June 2022 - talks - lists.cs.princeton.edu

CS Colloquium Series: week of April 11-15
by Emily C. Lawrence 19 Sep '22

19 Sep '22

Here is the full list of CS Colloquium talks for next week. All talks will be recorded. ~~~~~ CS Colloquium Speaker Speaker: Parastoo Abtahi, Stanford University Date: Monday, April 11, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Andrés Monroy-Hernández & Adam Finkelstein Event page: [ https://www.cs.princeton.edu/events/26186 | https://www.cs.princeton.edu/events/26186 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: From Haptic Illusions in Virtual Reality to Beyond-Real Interactions Abstract: Advances in audiovisual rendering have led to the commercialization of virtual reality (VR) hardware; however, haptic technology has not kept up with these advances. While haptic devices aim to bridge this gap by simulating the sensation of touch, there are many hardware limitations that make realistic touch interactions in VR challenging. In my research, I explore how by understanding human perception, we can design VR interactions that not only overcome the current limitations of VR hardware, but also extend our abilities beyond what is possible in the real world. In this talk, I will present my work on redirection illusions that leverage the limits of human perception to improve the perceived performance of encountered-type haptic devices, such as improving the position accuracy of drones, the speed of tabletop robots, and the resolution of shape displays when used for haptics in VR. I will then present a framework I have developed through the lens of sensorimotor control theory to argue for the exploration and evaluation of VR interactions that go beyond mimicking reality. Bio: Parastoo Abtahi is a final year computer science PhD candidate and a Gerald J. Lieberman fellow at Stanford University, where she is co-advised by Prof. James Landay and Prof. Sean Follmer. Her research area is human-computer interaction (HCI) and she works broadly on virtual reality interactions and spatial computing. Her research has been published at top HCI venues, such as the ACM Conference on Human Factors in Computing Systems (CHI) and the ACM User Interface Software and Technology Symposium (UIST), and has received two honorable mention paper awards. Her work has been supported by the Stanford Institute for Human-Centered Artificial Intelligence, the Hasso Plattner Design Thinking Research Program, and the VMware fellowship. Prior to Stanford, Parastoo received her bachelor’s degree in Electrical and Computer Engineering from the University of Toronto as part of the Engineering Science program. ~~~~~ CS Colloquium Speaker Speaker: Ferran Alet, Massachusetts Institute of Technology Date: Tuesday, April 12, 2022 Time: 12:30pm EST Location: CS 105 Host: Olga Russakovsky Event page: https://www.cs.princeton.edu/events/26187 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: A flexible framework for machine learning Abstract: In this last decade, we have seen a lot of progress in AI and machine learning using a single recipe: given a task, we train a single neural network to map inputs to outputs. In this talk, I will show that this one-neural-network-per-task framework can be extended to improve generalization. First, I will describe modular meta-learning, which achieves language-like generalization by training a set of composable neural modules. By having multiple neural networks per task, and multiple tasks per neural network, we are able to reuse information and achieve bigger data and computational efficiency. In the second part of my talk, I will describe tailoring, a general way of encoding inductive biases in neural networks by optimizing unsupervised objectives inside the prediction function, essentially having one neural network per input. Finally, I will describe my vision for creating a flexible ML framework that will enable training reinforcement learning policies within minutes rather than days, solving complex search and discovery problems, and improving our understanding of generalization in deep learning. Bio: Ferran Alet is a PhD candidate at MIT CSAIL advised by Leslie Kaelbling, Tomas Lozano-Perez, and Joshua Tenenbaum. His research is on machine learning and leverages techniques from meta-learning, learning to search, program synthesis, and insights from mathematics and the physical sciences. During his PhD, he created the MIT Embodied Intelligence Seminar, mentored 17 students, and won the MIT Outstanding Mentor award 2021. Ferran studied mathematics and physics in Barcelona thanks to CFIS, a program for doing two degrees, where he was the valedictorian of his promotion. In college, he participated in the ACM-ICPC programming contest, being the most decorated in the history of his regional phase (South Western Europe). In grad school, he earned a “La Caixa” fellowship and was responsible for the high-level planner of the MIT-Princeton team for the Amazon Robotics Challenge, which won the stowing task in 2017. You can find more information and papers at www.alet-et.al ~~~~~ CS Colloquium Speaker Speaker: Benedikt Bünz, Stanford University Date: Thursday, April 14, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Jonathan Mayer & Arvind Narayanan Event page: https://www.cs.princeton.edu/events/26188 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Improving the privacy, scalability, and ecological impact of blockchains Abstract: Blockchains are an exciting area of research that touches on many areas of Computer Science and beyond. This technology has the potential to enable a fast, cheap, and private financial system based on distributed consensus and cryptography, instead of trusted parties. Despite this potential, the reality still shows severe limitations of blockchains: (i) transactions can cost hundreds of dollars and take minutes to confirm, (ii) some blockchains offer little privacy, and (iii) proof-of-work consensus consumes too much energy. In this talk, I will discuss powerful techniques that follow a prover paradigm and can mitigate these limitations. The first technique, called Bulletproofs, is a general-purpose zero-knowledge proof system that is specifically designed to enable confidential blockchain transactions. Bulletproofs requires minimal trust assumptions and gives the shortest zero-knowledge proofs without trusted setup. The system is widely deployed and powers tens of thousands of private blockchain transactions per day. The second technique, called inner pairing products, is a way to aggregate many zero-knowledge proofs into a single short proof. This can significantly reduce on-chain data, leading to a significant increase in transactions per second that the chain can process. The third technique is a new concept called a verifiable delay function (VDF) that is vital for permission-less and eco-friendly consensus. VDFs are already deployed in Filecoin and Chia, and are planned for Ethereum 2.0, the upcoming upgrade to Ethereum. Bio: Benedikt Bünz is a PhD candidate at Stanford University, a member of Dan Boneh’s applied cryptography lab, and a recipient of the Microsoft Research Fellowship at the Simons Institute. His work on the science of Blockchains uses tools from applied cryptography, distributed systems, and algorithmic game theory. His research focuses on building new proof protocols for improving the privacy, scalability, and ecological impact of blockchains. Several of his research results have had a significant industry impact. His work on Bulletproofs, secures tens of thousands of private transactions on Blockchains like Monero or Signal’s Mobilecoin. His seminal work on Verifiable Delay Functions (VDFs) sparked the VDF Alliance, a multi-million dollar initiative composed of academic, non-profit, and corporate collaborators.

