CS Department Colloquium Series

Despite its relative infancy, there are a number of emerging quantum technologies for quantum computation, and it is unclear which will be the clear winner. Evaluation of these technologies at the architectural level, far beyond the small-scale prototypes of 1 to 2 qubits, is critical to producing viable systems capable of executing both near and long-term applications effectively. At a high level, we are tasked with asking and answering important sets of questions with each new technology developed. In this talk, I discuss two case studies involving emerging technologies: use of multivalued logic for quantum computation and use of 2.5D quantum architectures with bounded local “memory.” In the first part, we explore the use of a variety of optimization techniques for specialized and general-use of intermediate qudits, temporary occupancy of higher order states, to reduce circuit runtimes and reduce physical device requirements which directly translates into improved output quality. In the second part, we introduce a scalable 2.5D architecture composed of resonant cavities and evaluate its ability to support quantum error correction codes. In particular, we design an architecture which directly matches the requirements of known error correction codes to reduce physical device requirements while accelerating key logical operations. I will conclude with some current and future directions in this area.

Bio: Jonathan Baker is a final-year Ph.D. student in the Department of Computer Science at the University of Chicago, advised by Prof. Fred Chong. Prior to the University of Chicago, he received degrees in Computer Science, Chemistry, and Mathematics from the University of Notre Dame. His research is focused on interdisciplinary, full-stack optimization and the evaluation of emerging quantum systems. His work has been recognized with two IEEE Micro Top Picks awards and an honorable mention, and he has recently been named a Siebel Scholar. 

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This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/

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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This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/  

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

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This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/

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.

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This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/ 

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Over the past 60 years, relational databases have been a runaway success: they are deployed at every major organization and have produced hundreds of billions of dollars in market capitalization. However, there is a growing demand for analytics over unstructured data (e.g., videos, audio, text) given the rise of ML capabilities: previously, unstructured data did not fit cleanly with the relational database model (e.g., selecting pixels vs semantic content about objects in an image). Unfortunately, ML can be prohibitively expensive to deploy (e.g., 10 orders of magnitude more expensive than standard relational analytics) and can produce incorrect results. These problems are exacerbated by the scale of data. For example, the Tesla fleet of vehicles produces exabytes of data per day.

In this talk, I'll describe my work on new ML-based query systems to tackle the cost and reliability of unstructured data analytics. My first line of work accelerates large classes of queries by orders of magnitude while providing strong guarantees on query accuracy. I accomplish this by developing novel query processing algorithms, indexing methods, and execution engines for unstructured data queries. I'll also describe how to find errors in human labels and ML model outputs using novel data management systems. My systems can be used to automatically improve ML models and, perhaps surprisingly, have discovered a large number of errors in a popular autonomous vehicle dataset. My research has been deployed at an autonomous vehicle company and has enabled new forms of video analytics for ecologists at the Jasper Ridge biological preserve.

Bio: Daniel Kang is a sixth year PhD student in the Stanford DAWN lab, co-advised by Professors Peter Bailis and Matei Zaharia. His research focuses on systems to query unstructured data. In particular, he focuses on using cheap approximations to accelerate query processing algorithms and new programming models for ML data management. Daniel is collaborating with autonomous vehicle companies and ecologists to deploy his research. His work is supported in part by the NSF GRFP and the Google PhD fellowship.

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This talk will be recorded.

From roads to roofs, homes to high-rises, my inspiration is the promise of building cyber-physical infrastructure for human interaction and enabling smart city applications. Unfortunately, there are several challenges in achieving this vision due to the end of Moore's law, Dennard scaling, and our limited views on how computing systems are manufactured. To date, device manufacturing has focused primarily on miniaturization—packing the most functionality into the smallest form factor—despite our physical infrastructure being much larger in scale.  We need to think creatively, design devices in new form factors (made in structural forms like walls, tables, facades, etc.) and materials of various kinds (those with extreme mechanical strength) that make up our built environments. There remain several challenges at the nexus of device power, form factor, and scale for designing our cyber-physical infrastructure. 

