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March 19, 2019

Data Visualization Across Disciplines

Dr. Michelle Borkin
Northeastern University
1:00P
- 2:00P

Location

32-G449 (Kiva Room)
Seminar Room
Add to Calendar 2019-03-19 13:00:00 2019-03-19 14:00:00 America/New_York Data Visualization Across Disciplines Abstract: What can help enable both the treatment of heart disease and the discovery of newborn stars? Visualization. Specifically interdisciplinary data visualization, the sharing and co-development of tools and techniques across domains. Visualization is a powerful tool for data exploration and analysis. With data ever-increasing in quantity, having effective visualizations is necessary for knowledge discovery and data insight. In this talk I will share sample results from my own research and experience crossing disciplines and bringing together the knowledge and experts of computer science, astrophysics, radiology and medicine. I will present new visualization techniques and tools inspired by this work for the astronomical and medical communities including Glue, a multi-dimensional linked-data visual exploration tool.Bio: Dr. Michelle Borkin works on the development of novel visualization techniques and tools to enable new insights and discoveries in data. She works across disciplines to bring together computer scientists, doctors, and astronomers to collaborate on new analysis and visualization techniques, and cross-fertilize techniques across disciplines. Her research has resulted in the development of novel computer assisted diagnostics in cardiology, scalable visualization solutions for large network data sets, and novel astrophysical visualization tools and discoveries. Her main research interests include information and scientific visualization, hierarchical and multidimensional data representations, network visualization, visualization cognition, user interface design, human computer interaction (HCI), and evaluation methodologies. Dr. Borkin is an Assistant Professor in the Khoury College of Computer Sciences at Northeastern University. Prior to joining Northeastern, she was a Postdoctoral Research Fellow in Computer Science at the University of British Columbia, as well as Associate in Computer Science at Harvard and Research Fellow at Brigham & Women’s Hospital. She received her Ph.D. in Applied Physics at Harvard’s School of Engineering and Applied Sciences (SEAS) in 2014. She also has an MS in Applied Physics and a BA in Astronomy and Astrophysics & Physics from Harvard University. She was previously a National Science Foundation (NSF) Graduate Research Fellow, a National Defense Science and Engineering Graduate (NDSEG) Fellow, and a TED Fellow.*Snacks will be served 32-G449 (Kiva Room) Belfer sarah_donahue@hks.harvard.edu

Self-Directed Learning

Deepak Pathak
University of California Berkeley
4:00P
- 5:00P

Location

34-401
Grier Combined
Add to Calendar 2019-03-19 16:00:00 2019-03-19 17:00:00 America/New_York Self-Directed Learning Abstract:Generalization, i.e., the ability to adapt to novel scenarios, is the hallmark of human intelligence. While we have systems that excel at recognizing objects, cleaning floors, playing complex games and occasionally beating humans, they are incredibly specific in that they only perform the tasks they are trained for and are miserable at generalization. In this talk, I will present our initial efforts toward endowing artificial agents with a human-like ability to generalize in diverse scenarios. The main insight is to allow the agent to learn general-purpose skills in a completely self-directed manner, without optimizing for any external goal. These skills are then later repurposed to solve complex tasks. I will discuss how this framework can be instantiated to develop curiosity-driven agents (virtual as well as real) that can learn to play games, learn to walk, and learn to perform real-world object manipulation without any rewards or supervision. These self-directed robotic agents, after exploring the environment, can find their way in office environments, tie knots using rope, rearrange object configuration, and compose their skills in a modular fashion.Bio:Deepak Pathak is a Ph.D. candidate in Computer Science at UC Berkeley, advised by Prof. Trevor Darrell and Prof. Alexei A. Efros. His research spans computer vision, machine learning, and robotics. Deepak is a recipient of the Facebook Graduate Fellowship, the NVIDIA Fellowship, and the Snapchat Fellowship, and his research has been featured in popular press outlets, including The Wall Street Journal, The Economist, Quanta Magazine, Wired, and MIT Technology Review. Deepak received his Bachelor's in Computer Science from IIT Kanpur and was a recipient of the Gold Medal and the best undergraduate thesis award. He has also spent time at Facebook, Microsoft and founded VisageMap Inc. which was later acquired by FaceFirst Inc.For papers and open-sourced code see: https://people.eecs.berkeley.edu/~pathak/ 34-401 Belfer sarah_donahue@hks.harvard.edu

