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.
This talk is focused on the methods and technologies to answer the question ‘Why does it take a long time to process, analyze and derive insights from the data?’ Dr. Veeramachaneni is leading the ‘Human Data Interaction’ Project to develop methods that are at the intersection of data science, machine learning, and large scale interactive systems. With significant achievements in storage , processing, retrieval, and analytics, the answer to this question now lies in developing technologies that are based on intricately understanding the complexities in how scientists, researchers, analysts interact with data to analyze, interpret, and derive models from it. In this talk, Dr. Veeramachaneni will present how his team is building systems to transform this interaction for the signals domain using an example of physiological signals. Prediction studies on physiological signals are time-consuming: a typical study, even with a modest number of patients, usually takes from 6 to 12 months.
In this talk, he will describe a large-scale machine learning and analytics framework, BeatDB, to scale and speed up mining predictive models from these waveforms. BeatDB radically shrinks the time an investigation takes by: (a) supporting fast, flexible investigations by offering a multi-level parameterization, (b) allowing the user to define the condition to predict, the features, and many other investigation parameters (c) pre-computing beat-level features that are likely to be frequently used while computing on-the-fly less used features and statistical aggregates.
2016 MIT Digital Health Conference
Can data series from a broad patient population be relevant and reliable tools in predicting individual outcomes when compared to personal wellness sensor data? Or, simply put from a patient perspective, “Can what happen to them, happen to me?” Retrieving and making use of “like-me” signal data based on similarity presents challenges far beyond digital marketing’s effectiveness in making targeted book and movie recommendations. By investigating and understanding those unique challenges, our research group has developed an approach based upon locality sensitive hashing (LSH). We will provide an update on our progress towards adapting LSH for fast and accurate Signal Like-Me capability.
The MIT Trust Data Consortium aims to provide people, organizations, and computers the ability to manage access to their data more securely, efficiently, and equitably, while protecting personal data from incursion and corruption. As we have moved from the analog world to the digital world, our data, security, and governance systems have not kept pace. This has created numerous issues ranging from data insecurity (such as the large-scale government and private sector data losses of recent years) to a widening digital divide between rich and poor, including the global disenfranchisement of over 1.5 billion people who lack legal identity.
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.
The rapid, stable cycling of rechargeable batteries requires well-controlled phase transformations of the redox active materials in each electrode, between the charged and discharged states. In Li-ion batteries, common intercalation materials, such as graphite and iron phosphate, undergo phase separation (into Li-rich and Li-poor phases), which limits the power density and causes degradation. A general mathematical theory, supported by recent x-ray imaging experiments, will be presented that shows how phase separation can be controlled by electro-autocatalytic reactions. For Li-metal batteries, theoretical and experimental results will be presented for the stability of lithium electrodeposition, controlled by electrokinetic phenomena in charged porous separators.
Making your mark in the multi-billion dollar global sports industry is a challenge. So how do teams like the Red Sox and the Dallas Cowboys drive revenue? They create powerhouse brands that attract, engage, and retain fans by leveraging big data, creating cross-platform media and engagement plans, and using dynamic social media strategies to maximize live event experiences. Using theoretical and real-world examples, Ben Shields will share innovative best practices from the business of sports that are relevant to any consumer-driven enterprise.
New digital technologies, pervasive social media and countless apps have transformed the traditional B to C marketing templates. Now, businesses and consumers can increasingly co-create content, experiences and value. And often these collaborations yield persuasive results. But to achieve this brand leverage, businesses have to be willing to give up some control and also engage, creatively, with customers on a more personal level. Under what circumstances does it make sense for business to loosen brand control and what energy and investment is required of consumers to enable co-creation to make an impact? Similarly, what needs to be considered as we enter the world of co-decision making, in which customers have to allow apps to control selection and decisions?
ILP members, many of them Fortune 1000 companies, increasingly want to meet with MIT startups, to scout, to discuss, to partner, to invest, and more. Responding to that need, ILP’s Startup Initiative will boost our current database of near 1000 MIT startups. Going forward, the intent is to provide a web platform to gather real time developments, advertise opportunities and do more but also better matching. We are currently seeking feedback from the wider MIT innovation ecosystem on how we should proceed. There will be a stand at the Startup Exhibit where we can take questions and you can give your input. We're looking for input from both MIT startups and ILP members.