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Seminar Series on Security & Privacy in Machine Learning
by Emily C. Lawrence 02 Aug '22

02 Aug '22

Challenges and Opportunities in Security & Privacy in Machine Learning Weekly talks starting Tuesday, June 7 About the seminar series The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins). Timing : Every Tuesday at 1pm Eastern Time (Virtual talks) Website: [ https://vsehwag.github.io/SPML_seminar/ | https://vsehwag.github.io/SPML_seminar/ ] Mailing list: [ https://groups.google.com/forum/#!forum/spml-seminars/join | Link to mailing list ] Calendar: [ https://calendar.google.com/calendar/u/0?cid=N2FwbTVxYzJsOGM2bXBiNGY4am1oMj… | Link to calendar ] You can find all additional details on the website. If you are interested, we recommend signing up for the mailing list and sync the calendar to stay up to date with the seminar schedule. -- Vikash Sehwag PhD Candidate Princeton University [ https://vsehwag.github.io/ | https://vsehwag.github.io ]

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Meryem Essaidi will present her Pre FPO "User-Centered Algorithmic Mechanism Design" on Thursday, June 30th 2022, at 10:30am via Zoom.
by Nicki Mahler 28 Jun '22

28 Jun '22

Meryem Essaidi will present her Pre FPO "User-Centered Algorithmic Mechanism Design" on Thursday, June 30th 2022, at 10:30am via Zoom. Zoom link: https://princeton.zoom.us/j/93387293206 The members of her committee are as follows: Prof. Matt Weinberg (adviser) , Prof. Mark Braverman (reader), Prof. Rediet Abebe (reader), Prof. Zeev Dvir (examiner), and Prof. Huacheng Yu (examiner). All are welcome to attend. Title: "User-Centered Algorithmic Mechanism Design" Abstract: In algorithmic mechanism design, classical desiderata are typically seller-centered and aim to protecting them and maximizing their well-being. Examples of such objectives are efficiency, revenue or profit maximization, and strategyproofness. This often creates negative externalities that harm the users purchasing the goods and the people targeted by the market. Examples of such externalities are not treating the users fairly and equally, not protecting against malicious or adversarial sellers, and not maximizing the social welfare or utility of the users. Many frameworks attempt to mitigate these undesirable outputs by either setting constraints on current goals or by redesigning the goals altogether. In our work, we focus on the latter and design frameworks that center user well-being. We ask, how can we design new solutions that prioritize user-centered desiderata while maintaining the preexisting seller-centered desiderata or without too great a cost to the classical seller-centered desiderata such as profit-maximization and strategyproofness? The challenge stems from balancing (sometimes conflicting) interests of both parties and using tools from algorithmic game theory to ensure that all the agents involved will behave as expected and the desired output is achieved. In this thesis, we present three different settings where we study such problems. Project 1. First, in [1] we consider a revenue-maximizing seller with multiple items for sale to a single population of buyers. Our main result shows that for a single population of additive buyers with independent (but not necessarily identically distributed) item values, bundling all items together achieves a constant-factor approximation to the revenue-optimal itemsymmetric mechanism. We motivate this direction via fairness in ad auctions. In ad auction domains the items correspond to views from particular demographics, and recent works have therefore identified a novel fairness constraint: equally-qualified users from different demographics should be shown the same desired ad at equal rates. Prior work abstracts this to the following fairness guarantee: if an advertiser places an identical bid on two users, those two users should view the ad with the same probability. We first propose a relaxation of this guarantee from worst-case to Bayesian settings, which circumvents strong impossibility results from these works, and then study this guarantee through the lens of symmetries, as any item-symmetric auction is also fair (by this definition). Observe that in this domain, bundling all items together corresponds to concealing all demographic data. Project 2. Next, in [2] we consider a revenue-maximizing seller with a single item for sale to multiple buyers with