CS Colloquium Series: week of April 11-15
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.
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 [ http://www.alet-et.al/ | 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.
CS Colloquium Speaker Speaker: Jonathan Baker, University of Chicago Date: Monday, April 25, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Kyle Jamieson & Ravi Netravali Event page: https://www.cs.princeton.edu/events/26189 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Architecting Emerging Technologies for Quantum Computing Abstract: 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.
Here is the full list of CS Colloquium talks for next week. All talks will be recorded. ~~~~~ CS Colloquium Speaker Speaker: Daniel Kang, Stanford University Date: Monday, April 18, 2022 Time: 12:30pm EST Location: Zoom Webinar Host: Amit Levy Event page: https://www.cs.princeton.edu/events/26185 Please register here: https://princeton.zoom.us/webinar/register/WN_rQIK4ofARyidoIwnUSPT3w Title: Efficient and Accurate Systems for Querying Unstructured Data Abstract: 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. ~~~~~ CS Colloquium Speaker Speaker: Amy Ousterhout ‘13, University of California, Berkeley Date: Thursday, April 21, 2022 Time: 12:30pm EST Location: CS 105 Host: Jennifer Rexford Event page: https://www.cs.princeton.edu/events/26177 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Optimizing CPU Efficiency and Tail Latency in Datacenters Abstract: The slowing of Moore’s Law and increased concerns about the environmental impacts of computing are exerting pressure on datacenter operators to use resources such as CPUs and memory more efficiently. However, it is difficult to improve efficiency without degrading the performance of applications. In this talk, I will focus on CPU efficiency and how we can increase efficiency while maintaining low tail latency for applications. The key innovation is to reallocate cores between applications on the same server very quickly, every few microseconds. First I will describe Shenango, a system design that makes such frequent core reallocations possible. Then I will show how policy choices for core reallocation and load balancing impact CPU efficiency and tail latency, and present the policies that yield the best combination of both. Bio: Amy is a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She received her PhD in Computer Science from MIT and her BSE in Computer Science from Princeton University. Her research is on operating systems and distributed systems, and focuses on improving the efficiency, performance, and usability of applications in datacenters. She is a recipient of a Jacobs Presidential Fellowship at MIT, an NSF Graduate Research Fellowship, and a Hertz Foundation Fellowship.
CS Colloquium Speaker Speaker: Daniel Kang, Stanford University Date: Monday, April 18, 2022 Time: 12:30pm EST Location: Zoom Webinar Host: Amit Levy Event page: https://www.cs.princeton.edu/events/26185 Please register here: https://princeton.zoom.us/webinar/register/WN_rQIK4ofARyidoIwnUSPT3w Title: Efficient and Accurate Systems for Querying Unstructured Data Abstract: 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.
CS Colloquium Speaker Speaker: Amy Ousterhout ‘13, University of California, Berkeley Date: Thursday, April 21, 2022 Time: 12:30pm EST Location: CS 105 Host: Jennifer Rexford Event page: https://www.cs.princeton.edu/events/26177 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Optimizing CPU Efficiency and Tail Latency in Datacenters Abstract: The slowing of Moore’s Law and increased concerns about the environmental impacts of computing are exerting pressure on datacenter operators to use resources such as CPUs and memory more efficiently. However, it is difficult to improve efficiency without degrading the performance of applications. In this talk, I will focus on CPU efficiency and how we can increase efficiency while maintaining low tail latency for applications. The key innovation is to reallocate cores between applications on the same server very quickly, every few microseconds. First I will describe Shenango, a system design that makes such frequent core reallocations possible. Then I will show how policy choices for core reallocation and load balancing impact CPU efficiency and tail latency, and present the policies that yield the best combination of both. Bio: Amy is a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She received her PhD in Computer Science from MIT and her BSE in Computer Science from Princeton University. Her research is on operating systems and distributed systems, and focuses on improving the efficiency, performance, and usability of applications in datacenters. She is a recipient of a Jacobs Presidential Fellowship at MIT, an NSF Graduate Research Fellowship, and a Hertz Foundation Fellowship.
