April 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.

1 11

Sophia Yoo (ECE) will present her General Exam "SmartCookie: Distributed In-Network SYN Flooding Mitigation" on Tuesday, May 10, 2022 at 9am in CS 302.
by Nicki Mahler 09 May '22

09 May '22

Sophia Yoo (ECE) will present her General Exam "SmartCookie: Distributed In-Network SYN Flooding Mitigation" on Tuesday, May 10, 2022 at 9am in CS 302. Title: SmartCookie: Distributed In-Network SYN Flooding Mitigation Date: Tuesday, May 10, 2022 Time: 9 am - 10 am Location: CS 302 Committee: Jen Rexford, Prateek Mittal, Ravi Netravali Abstract: SYN-flooding attacks, a pervasive class of DDoS attacks, target the TCP three-way handshake to exhaust server resources, leading to poor performance for legitimate clients. In this work, we present SmartCookie, a distributed defense system for service providers to protect their servers from large-scale SYN flooding attacks. SmartCookie pushes the front line of defense to the network edge, placing an in-network SYN cookie proxy upstream of the servers and closer to the clients. Our design capitalizes on the distinctive capabilities of high-speed programmable switches by offloading some defense responsibilities to lightweight modules running within standard Linux kernels. Unlike traditional SYN cookie proxy defenses located at the server, SmartCookie is a split proxy with a novel division of labor between a high-speed, in-network switch and a lightweight server-side component. Our design protects downstream network bandwidth (by blocking attack traffic early in the path), scales to protect many servers at a time, and gracefully handles asymmetric routing. We implement SmartCookie with P4 (on Intel Tofino switches) and eBPF (on Linux servers), showing two orders of magnitude improvement over the standard Linux kernel's defense running on an 8-core CPU, and blocking attack rates of 136.9 million packets per second with no packet loss.

1 1

Minsung Kim will present his Pre FPO "Quantum and Quantum-Inspired Computation for Wireless Networks" on Friday, May 13, 2022 at 10am via Zoom
by Nicki Mahler 29 Apr '22

29 Apr '22

Minsung Kim will present his Pre FPO "Quantum and Quantum-Inspired Computation for Wireless Networks" on Friday, May 13, 2022 at 10am via Zoom. Zoom link: https://princeton.zoom.us/j/97528898987 The members of his committee are as follows: Examiners: Kyle Jamieson (adviser), Jennifer Rexford, and Yasaman Ghasempour (ECE); Readers: Ravi Netravali, Lin Zhong, and Davide Venturelli (NASA/USRA RIACS) All are welcome to attend. ============================================================== Abstract: A central design challenge for future generations of wireless networks is to meet the ever-increasing demand for wireless capacity. While significant progress has been made in designing advanced wireless technologies, the current computational capacity at base stations to support them has been consistently identified as the bottleneck, due to limitations in processing time. Quantum computing is a potential tool to overcome the tradeoff between the wireless performance and computational complexity. It exploits unique information processing capabilities based on quantum mechanics to perform fast calculations that are intractable by traditional digital methods. In this talk, I will present design directions of quantum compute-enabled base station systems in wireless networks and introduce our prototype systems that are implemented on real-world quantum processors. The prototypes are designed for quantum-accelerated near-optimal wireless signal processing in Multiple-Input Multiple-Output (MIMO) systems that could drastically increase wireless capacity for the cellular 5G New Radio roadmap, as well as in next generation wireless local area networks. I will provide design guidance of quantum, quantum-inspired classical, and classical-quantum hybrid optimization in the systems with underlying principles and technical details, and discuss future research directions based on the current challenges and opportunities.

1 0

Carlos Jimenez will present his General Exam "Consistency And Robustness Testing for Visual Question Answering" on Friday, May 6, 2022 at 2:30 PM in CS 401 and via zoom.
by Louis W. Riehl 29 Apr '22