independent and identically distributed valuations. Previous work showed that the only optimal, credible, strategyproof auction is the ascending price auction with reserves which has unbounded communication complexity. In this work, assuming the existence of cryptographically secure commitment schemes, we identify a new single-item auction that is credible, strategyproof, revenue optimal, and terminates in constant rounds in expectation for all distributions with finite monopoly price. Project 3. Finally, in [3] we study a problem inspired by regulated health insurance markets, such as those created by the government in the Affordable Care Act Exchanges or by employers when they contract with private insurers to provide plans for their employees. The market regulator can choose to do nothing, running a Free Market, or can exercise her regulatory power by limiting the entry of providers (decreasing consumer welfare by limiting options, but also decreasing revenue via enhanced competition). We investigate whether limiting entry increases or decreases the utility (welfare minus revenue) of the consumers who purchase from the providers, specifically in settings where the outside option of ”purchasing nothing” is prohibitively undesirable.

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Kevin Lee will be presenting his FPO "The Research-Practice Gap in User Authentication" on June 30th, 2022 at 2pm via Zoom.
by Nicki Mahler 27 Jun '22

27 Jun '22

Kevin Lee will be presenting his FPO "The Research-Practice Gap in User Authentication" on June 30th, 2022 at 2pm via Zoom. His committee members are as follows: Arvind Narayanan (adviser), Examiners: Andrés Monroy-Hernández, and Tom Ristenpart (Cornell); Readers: Jonathan Mayer and Prateek Mittal Zoom: [ https://princeton.zoom.us/j/99377975766 | https://princeton.zoom.us/j/99377975766 ] 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: [ https://docs.google.com/document/d/1unvunt1lEYqoMWlGaH1DQRBq7HwGF5tni5sefrj… | https://docs.google.com/document/d/1unvunt1lEYqoMWlGaH1DQRBq7HwGF5tni5sefrj… ] The gap between user authentication research and practice has led to weaknesses in critical, widely-deployed systems used by millions of people. In these systems, policy and process vulnerabilities—not software vulnerabilities—allow UI-bound, low-tech adversaries to exploit weaknesses to threaten user safety. The disconnect is caused partly by practice failing to heed advice from research. But it is also caused by research not understanding the practical constraints of these systems, while discouraging studies that try to do so. Ultimately, users are the ones who suffer when these weaknesses remain undiscovered. Here, we studied user authentication practices that were not necessarily cutting-edge, but broadly impacted user safety. We identified security policy and process flaws, quantified the risk of harm to users through manual measurements, and called for policy solutions to mitigate the risks. More broadly, we honed a methodology through these studies which can potentially bridge the research-practice gap in user authentication as well as in other topics in information security. First, we studied call center authentication for SIM swap requests at mobile carriers. We found flaws in their authentication policy and processes which could facilitate SIM swap attacks. Furthermore, we found that most websites did not stand up well against SIM swaps, demonstrating that users' accounts could easily be hijacked. Our results have influenced policy changes at carriers and websites, and have motivated ongoing rulemaking by the FCC. Next, we studied security and privacy risks of phone number recycling in the U.S. at mobile carriers. We found that most numbers we sampled were recycled and vulnerable to attacks on previous owners, while carriers had design weaknesses that could facilitate attacks. We have raised awareness about the risks of number recycling at carriers, and have communicated a practical constraint of SMS-based authentication to the research community. Finally, we studied password policies of top websites. Despite well-established recommendations from research, we found few websites actually following them, which could put accounts at risk of password compromise. We hypothesized reasons why these websites were not following best practices, and discussed ways the research community could engage website system administrators to bridge the research-practice gap.

