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2024-04-26 10:00:00
2024-04-26 11:30:00
America/New_York
Designing human-centric systems for learning physical skills
Abstract:Today, how humans learn physical skills is transforming profoundly owing to the technological advances in sensing, AR/VR, and AI. Amidst this excitement in innovation, however, it is critical not to lose sight of the multifaceted nature of human learning. Given that every learner is diverse and every learning experience is uniquely multidimensional, my vision is to use these emerging technologies for a learner-centric future for skill learning. In this talk, I will present the steps I have taken in my Ph.D. research toward my vision through designing, building, and studying tools for skill learning that are grounded in learners' and educators' experiences. I'll share three research projects focusing on adaptive motor skill learning, game-based fabrication skill learning, and reflection-based makerskill learning. These projects aim to enhance motivation, creativity, and self-reflection, thereby expanding the design space of learning tools beyond merely focusing on skill acquisition. My research also provides insights into how humans learn with technology, thus contributing to advancing our understanding of human learning of physical skills. Bio: Dishita Turakhia is a Ph.D. candidate in the Electrical Engineering and Computer Science (EECS) dept. at MIT with a research focus in Human-Computer Interaction and Design and a minor in Brain and Cognitive Science. Her research focuses on designing systems for learning physical skills at the intersection of HCI and learning sciences. Dishita is part of the EECS Rising Stars ('23 cohort), a Meta Ph.D. Research Fellowship recipient ('22-24), and holds the MIT Edwin S. Webster Graduate Fellowship ('18). She is a SERC and Grace Hopper scholar. Her research is supported by several grants, including two National Science Foundation grants, three MITili grants, and a MIT-JWEL grant. Before starting her Ph.D., she earned a dual master's degree in EECS (MS) and Architecture (SMArchS computation) from MIT ('17), and a master's (MSc) in Emergent Technology and Design (EmTech) from the AA School of Architecture ('11). Besides academic research, she has worked in the industry as a computational designer on several award-winning projects in London, Singapore, and Bern, and as a licensed architect in Mumbai, where she also co-led her design firm.
Events
April 26, 2024
Thesis Defense: Advancing Dexterous Manipulation via Machine Learning
Tao Chen
https://taochenshh.github.io/
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2024-04-26 13:00:00
2024-04-26 14:00:00
America/New_York
Thesis Defense: Advancing Dexterous Manipulation via Machine Learning
Abstract: Robots are becoming better at navigating and moving around, but they still struggle with using tools, which severely limits their usefulness for household tasks. Using tools requires dexterously manipulating everyday objects like hammers, scissors, knives, screwdrivers, etc. While simple for humans, manipulating everyday objects remains a long-standing challenge that requires breakthroughs in robotic hardware, sensing, perception, and control algorithms. This talk introduces machine learning techniques that substantially improve the state-of-the-art performance of dexterous manipulation controllers. It focuses specifically on in-hand object reorientation tasks. Previous works on this problem had limitations like using expensive sensors or hands, only working for a few objects, requiring the hand to face upward, slow object motion, etc. This talk discusses how we can go a step further by enabling a low-cost robot hand to dynamically reorient diverse objects in mid-air with the hand facing downward using an inexpensive depth camera. Bio: Tao Chen is a Ph.D. student advised by Prof. Pulkit Agrawal in Improbable AI lab at MIT CSAIL. His research focuses on robot learning, in particular, dexterous manipulation in robotics. He has received the Best Paper Award at the top robot learning conference, CoRL 2021, and has also published in the Science Robotics journal. Thesis committee: Pulkit Agrawal, Daniela Rus, Russ Tedrake
