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Conference Details - Agenda

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2018 MIT Research and Development Conference

Accelerating Big Impact Innovations
November 14-15, 2018
 

Day 2: Thursday, November 15, 2018

8:25

Samberg Conference Center, E52
Salon M/I/T

Reconvene at the Samberg Conference Center

8:05 - 8:35

New Methodologies in Materials Research for Accelerated Innovation
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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8:35 - 9:05

From Moore’s Law to Fabric as a Service
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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9:05 - 9:35

The MIT Quest for Intelligence
The MIT Quest for Intelligence (The Quest) aims to build on MIT’s rich history of innovation and impact in the study of intelligence, our next step towards the future. Comprised of two linked entities, The Core and The Bridge, The Quest aims to advance two fundamental intelligence challenges: Can we reverse engineer intelligence? And, how can we deploy our current and expanding understanding of intelligence to the benefit of society?

9:35 - 10:00

Networking Break

Samberg Conference Center, E52
Salon M/I/T

Track 5: Quest for Intelligence

10:00 - 10:40

Towards Learning Spoken Language through Vision
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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10:40 - 11:20

Vision and Language

11:20 - 11:40

Networking Break

11:40 - 12:20

Making Robots Behave
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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12:20 - 1:00

Interpretable AI
This talk introduces a new generation of machine learning methods that provide state of the art performance and are very interpretable, introducing optimal classification (OCT) and regression (ORT) trees for prediction and prescription with and without hyperplanes. This talk shows 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.
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Samberg Conference Center, E52
Dining Room 3/4

Track 6: Material Research

10:00 - 10:40

Advancing Grid-Connected Electrochemical Energy Storage Through Cost-Constrained Design
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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10:40 - 11:20

Chalcogenide Active Materials for Photonics, Photovoltaics, and Chemical Sensing
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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11:20 - 11:40

Networking Break

11:40 - 12:20

Inverse Materials Design Using Machine Learning and Simulations
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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12:20 - 1:00

Machine-learning-enhanced Chip-scale Spectrometers for Chemical Sensing
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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Samberg Conference Center, E52
Dining Room 5/6

Track 7: Design Technology

10:00 - 10:40

Designing the Future of Design
The act of designing is one of the most powerful activities a human being can engage in, both for the designer and for the people that are being designed for. At the same time, design is one of the most challenging topics to teach effectively in the engineering classroom, in part because of its inherently ambiguous and sociotechnical nature. In this talk, I’ll discuss my approach to uncovering the fundamental nature of design through controlled studies. I'll discuss findings from our recent experimental research, including work on the design of products to encourage users to behave more sustainably, and also strategies for helping designers work collaboratively with increasingly powerful computational tools. The goal of this research is to link design process with design outcomes in order to help students and practitioners better manage design ambiguity. Looking forward to the future of design, I’ll also discuss examples of design and engineering contexts that have defied expectations in attracting a broad range of new students to STEM fields through human-centered design.
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10:40 - 11:20

Materials-based Solutions to a Pair of 50-year-old Nuclear Challenges
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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11:20 - 11:40

Networking Break

11:40 - 12:20

Material Boundaries: The Future of Product Design and Manufacturing
Digital fabrication and computational materials are enabling the design and manufacturing of objects that are mass-customizable, interconnected, and can fundamentally adapt to users’ needs and requirements. This talk will present a series of research projects and technologies that push the boundaries of how materials and computers can be intertwined to create new products and experiences — from the nanoscale to a stadium, from a single person to a crowd — and that redefine how we perceive and interact with physical world.

12:20 - 1:00

Design: The Last Line of Defense
While Google's mission is to organize the world's information, this information needs to made understandable and usable. We hear a lot about data, and worse, “big data,” but far too little about its meaning and how to make it approachable for the people who need to use it. The solution is to treat data as a design problem, where it can be addressed by starting with end users and working back to the data in all its messy complexity. We can only make progress if we first consider audience and context, forcing us to reformulate the questions at hand and to reconsider the technical decisions and approaches made behind the scenes.
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1:00

Adjournment with Bagged Lunch

1:30 - 3:00

Post-Conference Field Visits
* Advanced Functional Fabrics of America (AFFOA): Heralding the dawn of the future fabric that can see, hear, sense, communicate, store energy, and more.
* The Engine: A home for tough tech founders building the next generation of world-changing companies.
* International Design Center (IDC): Center for radical design innovation for relevant solutions. IDC faculty and researchers forge unique collaborations with industry to innovate, excite and change the world through intense design innovation.
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