June 2020 - talks - lists.cs.princeton.edu

Sinong Geng will present his General Exam on June 17, 2020 at 3pm via Zoom.
by Nicki Mahler 03 Jul '20

03 Jul '20

Sinong Geng will present his General Exam on June 17, 2020 at 3pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/369310330 | https://princeton.zoom.us/j/369310330 ] The members of his committee are as follows: Ronnie Sircar (adviser), Mete Soner (ORFE), Ryan Adams, and Karthik Narasimhan Link to Abstract & Reading List: [ https://drive.google.com/file/d/17qk8jPp680v4RezIphsU-9k4P_eR3rqr/view?usp=… | https://drive.google.com/file/d/17qk8jPp680v4RezIphsU-9k4P_eR3rqr/view?usp=… ] [ https://drive.google.com/file/d/1FWncLeSw5yCQIQCCsGhyQhWQtU3vuf7a/view?usp=… | https://drive.google.com/file/d/1FWncLeSw5yCQIQCCsGhyQhWQtU3vuf7a/view?usp=… ] 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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Samuel Ginzburg will present his General Exam on June 30, 2020 at 2pm via Zoom
by Nicki Mahler 23 Jun '20

23 Jun '20

Samuel Ginzburg will present his General Exam on June 30, 2020 at 2pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/4412927805 | https://princeton.zoom.us/j/4412927805 ] The members of his committee are as follows: Michael Freedman (adviser), Jennifer Rexford, and Wyatt Lloyd. Link to Abstract and Reading List: [ https://docs.google.com/document/d/1bbtm3Yq-4TRAw8aTjyBRuQLwMOxibwCcDljdhki… | https://docs.google.com/document/d/1bbtm3Yq-4TRAw8aTjyBRuQLwMOxibwCcDljdhki… ] 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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Xingyuan Sun will present his General Exam on June 29, 2020 at 4pm via Zoom.
by Nicki Mahler 22 Jun '20

22 Jun '20

Xingyuan Sun will present his General Exam on June 29, 2020 at 4pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/99592593025 | https://princeton.zoom.us/j/99592593025 ] The members of his committee are as follows: Ryan Adams (adviser), Szymon Rusinkiewicz (adviser), and Sebastian Seung Link to Abstract and Reading List: [ https://docs.google.com/document/d/1De54uLq_HM6GDU6vonx4f311eCfD1HG0duZ4IyS… | https://docs.google.com/document/d/1De54uLq_HM6GDU6vonx4f311eCfD1HG0duZ4IyS… ] 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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Sumegha Garg will present her FPO "Implications of Space-Bounded Computation" on June 12, 2020 at 10:30am via Zoom.
by Nicki Mahler 08 Jun '20

08 Jun '20

Sumegha Garg will present her FPO "Implications of Space-Bounded Computation" on June 12, 2020 at 10:30am via Zoom. Zoom link: [ https://princeton.zoom.us/j/93 | https://princeton.zoom.us/j/93 ] The members of her committee are as follows: Examiners: Mark Zhandry, Gillat Kol, and Matt Weinberg; Readers: Mark Braverman (adviser) and Ran Raz. A copy of her thesis, is available upon request. Please email ngotsis(a)cs.princeton if you would like a copy of the thesis. Everyone is invited to attend her talk. The field of computational complexity theory studies the inherent difficulties of performing certain computational tasks with limited resources. While characterizing the minimum time required for a computational task has received more attention, understanding the memory requirements is as fundamental and fascinating. The focus of this thesis is understanding the limits of space-bounded computation and implications for various algorithmic problems. 1. Implications of bounded space for learning algorithms: [SVW16] and [Sha14] started the study of online learning under memory constraints. In a breakthrough result, [Raz16] showed that learning an unknown n-bit vector from random linear equations (in F2) requires either Ω(n2) space (memory) or 2 Ω(n) samples. Work in this thesis extends these memory-sample tradeoffs to a larger class of learning problems through an extractor-based approach, to when the learner is allowed a second pass over the stream of samples and, to proving security of Goldreich’s local pseudorandom generator against memory-bounded adversaries in the streaming model. 2. Implications of bounded space for randomized algorithms: It is largely unknown if randomization gives space-bounded computation any advantage over deterministic computation. The current best hope of the community is to derandomize randomized log-space computation with one-sided error, that is, prove RL = L. A work presented in this thesis, in a step towards answering this question, improved upon the state-of-the-art constructions of hitting sets, which are tools for derandomization. 3. Implications of bounded space for mirror games: In this thesis, we show the impossibility of winning the following streaming game under memory constraints. Alice and Bob take turns saying numbers belonging to the set {1, 2, ..., 2N}. A player loses if they repeat a number that has already been said. Bob, who goes second, has a memory-less strategy to avoid losing. Alice, however, needs at least Ω(N) memory to not lose.

