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2019-11-18 9:00:00
2019-11-18 16:00:00
America/New_York
CSAIL Alliances Member BT Insights
Belfer
sarah_donahue@hks.harvard.edu
Events
November 18, 2019
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2019-11-18 14:00:00
2019-11-18 15:00:00
America/New_York
How to Modernize Compiler Technology
Abstract: In this talk, I will show the path to the modernization of one important compiler technique -- vectorization. Vectorization was first introduced in the era of Cray vector processors during the 1980's. While vector supercomputers needed large vectors, which are mainly available by parallelizing loops, modern SIMD instructions efficiently work on short vectors. Thus, in 2000, Larsen et. al. introduced a new technique known as Superword Level Parallelism (SLP) based vectorization to fill this gap. First, I will show how we can take advantage of the power of modern computers for compilation, by using more accurate but expensive techniques to improve SLP vectorization. Due to the hardware resource constraints of the era, like many other compiler optimizations, SLP implementation was a greedy algorithm. In 2018, we introduced goSLP, which uses integer linear programming to find an optimal instruction packing strategy. Next, I will show how to truly modernize a compiler by automatically learning the necessary components of the compiler with Ithemal and Vemal. The actual cost of execution in a modern out-of-order, superscalar processor is much more complex than a linear model. Manually building such cost models as well as manually developing compiler optimizations is costly, tedious and is hard to keep up with the architectural changes. Ithemal is the first learnt cost model for predicting the throughput of x86 basic blocks with minimal human effort. Vemal is the first learnt policy for end-to-end vectorization as opposed to tuning heuristics. These data-driven techniques achieve state-of-the-art results while also reducing the development and maintenance burden of the compiler developer.
32-G449 Kiva/Patil
Belfer
sarah_donahue@hks.harvard.edu
Talk: Jascha Sohl-Dickstein
Google Brain
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2019-11-18 16:30:00
2019-11-18 17:30:00
America/New_York
Talk: Jascha Sohl-Dickstein
Title: Understanding infinite width neural networksAbstract: As neural networks become wider their accuracy improves, and their behavior becomes easier to analyze theoretically. I will give an introduction to a rapidly growing body of work which examines the learning dynamics and prior over functions induced by infinitely wide, randomly initialized, neural networks. Core results that I will discuss include: that the distribution over functions computed by a wide neural network often corresponds to a Gaussian process with a particular compositional kernel, both before and after training; that the predictions of wide neural networks are linear in their parameters throughout training; and that this perspective enables analytic predictions for how trainability of finite width networks depends on hyperparameters and architecture. These results provide for surprising capabilities -- for instance, the evaluation of test set predictions which would come from an infinitely wide trained neural network without ever instantiating a neural network, or the rapid training of 10,000+ layer convolutional networks. I will argue that this growing understanding of neural networks in the limit of infinite width is foundational for future theoretical and practical understanding of deep learning.
Seminar Room D463 (Star)
Belfer
sarah_donahue@hks.harvard.edu
November 19, 2019
Model-based imaging and image-based modelling
Daniel Alexander
University College London
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2019-11-19 11:30:00
2019-11-19 12:30:00
America/New_York
Model-based imaging and image-based modelling
My talk will update on three current research topics. 1.Microstructure imaging, which uses mathematical or computationalmodelling and machine learning to estimate and map microstructuralfeatures of tissue. Examples from my group's work include NODDI (Zhanget al Neuroimage 2012) for brain imaging and VERDICT (Panagiotaki etal Cancer Research 2014) for cancer imaging. I will describe movestowards a new paradigm combining multi-contrast measurements throughsophisticated computational models exploiting machine learning. 2.Data-driven disease progression models (e.g. Fonteijn et al Neuroimage2012; Young et al Brain 2014; Lorenzi et al Neuroimage 2017), whichaim to piece together longitudinal pictures of disease fromcross-sectional or short-term longitudinal data sets and thus gaindisease understanding, stratification systems, and predictivepower. The recent Subtype and Stage Inference (SuStaIn - Young et alNature Comms 2018) algorithm extends the idea to identify diseasesubgroups defined by distinct longitudinal trajectories of change. 3Image Quality Transfer (Alexander et al Neuroimage 2017; Tanno et alMICCAI 2017), which uses machine learning to estimate a high qualityimage, e.g. that we would have acquired from a one-off super-poweredscanner, from a lower quality image, e.g. acquired on a standardhospital scanner.
