- talks - lists.cs.princeton.edu

Arunesh Mathur will present his FPO "Identifying and measuring manipulative user interfaces at scale on the web" on Wednesday, August 26, 2020 at 2pm via Zoom.
by Nicki Mahler 21 Aug '20

21 Aug '20

Arunesh Mathur will present his FPO "Identifying and measuring manipulative user interfaces at scale on the web" on Wednesday, August 26, 2020 at 2pm via Zoom. Link to Zoom: [ https://princeton.zoom.us/j/92967251962 | https://princeton.zoom.us/j/92967251962 ] The members of his committee are as follows: Marshini Chetty (adviser); Readers: Marshini Chetty and Arvind Narayanan; Examiners: Jonathan Mayer, Brandon Stewart, and Janet Vertesi. A copy of his 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 his talk. Abstract follows below. Powerful and otherwise trustworthy actors on the web gain from manipulating users and pushing them into making sub-optimal decisions. While prior work has documented examples of such manipulative practices, we lack a systematic understanding of their characteristics and their prevalence on the web. Building up this knowledge can lead to solutions that protect individuals and society from their harms. In this dissertation, I focus on manipulative practices that manifest in the user interface. I first describe the attributes of manipulative user interfaces. I show that these interfaces engineer users' choice architectures by either modifying the information available to users, or by modifying the set of choices available to users—eliminating and suppressing choices that disadvantage the manipulator. I then present the core contribution of this dissertation: automated methods that combine web automation and machine learning to identify manipulative interfaces at scale on the web. Using these methods, I conduct three measurements. First, I examine the extent to which content creators fail to disclose their endorsements on social media—misleading users into believing they are viewing unbiased, non-advertising content. Collecting and analyzing a dataset of 500K YouTube videos and 2 million Pinterest pins, I discover that ~90% of these endorsements go undisclosed. Second, I quantify the prevalence of dark patterns on shopping websites. Analyzing data I collected from 11K shopping websites, I discover 1,818 dark patterns on 1,254 websites that mislead, deceive, or coerce users into making more purchases or disclosing more information than they would otherwise. Finally, I quantify the prevalence of dark patterns and clickbait in political campaign emails. Collecting and analyzing a dataset of over 100K campaign emails in the U.S., I discover a long tail of candidates and organizations that use these interfaces to influence public opinion and to solicit donations. The median political campaign sends emails containing such interfaces with a probability of ~30%. I conclude with how the lessons learned from these measurements can be used to build technical defenses and to lay out policy recommendations to mitigate the spread of these interfaces

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Sergiy Popovych will present his Pre FPO "Visual Feature Extraction with Unsupervised Metric Learning" on Friday August 7th, 2020 at 8:30pm ET via Zoom.
by Nicki Mahler 31 Jul '20

31 Jul '20

Sergiy Popovych will present his Pre FPO " Visual Feature Extraction with Unsupervised Metric Learning" on Friday August 7th, 2020 at 8:30pm ET via Zoom. Zoom Link: [ https://princeton.zoom.us/j/94304733448 | https://princeton.zoom.us/j/94304733448 ] The members of his committee are as follows: Sebastian Seung (Advisor); Readers: Sebastian Seung and Viren Jain (Google); Examiners: Stephan Saalfeld (Janelia), Yoram Singer, and Kai Li Please see abstract below. Abstract: In serial section electron microscopy (ssEM), a brain volume is cut into a series of ultrathin sections, each of which is imaged via electron microscopy. Then the 2D images are combined to create a 3D image stack, from which the connections between neurons can be reconstructed. Images obtained from serial section electron microscopy often contain distortions of both affine and non-affine nature. Before the 3D stack can be analyzed, these tissue distortions have to be corrected. The problem of correcting these distortions, also known as 3D image registration or stack alignment, poses significant challenges for ssEM neuron reconstruction. This thesis focuses on convolutional neural network (ConvNet) based method that can successfully align large stacks, even in presence of large discontinuous deformations. The key behind the proposed method is a metric learning based technique that learns to extract dense noiseless representation of ssEM images. In this technique, two ConvNets are trained simultaneously. The first ConvNet is a feature extractor that is applied to the images independently. The extracted features are passed to the second ConvNet, which predicts a corrective deformation field. The joint loss of the two networks is to minimize the mean square error between the extracted features after the corrective field is applied, while maximizing the difference between the features before the corrective field is applied. Special techniques are employed to encourage feature density and to avoid degenerate solutions. The proposed technique is evaluated on real world datasets and is shown to outperform other existing approaches. This thesis also presents an efficient distributed pipeline that can be used to efficiently apply the proposed method to multi-terabyte datasets.