32-D463
April 29, 2024
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2024-04-29 13:00:00
2024-04-29 14:00:00
America/New_York
ML-enabled Genetic Analysis of High-Content Phenotypes
Abstract: In my talk, I will discuss new machine learning (ML) approaches for human genetics. First, I will present ML-enhanced genetic analysis of histological traits, where we leverage a novel semantic autoencoder to compress histological images into trait embeddings for GWAS. In an application to multiple tissues from the GTEx dataset, we discover 4 genome-wide significant loci associated with histological changes, which we can visualise and interpret for each of the discovered variants thanks to our decoder.Second, I will introduce a new method combining machine learning and genetic causal inference for risk predictions. A key advantage of this method is that it doesn't require longitudinal data. This allows for risk prediction of late-onset diseases in large biobanks, where follow-up cases are often limited.Overall, these contributions demonstrate the transformative power of ML in human genetics. Our approaches enable more nuanced analyses of high-dimensional traits and facilitate biomarker discovery.Bio: Francesco Paolo Casale studied physics at the University of Naples Federico II, Italy. He received his PhD in statistical genetics at the University of Cambridge and the European Bioinformatics Institute in 2016, where he developed new computational methods for genetic association studies and contributed to landmark international projects such as the last phase of the 1000 Genomes Project and the Blueprint initiative. He conducted his postdoctoral studies at the Microsoft Research New England lab in Boston, working on deep generative models for imaging genetics and automated machine learning. In 2019, he joined insitro, a drug discovery and development company located in the bay area. There, he led the statistical genetics team, working at the intersection of human genetics, machine learning and functional genomics to enable target identification and characterization. Since January 2022, he is a Principal Investigator in Machine Learning in Biomedicine at the Helmholtz Munich Institute of AI for Health.
D507
April 30, 2024
Machine Learning Approaches for Healthcare Discovery, Delivery, and Equity
Yuzhe Yang
MIT-CSAIL
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2024-04-30 10:00:00
2024-04-30 11:00:00
America/New_York
Machine Learning Approaches for Healthcare Discovery, Delivery, and Equity
Abstract: Today's clinical systems frequently exhibit delayed diagnoses, sporadic patient visits, and unequal access to care. Can we identify chronic diseases earlier, potentially before they manifest clinically? Furthermore, can we bring comprehensive medical assessments into patient’s own homes to ensure accessible care for all? In this talk, I will present machine learning approaches to bridge the persistent gaps in healthcare discovery, delivery, and equity. I will first introduce an AI-powered digital biomarker that detects Parkinson’s disease multiple years before clinical diagnosis, using just nocturnal breathing signals. I will then discuss a simple self-supervised framework for contactless measurement of human vital signs using smartphones. Finally, I will discuss principled methods to achieve equitable healthcare decision-making systems across diverse subpopulations and distribution shifts for real-world deployment. Committee Members: Dina Katabi (advisor, MIT), Marzyeh Ghassemi (MIT), Daniel McDuff (Google & University of Washington) Bio: Yuzhe Yang is a Ph.D. candidate at MIT, advised by Dina Katabi. His research interests include machine learning and AI for human diseases, health and medicine. His research has been published in Nature Medicine, Science Translational Medicine, NeurIPS, ICML, and ICLR, and featured in media outlets such as WSJ, Forbes, and BBC. He is a recipient of the Rising Stars in Data Science, and PhD fellowships from MathWorks and Takeda.