This talk will introduce "computational infrastructure materials" that enable us to build energy-efficient sensing, actuation, and communication in the networked physical infrastructure (e.g., buildings, sidewalks) forms. Specifically, I will talk about how to enable our infrastructure materials (e.g., concrete, wood, composites) to do computation:  (1) as they bear large amounts of forces (~4000 lbs) (2) enable battery-free sensing and activity recognition in long distances (~70km), (3) actuate large-structures in response to user interaction and (4) enable battery-free wireless communication. Taken together, these capabilities in infrastructure materials enable a range of applications in the built environment, such as digital buildings, accessibility, and ultimately towards creating sustainable and resilient cyber-physical infrastructure for human interaction. I will conclude by discussing open problems and challenges for this emerging research area.

Bio: Sai Swaminathan is a Ph.D. Candidate at the Human-Computer Interaction Institute in the School of Computer Science of Carnegie Mellon University. He is advised by Scott Hudson in the DevLab. He works at the intersection of Human-Computer Interaction, Ubiquitous Computing, and Computational Materials. He has published award-winning work at top-tier HCI venues, including ACM CHI, IMWUT (UbiComp), UIST, and CSCW. His work has also been featured in news outlets such as the New Scientist, Makezine, and HacksterIO. He has worked at numerous research institutions such as the Manufacturing Science group at Oakridge National Lab (ORNL), Microsoft Research, INRIA, and Xerox Research. You can find out more about him at www.saiganesh.net

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Probabilistic programming supports probabilistic modeling, learning, and inference by representing sophisticated probabilistic models as computer programs in new programming languages. This talk presents efficient probabilistic programming-based techniques that address two fundamental challenges in scaling and automating structure learning and inference over complex data.

First, I will describe scalable structure learning methods that make it possible to automatically synthesize probabilistic programs in an online setting by performing Bayesian inference over hierarchies of flexibly structured symbolic program representations, for discovering models of time series data, tabular data, and relational data. Second, I will present fast compilers and symbolic analyses that compute exact answers to a broad range of inference queries about these learned programs, which lets us extract interpretable patterns and make accurate predictions in real time.

I will demonstrate how these techniques deliver state-of-the-art performance in terms of runtime, accuracy, robustness, and programmability by drawing on several examples from real-world applications, which include adapting to extreme novelty in economic time series, online forecasting of flu rates given sparse multivariate observations, discovering stochastic motion models of zebrafish hunting, and verifying the fairness of machine learning classifiers.

Bio: Feras Saad is a PhD candidate in Computer Science at MIT working at the intersection of programming languages, probabilistic machine learning, and computational statistics. His research is accompanied with a collection of popular open-source probabilistic programming systems used by collaborators at Intel, Takeda, Liberty Mutual, IBM, and the Bill & Melinda Gates Foundation for practical applications of structure learning and probabilistic inference. Feras' MEng thesis on probabilistic programming and data science has been recognized with the 1st Place Computer Science Thesis Award at MIT.

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This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/

Social learning helps humans and animals rapidly adapt to new circumstances, coordinate with others, and drives the emergence of complex learned behaviors. What if it could do the same for AI? This talk describes how Social Reinforcement Learning in multi-agent and human-AI interactions can address fundamental issues in AI such as learning and generalization, while improving social abilities like coordination. I propose a unified method for improving coordination and communication based on causal social influence. I then demonstrate that multi-agent training can be a useful tool for improving learning and generalization. I present PAIRED, in which an adversary learns to construct training environments to maximize regret between a pair of learners, leading to the generation of a complex curriculum of environments. Agents trained with PAIRED generalize more than 20x better to unknown test environments. Ultimately, the goal of my research is to create intelligent agents that can assist humans with everyday tasks; this means leveraging social learning to interact effectively with humans. I show that learning from human social and affective cues scales more effectively than learning from manual feedback. However, it depends on accurate recognition of such cues. Therefore I discuss how to dramatically enhance the accuracy of affect detection models using personalized multi-task learning to account for inter-individual variability. Together, this work argues that Social RL is a valuable approach for developing more general, sophisticated, and cooperative AI, which is ultimately better able to serve human needs. 