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Princeton AI Club guest speaker: Prof. Emtiyaz Khan - June 27th, at 10 am ET!
by Emily C. Lawrence 21 Jun '22

21 Jun '22

The Princeton AI Club is excited to announce its next guest speaker – [ https://emtiyaz.github.io/ | Prof. Emtiyaz Khan ] from the RIKEN Center for AI Project! His talk will focus on: How can we design AI systems that imitate humans and animals to quickly adapt to their surroundings? If you want to know the answer to this intriguing question, sign-up for his virtual talk [ https://forms.gle/ghtmESmGr2Lr52na9 | here ] , and join us on June 27 th , at 10 am Eastern Time (ET) ! The full details are below. Speaker: Prof. Emtiyaz Khan Date: Monday 27th June 2022, 10:00 AM ET Title: The Bayesian Learning Rule for Adaptive AI Abstract : Humans and animals have a natural ability to autonomously learn and quickly adapt to their surroundings. How can we design AI systems that do the same? In this talk, I will present Bayesian principles to bridge such gaps between humans and AI. I will show that a wide-variety of machine-learning algorithms are instances of a single learning-rule called the Bayesian learning rule. The rule unravels a dual perspective yielding new adaptive mechanisms for machine-learning based AI systems. My hope is to convince the audience that Bayesian principles are indispensable for an AI that learns as efficiently as we do. Reference: The Bayesian Learning Rule, (Preprint) M.E. Khan, H. Rue [ [ https://arxiv.org/abs/2107.04562 | arXiv ] ] [ [ https://twitter.com/EmtiyazKhan/status/1414498922584711171?s=20 | Tweet ] ] Bio: Emtiyaz Khan (also known as Emti) is a team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. He is also an external professor at the Okinawa Institute of Science and Technology (OIST). Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For more than a decade, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.

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Sergiy Popovych will present his FPO "Self-Supervised Metric Learning for Alignment of Petascale Connectomics Datasets" on Tuesday, June 21, 2022 at 6:00 PM via Zoom.
by Louis W. Riehl 14 Jun '22

14 Jun '22

Sergiy Popovych will present his FPO "Self-Supervised Metric Learning for Alignment of Petascale Connectomics Datasets" on Tuesday, June 21, 2022 at 6:00 PM via Zoom. Location: Zoom link: https://princeton.zoom.us/j/95852144614 The members of Sergiy’s committee are as follows: Examiners: H. Sebastian Seung (Adviser), Karthik Narasimhan, Stephan Saalfeld (Janelia Research Campus) Readers: Kai Li, 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: The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. These methods are often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. The problem of 3D stack alignment can be divided into two subproblems – image pair alignment and alignment globalization. Image pair alignment is. This dissertation presents a computational pipeline for aligning ssEM images with several key elements. First, a self‐supervised convolutional net are trained via metric learning to encode and align image pairs, followed by iterative finetuning of alignment. Second, a procedure called vector voting is used to remove outliers, further increasing robustness to image defects. Third, for speedup the series is divided into blocks that are distributed to computational workers for alignment. Fourth, the blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time‐consuming global optimization. The pipeline is used to align a female adult fly brain dataset, as well as a cubic millimeter of mouse visual cortex and is publicly available through two open source Python packages. Development of the presented pipeline constitutes a team effort by Seunglab members. Thomas Macrina and Barak Nehoran took lead roles in development of global alignment and vector voting based error recovery. The author of this dissertation Sergiy Popovych has taken a lead role in image pair alignment, seethrough‐based error recovery, as well as the development and architecture of the pipeline software stack. Nico Kemnitz, Manuel Castro, and Dodam Ih have contributed a large variety of software and research components. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Yuping Luo will present his FPO "Towards Designing Efficient and Effective Deep Model-based Reinforcement Learning Algorithms" on Tuesday, June 21, 2022 at 1:30 PM via Zoom.
by Louis W. Riehl 14 Jun '22