32-D463 (Star). Will be hybrid and have a Zoom link, please contact (yuzhe@mit.edu) for the link
Privacy-Preserving ML with Fully Homomorphic Encryption
Jordan Frery and Benoit Chevallier-Mames
Zama
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2024-04-30 14:00:00
2024-04-30 15:00:00
America/New_York
Privacy-Preserving ML with Fully Homomorphic Encryption
Abstract:In the rapidly evolving field of artificial intelligence, the commitment to data privacy and intellectual property protection during Machine Learning operations has become a foundational necessity for society and businesses handling sensitive data. This is especially critical in sectors such as healthcare and finance, where ensuring confidentiality and safeguarding proprietary information are not just ethical imperatives but essential business requirements.This presentation goes into the role of Fully Homomorphic Encryption (FHE), based on the open-source library Concrete ML, in advancing secure and privacy-preserving ML applications.We begin with an overview of Concrete ML, emphasizing how practical FHE for ML was made possible. This sets the stage for discussing how FHE is applied to ML inference, demonstrating its capability to perform secure inference on encrypted data across various models. After inference, we speak about another important FHE application, the FHE training and how encrypted data from multiple sources can be used for training without compromising individual user's privacy.FHE has lots of synergies with other technologies, in particular Federated Learning: we show how this integration strengthens privacy-preserving features of ML models during the full pipeline, training and inference.Finally, we address the application of FHE in generative AI and the development of Hybrid FHE models (which are the subject of our RSA 2024 presentation). This approach represents a strategic balance between intellectual property protection, user privacy and computational performance, offering solutions to the challenges of securing one of the most important AI applications of our times.Zoom: https://mit.zoom.us/j/91841471370BiosJordan Frery is a research scientist and engineer in machine learning at Zama. As a researcher, he published in different application domains such as fraud detection, author verification, and risk prediction. He holds a PhD in machine learning and has worked in the field for 8+ years, as a data and research scientist. His current work at Zama focuses on bridging the gap between machine learning and fully homomorphic encryption, with the goal of applying machine learning techniques to encrypted data.Benoit Chevallier-Mames is a security engineer and researcher serving as VP of Cloud & Machine Learning at Zama. He has spent 20+ years between cryptographic research and secure implementations in a wide range of domains such as side-channel security, provable security, whitebox cryptography, and fully homomorphic encryption. Prior to Zama, he securely implemented public-key algorithms on smartcards in Gemplus for seven years, worked for the French governmental ANSSI agency for one year, and then designed and developed whitebox implementations at Apple for 12 years. Benoit has co-written 15+ peer-reviewed papers and is the co-author of 50+ patents. He holds a PhD from Ecole Normale Superieure / Paris University and a master's degree from CentraleSupelec.
32-G882
May 01, 2024
Efficient Algorithms for Vector Similarities
Sandeep Silwal
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2024-05-01 11:00:00
2024-05-01 13:00:00
America/New_York
Efficient Algorithms for Vector Similarities
Title: Efficient Algorithms for Vector SimilaritiesAbstract:A key cog in machine learning is the humble embedding: a vector representation of real world objects such as text, images, graphs, and even molecules. It is common to curate massive datasets of embeddings by inferencing on a ML model of choice. However, the sheer dataset size and large dimensionality is often *the* bottleneck in effectively leveraging and learning from this rich dataset. Inspired by this computational bottleneck in modern machine learning pipelines, we study the following question: "How can we efficiently compute on large scale high dimensional data?"In this thesis, we focus on two aspects of this question.(1) Fast local similarity computation: we give faster algorithms to compute complicated notions of similarity, such as those between collections of vectors (think optimal transport), as well as dimensionality reduction techniques which preserve similarities. In addition to computational efficiency, other resource constraints such as space and privacy are also considered.(2) Fast global similairty analysis: we study algorithms for analyzing global relationships between vectors encoded in similarity matrices. Our algorithms compute on similarity matrices, such as distance or kernel matrices, without ever initializing them, thus avoiding a quadratic time and space bottleneck.Overall, my main message is that sublinear algorithms design principles are instrumental in designing scalable algorithms for big data. For the presentation, I will only cover a subset of results in each of the categories, with a big emphasis on simplicity.Thesis Committee:Piotr Indyk (Advisor) - MITRonitt Rubinfeld - MITHuy Nguyen - Northeastern University
Seminar Room G882 (Hewlett Room)
May 02, 2024
Thesis Defense: Learning to Model Atoms Across Scales
Xiang Fu
MIT CSAIL
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2024-05-02 13:30:00
2024-05-02 14:30:00
America/New_York
Thesis Defense: Learning to Model Atoms Across Scales
The understanding of atoms and how they interact forms the foundation of modern natural science, as well as material and drug discovery efforts. Computational chemistry methods such as density functional theory and molecular dynamics simulation can offer an unparalleled spatiotemporal resolution for observing microscopic mechanisms and predicting macroscopic phenomena. However, their computational cost limits the applicable domains and scales. This thesis presents machine learning methods for modeling atoms for tasks across different scales. First, we propose machine learning force fields that can decompose molecular interactions into fast and slow components, and then accelerate molecular simulations through multi-scale integration. Second, we propose an end-to-end workflow for learning time-integrated coarse-grained molecular dynamics using multi-scale graph neural networks. Third, we propose diffusion models for periodic material structures and their multi-scale extension to metal-organic frameworks. These machine-learning methods represent a new paradigm in high-throughput scientific discovery and molecular design.