32-D507
Belfer
sarah_donahue@hks.harvard.edu
November 20, 2019
Human-Computer Interaction for User Experience Design Online Course | MIT CSAIL Alliances
Daniela Rus, Randall Davis, Fredo Durand, Jim Glass, Daniel Jackson, David Karger, Wojciech Matusik and Stefanie Mueller
MIT CSAIL
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2019-11-20 0:00:00
2019-11-20 23:45:00
America/New_York
Human-Computer Interaction for User Experience Design Online Course | MIT CSAIL Alliances
Looking to enhance your user experience? Learn how to implement human computer interaction (HCI) concepts to gain a head start on future developments with MIT CSAIL.The HCI course will give you the opportunity to gain access to CSAIL faculty research, review a host of HCI concepts, earn an HCI certificate and learn how to integrate effective speech and vision tools into your current practice. The course is your opportunity to go deep into the HCI interface and influence the future of the user experience.
Belfer
sarah_donahue@hks.harvard.edu
The Structure of Optimal Private Tests for Simple Hypotheses
Audra McMillan
BU
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2019-11-20 16:00:00
2019-11-20 17:00:00
America/New_York
The Structure of Optimal Private Tests for Simple Hypotheses
Abstract: Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. In this talk we’ll address a basic question about privately testing simple hypotheses: given two distributions P and Q, and a privacy level \epsilon, how many i.i.d. samples are needed to distinguish P from Q subject to \epsilon-differential privacy, and what sort of tests have optimal sample complexity? Specifically, we'll characterize this sample complexity up to constant factors in terms of the structure of P and Q and the privacy level \epsilon, and show that this sample complexity is achieved by a certain randomized and clamped variant of the log-likelihood ratio test. This result is an analogue of the classical Neyman–Pearson lemma in the setting of private hypothesis testing. The characterization applies more generally to hypothesis tests satisfying essentially any notion of algorithmic stability, which is known to imply strong generalization bounds in adaptive data analysis, and thus our results have applications even when privacy is not a primary concern.Joint work with Clement Canonne, Gautam Kamath, Adam Smith and Jonathan Ullman.
32-G575
Belfer
sarah_donahue@hks.harvard.edu
November 21, 2019
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2019-11-21 8:00:00
2019-11-21 17:00:00
America/New_York
AI and the Work of the Future Congress 2019
The MIT Computer Science and Artificial Intelligence Lab (CSAIL), MIT Work of the Future (WOTF), MIT Initiative on the Digital Economy (IDE) present the MIT AI & the Work of the Future Congress.The AI age is here, transforming work and the skills required for humans to thrive. Join us as we gather leading academics, business leaders, entrepreneurs, education and labor leaders, and policy makersto explore the impact and future trends of technological disruption, rethink the nature of work, and chart a course for catalyzing innovation for inclusion and economic opportunity for all.Join MIT on November 21 to explore, rethink, and chart a course for the work of the future.Members of the MIT community can reach out to alliances@csail.mit.edu for ticket information.