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Niranjani Prasad will present her FPO "Methods for Reinforcement Learning in Clinical Decision Support" on June 11, 2020 at 3:30pm via Zoom.
by Nicki Mahler 04 Jun '20

04 Jun '20

Niranjani Prasad will present her FPO " Methods for Reinforcement Learning in Clinical Decision Support" on June 11, 2020 at 3:30pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/2211458833 | https://princeton.zoom.us/j/2211458833 ] The members of her committee are as follows: Examiners: Barbara Engelhardt (adviser), Sebastian Seung, and Mengdi Wang; Readers: Ryan Adams and Finale Doshi-Velez (Harvard University) A copy of her thesis, is available upon request. Please email ngotsis(a)cs.princeton if you would like a copy of the thesis. Everyone is invited to attend her talk. Abstract follows below. The administration of routine interventions, from breathing support to pain management, constitutes a major part of inpatient care. Thoughtful treatment is crucial to improving patient outcomes and minimizing costs, but these interventions are often poorly understood, and clinical opinion on best protocols can vary significantly. Through a series of case studies of key critical care interventions, this thesis develops a framework for clinician-in-loop decision support. The first of these explores the weaning of patients from mechanical ventilation: patient admissions are modelled as Markov decision processes, and model-free batch reinforcement learning algorithms are employed to learn personalized regimes of sedation and ventilator support, that show promise in improving outcomes when assessed against current clinical practice. The second part of this thesis is directed towards effective reward design when formulating clinical decisions as a reinforcement learning task. In tackling the problem of redundant testing in critical care, methods for Pareto-optimal reinforcement learning are integrated with known procedural constraints in order to consolidate multiple, often conflicting, clinical goals and produce a flexible optimized ordering policy. The challenges here are probed further to examine how decisions by care providers, as observed in available data, can be used to restrict the possible convex combinations of objectives in the reward function, to those that yield policies reflecting what we implicitly know from the data about reasonable behaviour for a task, and that allow for high-confidence off-policy evaluation. The proposed approach to reward design is demonstrated through synthetic domains as well as in planning in critical care. The final case study considers the task of electrolyte repletion, describing how this task can be optimized using the MDP framework and analysing current clinical behaviour through the lens of reinforcement learning, before going on to outline the steps necessary in enabling the adoption of these tools in current healthcare systems.

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Rescheduled: Arushi Gupta will present her General Exam on June 8, 2020 at 4pm via Zoom (Correction)
by Nicki Mahler 04 Jun '20

04 Jun '20

Correction: Arushi Gupta will present her General Exam on June 8, 2020 at 4pm via Zoom. Zoom link: [ https://princeton.zoom.us/my/arushig | https://princeton.zoom.us/my/arushig ] The members of her committee are as follows: Sanjeev Arora (adviser), Jia Deng, and Yoram Singer Link to Abstract and Reading List: [ https://docs.google.com/document/d/1RqVbnNpZaIjJGQgEFyhgDJ3RHahAH_aQ3uf9cGz… | https://docs.google.com/document/d/1RqVbnNpZaIjJGQgEFyhgDJ3RHahAH_aQ3uf9cGz… ] [ https://docs.google.com/document/d/10vi9PuoScggY-StK5nbewyHJFf1bwoOSRfWbTWe… | https://docs.google.com/document/d/10vi9PuoScggY-StK5nbewyHJFf1bwoOSRfWbTWe… ] 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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Arushi Gupta will present his General Exam on June 5, 2020 at 11:30am via Zoom
by Nicki Mahler 04 Jun '20

04 Jun '20

Arushi Gupta will present his General Exam on June 5, 2020 at 11:30am via Zoom. Zoom link: [ https://princeton.zoom.us/my/arushig | https://princeton.zoom.us/my/arushig ] The members of his committee are as follows: Sanjeev Arora (adviser), Jia Deng, and Yoram Singer Link to Abstract and Reading List: [ https://docs.google.com/document/d/1RqVbnNpZaIjJGQgEFyhgDJ3RHahAH_aQ3uf9cGz… | https://docs.google.com/document/d/1RqVbnNpZaIjJGQgEFyhgDJ3RHahAH_aQ3uf9cGz… ] [ https://docs.google.com/document/d/10vi9PuoScggY-StK5nbewyHJFf1bwoOSRfWbTWe… | https://docs.google.com/document/d/10vi9PuoScggY-StK5nbewyHJFf1bwoOSRfWbTWe… ] 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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