32-370
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2024-05-02 14:00:00
2024-05-02 16:00:00
America/New_York
Designing for Participation and Power in Data Collection and Analysis
Thesis committee: Arvind Satyanarayan, Daniel Jackson, Catherine D’Ignazio, J. Nathan MatiasAbstract:Technologies that mediate social participation are an increasingly important area for design, enabling people to create, share, and discuss information. While increased participation is generally considered empowering, it can also be a double-edged sword, as involuntary participation in systems can lead to disempowerment. In this dissertation, I apply the lens of participation and power to two problem domains: accessible data visualization and ethical data collection. First, existing approaches to accessible data visualization reinforce blind and low-vision (BLV) users' dependence on sighted assistance. In contrast, I design systems that empower BLV users to conduct self-guided data exploration and create non-visual representations without using visual idioms. Second, existing data ethics procedures are often designed to offer people more choices, but can serve to placate users and consolidate data collectors' power. I develop systems and frameworks that offer novel approaches to data protection by reframing people's non-compliance with data collection as a form of socio-technical design. Altogether, this work demonstrates how the lens of participation and power deepens our understanding of technology's social implications and inspires novel approaches to design.
32-G882
May 06, 2024
Thesis Defense: Towards Object-based SLAM
Yihao Zhang
MIT MechE
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2024-05-06 10:00:00
2024-05-06 11:30:00
America/New_York
Thesis Defense: Towards Object-based SLAM
Abstract:Simultaneous localization and mapping (SLAM) is a fundamental capability for a robot to perceive its surrounding environment. The research area has developed for more than two decades from the original sparse landmark-based SLAM to dense SLAM, and now there is a demand for semantic understanding of the environment beyond pure geometric understanding. This thesis makes a number of contributions to help realize object-based SLAM, in which the map consists of a set of objects with their semantic categories recognized and their poses and shapes estimated. Such a map provides vital object-level semantic and geometric perception for applications such as augmented reality (AR), mixed reality (MR), mobile manipulation, and autonomous driving.In order to perform object-based SLAM, the sensor measurements have to undergo a series of processes. First, objects are semantically segmented in the sensor measurements. This step is typically done by a neural network. As robots are often required to bootstrap from some initial labeled datasets and adapt to different environments where labeled data are unavailable, it is important to enable semi-supervised learning to improve the robot performance with the unlabeled data collected by the robot itself. Second, after the objects are segmented, measurements for each object across different viewpoints have to be associated together for downstream processing. Lastly, the robot must be able to extract the object pose and shape information from the measurements without access to the detailed CAD models of the objects. This thesis studies these three aspects of object-based SLAM, namely semi-supervised learning of semantic segmentation in a robotics context, data association for object-based SLAM, and category-level object pose and shape estimation.For category-level object pose and shape estimation, we developed ShapeICP (ICP: iterative closest point), an algorithm that does not use pose-annotated data and generates meshes as the object shape representation. For data association, we developed DAF-SLAM (DAF: data association free) to estimate the associations in the back-end instead of relying on sensor-dependent front-end methods. For semi-supervised learning, we applied temporal semantic consistency inspired by the photometric consistency technique in the traditional SLAM methods. Each contribution is evaluated via experimental datasets, demonstrating improvements over previous techniques.Committee Members:John J. Leonard (Advisor), Department of Mechanical EngineeringFaez Ahmed, Department of Mechanical EngineeringNicholas Roy, Department of Aeronautics and Astronautics