Kresge Auditorium
Belfer
sarah_donahue@hks.harvard.edu
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2019-11-21 13:00:00
2019-11-21 13:45:00
America/New_York
Machine Learning for the Detection of Clinical Deterioration
Title: Machine Learning for the Detection of Clinical DeteriorationBiography: Farah Shamout is an Assistant Professor & Emerging Scholar at NYU Abu Dhabi in Computer Engineering, who recently set up the Machine Learning in Healthcare Research (MLHR) Laboratory. Prior to joining NYU Abu Dhabi, Farah completed her doctoral studies in Engineering Science at the University of Oxford as a Rhodes Scholar. She received her Bachelor of Science cum laude in Computer Engineering from New York University Abu Dhabi. Her research focuses on designing robust, interpretable, and generalisable machine learning models to inform decision-making in healthcare.Abstract: Clinical deterioration often goes undetected on hospital wards. Assessment of clinical deterioration preceding specific adverse outcomes, such as unplanned ICU admission, cardiac arrest, or mortality, is traditionally done using Early Warning Score (EWS) systems. Those scores consider a limited set of physiological variables, do not account for temporal changes leading to adverse events, are heuristic in nature, or do not generalize well for unseen patient cohorts.Increased access to Electronic Health Records (EHR) motivates the development of more robust and flexible machine learning techniques to improve predictions of clinical deterioration. This talk will focus on the opportunities and challenges of modelling EHR data and the results of the work in which we develop EWS systems using deep learning. It will also present ongoing projects at the MLHR Lab.Room: 32-G882
32-G882
Belfer
sarah_donahue@hks.harvard.edu
Synthetic Control (NeurIPS 2019 tutorial)
Devavrat Shah and Alberto Abadie
MIT
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2019-11-21 15:00:00
2019-11-21 17:00:00
America/New_York
Synthetic Control (NeurIPS 2019 tutorial)
Abstract:The synthetic control method, introduced in Abadie and Gardeazabal(2003), has emerged as a popular empirical methodology for estimating a causal effects with observational data, when the “gold standard” of a randomized control trial is not feasible. In a recent survey on causal inference and program evaluation methods in economics, Athey and Imbens (2015) describe the synthetic control method as “arguably the most important innovation in the evaluation literature in the last fifteen years”. While many of the most prominent application of the method, as well as its genesis, were initially circumscribed to the policy evaluation literature, synthetic controls have found their way more broadly to social sciences, biological sciences, engineering and even sports. However, only recently, synthetic controls have been introduced to the machine learning community through its natural connection to matrix and tensor estimation in Amjad, Shah and Shen (2017) as well as Amjad, Misra, Shah and Shen (2019).In this tutorial, we will survey the rich body of literature on methodical aspects, mathematical foundations and empirical case studies of synthetic controls. We willprovide guidance for empirical practice, with special emphasis on feasibility and data requirements, and characterize the practical settings where synthetic controls may be useful and those where they may fail. We will describe empirical case studies from policy evaluation, retail, and sports. Moreover, we will discuss mathematical connections of synthetic controls to matrix and tensor estimation, high dimensional regression, and time series analysis. Finally, we will discuss how synthetic controls are likely to be instrumental in the next wave of development in reinforcement learning using observational data.Bios:Devavrat Shah is a Professor with the department of Electrical Engineering and Computer Science and Director of Statistics and Data Science at the Massachusetts Institute of Technology. His current research interests are at the interface of Statistical Inference and Social Data Processing. His work has been recognized through prize paper awards in Machine Learning, Operations Research and Computer Science, as well as career prizes 2008 ACM Sigmetrics Rising Star Award, 2010 Erlang prize from the INFORMS Applied Probability Society and 2019 ACM Sigmetrics Test of Time Paper Award. He is a distinguished young alumni of his alma mater IIT Bombay. He has authored monographs “Gossip algorithms” and “Explaining the success of nearest neighbors in prediction’’. He co-founded machine learning start-up Celect, Inc. which is part of Nike, Inc. since August 2019. Alberto Abadie is an econometrician and empirical microeconomist, with broad disciplinary interests that span economics, political science and statistics. Professor Abadie received his Ph.D. in Economics from MIT in 1999. Upon graduating, he joined the faculty at the Harvard Kennedy School, where he was promoted to full professor in 2005. He returned to MIT in 2016, where he is Professor of Economics and Associate Director of the Institute for Data, Systems, and Society (IDSS).His research areas are econometrics, statistics, causal inference, and program evaluation. Professor Abadie’s methodological research focuses on statistical methods to estimate causal effects and, in particular, the effects of public policies, such as labor market, education, and health policy interventions. He is Associate Editor of Econometrica and AER: Insights, and has previously served as Editor of the Review of Economics and Statistics and Associate Editor of the Journal of Business and Economic Statistics. He is a Fellow of the Econometric Society.