CBMM Special Seminar: The State of Autonomous Driving

Dr. Amnon Shashua
President and CEO Mobileye, an Intel company; Senior Vice President, Intel Corporation; Sachs Professor of Computer Science | Hebrew University
4:30P
- 5:30P

Location

10-250
MIT 10-250, Huntington Hall, 22 Memorial Drive, Cambridge MA 02139
Add to Calendar 2019-03-19 16:30:00 2019-03-19 17:30:00 America/New_York CBMM Special Seminar: The State of Autonomous Driving *Please note change of location - this talk will be held in MIT 10-250.*Speaker Biography: Professor Amnon Shashua is senior vice president at Intel Corporation and president and chief executive officer of Mobileye, an Intel company. He leads Intel’s global organization focused on advanced driving assist systems (ADAS), highly autonomous and fully autonomous driving solutions and programs.Professor Shashua holds the Sachs Chair in computer science at the Hebrew University of Jerusalem. Prof. Shashua’s field of expertise is computer vision and machine learning. Prof. Shashua received the MARR Prize Honorable Mention in 2001, the Kaye Innovation Award in 2004, and the Landau Award in Exact Sciences in 2005. Since 2010 Prof. Shashua is the co-founder, Chief Technology Officer and Chairman of OrCam, an Israeli company that recently launched an assistive product for the visually impaired based on advanced computerized visual interpretation capabilities.---This talk is co-hosted by the Center for Brains, Minds, and Machines (CBMM) and MIT Quest for Intelligence. 10-250 Belfer sarah_donahue@hks.harvard.edu

March 20, 2019

Genomic analysis pipeline: overview, challenges, and proposed solutions

Idoia Ochoa
Univ of Illinois Urbana
11:30A
- 1:00P

Location

32-G575
Add to Calendar 2019-03-20 11:30:00 2019-03-20 13:00:00 America/New_York Genomic analysis pipeline: overview, challenges, and proposed solutions In this talk we will give an overview of the genomic analysis pipeline, from data generation to its analysis. In doing so, we will identify the main challenges arising in the genomic setting. These include dealing with errors introduced during the sequencing process, designing state-of-the-art specialized compressors to deal with the ever growing amount of genomic data being generated, as well as improving the accuracy of the current tools used for the analysis.We will emphasize on some of the effort being carried out by the international community to design a standard under the International Standardization Organization (ISO), denoted MPEG-G, for genomic information representation. We will also introduce a new filtering tool intended to improve the accuracy of variant calling, the last step of the genomic analysis pipeline whose output is generally the starting point for analysis in the personalized medicine paradigm. We will conclude the talk with some thoughts of where the community is going and the challenges that we will face in the near future.Dr. Idoia Ochoa is an assistant professor in the Electrical and Computer Engineering department at the University of Illinois at Urbana-Champaign (UIUC). Prior to that, Dr. Ochoa obtained a Ph.D. from the Electrical Engineering Department at Stanford University, in 2016. She received her M.Sc. from the same department in 2012. During her time at Stanford she conducted internships at Google and Genapsys, and served as a technical consultant for the HBO's TV show ``Silicon Valley''.Dr. Ochoa's main interests lie in the field of bioinformatics and computational genomics, and she uses a multidisciplinary approach that combines tools from information theory, signal processing, and machine learning, among others. Her main contributions include the design of several lossless and lossy compression schemes tailored to raw and aligned genomic data, as well as denoising schemes to reduce the noise present in such data. She has also developed compression schemes for other types of omics data, as well as schemes to perform similarity queries on compressed databases without the need of decompression. Finally, she has developed new methods for the discovery of gene networks specific to different cancer types.Dr. Ochoa is also part of the group of experts who is developing, under the International Standardization Organization (ISO), the new MPEG-G standard for genomic information representation. She is also part of the Center for Science of Information, an NSF Science and Technology Center, and she is the recipient of several US-based grants. 32-G575 Belfer sarah_donahue@hks.harvard.edu