32-G882 (https://mit.zoom.us/j/92202523862)
May 07, 2024
Quest | CBMM Seminar Series: Invariance and equivariance in brains and machines
Bruno Olshausen
UC Berkeley
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2024-05-07 16:00:00
2024-05-07 17:30:00
America/New_York
Quest | CBMM Seminar Series: Invariance and equivariance in brains and machines
Abstract: The goal of building machines that can perceive and act in the world as humans and other animals do has been a focus of AI research efforts for over half a century. Over this same period, neuroscience has sought to achieve a mechanistic understanding of the brain processes underlying perception and action. It stands to reason that these parallel efforts could inform one another. However recent advances in deep learning and transformers have, for the most part, not translated into new neuroscientific insights; and other than deriving loose inspiration from neuroscience, AI has mostly pursued its own course which now deviates strongly from the brain. Here I propose an approach to building both invariant and equivariant representations in vision that is rooted in observations of animal behavior and informed by both neurobiological mechanisms (recurrence, dendritic nonlinearities, phase coding) and mathematical principles (group theory, residue numbers). What emerges from this approach is a neural circuit for factorization that can learn about shapes and their transformations from image data, and a model of the grid-cell system based on high-dimensional encodings of residue numbers. These models provide efficient solutions to long-studied problems that are well-suited for implementation in neuromorphic hardware or as a basis for forming hypotheses about visual cortex and entorhinal cortex.Bio: Professor Bruno Olshausen is a Professor in the Helen Wills Neuroscience Institute, the School of Optometry, and has a below-the-line affiliated appointment in EECS. He holds B.S. and M.S. degrees in Electrical Engineering from Stanford University, and a Ph.D. in Computation and Neural Systems from the California Institute of Technology. He did his postdoctoral work in the Department of Psychology at Cornell University and at the Center for Biological and Computational Learning at the Massachusetts Institute of Technology. From 1996-2005 he was on the faculty in the Center for Neuroscience at UC Davis, and in 2005 he moved to UC Berkeley. He also directs the Redwood Center for Theoretical Neuroscience, a multidisciplinary research group focusing on building mathematical and computational models of brain function (see http://redwood.berkeley.edu).Olshausen's research focuses on understanding the information processing strategies employed by the visual system for tasks such as object recognition and scene analysis. Computer scientists have long sought to emulate the abilities of the visual system in digital computers, but achieving performance anywhere close to that exhibited by biological vision systems has proven elusive. Dr. Olshausen's approach is based on studying the response properties of neurons in the brain and attempting to construct mathematical models that can describe what neurons are doing in terms of a functional theory of vision. The aim of this work is not only to advance our understanding of the brain but also to devise new algorithms for image analysis and recognition based on how brains work.