32-G449 (Stata Center - Patil/Kiva Conference Room)
Belfer
sarah_donahue@hks.harvard.edu
Artificial Intelligence and the “Barrier of Meaning”
Melanie Mitchell
Portland State University, Santa Fe Institute
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2019-11-21 15:00:00
2019-11-21 16:00:00
America/New_York
Artificial Intelligence and the “Barrier of Meaning”
Abstract: In 1986, the mathematician and philosopher Gian-Carlo Rota wrote, “I wonder whether or when artificial intelligence will ever crash the barrier of meaning.” Here, the phrase “barrier of meaning” refers to a belief about humans versus machines: humans are able to “actually understand” the situations they encounter, whereas AI systems (at least current ones) do not possess such understanding. The internal representations learned by (or programmed into) AI systems do not capture the rich “meanings” that humans bring to bear in perception, language, and reasoning. In this talk I will assess the state of the art of artificial intelligence in several domains, and describe some of their current limitations and vulnerabilities, which can be accounted for by a lack of true understanding of the domains they work in. I will explore the following questions: (1) To be reliable in human domains, what do AI systems actually need to “understand”? (2) Which domains require human-like understanding? And (3) What does such understanding entail?Speaker Biography: Melanie Mitchell is Professor of Computer Science at Portland State University and External Professor at the Santa Fe Institute. Her research interests include artificial intelligence, machine learning, and complex systems, and she is the author of numerous scholarly papers and several books in these fields. Her general-audience book, Complexity: A Guided Tour, won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her newest book, Artificial Intelligence: A Guide for Thinking Humans, was recently published by Farrar, Straus, and Giroux. Melanie can be contacted via her website, melaniemitchell.me, and on Twitter, @melmitchell1.
Seminar Room G882 (Hewlett Room)
Belfer
sarah_donahue@hks.harvard.edu
November 22, 2019
What Needs to be Added to Machine Learning?
Leslie Valiant
Harvard University
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2019-11-21 19:00:00
2019-11-21 21:00:00
America/New_York
What Needs to be Added to Machine Learning?
IEEE Computer Society and GBC/ACM7:00 PM, Thursday, 21 November 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.What Needs to be Added to Machine Learning?Leslie Valiant, Harvard UniversitySupervised learning is a cognitive phenomenon which has proved amenable both to theoretical analysis and exploitation as a technology. However, not all of cognition can be accounted for directly by supervised learning. The question we ask here is whether one can build on the success of machine learning to address the broader goals of artificial intelligence. We regard reasoning as the major component of cognition that needs to be added. We suggest that the central challenge therefore is to unify the formulation of these two phenomena, learning and reasoning, into a single framework with a common semantics. In such a framework one would aim to learn rules with the same success that predicates can be learned by means of machine learning, and, at the same time, to reason with them with guarantees analogous to those of standard logic. We discuss how Robust Logic fulfils the role of such a theoretical framework. We also discuss the challenges of testing this experimentally on a significant scale, for tasks where one hopes to exceed the performance offered by learning alone.Leslie Valiant was educated at King's College, Cambridge; Imperial College, London; and at Warwick University where he received his Ph.D. in computer science in 1974. He is currently T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University, where he has taught since 1982. Before coming to Harvard he had taught at Carnegie Mellon University, Leeds University, and the University of Edinburgh. His work has ranged over several areas of theoretical computer science, particularly complexity theory, learning, and parallel computation. He also has interests in computational neuroscience, evolution and artificial intelligence and is the author of two books, Circuits of the Mind, and Probably Approximately Correct. He received the Nevanlinna Prize at the International Congress of Mathematicians in 1986, the Knuth Award in 1997, the European Association for Theoretical Computer Science EATCS Award in 2008, and the 2010 A. M. Turing Award. He is a Fellow of the Royal Society (London) and a member of the National Academy of Sciences (USA).See https://amturing.acm.org/award_winners/valiant_2612174.cfm for a more detailed description of his work or read "Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World".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.
Seminar Room G449 (Patil/Kiva)
Belfer
sarah_donahue@hks.harvard.edu
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2019-11-22 9:00:00
2019-11-22 13:00:00
America/New_York
AI and the Work of the Future Unconference
The MIT Computer Science and Artificial Intelligence Lab (CSAIL), MIT Work of the Future (WOTF), MIT Initiative on the Digital Economy (IDE) present the MIT AI & the Work of the Future Unconference, following the Nov 21st event.After the Congress, keep the conversation going. Cap off your time on campus by participating in the AI & Work of the Future Unconference on Friday, November 22.Join fellow participants and bring your biggest questions and most innovative ideas to this half-day event. No pre-determined topics, keynotes, or panels - we’ll amplify and facilitate the AI and future of work-driven conversations that matter most to you and your organization.New to the unconference format? Learn more about what’s in store here:https://www.forbes.com/sites/rebeccabagley/2014/08/18/how-unconferences-unleash-innovative-ideas/#57f57eeb645bMembers of the MIT community can reach out to alliances@csail.mit.edu for ticket information.
