August 2023 - talks - lists.cs.princeton.edu

Princeton Robotics Seminar: Vikas Sindhwani, Friday June 9 at 11am
by Emily C. Lawrence 12 Sep '23

12 Sep '23

Speaker: Vikas Sindhwani, Senior Staff Research Scientist, Google Deepmind Date: Friday, June 9, 2023 Time: 11am ET Location: Virtual, Zoom link: [ https://princeton.zoom.us/my/robotics | https://princeton.zoom.us/my/robotics ] Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Large Language Models with Eyes, Arms and Legs Abstract: To become useful in human-centric environments, robots must demonstrate language comprehension, semantic understanding and logical reasoning capabilities working in concert with low-level physical skills. With the advent of modern "foundation models" trained on massive datasets, the algorithmic path to developing general-purpose “robot brains” is (arguably) becoming clearer, though many challenges remain. In the first part of this talk, I will attempt to give a flavor of how state-of-the-art multimodal foundation models are built, and how they can be bridged with low-level control. In the second part of the talk, I will summarize a few surprising lessons on control synthesis observed while solving a collection of Robotics benchmarks at Google. I will end with some emerging open problems and opportunities at the intersection of dynamics, control and foundation models. Bio: Vikas Sindhwani is Research Scientist at Google Deepmind in New York where he leads a research group focused on solving a range of planning, perception, learning, and control problems arising in Robotics. His interests are broadly in core mathematical foundations of statistical machine learning, and in end-to-end design aspects of building large-scale and robust AI systems. He received the best paper award at Uncertainty in Artificial Intelligence (UAI-2013), the IBM Pat Goldberg Memorial Award in 2014, and was finalist for Outstanding Planning Paper Award at ICRA-2022. He serves on the editorial board of Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Pattern Analysis and Machine Intelligence; he has been area chair and senior program committee member for NeurIPS, International Conference on Learning Representations (ICLR) and Knowledge Discovery and Data Mining (KDD). He previously headed the Machine Learning group at IBM Research, NY. He has a PhD in Computer Science from the University of Chicago and a B.Tech in Engineering Physics from Indian Institute of Technology (IIT) Mumbai. His publications are available at: http://vikas.sindhwani.org/

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CS seminar speaker Sriram Sankararaman: Tuesday, September 5 at 4:30pm
by Emily C. Lawrence 29 Aug '23

29 Aug '23

Speaker: Sriram Sankararaman, from UCLA Date: Tuesday, September 5, 2023 Time: 4:30pm ET Location: CS 105 Host: Ben Raphael Event page: [ https://www.cs.princeton.edu/events/26490 | https://www.cs.princeton.edu/events/26490 ] Title: The Role of Archaic Admixture in Human Evolution Abstract: Over the past decade, the ability to sequence genomes from both present-day and archaic humans (including our closest evolutionary relatives, the Neanderthals) has transformed our understanding of human history. Analyzing these genome sequences paints a picture of human history in which present-day humans migrated out of Africa but exchanged genes with multiple archaic human populations. I will describe statistical methods that identify segments of DNA inherited from archaic humans that are surviving in our genomes today and how these maps of introgressed archaic DNA are providing insights into human migration and biology. Despite this progress, our understanding of the contribution of archaic introgression to populations in Africa remains limited, in part due to the challenges in obtaining ancient DNA in Africa. Leveraging recently developed approaches that enable inferences about archaic populations without access to their genome sequences, we show that west African populations today inherit substantial genetic ancestry from an as-yet-unidentified archaic ghost population that diverged prior to the split of modern humans and Neanderthals. Finally, we combine maps of introgressed Neanderthal DNA with phenotypic datasets collected in hundreds of thousands of individuals to assess the contribution of introgressed Neanderthal DNA to complex traits. I will discuss the implications of these results for our understanding of human evolution as well as the statistical challenges that need to be solved in this endeavor. Bio: Sriram Sankararaman is a professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA. His research interests lie at the interface of computer science, statistics and biology. His lab develops machine learning algorithms to analyze genomic data and biomedical data with the broad goal of understanding the interplay between evolution, genomes and traits. He received a B.Tech. in Computer Science from the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a postdoctoral fellow in Harvard Medical School before joining UCLA. He is a recipient of a NSF Career Award, NIH Pathway to Independence Award, and fellowships from Microsoft Research, the Sloan Foundation, the Okawa Foundation and the Simons Institute.