Planning from a Few Samples

Geoff Gordon
Microsoft Research Montreal
3:00P
- 4:00P

Location

32-D463 (Stata Center - Star Conference Room)
Add to Calendar 2019-03-20 15:00:00 2019-03-20 16:00:00 America/New_York Planning from a Few Samples Abstract: We study the problem of planning when experience is expensive: we are given a few trajectories sampled from our target environment, and we wish to compute a good policy or value function. In such problems, we can’t afford to use “deep RL” methods like DQN or REINFORCE: these methods can need millions or even billions of samples to reach a useful solution. On the other hand, we are willing to assume that we can gather a representative sample of trajectories; e.g., we might have access to an expert who can steer the system into interesting regions of state space. Given these problem characteristics, we seek an algorithm that is reliable, fast, and data-efficient. To this end, we design a new approximate optimality criterion for planning: we frame the problem as a fixed-point iteration based on a variational inequality, and use projection and subsampling to reduce the problem to a tractable size. We demonstrate that our approach can solve simple planning problems quickly and reliably from moderate amounts of data.Bio: Dr. Gordon is Research Director of Microsoft Research Montreal. He is on leave from the Department of Machine Learning at Carnegie Mellon University, where he has served as Professor, Interim Department Head, and Associate Department Head for Education. His research interests include artificial intelligence, statistical machine learning, game theory, multi-robot systems, and planning in probabilistic, adversarial, and general-sum domains. His previous appointments include Visiting Professor at the Stanford Computer Science Department and Principal Scientist at Burning Glass Technologies in San Diego. Dr. Gordon received his B.A. in Computer Science from Cornell University in 1991, and his Ph.D. in Computer Science from Carnegie Mellon University in 1999. 32-D463 (Stata Center - Star Conference Room) Belfer sarah_donahue@hks.harvard.edu

CBMM Special Seminar: Self-Learning Systems

Dr. Demis Hassabis
4:00P
- 5:00P

Location

34-101
MIT Lecture Hall 34-101, 50 Vassar Street, Cambridge MA 02139
Add to Calendar 2019-03-20 16:00:00 2019-03-20 17:00:00 America/New_York CBMM Special Seminar: Self-Learning Systems Speaker Biography: Demis is a former child chess prodigy, who finished his A-levels two years early before coding the multi-million selling simulation game Theme Park aged 17. Following graduation from Cambridge University with a Double First in Computer Science, he founded the pioneering videogames company Elixir Studios producing award winning games for global publishers such as Vivendi Universal. After a decade of experience leading successful technology startups, Demis returned to academia to complete a PhD in cognitive neuroscience at UCL, followed by postdocs at MIT and Harvard, before founding DeepMind. His research into the neural mechanisms underlying imagination and planning was listed in the top ten scientific breakthroughs of 2007 by the journal Science. Demis is a 5-times World Games Champion, a Fellow of the Royal Society of Arts, and the recipient of the Royal Society’s Mullard Award and the Royal Academy of Engineering's Silver Medal.---This talk is co-hosted by the Center for Brains, Minds, and Machines (CBMM) and MIT Quest for Intelligence. 34-101 Belfer sarah_donahue@hks.harvard.edu