Belfer
sarah_donahue@hks.harvard.edu
November 25, 2019
A Speed-of-Light Internet Service Provider
Duke University and Emerald Innovations
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2019-11-25 14:00:00
2019-11-25 15:00:00
America/New_York
A Speed-of-Light Internet Service Provider
Abstract: A variety of network applications, including electronic commerce and games, are either enabled by or benefit greatly from low latency communications. Studies have shown, however, that over medium and long distances the time to send a packet from one city to another on the public Internet is typically more than three times larger than the lower bound implied by the speed of light in free space. Hence for applications like high-frequency trading, where the winner of a communications race receives all the benefits, special purpose networks have been deployed. For example, between New Jersey and Chicago a succession of networks has been deployed, first fiber-based and then microwave-based, with each network reducing latency by a fraction of a millisecond over the previous. This talk explores the possibility of using the same radio technology to build a network backbone spanning the 120 largest population centers in the United States. The design places radios on existing towers, using topographic maps to ensure line-of-sight connectivity between towers. The impact of weather on the network is evaluated using historical weather data. Our analysis suggests that it should be possible to achieve mean speeds of over 95% of the speed of light over medium and long distances at a transmission cost of under $1 per GB.Joint work with Sangeetha A. J., Anthony Aquirre, Waqar Aqeel, Nadi Bozkurt, Bala Chandrasekaran, Brighten Godfrey, Greg Laughlin, Ankit Singla, and Muhammad Tirmazi.Bio: Bruce Maggs received the S.B., S.M., and Ph.D. degrees in computer science from the Massachusetts Institute of Technology in 1985, 1986, and 1989, respectively. His advisor was Charles Leiserson. After spending one year as a Postdoctoral Associate at MIT, he worked as a Research Scientist at NEC Research Institute in Princeton from 1990 to 1993. In 1994, he moved to Carnegie Mellon, where he stayed until joining Duke University in 2009 as a Professor in the Department of Computer Science. While on a two-year leave-of-absence from Carnegie Mellon, Maggs was a founding employee of Akamai Technologies, serving as its first Vice President for Research and Development. In 2018 he was part of a large team that received the inaugural SIGCOMM Networking Systems Award for the Akamai CDN, and was named an ACM Fellow.
32-G449 (Kiva)
Belfer
sarah_donahue@hks.harvard.edu
November 27, 2019
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2019-11-27 16:00:00
2019-11-27 17:00:00
America/New_York
Discriminatory and Liberatory Algorithms: Restructuring Algorithmic “Fairness”
Abstract: The nascent field of Algorithmic Fairness recognizes that algorithms have the potential to perpetuate and codify systemic discrimination and attempts the noble goal of defining notions of “Fairness” that will mitigate this. The past year, however, has seen many critiques of the field’s direction and methodologies, illustrating how the field itself is in danger or perpetuating and codifying systems of discrimination.This talk will review Fairness and Abstraction in Sociotechnical Systems, a work that outlines five sociotechnical “traps” that Algorithmic Fairness seems to routinely fall into. I will then present ongoing work where we introduce Discriminatory & Liberatory Algorithms (DLA): a framework to restructure the terminology, methodology, and role of Algorithmic “Fairness” through a sociotechnical lens. The merit of this will be argued via its lack of susceptibility to these five “traps.”
32-G575
Belfer
sarah_donahue@hks.harvard.edu
December 05, 2019
TBA
Archana Venkataraman
John Hopkins University
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2019-12-05 11:30:00
2019-12-05 12:30:00
America/New_York
TBA
32-D507
Belfer
sarah_donahue@hks.harvard.edu
The Non-Stochastic Control Problem
Elad Hazan
Princeton University
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2019-12-05 16:00:00
2019-12-05 17:00:00
America/New_York
The Non-Stochastic Control Problem
Abstract:Linear dynamical systems are a continuous subclass of reinforcement learning models that are widely used in robotics, finance, engineering, and meteorology. Classical control, since the work of Kalman, has focused on dynamics with Gaussian i.i.d. noise, quadratic loss functions and, in terms of provably efficient algorithms, known statespace realization and observed state. We'll discuss how to apply new machine learning methods which relax all of the above: efficient control with adversarial noise, general loss functions, unknown systems, and partial observation.Bio:Elad Hazan is a professor of computer science at Princeton University. His research focuses on the design and analysis of algorithms for basic problems in machine learning and optimization. Amongst his contributions are the co-development of the AdaGrad optimization algorithm, and the first sublinear-time algorithms for convex optimization. He is the recipient of the Bell Labs prize, (twice) the IBM Goldberg best paper award in 2012 and 2008, a European Research Council grant, a Marie Curie fellowship and Google Research Award (twice). He served on the steering committee of the Association for Computational Learning and has been program chair for COLT 2015. In 2017 he co-founded In8 inc. focusing on efficient optimization and control, acquired by Google in 2018. He is the co-founder and director of Google AI Princeton.