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Princeton Symposium on Biological & Artificial Intelligence - October 19-20, 2023 - Free Registration
by Elizabeth Wang 17 Aug '23

17 Aug '23

Please see attached and below regarding an upcoming symposium.

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Mingzhe Wang will present his FPO "Deep Learning for Automated Theorem Proving" on Monday, August 21, 2023 at 3:00 PM in CS 301 and Zoom.
by CS Grad Department 07 Aug '23

07 Aug '23

Mingzhe Wang will present his FPO "Deep Learning for Automated Theorem Proving" on Monday, August 21, 2023 at 3:00 PM in CS 301 and Zoom. Location: Zoom link: [ https://princeton.zoom.us/j/3294749300 | https://princeton.zoom.us/j/3294749300 ] The members of Mingzhe’s committee are as follows: Examiners: Jia Deng (Adviser), Danqi Chen, Karthik Narasimhan Readers: Andrew Appel, Sanjeev Arora 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 his talk. Abstract follows below: Automated theorem proving is a fundamental AI task with broad applications to the formalization of mathematics, software and hardware verification, and program synthesis. Deep learning has emerged as a promising approach for guiding theorem provers. In this dissertation, we present our work on developing deep learning approaches for automated theorem proving. First, we propose FormulaNet, a deep learning-based approach to the problem of premise selection. FormulaNet represents a logical formula as a graph that is invariant to variable renaming and then embeds the graph into a vector via a novel graph embedding method that preserves the information of edge ordering. Our approach achieves state-of-the-art results on the HolStep dataset, improving the classification accuracy from 83% to 90.3%. Next, we propose MetaGen, a neural generator that automatically synthesizes theorems and proofs for the purpose of training a theorem prover. On real-world tasks, we demonstrate that synthetic data from our approach improves the theorem prover and advances the state of the art of automated theorem proving in Metamath. Finally, we extend our theorem generator to the interactive theorem prover Lean. We propose TermGen, a neural generator that automatically synthesizes theorems and proofs in Lean by directly constructing proof terms. We also propose a method of expert iteration for training TermGen to synthesize short theorems. At last, we present a case study of generating formal specifications of while-loop programs through a rule-based theorem generator.

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Sven Dorkenwald will present his FPO "Analysis of neuronal wiring diagrams" on Friday, August 18, 2023 at 1:00 PM in Neuroscience Institute A32 and Zoom.
by CS Grad Department 01 Aug '23