March 21, 2019

A unified program synthesis framework for automating end-user programming tasks

Xinyu Wang
University of Texas, Austin
4:00P
- 5:00P

Location

32-G449
Patil/Kiva
Add to Calendar 2019-03-21 16:00:00 2019-03-21 17:00:00 America/New_York A unified program synthesis framework for automating end-user programming tasks Abstract: Programming has started to become an essential skill for an increasing number of people, including novices without formal programming background. As a result, there is an increasing need for technology that can provide basic programming support to such non-expert computer end-users. Program synthesis, as a technique for automatically generating programs from high-level specifications, has been used to automate real-world programming tasks in a number of application domains (such as spreadsheet programming and data science) that non-expert users struggle with. However, developing specialized synthesizers for these domains is notoriously hard. In this talk, I will describe a unified program synthesis framework that can be applied broadly to automating tasks across different application domains. This framework is also efficient and achieves orders of magnitude improvement in terms of synthesis speed compared to existing techniques. In particular, I have used this framework to build synthesizers for three different application domains and achieved up to 450x speed-up compared to state-of-the-art synthesis techniques. Bio: Xinyu Wang is a PhD candidate at UT Austin advised by Isil Dillig. He works at the intersection of programming languages, software engineering and formal methods. He is interested in developing foundational program synthesis techniques that are applicable to automating real-world programming tasks. 32-G449 Belfer sarah_donahue@hks.harvard.edu

Google PhD Panel @ CSAIL, March 21st 4-5pm in Star (32-D463)

Panel of PhD Researchers from Google
Google-CSAIL Alliances
4:00P
- 5:00P

Location

Star Seminar Room(32-D463)
Star Seminar Room(32-D463)
Add to Calendar 2019-03-21 16:00:00 2019-03-21 17:00:00 America/New_York Google PhD Panel @ CSAIL, March 21st 4-5pm in Star (32-D463) What: Google PhD Panel @ CSAILWhen: 4-5pm on Thursday, March 21st Where: CSAIL STATA Center, Star Seminar Room (32-D463)Abstract: Google's PhD engineers work on an incredibly wide variety of technologies and research areas. If you are curious about what it is like incorporating research in daily technical challenges, then please join us on Thursday, March 21st from 4-5pm at CSAIL (Star Seminar Room; 32-D463) as Googlers from across the business share day-in-the-life stories about their work and provide an inside look on the Google experience.If you would like to RSVP with Google ahead of time, you may do so here: https://docs.google.com/forms/d/e/1FAIpQLSfmsOQjhK5xmXiZn-rX5BUVnNxm_9_ZWfguLgwQJr5wcOtjvw/viewform Registration is appreciated but not required. If you have any questions about the panel please feel free to reach out to me directly at pmarsena@mit.edu Star Seminar Room(32-D463) Belfer sarah_donahue@hks.harvard.edu

Quantitative Computational Syntax: some case studies

Paola Merlo
University of Geneva
5:00P
- 6:00P

Location

46-5165
Add to Calendar 2019-03-21 17:00:00 2019-03-21 18:00:00 America/New_York Quantitative Computational Syntax: some case studies In the computational study of intelligent behaviour, the domain of language is distinguished by the complexity of the representations and the sophistication of the domain theory that is available. It also has a large amount of observational data available for many languages. The main scientific challenge for computational approaches to language is the creation of theories and methods that fruitfully combine large-scale, corpus-based approaches with the linguistic depth of more theoretical methods. I report here on some recent and current work on word order universals and argument structure that exemplifies the quantitative computational syntax approach. First, we demonstrate that typological frequencies of noun phrase orderings, universal 20, are systematically correlated to abstract syntactic principles at work in structure building and movement. Then, we investigate higher level structural principles of efficiency and complexity. In a large-scale, computational study, we confirm a trend towards minimization of the distance between words, in time and across languages. In the third case study, much like the comparative method in linguistics, cross-lingual corpus investigations take advantage of any corresponding annotation or linguistic knowledge across languages. We show that corpus data and typological data involving the causative alternation exhibit interesting correlations explained by the notion of spontaneity of an event. Finally, time permitting, I will discuss current work investigating on whether the notion of similarity in the intervention theory of locality is related to current notions of similarity in word embedding space.BioPaola Merlo is faculty in the linguistics department of the University of Geneva. She is the head of the interdisciplinary research group Computational Learning and Computational Linguistics (CLCL). The group is concerned with interdisciplinary research combining linguistic modelling with machine learning techniques. The scope of her current research includes fundamental issues in the statistical nature of language, empirical evaluations of linguistic proposal about the lexical semantics of verbs and language universals of word order and statistical models of syntactic and semantic parsing. Prof. Merlo has been the editor of the journal of the Association for Computational Linguistics, Computationall Linguistics, and has been member of the executive committee of the EACL and of the ACL. Prof. Merlo holds a doctorate in Computational Linguistics from the University of Maryland, USA. She has been associate research fellow at the Institute for Cognitive Science at the University of Pennsylvania, and has been visiting scholar at Rutgers, Edinburgh, and Stanford.-----About CompLang: CompLang is a student-run discussion group on language and computation. The aim of the group is to bring together the language community at MIT and nearby, learn about each other's research, and foster cross-laboratory collaborations. The broad topic of the meetings is using computational models to study scientific questions about language. We will discuss work from computational linguistics, psycholinguistics, cognitive science, natural language processing and formal linguistics. Please visit http://complang.mit.edu for future events. 46-5165 Belfer sarah_donahue@hks.harvard.edu