Here is the list of CS Colloquium talks for next week. This talk will be recorded. ~~~~~ CS Colloquium Speaker Speaker: Jonathan Baker, University of Chicago Date: Monday, April 25, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Kyle Jamieson & Ravi Netravali Event page: https://www.cs.princeton.edu/events/26189 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Architecting Emerging Technologies for Quantum Computing Abstract: 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.
CS Department Colloquium Series Speaker: Prof. Deming Chen, University of Illinois at Urbana-Champaign (UIUC) Date: Monday, Sept 19, 2022 Time: 12:30pm EST Location: CS 105 Host: Kai Li Event page: [ https://www.cs.princeton.edu/events/26244 | https://www.cs.princeton.edu/events/26244 ] Title: Programmability, Scalability, and Security for Reconfigurable Computing in the Cloud Abstract: Reconfigurable Computing uses FPGAs (Field-Programmable Gate Arrays) as an alternative to microprocessors to enable high-performance and low-energy customized computing. It is becoming a mainstream technology as evident by Intel’s $16.7B acquisition of Altera in 2015 and AMD’s $49B acquisition of Xilinx in 2022. However, challenges remain in terms of FPGA programmability, scalability, and security before reconfigurable computing makes a transformative impact in the computing world, especially in the cloud. In this talk, Dr. Chen will present some new concepts and research results that demonstrate initial promises to overcome these challenges, including shared virtual memory system for computing with FPGAs, scalable high-level synthesis for FPGA programming, and trusted execution environment with accelerators. These results are developed within the AMD-Xilinx Center of Excellence and the Hybrid-Cloud Thrust of the IBM-Illinois Discovery Accelerator Institute at UIUC. Bio: Deming Chen is the Abel Bliss Professor of the Grainger College of Engineering at University of Illinois at Urbana-Champaign (UIUC). His current research interests include reconfigurable computing, hybrid cloud, system-level design methodologies, machine learning and acceleration, and hardware security. He has published more than 250 research papers, received ten Best Paper Awards and one ACM/SIGDA TCFPGA Hall-of-Fame Paper Award, and given more than 130 invited talks. He is an IEEE Fellow, an ACM Distinguished Speaker, and the Editor-in-Chief of ACM Transactions on Reconfigurable Technology and Systems (TRETS). He is the Director of the AMD-Xilinx Center of Excellence and the Hybrid-Cloud Thrust Co-Lead of the IBM-Illinois Discovery Accelerator Institute at UIUC. He has been involved in several startup companies, such as AutoESL and Inspirit IoT. He received his Ph.D. from the Computer Science Department of UCLA in 2005. This talk will be recorded and live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ]
CS Department Colloquium Series Speaker: Prof. Tandy Warnow, University of Illinois at Urbana-Champaign (UIUC) Date: Monday, Sept 19, 2022 Time: 10:30am EST Location: CS 105 Host: Ben Raphael Event page: https://www.cs.princeton.edu/events/26245 Title: New Algorithms for Large-scale Species Tree Estimation Abstract: Constructing the Tree of Life (i.e., a species tree containing all of the extant species) is a Scientific Grand Challenge that is surprisingly difficult from a computational and statistical perspective. One of the challenges is that different parts of the genome evolve down different trees, due to processes such as incomplete lineage sorting (ILS) and gene duplication and loss (GDL). In this talk, I will present new algorithms that can estimate species trees under both processes with high accuracy, even on very large datasets (thousands of species and genes). Moreover, our new methods for species tree estimation addressing GDL do not require knowledge of orthology. Some of this work is unpublished. Bio: Tandy Warnow is the Grainger Distinguished Chair in Engineering in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Tandy received her PhD in Mathematics at UC Berkeley under the direction of Gene Lawler, and her research focuses on reconstructing complex and large-scale evolutionary histories. She was awarded the David and Lucile Packard Foundation Award (1996), a Radcliffe Institute Fellowship (2003), and the John Simon Guggenheim Foundation Fellowship (2011). She was elected a Fellow of the Association for Computing Machinery (ACM) in 2015 and of the Association for the Advancement of Science (AAAS) in 2021. This talk will not be recorded or live-streamed.