29 Apr '22

Carlos Jimenez will present his General Exam "Consistency And Robustness Testing for Visual Question Answering" on Friday, May 6, 2022 at 2:30 PM in CS 401 and via zoom. Zoom link: [ https://princeton.zoom.us/j/96002443578 | https://princeton.zoom.us/j/96002443578 ] Committee Members: Karthik Narasimhan (advisor), Danqi Chen, Olga Russakovsky Abstract: We introduce CARETS as a new systematic test suite to measure consistency and robustness of modern visual question answering (VQA) models through a series of fine-grained capability tests. In contrast to existing VQA test sets, CARETS features balanced question generation to create pairs of instances to test models, with each pair focusing on a specific capability such as rephrasing, logical symmetry or image obfuscation. We evaluate six modern VQA systems on CARETS and identify several actionable weaknesses in model comprehension, especially with concepts such as negation, disjunction, or hypernym invariance. We show that even the most sophisticated models are sensitive to aspects such as swapping the order of terms in a conjunction or varying the number of answer choices mentioned in a question; underscoring the need for improved robustness and reliability of state-of-the-art machine learning models. Reading List: [ https://docs.google.com/document/d/1rnOlJVkDNgI7CJsjVN78sEPN-OIa3M9i661f_ol… | https://docs.google.com/document/d/1rnOlJVkDNgI7CJsjVN78sEPN-OIa3M9i661f_ol… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

1 0

Sayash Kapoor will present his General Exam "Leakage and the reproducibility crisis in ML-based science" on Friday, May 6, 2022 at 9:00 AM in CS 402 and via zoom.
by Louis W. Riehl 29 Apr '22

29 Apr '22

Sayash Kapoor will present his General Exam "Leakage and the reproducibility crisis in ML-based science" on Friday, May 6, 2022 at 9:00 AM in CS 402 and via zoom. Zoom link: [ https://princeton.zoom.us/j/99164890280 | https://princeton.zoom.us/j/99164890280 ] Committee Members: Arvind Narayanan (advisor), Brandon Stewart, Olga Russakovsky Abstract: The use of Machine Learning (ML) methods for prediction and forecasting has become widespread across the quantitative sciences. However, there are many known methodological pitfalls, including data leakage, in ML-based science. In this work, we systematically investigate reproducibility issues in ML-based science. We show that data leakage is indeed a widespread problem and has led to severe reproducibility failures. Specifically, through a survey of literature in research communities that adopted ML methods, we find 15 fields where errors have been found, collectively affecting 304 papers and in some cases leading to wildly overoptimistic conclusions. Based on our survey, we present a fine-grained taxonomy of 8 types of leakage that range from textbook errors to open research problems. We argue for fundamental methodological changes to ML-based science so that cases of leakage can be caught before publication. To that end, we propose model cards for reporting scientific claims based on ML models and find that this would address all types of leakage identified in the 304 papers with errors in our survey. To investigate the impact of reproducibility errors and the efficacy of our model cards, we undertake a reproducibility study in a field where complex ML models are believed to vastly outperform older statistical models such as Logistic Regression (LR): Civil War prediction. We find that all papers claiming the superior performance of complex ML models compared to LR models fail to reproduce due to data leakage, and complex ML models don’t perform substantively better than decades-old LR models. While none of these errors could have been caught by reading the papers, our model cards enable the detection of leakage in each case. Reading List: [ https://docs.google.com/document/d/10Zt4UQTATXKb-aEOmNFi99xu4MbQiHcAXWc1hS3… | https://docs.google.com/document/d/10Zt4UQTATXKb-aEOmNFi99xu4MbQiHcAXWc1hS3… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

1 0

Jiaqi Su will present her FPO "Studio-quality Speech Enhancement" on Thursday, May 5, 2022 at 3:30 PM in Friend 125 and Zoom.
by Louis W. Riehl 28 Apr '22

28 Apr '22

Jiaqi Su will present her FPO "Studio-quality Speech Enhancement" on Thursday, May 5, 2022 at 3:30 PM in Friend 125 and Zoom. Location: Zoom link: https://princeton.zoom.us/j/6073737365 & Friend 125 The members of Jiaqi’s committee are as follows: Examiners: Adam Finkelstein (Adviser), Olga Russakovsky, Karthik Narasimhan Readers: Szymon Rusinkiewicz, Zeyu Jin (Adobe Research) A copy of her 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 her talk. Abstract follows below: Modern speech content such as podcasts, video narrations, and audiobooks typically requires high-quality audio to support a strong sense of presence and a pleasant listening experience. However, real-world recordings captured with consumer-grade equipment often suffer from quality degradations including noise, reverberation, equalization distortion, and loss of bandwidth. Conventional speech enhancement methods typically focus on removing a subset of quality degradations with the goal of improving signal clarity and intelligibility, but they fall short of studio quality. This dissertation addresses speech enhancement with a focus on improving the perceptual quality and aesthetics of recorded speech. It describes how to improve single-channel real-world consumer-grade recordings to sound like professional studio recordings – studio-quality speech enhancement. In pursuit of this problem, we identify three challenges: objective functions misaligned with human perception, the shortcomings of commonly used audio representations (i.e., spectrogram and waveform), and the lack of available high-quality speech data for training. This dissertation presents a waveform-to-waveform deep neural network solution that consists of two steps: (1) enhancement by removing all quality degradations at limited bandwidth (i.e., 16kHz sample rate), and (2) bandwidth extension from 16kHz to 48kHz to produce a high-fidelity signal. The first enhancement stage relies on a perceptually-motivated GAN framework that combines both waveform and spectrogram representations, and learns from simulated data covering a broad range of realistic recording scenarios. Next, the bandwidth extension stage shares a similar design as the enhancement method, but focuses on filling in missing high frequency details at 48kHz. Finally, we extend the studio-quality speech enhancement problem to a more general problem called acoustic matching to convert recordings to an arbitrary acoustic environment. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