March 22, 2019

Proactive Work Stealing for Futures

I-Ting Angelina Lee
Washington University in St. Louis, Department of Computer Science and Engineering
11:00A
- 12:00P

Location

32-G463 (Star)
Add to Calendar 2019-03-22 11:00:00 2019-03-22 12:00:00 America/New_York Proactive Work Stealing for Futures Refreshments, 10:45amAbstract:The use of futures provides a flexible way to express parallelism and can generate arbitrary dependences among parallel subcomputations. The additional flexibility that futures provide comes with a cost, however. When scheduled using work stealing, a program with futures, compared to a program that uses only fork-join parallelism, can incur a much higher number of "deviations," a better metric for evaluating the performance of parallel executions. Consequently, most practical implementations of task parallel platforms do not utilize the classic work stealing algorithm to handle the use of futures.In this work, we propose proactive work stealing for futures and show that, the proposed algorithm can provide provably efficient execution time and equal or better bounds on the number of deviations compared to classic work stealing.Bio:I-Ting Angelina Lee is an assistant professor in the Computer Science and Engineering department in Washington University in St. Louis. Her research focuses on advancing software technologies for parallel computing. She is interested in many aspects of parallel computing, including designing programming models and linguistic constructs to simplify parallel programming, developing runtime and operating system support to execute multithreaded programs efficiently, and building software tools to aid debugging and performance engineering of multithreaded code. Belfer sarah_donahue@hks.harvard.edu

Brains, Minds + Machines Seminar Series: Probing memory circuits in the primate brain: from single neurons to neural networks

Julio Martinez-Trujillo
Cognitive Neurophysiology Laboratory, Western University
4:00P
- 5:00P

Location

46-3002
Singleton Auditorium (46-3002), MIT Bldg 46-3002, 43 Vassar Street, Cambridge MA 02139
Add to Calendar 2019-03-22 16:00:00 2019-03-22 17:00:00 America/New_York Brains, Minds + Machines Seminar Series: Probing memory circuits in the primate brain: from single neurons to neural networks Abstract: The brain’s memory systems are like time machines for thought: they transport sensory experiences from the past to the present, to guide our current decisions and actions. Memories have been classified into long-term, stored for time intervals of days, months, or years, and short-term, stored for shorter intervals of seconds or minutes. There is a consensus that these two types of memories involve different brain systems and have different underlying mechanisms. In this talk I will present data from different experiments in non-human primates examining brain circuits and mechanisms of both short-term memory and long-term memory.Biography: Julio Martinez-Trujillo is Professor in the Department of Physiology and Pharmacology and Scientist at the Robarts Research Institute. He holds an Academic Chair in Autism. Prior to joining Western University in 2014, he was Associate Professor in the Department of Physiology and Canada Research Chair in Neuroscience at McGill University. 46-3002 Belfer sarah_donahue@hks.harvard.edu