CS Department Colloquium Series Speaker: Prof. Tandy Warnow, University of Illinois at Urbana-Champaign (UIUC) Date: Monday, Sept 19, 2022 Time: 10:30am EST Location: CS 105 Host: Ben Raphael Event page: https://www.cs.princeton.edu/events/26245 Title: New Algorithms for Large-scale Species Tree Estimation Abstract: Constructing the Tree of Life (i.e., a species tree containing all of the extant species) is a Scientific Grand Challenge that is surprisingly difficult from a computational and statistical perspective. One of the challenges is that different parts of the genome evolve down different trees, due to processes such as incomplete lineage sorting (ILS) and gene duplication and loss (GDL). In this talk, I will present new algorithms that can estimate species trees under both processes with high accuracy, even on very large datasets (thousands of species and genes). Moreover, our new methods for species tree estimation addressing GDL do not require knowledge of orthology. Some of this work is unpublished. Bio: Tandy Warnow is the Grainger Distinguished Chair in Engineering in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Tandy received her PhD in Mathematics at UC Berkeley under the direction of Gene Lawler, and her research focuses on reconstructing complex and large-scale evolutionary histories. She was awarded the David and Lucile Packard Foundation Award (1996), a Radcliffe Institute Fellowship (2003), and the John Simon Guggenheim Foundation Fellowship (2011). She was elected a Fellow of the Association for Computing Machinery (ACM) in 2015 and of the Association for the Advancement of Science (AAAS) in 2021. This talk will not be recorded or live-streamed. Speaker: Prof. Deming Chen, University of Illinois at Urbana-Champaign (UIUC) Date: Monday, Sept 19, 2022 Time: 12:30pm EST Location: CS 105 Host: Kai Li Event page: https://www.cs.princeton.edu/events/26244 Title: Programmability, Scalability, and Security for Reconfigurable Computing in the Cloud Abstract: Reconfigurable Computing uses FPGAs (Field-Programmable Gate Arrays) as an alternative to microprocessors to enable high-performance and low-energy customized computing. It is becoming a mainstream technology as evident by Intel’s $16.7B acquisition of Altera in 2015 and AMD’s $49B acquisition of Xilinx in 2022. However, challenges remain in terms of FPGA programmability, scalability, and security before reconfigurable computing makes a transformative impact in the computing world, especially in the cloud. In this talk, Dr. Chen will present some new concepts and research results that demonstrate initial promises to overcome these challenges, including shared virtual memory system for computing with FPGAs, scalable high-level synthesis for FPGA programming, and trusted execution environment with accelerators. These results are developed within the AMD-Xilinx Center of Excellence and the Hybrid-Cloud Thrust of the IBM-Illinois Discovery Accelerator Institute at UIUC. Bio: Deming Chen is the Abel Bliss Professor of the Grainger College of Engineering at University of Illinois at Urbana-Champaign (UIUC). His current research interests include reconfigurable computing, hybrid cloud, system-level design methodologies, machine learning and acceleration, and hardware security. He has published more than 250 research papers, received ten Best Paper Awards and one ACM/SIGDA TCFPGA Hall-of-Fame Paper Award, and given more than 130 invited talks. He is an IEEE Fellow, an ACM Distinguished Speaker, and the Editor-in-Chief of ACM Transactions on Reconfigurable Technology and Systems (TRETS). He is the Director of the AMD-Xilinx Center of Excellence and the Hybrid-Cloud Thrust Co-Lead of the IBM-Illinois Discovery Accelerator Institute at UIUC. He has been involved in several startup companies, such as AutoESL and Inspirit IoT. He received his Ph.D. from the Computer Science Department of UCLA in 2005. This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/
participants (1)
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Emily C. Lawrence