Bio: Natasha Jaques holds a joint position as a Senior Research Scientist at Google Brain and Visiting Postdoctoral Scholar at UC Berkeley. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. Natasha completed her PhD at MIT, where her thesis received the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing. Her work has also received Best Demo at NeurIPS, an honourable mention for Best Paper at ICML, Best of Collection in the IEEE Transactions on Affective Computing, and Best Paper at the NeurIPS workshops on ML for Healthcare and Cooperative AI. She has interned at DeepMind, Google Brain, and was an OpenAI Scholars mentor. Her work has been featured in Science Magazine, Quartz, IEEE Spectrum, MIT Technology Review, Boston Magazine, and on CBC radio. Natasha earned her Masters degree from the University of British Columbia, and undergraduate degrees in Computer Science and Psychology from the University of Regina.

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This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/ 

One of computer science's greatest insights has been in understanding the power and versatility of *proofs*, which were revolutionized in the 1980s to utilize *interaction* as well as other resources such as randomization and computational hardness. Today, they form the backbone of both theoretical and practical cryptography and are simultaneously the source of deep connections to areas such as complexity theory, game theory, and quantum computation.

In this talk, I will discuss general-purpose tools, techniques, and abstractions for two key aspects of cryptographic proof systems that have been poorly understood for decades:

1) Can we remove interaction from interactive proofs? Already in the 1980s, Fiat and Shamir proposed a heuristic *but unproven* methodology for removing interaction from interactive proofs, which is now ubiquitous and essential for practical applications. However, it remained open for over 30 years to prove the security of this transformation in essentially any setting of interest. 

My work on the Fiat-Shamir transform has led to resolutions to many long-standing open problems, including (i) building non-interactive zero knowledge proof systems based on lattice cryptography, (ii) establishing the existence of highly efficient and succinct non-interactive proof systems, and (iii) demonstrating that foundational protocols from the 80s fail to compose in parallel.

2) Are classical interactive protocols secure against quantum computers? At its heart, the problem of analyzing and ruling out quantum attacks on cryptographic protocols is the issue of “rewinding.” The inability to rewind a quantum attack stems from the no-cloning theorem, a fundamental property of quantum information. As a result, very few classical protocols were known to be secure against quantum attacks. 

In a recent work, I showed how to overcome these difficulties and settle many foundational questions on post-quantum cryptographic proof systems. Our main technique is showing how to efficiently extract certain pieces of (classical) information from a quantum attacker without disturbing its internal state. 

Bio: Alex Lombardi is a graduate student at MIT advised by Vinod Vaikuntanathan. He is the recipient of an MIT Presidential fellowship, an NDSEG fellowship, and a Charles M. Vest NAE Grand Challenges fellowship. He is broadly interested in cryptography and theoretical computer science with a focus on cryptographic proof systems and post-quantum security. 

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This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/  

Bugs in systems software like file systems, databases, and operating systems can have serious consequences, ranging from security vulnerabilities to data loss, and these bugs affect all the applications built on top. Systems verification is a promising approach to improve the reliability of our computing infrastructure, since it can eliminate whole classes of bugs through machine-checked proofs that show a system always meets its specification.

In this talk, I’ll present a line of work culminating in a verified, concurrent file system called DaisyNFS. The file system comes with a proof that shows operations appear to execute correctly and atomically (that is, all-or-nothing), even if the computer crashes and when processing concurrent operations. I’ll describe how a combination of design and verification techniques make it possible to carry out the proof for an efficient implementation.

Bio: Tej Chajed is a final-year PhD student at MIT advised by Frans Kaashoek and Nickolai Zeldovich. His research is on systems verification, ranging from developing new foundations through designing and verifying high-performance systems. Before MIT, he completed his undergraduate degree in Electrical Engineering and Computer Science at UIUC. His work has been in part supported by an NSF graduate research fellowship.

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This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/.