14 Jun '22

Yuping Luo will present his FPO "Towards Designing Efficient and Effective Deep Model-based Reinforcement Learning Algorithms" on Tuesday, June 21, 2022 at 1:30 PM via Zoom. Location: Zoom link: [ https://princeton.zoom.us/j/94826666865 | https://princeton.zoom.us/j/94826666865 ] The members of Yuping’s committee are as follows: Examiners: Sanjeev Arora (Adviser), Karthik Narasimhan, Tengyu Ma (Stanford University) Readers: Elad Hazan, Jason Lee 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: Recent advances in deep reinforcement learning have demonstrated its great potential for real-world problems. However, two concerns prevent reinforcement learning from being applied: Efficiency and Efficacy. This dissertation studies how to design improving the efficiency and efficacy of reinforcement learning by designing deep model-based algorithms. The access to dynamics models empowers the algorithms to plan, which is key to sequential decision making. Four topics are covered in this dissertation, which are online reinforcement learning, the expressivity of neural networks in deep reinforcement learning, offline reinforcement learning, and safe reinforcement learning. For online reinforcement learning, we present an algorithmic framework with theoretical guarantees by utilizing a lower bound of performance the policy learned in the learned environment can obtain in the real environment, and we also empirically verify the efficiency of our proposed method. For expressivity of neural networks in deep reinforcement learning, we prove that in some scenarios, the model-based approaches can require much less representation power to approximate a near-optimal policy than model-free approaches, and empirically show that this can be an issue in physically simulation environments and a model-based planner can help. For offline reinforcement learning, we devise an algorithm that stays close to the provided expert demonstration set to reduce distribution shift, and we also conduct experiments to demonstrate the efficacy of our methods to improve the success rate for simulated robotics environments. For safe reinforcement learning, we propose a method that uses the learned dynamics model to certify safe states, and our experiments show that our method can learn a decent policy without a single safety violation during training in a set of simple but challenging tasks while baseline algorithms have hundreds of safety violations. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Paul Krueger will present his Pre-FPO "Metacognition: learning how to think" on Wednesday, June 8, 2022 at 3:00 PM via Zoom.
by Louis Riehl 07 Jun '22

07 Jun '22

Paul Krueger will present his Pre-FPO "Metacognition: learning how to think" on Wednesday, June 8, 2022 at 3:00 PM via Zoom. Zoom link: https://us04web.zoom.us/j/2902028164?pwd=ZHdhZjZKWm4vK0pZai9nbWpkNlo4Zz09 Committee Members: Tom Griffiths (Examiner, advisor), Ryan Adams (Examiner), Jonathan Cohen (examiner), Nathaniel Daw (reader), Karthik Narasimhan (reader). All are welcome to attend. Title: Metacognition: learning how to think Abstract: Reinforcement learning has been used successfully to account for human decision-making and to produce intelligent behavior in artificial agents. However, people's internal cognitive processes generally remain obscure with these models. The same formalism used to account for external actions can also be applied to cognitive processes themselves. In particular, using the recently developed framework of resource rationality we can describe how people make efficient use of limited cognitive resources. Using this approach, we apply tools from machine learning to derive a normative account of how people ought to think efficiently. As a proof of concept, I will show how this framework can be used to automatically derive decision-making heuristics. I will present results from a classic multi-alternative risky choice task that allows us to trace people's cognitive processes. Using our method, we both re-discovered previously known heuristics, and discovered heuristics that had been overlooked. We found that people did indeed use the same heuristics that our method predicted, and furthermore adapted their heuristics to the structure of the decision environment in a rational way. Our approach bridges classic rationality with research on heuristics and biases by providing a general framework that can be used to apply rationality to any decision-making process while accounting for heuristics, thus providing a more realistic normative standard. In the second part of the talk, I will discuss a new framework for how model-based and model-free reinforcement learning systems can interact cooperatively, and the potential to account for human decision-making with this approach. In particular, this cooperation may be used to mediate the tension between goals and habits. I will close by discussing ongoing work to develop tools to solve meta-MDPs, the formalism used for our resource-rational approach.