1 0

Jordan Holland will present his FPO "A Generic Framework for Network Traffic Analysis" on May 3, 2022 at 10am via Zoom
by Nicki Mahler 28 Apr '22

28 Apr '22

Jordan Holland will present his FPO "A Generic Framework for Network Traffic Analysis" on May 3, 2022 at 10am via Zoom. Zoom link: [ https://princeton.zoom.us/j/92501308623 | https://princeton.zoom.us/j/92501308623 ] Jordan's committee is as follows: Advisers: Nick Feamster and Prateek Mittal; Readers: Jonathan Mayer and Vitaly Shmatikov (Cornell Tech); Examiners: Nick Feamster, Ravi Netravali, and Prateek Mittal All are welcome to attend. Abstract: Researchers and practitioners rely on network traffic analysis techniques for a variety of critical network security and network management tasks. Ever-increasing traffic volumes and encryption rates have rendered traditional, signature-based solutions less effective. As such, newly developed methods almost universally leverage machine techniques. The development of new machine-learning based traffic analysis techniques shares a common methodological pipeline: curate a network traffic dataset, create a system to separate and associate labels with the traffic (e.g. flows, applications), engineer features for the task, and finally train models using the engineered features. Although this methodological pipeline shared across tasks, each instantiated pipeline is custom-built for the task at hand, requiring new traffic processing systems, features, and models. This dissertation questions the assumption that each stage in the shared methodological pipeline should be custom-built to each task, exploring if several stages of the common pipeline can be better accomplished using generic techniques. First, we examine the process of feature engineering and model training--two of the most manual and painstaking steps for any traffic analysis task. We develop nPrint, a unified packet representation that is amenable to representation learning and model training for a variety of tasks. We then integrate nPrint with automated machine learning to produce nPrintML, a generic feature engineering and model training solution. Next, we study the data collection and data processing steps of the common traffic analysis pipeline. Unlike other disciplines, such as image recognition, no standard dataset format or ''canonical'' task exists, forcing researchers to develop custom dataset formats and processing systems for each task. We survey existing literature to show that this approach has led to a reproducibility crisis, finding that the lack of a standardized dataset format and the extensive usage of ambiguous terminology are primary causes. We use these findings to develop pcapML, a system that enables reproducible network traffic analysis by providing a standardized dataset format that removes ambiguity in the definitions of traffic analysis tasks. The contributions chart new directions in network traffic analysis, demonstrating that generic methods can outperform many custom-built approaches and significantly enhance the ability to develop, reproduce, and compare new methods.

1 0

Rohan Prabhakar will present his General Exam "Using an On-Hardware Predictor For Function Multi-Versioning" on Thursday, May 5, 2022 at 9:00 AM via Zoom.
by Louis W. Riehl 28 Apr '22

28 Apr '22

Rohan Prabhakar will present his General Exam "Using an On-Hardware Predictor For Function Multi-Versioning" on Thursday, May 5, 2022 at 9:00 AM via Zoom. Zoom link: [ https://princeton.zoom.us/my/rohan.bp | https://princeton.zoom.us/my/rohan.bp ] Committee Members: David Wentzlaff (advisor), David August, Ruby Lee (ECE) Abstract: This work explores a system architecture that integrates traditional CPU components with a general-purpose, on-hardware, neural network predictor that has broad access to a wide variety of system features and state. We study the utility of such a system in runtime function multi-versioning, a program optimization technique that involves storing several versions of the same function in the program binary, each compiled with different compiler optimization flags. The key premise behind such multi-versioning is that the best choice of optimization flags is not constant but a function of microarchitectural state, input, and application phase. We approximate the availability of such a neural network predictor by building a software-only system that uses the integrated GPU of a commercially available processor to perform inference on a Long Short Term Memory (LSTM) that is continually supplied with hardware performance counter data. Our system combines this neural network, a static Source-to-Source transformation built using the Clang frontend, and a shared memory model to allow efficient runtime function multi-versioning of C and C++ programs. The static Source-to-Source transformation approach we employ permits using both different optimization flags and different compilers for the same function, all within one program. To evaluate the system and its performance, we use programs that are part of the SPEC 2017 benchmark suite. Reading List: [ http://docs.google.com/document/d/1EBIfSkfxU69gfimtwL_XnRXb6mYvszzflMTi3bMh… | http://docs.google.com/document/d/1EBIfSkfxU69gfimtwL_XnRXb6mYvszzflMTi3bMh… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