Singleton Auditorium (46-3002)
Siva Vaidhyanathan - Digital Hegemony and Digital Sovereignty
Siva Vaidhyanathan
University of Virginia
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2024-05-07 16:00:00
2024-05-07 17:00:00
America/New_York
Siva Vaidhyanathan - Digital Hegemony and Digital Sovereignty
Abstract: Through the first 30 years of the development of the internet, we were promised a global “network of networks” that would offer free speech, democratic empowerment, and the spread of democracy. Leaders from Ronald Reagan to Margaret Thatcher to Barack Obama all promised that technology would unite and enlighten the world. Somehow it all went differently, and now we live in a world traversed by networks dominated by hegemons like the United States, Russia, and China. In this talk, Professor Siva Vaidhyanathan will explain the idea of “digital sovereignty,” the ways that a nation state creates and enforces its own sense of what should be allowed and watched on digital networks, resisting digital hegemony through strategies of digital sovereingty. There are many models of “digital sovereignty,” each offering a distinct set of value and opportunities, as well as methods of oppression. This talk will focus on how the Russian invasion of Ukraine exposes the dangers and necessities of digital sovereignty.Bio:Siva Vaidhyanathan is the Robertson Professor of Media Studies and director of the Center for Media and Citizenship at the University of Virginia. He is the author of Antisocial Media: How Facebook Disconnects Us and Undermines Democracy (2018), Intellectual Property: A Very Short Introduction (2017), The Googlization of Everything -- and Why We Should Worry (2011), Copyrights and Copywrongs: The Rise of Intellectual Property and How it Threatens Creativity ( 2001), and The Anarchist in the Library: How the Clash between Freedom and Control is Hacking the Real World and Crashing the System (2004). He also co-edited (with Carolyn Thomas) the collection, Rewiring the Nation: The Place of Technology in American Studies (2007). Vaidhyanathan is a columnist for The Guardian and has written for many other periodicals, including The New York Times, Wired, Bloomberg View, American Scholar, Reason, Dissent, The Chronicle of Higher Education, The New York Times Magazine, Slate.com, BookForum, Columbia Journalism Review, Washington Post, The Virginia Quarterly Review, The New York Times Book Review, and The Nation. He is a frequent contributor to public radio programs. And he has appeared on news programs on BBC, CNN, NBC, CNBC, MSNBC, ABC, and on The Daily Show with Jon Stewart on Comedy Central. In 2015 he was portrayed on stage at the Public Theater in a play called Privacy. After five years as a professional journalist, he earned a Ph.D. in American Studies from the University of Texas at Austin. Vaidhyanathan has also taught at Wesleyan University, the University of Wisconsin at Madison, Columbia University, New York University, McMaster University, and the University of Amsterdam. He is a fellow at the New York Institute for the Humanities and a Faculty Associate of the Berkman Center for Internet and Society at Harvard University. He was born and raised in Buffalo, New York and resides in Charlottesville, Virginia.This talk will also be streamed over Zoom: https://mit.zoom.us/j/95568018736.
Kiva (G449)
Parallel Derandomization for Chernoff-like Concentrations
Mohsen Ghaffari
CSAIL MIT
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2024-05-07 16:15:00
2024-05-07 17:15:00
America/New_York
Parallel Derandomization for Chernoff-like Concentrations
Abstract: Randomized algorithms frequently use concentration results such as Chernoff and Hoeffding bounds. A longstanding challenge in parallel computing is to devise an efficient method to derandomize parallel algorithms that rely on such concentrations. Classic works of Motwani, Naor, and Naor [FOCS'89] and Berger and Rompel [FOCS'89] provide an elegant parallel derandomization method for these, via a binary search in a k-wise independent space, but with one major disadvanage: it blows up the computational work by a (large) polynomial. That is, the resulting deterministic parallel algorithm is far from work-efficiency and needs polynomial processors even to match the speed of single-processor computation. This talk overviews a duo of recent papers that provide the first nearly work-efficient parallel derandomization method for Chernoff-like concentrations.Based on joint work with Christoph Grunau and Vaclav Rozhon, which can be accessed via https://arxiv.org/abs/2311.13764 and https://arxiv.org/abs/2311.13771.
