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RECENT VIDEOS

30 Results | Page 1 | 2 | Last | Next
 

11.15.2018
25 mins
ILP Video

Materials-based Solutions to a Pair of 50-year-old Nuclear Challenges (Track 7)

Michael Short
Norman C Rasmussen Career Development Associate Professor in Nuclear Engineering
MIT Department of Nuclear Science & Engineering
The largest technical impediments to the long-term viability of current nuclear reactors and the potential future of advanced ones are crud buildup and radiation damage. Can a coating with optical properties that match those of the surrounding water help eliminate the adhesion of oxide particles to fuel cladding surfaces? What steps are being taken to further the study of radiation damage and how can transient grating spectroscopy (TGS) help to provide more data? Professor Michael Short will discuss how recent science-first approaches to these problems are helping to stop these technical impediments.
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11.15.2018
34 mins
ILP Video

Designing the Future of Design (Track 7)

Qifang Bao
PhD student of:
Maria Yang
Associate Professor of Mechanical Engineering and Engineering Systems
MacVicar Faculty Fellow
MIT Department of Mechanical Engineering
Optical spectrum analysis is the cornerstone of spectroscopic chemical sensing, optical network performance monitoring, RF spectrum analysis, and hyperspectral infrared imaging. On-chip spectrometers have recently emerged as a promising alternative to their benchtop counterparts with apparent size, weight, and power advantages. We demonstrate a novel on-chip digital Fourier transform (dFT) spectrometer that can acquire high-resolution spectra within a millimeter-sized footprint. The device, fabricated and packaged using industry-standard silicon photonics technology, offers dramatically boosted signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further implemented machine learning regularization techniques to spectrum reconstruction and achieved significant noise suppression and spectral resolution enhancement beyond the classical Rayleigh criterion. Potential applications of the device in industrial process control, ubiquitous chemical identification, environmental monitoring, and optical communications will be discussed.
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11.15.2018
32 mins
ILP Video

New Methodologies in Materials Research for Accelerated Innovation

Carl V. Thompson
Stavros Salapatas Professor of Materials Science and Engineering
Director, Materials Research Laboratory (MRL)
MIT Department of Materials Science and Engineering
Materials research enables and advances technologies that meet challenges and opportunities in energy, sustainability, health, learning, and security. Inherently multidisciplinary in nature, and involving faculty in almost every department at MIT, materials research links methods and mechanisms of materials synthesis to nano- and micro-scale structure and the structure of materials to their properties. Iterative investigation of these linkages promotes a cycle of innovation delivering broadly applicable new materials. Development of computational techniques for materials discovery, design, and synthesis from data mining, machine learning, and first principles calculations of physical and chemical properties expedites innovation. New tools for probing atomic-scale structure and chemistry and for nano- and micro-scale in situ observations of materials synthesis and the responses of materials to applied forces and fields enhance progress. New methods for quantifying the sustainability and potential market impact of new materials technologies provide a holistic context for materials research. MIT researchers are playing lead roles in development of these new methodologies.
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11.15.2018
41 mins
ILP Video

From Moore?s Law to Fabric as a Service

Yoel Fink
CEO, AFFOA
Former Director, MIT Research Laboratory of Electronics
Our clothes help define us, yet the fabrics we wear have remained functionally unchanged for thousands of years. Recent breakthroughs in fiber materials and manufacturing processes allow us to design and wear fabrics that see, hear, communicate, change color, and monitor health ? heralding the dawn of a ?fabric revolution.? Our mission at Advanced Functional Fabrics of America (AFFOA) is to lead the convergence of advanced technology into fibers (?Moore?s Law for fibers?) resulting in fabric products that deliver value-added services to the user (?Fabrics as a service?).
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11.15.2018
41 mins
ILP Video

Interpretable AI (Track 5)

Jack Dunn, PhD
Co-Founding Partner of Interpretable AI
We introduce a new generation of machine learning methods that provide state of the art performance and are very interpretable. We introduce optimal classification (OCT) and regression (ORT) trees for prediction and prescription with and without hyperplanes. We show that (a) Trees are very interpretable, (b) They can be calculated in large scale in practical times and (c) In a large collection of real world data sets they give comparable or better performance than random forests or boosted trees. Their prescriptive counterparts have a significant edge on interpretability and comparable or better performance than causal forests. Finally, we show that optimal trees with hyperplanes have at least as much modeling power as (feedforward, convolutional and recurrent) neural networks and comparable performance in a variety of real world data sets. These results suggest that optimal trees are interpretable, practical to compute in large scale and provide state of the art performance compared to black box methods. (joint work with Jack Dunn)
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11.15.2018
32 mins
ILP Video

Chalcogenide Active Materials for Photonics, Photovoltaics, and Chemical Sensing (Track 6)

Rafael Jaramillo
Assistant Professor of Materials Science & Engineering
MIT Department of Materials Science and Engineering
Chalcogenide materials interact strongly with light, have widely-tunable semiconducting properties, and are the basis for many applications in optics and electronics. This presentation consists of our work developing new materials for photonics and photovoltaics. We propose layered, two-dimensional chalcogenides as a new class of active materials for controlling light in integrated photonics systems using the concept of resonant, martensitic phase transformations. We propose sulfide perovskites as a new class of materials for thin film photovoltaics, mimicking the excellent PV performance of lead halide perovskites but without problems of stability or toxicity. Finally, we discuss a new application of ?old? materials: low-cost chemical sensors based on the photoconductive response of binary metal chalcogenides.
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11.15.2018
35 mins
ILP Video