1 0

Jens Tuyls will present his General Exam "Multi-Stage Episodic Control for Strategic Exploration in Text Games" on Wednesday, May 4, 2022 at 3 pm in CS 401 and via Zoom.
by Louis W. Riehl 27 Apr '22

27 Apr '22

Jens Tuyls will present his General Exam "Multi-Stage Episodic Control for Strategic Exploration in Text Games" on Wednesday, May 4, 2022 at 3 pm in CS 401 and via Zoom. Zoom Link: https://princeton.zoom.us/j/94658001342 Committee Members: Karthik Narasimhan (advisor), Danqi Chen, Sham Kakade (Harvard University) Abstract: Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards. The interplay of these two factors is particularly demanding because large action spaces require extensive exploration, while sparse rewards provide limited feedback. This work proposes to tackle the explore-vs-exploit dilemma using a multi-stage approach that explicitly disentangles these two strategies within each episode. Our algorithm, called eXploit-Then-eXplore (XTX), begins each episode using an exploitation policy that imitates a set of promising trajectories from the past, and then switches over to an exploration policy aimed at discovering novel actions that lead to unseen state spaces. This policy decomposition allows us to combine global decisions about which parts of the game space to return to with curiosity-based local exploration in that space, motivated by how a human may approach these games. Our method significantly outperforms prior approaches by 27% and 11% average normalized score over 12 games from the Jericho benchmark (Hausknecht et al., 2020) in both deterministic and stochastic settings, respectively. On the game of Zork1, in particular, XTX obtains a score of 103, more than a 2x improvement over prior methods, and pushes past several known bottlenecks in the game that have plagued previous state-of-the-art methods. Reading List: https://docs.google.com/document/d/1y7viEOlx5HiUZWqM1AnHogpBnsWPFUBgLhZo279… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

1 1

Mengying (Molly) Pan will present her General Exam "Query-Driven Packet Control in the Data Plane" on Wednesday, May 4, 2022 at 9:00 AM in CS 301and via zoom.
by Louis W. Riehl 27 Apr '22

27 Apr '22

Mengying (Molly) Pan will present her General Exam "Query-Driven Packet Control in the Data Plane" on Wednesday, May 4, 2022 at 9:00 AM in CS 301and via zoom. Zoom link: https://princeton.zoom.us/my/mollypan Committee Members: Jennifer Rexford (advisor), David Walker, Ravi Netravali Abstract: As network traffic grows in both volume and diversity, operators are longing for a flexible, real-time control over the packets. For example, operators may want to rate-limit large flows, report heavy downloaders, or drop unsolicited incoming packets—none of which is feasible in traditional fixed-function switches. Fortunately, the last decade has seen the emergence of high-speed programmable switches, which is making all these applications possible by integrating measurement and control entirely in the data plane. This enables operators to define their own packet control programs that can run and adapt at line rate. However, programming control applications on the hardware is hard. The existing data plane languages like P4 are simply too low-level for anyone other than the domain experts to write. Moreover, it requires expertise in network measurement to choose and configure data structures, and careful simulation and testing to confidently deploy such low-level programs. In this talk, we present CatQL, a high-level programming language for writing query-driven packet control in the data plane. It strikes a balance between expressiveness and restrictiveness. Borrowing concepts from query and functional languages, CatQL is intuitive, expressive and concise, allowing operators to easily specify a wide range of practical packet control intentions within ten lines of code. Meanwhile, by modeling the key hardware constraints, our CatQL compiler takes an input program, searches for the best choices and configurations of data structures, and translates it into a P4 program that fits onto the Intel Tofino target. Finally, we develop our library of P4 data structures in a modular and layered manner to allow a repeatable and efficient formal verification of their correctness properties. Overall, with CatQL, we hope to provide a simple, performant and reliable language for operators to easily write and deploy packet control applications. Reading List: https://docs.google.com/document/d/1hXf5VkrVW1OkhQOeY9x8xzRS1WRyKZNYIRQLHro… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

1 0