March 28, 2019

Julia Programming -- Humans compose when software does

Professor Alan Edelman
MIT
7:00P
- 9:00P

Location

Kiva
Add to Calendar 2019-03-28 19:00:00 2019-03-28 21:00:00 America/New_York Julia Programming -- Humans compose when software does IEEE Computer Society and GBC/ACM7:00 PM, Thursday, 28 March 2019MIT Room 32-G449 (Kiva)This talk will be webcast on the MIT CSAIL Youtube channel http://www.youtube.com/channel/UCYs2iUgksAhgoidZwEAimmg/live beginning at 7pm.Julia Programming -- Humans compose when software doesAlan Edelman, MITWe have found that the Julia language, through its composable abstractions, multiple dispatch, and expressive typing is accelerating the formation of bridges at MIT. As physical bridges may be made from steel and provide infrastructure for many to cross, it seems Julia's language elements are the medium that allow people to connect to solve hard problems. In this talk we will give a quick introduction to Julia, and then speak in depth about some of Julia's special features.Professor Alan Edelman (Math,CSAIL,CCE) loves to prove pure and applied theorems, program computers and everything in between. He has received many prizes including a Gordon Bell Prize, a Householder Prize, and a Charles Babbage Prize, is a fellow of IEEE, AMS, and SIAM, and is a founder and chief scientist of Interactive Supercomputing and Julia Computing, Inc. He passionately believes in more interactions between classical computer science and computational science. Edelman's research interests include Julia, machine learning, high-performance computing, numerical computation, linear algebra, random matrix theory and geometry. He has consulted for IBM, Pixar, Akamai, Intel, and Microsoft among other corporations.This joint meeting of the Boston Chapter of the IEEE Computer Society and GBC/ACM will be held in MIT Room 32-G449 (the Kiva conference room on the 4th floor of the Stata Center, buildng 32 on MIT maps) . You can see it on this map of the MIT campus.Up-to-date information about this and other talks is available online at http://ewh.ieee.org/r1/boston/computer/. You can sign up to receive updated status information about this talk and informational emails about future talks at http://mailman.mit.edu/mailman/listinfo/ieee-cs, our self-administered mailing list. Belfer sarah_donahue@hks.harvard.edu

April 01, 2019

New horizons for electronic systems: Devices, design methods and applications

Giovanni De Micheli
EPFL Lausanne, Switzerland
1:00P
- 2:00P

Location

32-G882
Add to Calendar 2019-04-01 13:00:00 2019-04-01 14:00:00 America/New_York New horizons for electronic systems: Devices, design methods and applications Three new game changers enable the design of emerging electronic systems:the use of new devices and materials, the combined integration ofcircuits and sensors and the application of new design methods and toolsthat support the correct composition of complex systems from regularcomponents. These ingredients are crucial to design electronic systems in variouskey areas, such as precision medicine, the Internet of Things, and environmentalmonitoring/protection.I will first present the landscape of emerging areas, such as cybermedical systems. Then I will describe enhanced-functionality devicesexploiting new materials and geometries and their application into circuits,as well as new design methods and tools for the creation of complex systems.Finally, I will focus on new methods for synthesis of integratedcircuits based on majority algebra and i willconclude by presenting results from experimental design tools.Short bioGiovanni De Micheli is Professor and Director of the Institute of Electrical Engineering at EPFL Lausanne, Switzerland and program leader of the Nano-Tera.ch program. Previously, he was Professor of Electrical Engineering at Stanford University.Prof. De Micheli is a Fellow of ACM and IEEE, a member of the Academia Europaea and an International Honorary member of the American Academy of Arts and Sciences. His research interests include several aspects of design technologies for integrated circuits and systems, such as synthesis for emerging technologies, networks on chips and quantum computing.Prof. De Micheli is the recipient of various awards, such as the 2016 IEEE/CS Harry Goode award for seminal contributions to design and design tools of Networks on Chips, the 2016 EDAA Lifetime Achievement Award, and the D. Pederson Award for the best paper on the IEEE Transactions on CAD/ICAS in both 1987 and 2018.*Refreshments will be provided at 12:45 pm 32-G882 Belfer sarah_donahue@hks.harvard.edu