Inverse Materials Design Using Machine Learning and Simulations (Track 6)

Rafael Gomez-Bombarelli
Toyota Assistant Professor in Materials Processing
MIT Department of Materials Science and Engineering
Machine learning is disrupting multiple fields of human endeavor: healthcare, transportation, finance, communications, etc. Materials design is no exception in this disruption. Data-driven approaches can access the information embedded in years of experiments, perform rapid optimization of high-dimensional experimental conditions and design parameters, or design new molecules automatically. The Gomez-Bombarelli group at MIT combines cutting-edge machine learning models on experimental data with automation in physics-based atomistic simulations (molecular dynamics, electronic structure) to rapidly design and optimize new materials in multiple areas, such as: inverse chemical design of small molecules (drug-like molecules that optimally bind biological sites, organic-light emitting diode emitters, and organic battery electrolytes); virtual discovery of soft materials (lithium-conducting polymers and OLED transport materials); and chemical reactivity in the condensed phase (zeolite design for catalysis and chemical and thermal stability of organic electronics). There is great interest in using machine learning as the connector between multiple time and length scales: from electronic structure, to atomistic molecular dynamics, to coarse-grained models.
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11.15.2018
32 mins
ILP Video

Machine-learning-enhanced Chip-scale Spectrometers for Chemical Sensing (Track 6)

Juejun (JJ) Hu
Associate Professor, MIT Department of Material Sciences and Engineering
Optical spectrum analysis is the cornerstone of spectroscopic chemical sensing, optical network performance monitoring, RF spectrum analysis, and hyperspectral infrared imaging. On-chip spectrometers have recently emerged as a promising alternative to their benchtop counterparts with apparent size, weight, and power advantages. We demonstrate a novel on-chip digital Fourier transform (dFT) spectrometer that can acquire high-resolution spectra within a millimeter-sized footprint. The device, fabricated and packaged using industry-standard silicon photonics technology, offers dramatically boosted signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further implemented machine learning regularization techniques to spectrum reconstruction and achieved significant noise suppression and spectral resolution enhancement beyond the classical Rayleigh criterion. Potential applications of the device in industrial process control, ubiquitous chemical identification, environmental monitoring, and optical communications will be discussed.
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11.15.2018
37 mins
ILP Video

Vision and Language (Track 5)

Vikash Mansinghka
Research Scientist
MIT Department of Brain and Cognitive Scienc
Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT?s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR. He served on DARPA?s Information Science and Technology advisory board from 2010-2012, and currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation. He was an advisor to Google DeepMind and has co-founded two AI-related startups, one acquired and one currently operational.
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11.15.2018
40 mins
ILP Video

Making Robots Behave (Track 5)

Leslie Kaelbling
Panasonic Professor of Computer Science and Engineering
Margaret MacVicar Faculty Fellow
MIT Department of Electrical Engineering and Computer Science
The fields of AI and robotics have made great improvements in many individual subfields, including in motion planning, symbolic planning, probabilistic reasoning, perception, and learning. Our goal is to develop an integrated approach to solving very large problems that are hopelessly intractable to solve optimally. We make a number of approximations during planning, including serializing subtasks, factoring distributions, and determinizing stochastic dynamics, but regain robustness and effectiveness through a continuous state-estimation and replanning process. I will describe our initial approach to this problem, as well as recent work on improving effectiveness and efficiency through multiple types of learning.
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11.15.2018
38 mins
ILP Video

Towards Learning Spoken Language through Vision (Track 5)

Jim Glass
Senior Research Scientist
Head, Spoken Language Systems Group (SLS)
MIT Computer Science and Artificial Intelligence Laboratory
Despite continuous advances over many decades, automatic speech recognition remains fundamentally a supervised learning scenario that requires large quantities of annotated training data to achieve good performance. This requirement is arguably the major reason that less than 2% of the worlds' languages have achieved some form of ASR capability. Such a learning scenario also stands in stark contrast to the way that humans learn language, which inspires us to consider approaches that involve more learning and less supervision.

In our recent research towards unsupervised learning of spoken language, we are investigating the role that visual contextual information can play in learning word-like units from unannotated speech. This talk will outline our ongoing research in CSAIL to develop deep learning models that are able to associate images with unconstrained spoken descriptions, and present analyses that indicate that the models are learning correspondences between associated objects in images and their spoken instantiation.
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11.15.2018
37 mins
ILP Video

Advancing Grid-Connected Electrochemical Energy Storage Through Cost-Constrained Design (Track 6)

Fikile Brushett
Cecil and Ida Green Career Development Chair, Associate Professor of Chemical Engineering
MIT Department of Chemical Engineering
Electrochemical energy storage is emerging as a critical technology to enable sustainable electricity generation by alleviating intermittency from renewable sources, reducing transmission congestion, enhancing grid resiliency, and decoupling generation from demand. While several different rechargeable batteries have been proposed for and demonstrated in these applications, further cost reductions are needed for ubiquitous adoption. As such, recent research has focused on the discovery and development of new chemistries. Though exciting, most of these emerging concepts only consider new materials in isolation rather than as part of a battery system. Understanding the critical relationships between materials properties and overall battery price is key to enabling systematic improvements. In this presentation, I will discuss an approach to mapping feasible design spaces for incipient energy storage systems through techno-economic modeling and to using this knowledge to identify critical pathways at an early stage in the research and development process. While redox flow batteries will be used as an exemplar technology, the methods to be described here are applicable to a wide range of electrochemical systems and envisioned applications.
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