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CORRECTION: Uthsav Chitra will present his General Exam "Techniques for Analyzing Network Interactions " on July 29, 2020 at 3:30pm via Zoom.
by Nicki Mahler 23 Jul '20

23 Jul '20

Uthsav Chitra will present his General Exam "Techniques for Analyzing Network Interactions " on July 29, 2020 at 3:30pm via Zoom. Zoom address: [ https://princeton.zoom.us/j/244319523 ] [ https://princeton.zoom.us/j/98645694130 | https://princeton.zoom.us/j/98645694130 ] The members of his committee are as follows: Ben Raphael (adviser), Bernard Chazelle, and Christopher Musco (NYU) Link to Abstract and Reading list: [ https://docs.google.com/document/d/13hiLv3k2mssuZpeY50LpTnzHL-d9xmIU8UTDLD6… | https://docs.google.com/document/d/13hiLv3k2mssuZpeY50LpTnzHL-d9xmIU8UTDLD6… ] 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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Chitra Uthsav will present his General Exam "Techniques for Analyzing Network Interactions " on July 29, 2020 at 3:30pm via Zoom.
by Nicki Mahler 22 Jul '20

22 Jul '20

Chitra Uthsav will present his General Exam "Techniques for Analyzing Network Interactions " on July 29, 2020 at 3:30pm via Zoom. Zoom address: [ https://princeton.zoom.us/j/244319523 ] [ https://princeton.zoom.us/j/98645694130 | https://princeton.zoom.us/j/98645694130 ] The members of his committee are as follows: Ben Raphael (adviser), Bernard Chazelle, and Christopher Musco (NYU) Link to Abstract and Reading list: [ https://docs.google.com/document/d/13hiLv3k2mssuZpeY50LpTnzHL-d9xmIU8UTDLD6… | https://docs.google.com/document/d/13hiLv3k2mssuZpeY50LpTnzHL-d9xmIU8UTDLD6… ] 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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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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Jonathan Balkind will present his Pre-FPO "Open Source Frameworks for Enabling Full-Stack Hardware-Software Research" on Friday, June 10, 2020 at 9am via Zoom
by Nicki Mahler 02 Jul '20

02 Jul '20

Jonathan Balkind will present his Pre-FPO "Open Source Frameworks for Enabling Full-Stack Hardware-Software Research" on Friday, June 10, 2020 at 9am via Zoom. Zoom information: Time: Jul 10, 2020 09:00 AM Eastern Time (US and Canada) [ https://princeton.zoom.us/j/99108232595?pwd=eXBzL3ZMdGdPY2N6cTB0a3pKVHd5QT09 | https://princeton.zoom.us/j/99108232595?pwd=eXBzL3ZMdGdPY2N6cTB0a3pKVHd5QT09 ] Meeting ID: 991 0823 2595 Password: 765960 The members of his committee are as follows: David Wentzlaff (adviser); Readers: Christopher Batten (Cornell University) and Amit Levy; Examiners: Aarti Gupta and Margaret Martonosi. Abstract: Hardware-software research routinely starts with modelling. First order models can capture much of the desired system behaviour needed to begin evaluating architectural research ideas. However, when those ideas come closer to realisation in real chips or they focus on cross-layer optimisations, more precise tools must be brought to bear in evaluating them. This dissertation focuses on the development of two such tools, intended to enable research across the hardware-software stack. The tools described in this dissertation have been open-sourced in both their designs and characterisation in order to enable others to evaluate their research ideas with high fidelity. The first tool is OpenPiton, a full-stack manycore research platform and the world’s first open-source, general-purpose, multithreaded manycore processor. OpenPiton provides a toolbox for researchers to design new manycore processors incorporating their chosen research modifications. This toolbox includes scripts and tools necessary to produce a variety of processor configurations and take those instances all the way to realisation in real silicon chips. By embracing open-source design, the system supports modification at all levels of the stack, from underlying circuits to software running above, and as a result has been adopted in a variety of research areas across computing. The second tool presented in this dissertation is the Bring Your Own Core (BYOC) platform. BYOC is designed to enable heterogeneous-ISA systems research in architecture, systems software, and languages. BYOC brings together cores of multiple ISAs in a single, cache-coherent, manycore system and introduces a new interface for new cores to be rapidly connected to the system. This dissertation describes the integration of several new cores into OpenPiton to build this BYOC platform and the means taken to specifically support these cores existing together in a heterogeneous configuration. This dissertation also details a characterisation of an FPGA prototype named Dicho, built with a combination of an OpenSPARC T1 SPARC V9 core and an Ariane RISC-V core.

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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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