April 03, 2019

Lineage calling can identify antibiotic resistant clones within minutes

Karel Brinda, Harvard TH Chan School of Public Health
11:30A
- 1:00P

Location

32-G575
Add to Calendar 2019-04-03 11:30:00 2019-04-03 13:00:00 America/New_York Lineage calling can identify antibiotic resistant clones within minutes Surveillance of circulating drug resistant bacteria is essential for healthcare providers to deliver effective empiric antibiotic therapy. However, the results of surveillance may not be available on a timescale that is optimal for guiding patient treatment. Here we present a method for inferring characteristics of an unknown bacterial sample by identifying the presence of sequence variation across the genome that is linked to a phenotype of interest, in this case drug resistance. We demonstrate an implementation of this principle using sequence k-mer content, matched to a database of known genomes. We show this technique can be applied to data from an Oxford Nanopore device in real time and is capable of identifying the presence of a known resistant strain in 5 minutes, even from a complex metagenomic sample. This flexible approach has wide application to pathogen surveillance and may be used to greatly accelerate diagnoses of resistant infections.Dr. Karel Brinda’s research lies at the intersection of computer science, applied mathematics, biology and epidemiology. He develops methods for rapid prediction of antibiotic resistance from sequencing data and for epidemiological surveillance. Dr. Brinda’s previous work focused on resource-frugal methods for sequence data analysis.Dr. Brinda is a Research Associate in the Harvard Chan School of Public Health and Harvard Medical School. He received PhD in computer science from Universite Paris-Est, France. Besides bioinformatics, Dr. Brinda also works on methods for automatic generation of tactile maps for blind users. 32-G575 Belfer sarah_donahue@hks.harvard.edu

EECS Special Seminar: Secure Computer Hardware in the Age of Pervasive Security Attacks

Mangjia Yan
University of Illinois at Urbana-Champaign
4:00P
- 5:00P

Location

32-G449
Add to Calendar 2019-04-03 16:00:00 2019-04-03 17:00:00 America/New_York EECS Special Seminar: Secure Computer Hardware in the Age of Pervasive Security Attacks Abstract: Recent attacks such as Spectre and Meltdown have shown how vulnerable modern computer hardware is. The root cause of the problem is that computer architects have traditionally focused on performance and energy efficiency. Security has never been a first-class requirement. Moving forward, however, this has to radically change: we need to rethink computer architecture from the ground-up for security.As an example of this vision, in this talk, I will focus on speculative execution in out-of-order processors --- a core computer architecture technology that is the target of the recent attacks. I will describe InvisiSpec, the first robust hardware defense mechanism against speculative (a.k.a transient) execution attacks. The idea is to make loads invisible in the cache hierarchy, and only reveal their presence at the point when they are safe. Once an instruction is deemed safe, our hardware is able to cheaply modify the cache coherence state in a consistent manner. Further, to reduce the cost of InvisiSpec and increase its protection coverage, I propose Speculative Taint Tracking (STT). This is a novel form of information flow tracking that is specifically designed for speculative execution. It reduces cost by allowing tainted instructions to become safe early, and by effectively leveraging the predictor hardware that is ubiquitous in modern processors. Further improvements of InvisiSpec-STT can be attained with new compiler techniques. Finally, I will conclude my talk by describing ongoing and future directions towards designing secure processors. Host: Martin RinardBIO: Mengjia Yan is a Ph.D. student at the University of Illinois at Urbana-Champaign (UIUC), working with Professor Josep Torrellas. Her research interest lies in the areas of computer architecture and hardware security, with a focus on defenses against transient execution attacks and cache-based side channel attacks. Her work has appeared in some of the top venues in computer architecture and security, and has sparked a large research collaboration initiative between UIUC and Intel. Mengjia received the UIUC College of Engineering Mavis Future Faculty Fellow, the Computer Science W.J. Poppelbaum Memorial Award, a MICRO TopPicks in Computer Architecture Honorable Mention, and was invited to participate in two Rising Stars workshops. 32-G449 Belfer sarah_donahue@hks.harvard.edu

