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Seminar Series

May 16

Justin Holmgren: Securing Computation on Untrusted Platforms

Justin Holmgren

Part Of

12:30P
- 2:30P

Location

Patil/Kiva G449
Add to Calendar 2018-05-16 12:30:00 2018-05-16 14:30:00 America/New_York Justin Holmgren: Securing Computation on Untrusted Platforms Abstract:In today's networked world, weak devices increasingly rely on remote servers both to store data and to perform costly computations. Unfortunately, these servers may be easily hackable or otherwise untrustworthy. Therefore, without assuming honest behavior on the server's part, we would like to guarantee two basic security objectives:1. (Correctness) It is possible to verify the correctness of the server's computations much more efficiently than by re-executing the computation.2. (Privacy) A server learns nothing about the computation it performs, other than (perhaps) the output.I will present recent results that achieve both these goals for arbitrary computations, and I will conclude with a discussion of open problems and future directions.Thesis Committee: Ran Canetti, Shafi Goldwasser and Vinod Vaikuntanathan Patil/Kiva G449

May 14

Improving Clinical Decisions Using Correspondence Within and Across Electronic Health Records

Jen Gong
MIT - CSAIL

Part Of

12:00P
- 1:00P

Location

Star (32-D463)
Add to Calendar 2018-05-14 12:00:00 2018-05-14 13:00:00 America/New_York Improving Clinical Decisions Using Correspondence Within and Across Electronic Health Records Abstract:Electronic Health Record (EHR) adoption and large-scale retrospective analyses of health care data are part of a broader conversation about health care quality and cost in the United States. Clinical decision-making aids are one method of helping to improve quality and lower cost of care. In this thesis, we present three methods of leveraging correspondences across elements in health care records to aid clinicians in making care decisions. We focus on the critical care environment, where patient state can rapidly change and many care decisions need to be made in short periods of time.First, we introduce a method to leverage correspondences between structured fields from two different EHR systems to a shared space of clinical concepts encoded in an existing domain ontology. We use these correspondences to enable the transfer of machine learning models across different or evolving EHR systems. Second, we introduce a method to learn correspondences between structured health record data and topic distributions of clinical notes written by care team members. Finally, we present a method to characterize care processes by learning correspondences between observations of patient state and actions taken by care team members.Bio:Jen Gong is a Ph.D. candidate in the Data Driven Inference Group at MIT, supervised by John Guttag. Her research focuses on the application of machine learning to healthcare. She is interested in how different modalities of health care data (e.g., structured health record data, clinical notes, physiological time-series) and auxiliary sources (e.g., data from similar patient populations, expert-encoded ontologies) can be leveraged to improve clinical decision-making aids. Prior to MIT, Jen received an A.B. in Applied Mathematics from Harvard College.Committee: John Guttag, Collin Stultz, Jenna Wiens Star (32-D463)

May 07

Computational Design for the Next Manufacturing Revolution

Adriana Schulz
MIT CSAIL Computational Fabrication Group

Part Of

1:00P
- 3:00P

Location

32-D463 (Stata Center - Star Conference Room)
Add to Calendar 2018-05-07 13:00:00 2018-05-07 15:00:00 America/New_York Computational Design for the Next Manufacturing Revolution Abstract:Over the next few decades, we are going to transition to a new economy where highly complex, customizable products are manufactured on demand by flexible robotic systems. In many fields, this shift has already begun. 3D printers are revolutionizing production of metal parts in the aerospace, automotive, and medical industries. Whole-garment knitting machines allow automated production of complex apparel and shoes. Manufacturing electronics on flexible substrates makes it possible to build a whole new range of products for consumer electronics and medical diagnostics. Collaborative robots, such as Baxter from Rethink Robotics, allow flexible and automated assembly of complex objects. Overall, these new machines enable batch-one manufacturing of products that have unprecedented complexity.In my talk, I argue that the field of computational design is essential for the next revolution in manufacturing. To build increasingly functional, complex and integrated products, we need to create design tools that allow their users to efficiently explore high-dimensional design spaces by optimizing over a set of performance objectives that can be measured only by expensive computations. I will discuss how to overcome these challenges by 1) developing data-driven methods for efficient exploration of these large spaces and 2) performance-driven algorithms for automated design optimization based on high-level functional specifications. I will showcase how these two concepts are applied by developing new systems for designing robots, drones, and furniture. I will conclude my talk by discussing open problems and challenges for this emerging research field. Thesis Advisor: Wojciech MatusikThesis Committee: Daniela Rus and Eitan Grinspun 32-D463 (Stata Center - Star Conference Room)