present his FPO "Inferring the Biological Time of Single Cells using Supervised Dimensionality Reduction and Trees" on Monday, May 6, 2024 at 11:00 AM in CS 301. Location: CS 301 The members of Alexander's committee are as follows: Examiners: Ben Raphael (Adviser), Yuri Pritykin, Michelle Chan Readers: Ellen Zhong, Li Ding (Washington University in St. Louis) Everyone is invited to attend his talk. Abstract follows below: Single-cell omics measurements have exploded in growth over the past decade. This explosion has allowed researchers to probe human health and biology with unprecedented resolution. Currently, all these types of measurements are destructive, thus they only provide static snapshots of important dynamic biological processes such as development, cancer progression, and cell cycle. As cells differentiate/progress biologically asynchronously in most tissues, a major computational task often called trajectory inference is to infer the latent biological time also known as pseudotime of every cell. This inverse problem in general is quite challenging and is further complicated by the fact that single-cell omics measurements like scRNA-seq and scATAC-seq are highly sparse and highdimensional. Much of my work has shown that simple linear supervised dimensionality reduction techniques that rely on cell type information can outperform complex non-linear dimensionality reduction techniques when used in conjunction with state-of-the-art trajectory inference methods in a large benchmark. Moreover, we investigate the difficulties of benchmarking trajectory inference methods in the absence of ground truth showcasing that the implicit goal of many methods is not to identify intermediate/transient cell types but rather order cell types. In addition, we introduce a novel supervised linear dimensionality reduction technique called BCA that when applied to simulated and real datasets is better able to uncover intermediate cell types. Lastly, we have been interested in modeling the relationship between cell lineages of inferred phylogenies from single-cell lineage tracing data and scRNA-seq trajectories (the partial ordering of cells induced by pseudotimes). We have found that by using a novel irreversible continuous state model of pseudotime on a rooted tree that we are better able to model unobserved ancestral pseudotimes in simulated and real phylogenies.

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Lingjie Mei will present his General Exam "Procedurally Generating 3D Indoor Scenes for Computer Vision and Robotics" on Monday, May 6, 2024 at 3:00 PM in Friend 109 and via zoom.
by CS Grad Department 01 May '24

01 May '24

Lingjie Mei will present his General Exam "Procedurally Generating 3D Indoor Scenes for Computer Vision and Robotics" on Monday, May 6, 2024 at 3:00 PM in Friend 109 and via zoom. Zoom link: https://princeton.zoom.us/j/8415068766 Committee Members: Jia Deng (advisor), Felix Heide, Ellen Zhong Abstract: Synthetic data rendered by conventional computer graphics has seen increasing adoption in computer vision and AI research, especially for 3D vision and embodied AI. Synthetic data can be rendered in unlimited quantities and can automatically provide high-quality 3D ground truth, enabling large-scale training of computer vision models and embodied agents. We propose a synthetic data generator that can produce infinite photorealistic 3D indoor scenes. Our system is entirely procedural: every asset, including furniture, architecture elements, appliances, and other day-to-day objects, is generated from scratch via mathematical rules and randomized parameters. We also introduce a constraint-based arrangement system, which consists of a domain-specific language for expressing diverse constraints and preferences on indoor scene composition, and a solver that generates floorplans and scene layouts that maximally satisfy the constraints. The scenes are photorealistic, generated with pixel-perfect annotations, and can be exported to real-time simulators and run at interactive frame rates. Overall, anyone can directly use and customize our system to generate infinite high-quality indoor scene data for a variety of computer vision tasks, including object detection, semantic segmentation, optical flow, and 3D reconstruction. Robot scientists can also use the data in simulation for manipulation and grasping. We expect this to be a useful tool for researchers beyond. Reading List: https://docs.google.com/document/d/1mmf5W0sKZkLgWVKXpCA7TkSSWUfBoeuKFXWHw8k… 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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