April 11, 2019

April 17, 2019

April 18, 2019

Kyrix: A Detail-on-Demand Visualization System

Professor Michael Stonebraker
MIT
7:00P
- 9:00P

Location

32-G449
Kiva
Add to Calendar 2019-04-18 19:00:00 2019-04-18 21:00:00 America/New_York Kyrix: A Detail-on-Demand Visualization System IEEE Computer Society and GBC/ACM7:00 PM, Thursday, 18 April 2019MIT Room 32-G449 (Kiva)This talk will be webcast on the MIT CSAIL Youtube channel http://www.youtube.com/channel/UCYs2iUgksAhgoidZwEAimmg/live beginning at 7pm.Kyrix: A Detail-on-Demand Visualization SystemMike Stonebraker, MITKyrix is a "detail-on-demand" visualization system. As such, it supports a pan/zoom/jump interface similar to Google Maps. The benefit of such systems is the interface can be learned quickly and no user manual is required. Also, it facilitates browsing over large amounts of data, drilling into areas of interest to get more information. Although Kyrix is a natural on geographic data, it can also be used on any kind of data that is amenable to a two-dimensional layout.Many detail-on-demand systems have been constructed in the past; mostly hard-coded to support a single application. In contrast, Kyrix is easily programmable to support any kinds of objects, not just maps or satellite imagery.Kyrix is now operational, and we will demo the system on a Massachusetts General Hospital (MGH) application using 30T of EEG sleep study data. We also have a genomics browser (in conjunction with Paradigm4), a browser into internet traffic (in conjunction with Recorded Futures), and a browser for the MIT Data Civilizer data integration system.We also, show how we achieve end-to-end response time of 500 msec. or less and discuss the concept of supporting multiple co-ordinated viewports on the screen at once.More details can be obtained from our CIDR paper on Kyrix, available from http://cidrdb.org/cidr2019/papers/p70-tao-cidr19.pdfDr. Stonebraker has been a pioneer of data base research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T. he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, the SciDB array DBMS, and the Data Tamer data curation system. Presently he serves as Chief Technology Officer of Paradigm4 and Tamr, Inc. Professor Stonebraker was awarded the ACM System Software Award in 1992 for his work on INGRES. Additionally, he was awarded the first annual SIGMOD Innovation award in 1994, and was elected to the National Academy of Engineering in 1997. He was awarded the IEEE John Von Neumann award in 2005 and the 2014 Turing Award, and is presently an Adjunct Professor of Computer Science at M.I.T.See http://amturing.acm.org/award_winners/stonebraker_1172121.cfm for more biographical details.This joint meeting of the Boston Chapter of the IEEE Computer Society and GBC/ACM will be held in MIT Room 32-G449 (the Kiva conference room on the 4th floor of the Stata Center, building 32 on MIT maps) . You can see it on this map of the MIT campus.Up-to-date information about this and other talks is available online at http://ewh.ieee.org/r1/boston/computer/. You can sign up to receive updated status information about this talk and informational emails about future talks at http://mailman.mit.edu/mailman/listinfo/ieee-cs, our self-administered mailing list. 32-G449 Belfer sarah_donahue@hks.harvard.edu

April 24, 2019

April 26, 2019

Communicating gradients for learning using activity dynamics

Blake Richards
Canadian Institute for Advanced Research (CIFAR)
4:00P
- 5:00P

Location

Singleton Auditorium (46-3002)
MIT Bldg 46-3002, 43 Vassar Street, Cambridge MA 02139
Add to Calendar 2019-04-26 16:00:00 2019-04-26 17:00:00 America/New_York Communicating gradients for learning using activity dynamics Abstract: Theoretical and empirical results in the neural networks literature demonstrate that effective learning at a real-world scale requires changes to synaptic weights that approximate the gradient of a global loss function. For neuroscientists, this means that the brain must have mechanisms for communicating loss gradients between regions, either explicitly or implicitly. Here, I describe our research into potential means of communicating loss gradients using the dynamics of activity in a population of neurons. Using a combination of computational modelling and two-photon imaging data, I will present evidence suggesting that the neocortex may encode loss gradients related to motor learning and sensory prediction using the temporal derivative of activity in populations of pyramidal neurons. Singleton Auditorium (46-3002) Belfer sarah_donahue@hks.harvard.edu