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Teague Tomesh will present his Pre FPO "On the Codesign of Quantum Computing Algorithms and Architectures" on Monday, June 6, 2022 at 12pm in CS 301 and Zoom
by Nicki Mahler 03 Jun '22

03 Jun '22

Teague Tomesh will present his Pre FPO "On the Codesign of Quantum Computing Algorithms and Architectures" on Monday, June 6, 2022 at 12pm in CS 301 and Zoom Zoom link: [ https://princeton.zoom.us/j/97972616777 | https://princeton.zoom.us/j/97972616777 ] Committee: - Margaret Martonosi (Advisor) - Frederic Chong (UChicago, reader) - Kyle Jamieson (reader) - Amit Levy (examiner) - Steve Lyon (examiner) All are welcome to attend. Title: On the Codesign of Quantum Computing Algorithms and Architectures Abstract: Recent years have seen tremendous advances in quantum computing. Full-stack quantum computing systems have moved from the lab into industry and are now available to the general public. However, these machines are still lacking in terms of their computational resources and are characterized as Noisy Intermediate-Scale Quantum (NISQ) computers. Eventually, we would like to build larger, more robust systems that are able to tolerate errors and execute much longer quantum programs. Traversing this resource gap between the devices available today and the applications we aim to target in the future is a daunting task. I will present examples from my work which rely on codesign to close the resource gap. Codesign techniques are especially relevant when considering quantum computing architectures because the resource constraints can vary widely between different hardware technologies and application domains. I will discuss ways in which the design of quantum algorithms and architectures can be greatly improved by incorporating salient information from different layers up and down the hardware-software stack. The scope of this talk will encompass applications of quantum computing to machine learning and simulating nature, distributed quantum computation, and the benchmarking of quantum computers. With all of these example I aim to show how codesign of the quantum hardware-software stack can provide a viable path forward as we design tomorrow’s quantum computing architectures.

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Eli Lucherini will present his Pre-FPO "Analysis and Critique of Simulation Research on Recommender Systems" on Thursday, June 9, 2022 at 10:00 AM via Zoom.
by Louis W. Riehl 02 Jun '22

02 Jun '22

Eli Lucherini will present his Pre-FPO "Analysis and Critique of Simulation Research on Recommender Systems" on Thursday, June 9, 2022 at 10:00 AM via Zoom. Zoom link: [ https://princeton.zoom.us/j/3276145367 | https://princeton.zoom.us/j/3276145367 ] Committee Members: Arvind Narayanan (Examiner, advisor), Olga Russakovsky (Examiner), Brandon Stewart (examiner), Jonathan Mayer (reader), Andrés Monroy-Hernández (reader). All are welcome to attend. Title: Analysis and Critique of Simulation Research on Recommender Systems Abstract: Simulation has emerged as a popular method to study the long-term societal consequences of recommender systems. This approach allows researchers to specify their theoretical model explicitly and observe the evolution of system-level outcomes over time. However, performing simulation-based studies often requires researchers to build their own simulation environments from the ground up, which creates a high barrier to entry, introduces room for implementation error, and makes it difficult to disentangle whether observed outcomes are due to the model or the implementation. I will present T-RECS, an open-sourced Python package designed for researchers to simulate recommendation systems and other types of sociotechnical systems in which an algorithm mediates the interactions between multiple stakeholders, such as users and content creators. T-RECS promotes reproducibility in this area of research, provides a unified language for simulating sociotechnical systems, and removes the friction of implementing simulations from scratch. More broadly, I will present a comparative analysis and offer a critique of current simulation research, showing how common shortcomings such as inconsistent results across studies and reproducibility issues can be addressed by the use of tools such as T-RECS. References: 1. E. Lucherini, M. Sun, A. Winecoff, A. Narayanan. T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems. Under submission. 2. A. Winecoff, M. Sun, E. Lucherini, A. Narayanan. Simulation as Experiment: An Empirical Critique of Simulation Research on Recommender Systems. At the Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research, RecSys ’21. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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