01 Aug '23

Sven Dorkenwald will present his FPO "Analysis of neuronal wiring diagrams" on Friday, August 18, 2023 at 1:00 PM in Neuroscience Institute A32 and Zoom. Location: Zoom link: [ https://princeton.zoom.us/j/97076372854 | https://princeton.zoom.us/j/97076372854 ] The members of Sven’s committee are as follows: Examiners: H. Sebastian Seung (Adviser), Michael Freedman, Mala Murthy Readers: Felix Heid, Viren Jain (Google) 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 his talk. Abstract follows below: To understand how the brain works, one must know its neurons and their connections. Maps of synaptic connections between neurons can be created by acquiring and analyzing electron microscopic (EM) brain images, but until recently, even the largest datasets were insufficient to map full neuronal circuits in most organisms or even small brains. Advances in automated EM image acquisition and analysis have now given rise to neuronal reconstructions of mammalian brain circuits and entire insect brains. Errors in these automated reconstructions must still be corrected through proofreading. This thesis introduces new methods to bridge the gap between automated neuron reconstructions and the analysis of neuronal wiring diagrams. First, it presents a proofreading infrastructure that facilitates correction of whole neurons in up to petascale (∼ 1mm3 brain tissue) datasets by communities of scientists and proofreaders. Second, it embeds this proofreading system into an analysis infrastructure to enable queries of the connectome data while it is actively being edited. Third, it presents a self-supervised learning technique for efficient inference of semantic information which is crucial for circuit analyses. These methods have already been used by hundreds of users and enabled numerous neuroscientific analyses. Additionally, this thesis presents the analysis of the largest connectivity map to date between cortical neurons of a defined type (L2/3 pyramidal neurons in mouse visual cortex) which identified constraints on the learning algorithms employed by the cortex. Finally, this thesis reports on the completion of the wiring diagram of the Drosophila melanogaster brain containing ∼130,000 neurons and describes how this work enabled it. The Drosophila connectome was created through a years-long community-based effort and is an incredible resource for the science community and a milestone for connectomics on the way to large mammalian brains. The methods presented in this thesis will be useful for the analysis of larger brain samples as we aim for connectomes of whole mammalian brains.

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Sulin Liu (ECE) will present "Scalable and Interpretable Learning with Probabilistic Models for Knowledge Discovery" on Aug 7, 2023 at 2:30 pm in CSML Room 103 & Zoom
by Gradinfo 01 Aug '23

01 Aug '23

Sulin Liu (ECE) will present "Scalable and Interpretable Learning with Probabilistic Models for Knowledge Discovery" on Aug 7, 2023 at 2:30 pm in CSML Room 103 & Zoom Details can be found here: [ https://ece.princeton.edu/events/scalable-and-interpretable-learning-probab… | https://ece.princeton.edu/events/scalable-and-interpretable-learning-probab… ] Abstract: Novel machine learning methods are at the heart of a transformation underway in science and engineering. Probabilistic models have served as the foundation learning models for knowledge discovery. As surrogate models, they enable efficient black-box optimization or active learning of the system behavior under limited budget to evaluate/query the complex system. Another important use case is to use probabilistic models as generative models to generate de novo designs with desired properties or samples from the equilibrium distribution of physical systems. However, to fully unleash the potential of probabilistic models for knowledge discovery, it is imperative to develop models that are scalable to growing data size and complexity while remaining interpretable to domain experts. In this dissertation, I start with the development of a novel approach that addresses the sparse solution identification problem in Bayesian optimization with probabilistic surrogate models. The discovery of sparse solutions not only enhances the interpretability of the solutions to humans for understanding the system behavior but also facilitates easier streamlined deployment and maintenance with a reduced number of parameters. Next, I present a novel approach utilizing deep learning to enhance the scalability of Gaussian process inference. Gaussian processes are extensively used as probabilistic surrogate models in knowledge discovery, but their practical usage is hindered by the high cost associated with the identification of kernel hyperparameters in GP regression, which involves costly marginal likelihoods. I show how to side-step the need for expensive marginal likelihoods by employing “amortized” inference over hyperparameters. This is achieved through training a single neural network, which consumes a set of data and produces an estimate of the kernel function, useful across different tasks. Finally, I introduce a new family of generative models — marginalization models — for high-dimensional discrete data, which is ubiquitous in science discovery. Marginalization models offer scalable and flexible generative modeling with tractable likelihoods through explicit modeling of all induced marginal distributions with neural networks. Direct modeling of the marginals enables efficient marginal inference and scalable training of any-order generative models for sampling from a given (unnormalized) probability function, which overcomes major limitations of previous methods with tractable likelihoods Zoom: [ https://princeton.zoom.us/j/95051121688?pwd=MFlkRUN6YW5VNmtqMkREdklZSTMxZz09 | https://princeton.zoom.us/j/95051121688?pwd=MFlkRUN6YW5VNmtqMkREdklZSTMxZz09 ] Advisers: Peter Ramadge & Ryan Adams

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