32-D463 (Stata Center, Star Conference Room)
Belfer
sarah_donahue@hks.harvard.edu
December 06, 2019
First Friday Lunch with Julian Shun: High-Performance Frameworks for Static and Streaming Graph Processing
Julian Shun
Assistant Professor, MIT EECS
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2019-12-06 12:00:00
2019-12-06 13:00:00
America/New_York
First Friday Lunch with Julian Shun: High-Performance Frameworks for Static and Streaming Graph Processing
There has been significant interest in high-performance graphprocessing due to their applications in many domains, including socialnetwork and Web analytics, machine learning, biology, and physicalsimulations. However, writing efficient parallel graph programs forprocessing the large-scale graphs available today can be verydifficult and time consuming, and therefore it is important to havetools that make the task easier. This talk will cover our recent workon high-level frameworks for parallel graph processing, for bothstatic graphs and streaming graphs. I will first present GraphIt, adomain-specific language that separates algorithm logic fromperformance optimizations to achieve high performance across differentstatic graph algorithms and datasets. I will then present Aspen, aframework for processing streaming graphs that introduces a newpurely-functional compressed tree data structure to enable graphqueries and updates to be performed concurrently with low latency. Oursolutions provide high-level programming interfaces that simplify thetask of writing parallel graph programs, while achieving highperformance at the same time.About First Friday LunchesFirst Friday Lunches are informal gatherings open to Alliances members and the CSAIL community, who would like to come by the lab for a discussion on a current project. Most lunches will feature a Faculty Researcher but a few will feature a PhD student, post-doc or Research Scientist. If you are not local but still want to participate, call in numbers as well as skype/webex connections will be made available. Please register for the lunch and indicate that you would like to participate remotely.About Julian ShunJulian Shun is the Douglas T. Ross Career Development AssistantProfessor of Software Technology in EECS and CSAIL at MIT. Prior tocoming to MIT, he was a Miller Research Fellow at UC Berkeley. Hereceived his Ph.D. from Carnegie Mellon University and his B.A. fromUC Berkeley. His research focuses on the theory and practice ofparallel algorithms and programming frameworks. He has received theNSF CAREER Award, DOE Early Career Award, ACM Doctoral DissertationAward, CMU School of Computer Science Doctoral Dissertation Award,Facebook Graduate Fellowship, best paper awards at PLDI and SPAA, andbest student paper award at DCC.
Star Conference Room, 32-D463
Belfer
sarah_donahue@hks.harvard.edu
December 10, 2019
SystemsThatLearn@CSAIL Lecture Series: Using Technology to Track and Trace Live Goods from Supplier to Retailer
Martin Wolfe
Vice President & Global Consumer Industries CTO, IBM
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2019-12-10 16:00:00
2019-12-10 17:00:00
America/New_York
SystemsThatLearn@CSAIL Lecture Series: Using Technology to Track and Trace Live Goods from Supplier to Retailer
This talk covers work being done in the Retail and Consumer Products industries where there is a need to track and trace live goods including plants and food. The technical aspects of how to implement this will be covered including where Internet of Things (IoT) sensors are leveraged to provide location, status, and state to know previous, current, and future provenance of products materials, and consumer goods. Blockchain, sensors, and analytics will be covered in specifically how they are used in various examples.Martin Wolfe is currently the Global CTO for Retail, Consumer Products, and Agribusiness at IBM. He is a Distinguished Engineer with backgrounds in systems engineering, software development, cloud computing, Blockchain technology and leads the definition of architecture standards in Consumer focused industries.
Belfer
sarah_donahue@hks.harvard.edu