Our laboratory focuses on the science and applications of nanocrystals, especially semiconductor nanocrystal (aka quantum dots). Our research ranges from the very fundamental to applications in electro-optics and biology. There is an ongoing effort to address the challenges of making new compositions and morphologies of nanocrystals and nanocrystal heterostructures, and new ligands so that the nanocrystals can be incorporated into hybrid organic/inorganic devices, or biological systems. We are collaborating with a number of biology and medical groups to design nanocrystal probes that meet specific challenges.
Accelerated penetration of distributed energy resources (DER) for power generation and demand response (DR), the notion of just-in-time flexible consumption, are enabling the transformation from the current power grid structure to a modernized, cyber-enabled grid. In order to carry out an efficient design of Transactive Systems, a tightly integrated design of wholesale and retail markets and pricing policies is needed that incentivizes end-users' participation, accommodates physical constraints, and enables global objectives through local and distributed decision-making.
This talk will outline how this integrated design of wholesale and retail markets can be carried out. The two streams of research investigations from our lab will be featured: one is a dynamic wholesale market mechanism with the ability to make decisions at multiple time-scales, and the other is a hierarchical architecture capable of achieving volt-var control in the presence of large penetration of DERs and DRs. We will discuss how the results from these can be combined to result in an overall hierarchical Transactive architecture for smart distribution grids.
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.
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.
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.
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.
MIT Media Lab founder Nicholas Negroponte takes you on a journey through the last 30 years of tech. The consummate predictor highlights interfaces and innovations he foresaw in the 1970s and 1980s that were scoffed at then but are ubiquitous today. And he leaves you with one last (absurd? brilliant?) prediction for the coming 30 years.
Growing evidence supports a critical role of metal-coordination complex crosslinking in soft biological material properties such as underwater adhesion and self-healing. Given their exploitation in such desirable material applications in nature, bio-inspired metal-coordinate complex crosslinking no doubt provides unique possibilities to further advance synthetic polymer materials engineering. Using bio-inspired metal-binding polymers, initial efforts to mimic these material properties have shown promise. In addition, novel opportunities for new fundamental insights on how hierarchical polymer network mechanics can be strongly coupled to supramolecular crosslink dynamics are also emerging. Early lessons from studies of these hierarchical chemo-mechanical couplings will be presented.
From flexible hybrid electronics, to integrated photonic devices, to functional fibers to smart manufacturing, the new U.S. Federal government institutes are advancing new manufacturing technologies and accelerating the pace of manufacturing innovation. This session will review MIT's involvement in these institutes and discuss opportunities for industry partners to participate in developing new products and capabilities.
Despite billions invested in cancer research, our understanding of the disease, treatment, and prevention remains limited. Natural language processing (NLP) can offer new insights by mining the rich but underutilized information encoded in physicians’ observations and clinical findings, which are still primarily recorded as free-form text. NLP-based models can make a difference in clinical practice by improving models of disease progression, preventing over-treatment, and narrowing down on a cure.