32-G882
May 10, 2024
Pseudorandom Error-Correcting Codes
Miranda Christ (Columbia University)
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2024-05-10 10:30:00
2024-05-10 12:00:00
America/New_York
Pseudorandom Error-Correcting Codes
We construct pseudorandom error-correcting codes (or simply pseudorandom codes), which are error-correcting codes with the property that any polynomial number of codewords are pseudorandom to any computationally-bounded adversary. Efficient decoding of corrupted codewords is possible with the help of a decoding key.We build pseudorandom codes that are robust to substitution and deletion errors, where pseudorandomness rests on standard cryptographic assumptions. Specifically, pseudorandomness is based on either 2^{O(\sqrt{n})}-hardness of LPN, or polynomial hardness of LPN and the planted XOR problem at low density.As our primary application of pseudorandom codes, we present an undetectable watermarking scheme for outputs of language models that is robust to cropping and a constant rate of random substitutions and deletions. The watermark is undetectable in the sense that any number of samples of watermarked text are computationally indistinguishable from text output by the original model. This is the first undetectable watermarking scheme that can tolerate a constant rate of errors.Our second application is to steganography, where a secret message is hidden in innocent-looking content. We present a constant-rate stateless steganography scheme with robustness to a constant rate of substitutions. Ours is the first stateless steganography scheme with provable steganographic security and any robustness to errors.This is based on joint work with Sam Gunn: https://eprint.iacr.org/2024/235
32-G882 Hewlett Room
May 24, 2024
Dynamic Adaptive Optimization: Recovering from Hardware Errors and Software Crashes in a Distributed Virtual Machine
University of California, Santa Cruz
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2024-05-24 14:00:00
2024-05-24 15:00:00
America/New_York
Dynamic Adaptive Optimization: Recovering from Hardware Errors and Software Crashes in a Distributed Virtual Machine
Abstract: TidalScale was a startup aquired by HPE in December 2022. TidalSale developed a software architecture called distributed virtual machines. Today's virtual machines in widespread use today allows multiple operating systems to share a server. TidalScale inverts this paradigm. A single virtual machine running on TidalScale runs a single operating system instance across a cluster of standard servers. This virtual machine sits between an operating system and a cluster of servers. It runs on premise or in the cloud. Because they are virtual, resources like processors and memory can migrate among nodes in the cluster. The virtual machine dynamically self-optimizes resource placement in real time under contol of a set of machine learning algorithms. Servers can automatically and dynamically be added and removed depending on fluctuationg workloads, allowing for dynamic hardware scalability, but also increasing reliability and resiliency. In this talk, we specifically show how these servers automatically, without any human intervention, recover from most hardware failures, and and provide excellent restart performance should OS failures occur.Bio: Ike Nassi is a consultant and an Adjunct Professor of Computer Science at UC Santa Cruz, a Founding Trustee at the Computer History Museum and an advisory board member of TTI/Vanguard. Ike was the founder of TidalScale, sold to HPE Dec. 2022. Previously, he was an Executive Vice President and Chief Scientist at SAP. Ike started or helped to start four companies: Encore Computer Corporation building hierarchical strongly coherent shared memory symmetric multiprocessors (1984); InfoGear Technology, which developed both Internet appliances (including the first iPhone) (1996); Firetide, an early wireless mesh networking company (2000), and TidalScale (2012). He was SVP for Software at Apple Computer and a Corporate Officer. He worked at Visual Technology, and Digital Equipment Corporation. In the past, Dr. Nassi was a Visiting Scholar at Stanford University, twice a Research Scientist at MIT, and a Visiting Scholar at University of California, Berkeley. He has served on the board of the Anita Borg Institute for Women and Technology, and the IEEE Computer Society Industry Advisory Board. He holds a PhD in Computer Science from Stony Brook University.He was awarded two certificates for Distinguished Service from the Department of Defense, one for his work on the design of the programming language Ada and one for his work on DARPA ISAT. He is a Life Fellow of IEEE and a Life member of ACM. He is named on over 35 patents.
32-G575
June 07, 2024
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2024-06-07 9:00:00
2024-06-07 18:00:00
America/New_York
CSAIL + Imagination in Action Symposium 2024
The symposium will showcase the extraordinary and substantive contributions CSAIL research groups have made, and highlight the remarkable impacts of our work.
Kirsch Auditorium
December 01, 2024
Cancelled
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