New England WiML-Partner Event Series     

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New England WiML-Partner Event Series - 2019 Events


As part of AI Research Week, IBM Research Cambridge is proud to announce its 2nd annual flagship event for women in machine learning (ML) and artificial intelligence (AI). In hosting this event, our goal is to encourage and support local women, especially students, post-docs, and early career researchers in ML and AI to build and navigate successful careers. To that end, we offer seminars from thought-leading women researchers and engineers, and opportunities to present research as well as meet and interact with other women in the field, modeled off of the annual WiML Workshop. Our workshop event will feature keynote seminars, a panel discussion, and roundtable discussions. A detailed agenda is below. Click here to register

Please mark your calendar for Thursday, September 19 at 3pm on MIT's campus.

 

Details

Date: Thursday, September 19th, 2019

Time: 2:45 PM - 6:00 PM

Location: Samberg Conference Center, 7th Floor, MIT Building E52, Cambridge, MA

 

The event is OPEN and FREE - but please register.

 

Schedule

2:45 pm Registration Registration Desk
3:00 pm Welcome and Introduction Lisa Amini
3:15 pm

Keynote

CS for Everyone: A National Invitation

Carla Brodley
3:45 pm

Keynote

Doing for our robots what nature did for us

Leslie Kaelbling
4:15 pm

Panel Discussion

Interdisciplinary AI: Bringing AI to New Domains

Moderator: Laura Haas
5:00 pm Roundtables, technical and career topics See below

 

About Our Speakers

C_Brodley Carla E. Brodley is the Dean of the Khoury College of Computer Sciences at Northeastern University. Prior to joining Northeastern, she was a professor of the Department of Computer Science and the Clinical and Translational Science Institute at Tufts University (2004-2014). Before joining Tufts she was on the faculty of the School of Electrical Engineering at Purdue University (1994-2004).

A Fellow of the ACM and the Association for the Advancement of Artificial Intellignce (AAAI), Dean Brodley's interdisciplinary machine learning research led to advances not only in computer and information science, but in many other areas including remote sensing, neuroscience, digital libraries atrophysics, content-based image retrieval of medical images, computational biology, chemistry, evidence-based medicine, and predictive medicine.

Dean Brodely's numerous leadership positions in computer science as well as her chosen research fields of machine learning and data mining include serving as program co-chiar of ICML, co-chair of AAAI, and serving as associated editor of the Journal of AI Research, and the Journal of Machine Learning Research. She has previously served on the Defense Science Study Group, the board of the International Machine Learning Society, the AAAI Council, the executive committee of the Northeast Big Data Hub, and DARPA's Information Science and Technology (ISAT) Board. She is currently serving on the CRA Board of Directors, as a member-at-large of the section on Information, Computing, and Communication of AAAS, and as a member of the advisory committee for the NSF's Directorate of Computer and Information Science and Engineering.

 

L_Kaebling Leslie Pack Kaelbling is a Professor of Computer Science and Engineering at MIT. She has previously held positions at Brown University, the Artificial Intelligence Center of SRI International, and at Teleos Research. Prof. Kaelbling has done substanial research on designing situated agents, mobile robotics, reinforement learning, and decision-theoretic planning. In 2000, she founded the Journal of Machine Learning Research, a high-quality journal that is both freely available electronically as well as published in archival form; she currently servies as editor-in-chief. 

Prof. Kaelbling is an NSF Presidential Faculty Fellow, a former member of the AAAI Executive Council, the 1997 recipient of the IJCAI Computers and Thought Award, a trustee of IJCAII and a fellow of the AAAI. She received an A. B. in philosophy in 1983 and a Ph.D. in computer science in 1990, both from Stanford University.

 

L_Haas Dr. Haas is currently the dean of the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. She spent 36 years at IBM, where she rose to the level of IBM Fellow. Within IBM, she most recently served as Director of the Accelerated Discovery Lab (2011-2017); she was Director of Computer Science at IBM's Almaden research center from 2005 to 2011, and had worldwide responsibility for IBM Research's exploratory science program from 2009 through 2013. Dr. Haas is known for her foundational research on information integration technology.

 

 

 

 

 

 

About our panelists

Dr. Rose Yu is an Assisstant Professor in the Khoury College of Computer Science at Northeastern University. Previously, she was a postdoctoral researcher in Caltech Computing and Mathematical Sciences. She earned her Ph.D. in computer science at the University of Southern California and was a visiting researcher at Stanford University. Her research focuses on developing machine learning techniques for large-scale time series and spatiotemporal data. She is generally interested in theory and applications of deep learning, tensor optimization, and spatiotemporal modeling. Her work has been successfully applied to intelligent transportation, climate informatics, and aerospace control. Among her awards, she has won the best dissertation award in USC computer science, best paper award at NIPS time series workshop, and was nominated as one the "MIT Rising Stars in EECS".

 

D_Bragg Dr. Danielle Bragg is a postdoctoral researcher at Microsoft Research. Her research focuses on developing computational systems that expand  access to information, in particular for poeple with disabilities (sign language users and low-vision readers). Her work is interdisciplinary, combining human-computer interaction, applied machine learning, and accessibility. She holds a Ph.D. in computer science from the University of Washington and a BA in applied mathematics from Harvard University.

 

 

 

 

Elizabeth Wood co-founded and co-runs JURA Bio Inc., an early-stage therapeutics start up focusing on developing and delivering cell-based therapies for the treatment of autoimmune and immune-related neurodegenerative diseases. Before JURA, Wood was a post-doc in the lab of Adam Cohen at Harvard, after starting her PhD with Angela Belcher and Markus Buehler at MIT, and then later finishing it with Claus Helix-Neilsen at the Technical University of Denmark. She has also worked at the University of Copenhagen's Biocenter with Kresten Lindorff-Larsen, integrating computational methods with experimental studies to understand how the ability of proteins to change their shape ehlp modulate their function. Wood is a visiting scientist at the Broad Institute, where she serves on the steering committee for the Machine Inference Algorithm's Initiative. She is one of the leard organizers of the Learning Meaningful Representations of Life workshops at NeurIPS '19 and encourages you submit an abstract before 9/20.

 

 

Julie Norville co-founded and co-runs JURA Bio Inc., an early-stage therapeutics start up focusing on developing and delivering cell-based therapies for the treatment of autoimmune and immune-related neurodegenerative diseases. Before JURA, Norville was a Research Fellow in Genetics at the laboratory of George Church at Harvard Medical School, performing some of the first demonstrations of CRISPR in eukaryotes (human cells, plants, and yeast) and developed tools for the radical recoding of genomes. For her doctoral studies in EECS at MIT, Julie worked with Angela Belcher, Thomas Knight, and Gerald Sussman to modularly design biological systems at the DNA (using libraries of parts), protein (using proteins that programmably crystallize in the presence of calcium), and cell level (using cells embedded in paper and interleaved with membranes to allow communication). She is a recipient of a National Academy Keck Futures Initiative grant. She built one of the first photovoltaics made using a membrane protein from photosynthesis, developed fabrication techniques for building labs-on-chips, and worked at Intel's semiconductor fab in Mask Operations in Santa Clara, California.

 

 

Elsa Olivetti is the Atlantic Richfield Associate Professor of Energy Studies in the Department of Materials Science and Engineering. Her research focuses on improving the environment and economic sustainability of materials using methods informed by materials economics, machine learning, and techno-economic analysis. She has received the NSF Career award for her experimental research focused on beneficial use of industrial waste materials. Dr. Olivetti received her B.S. degree in Engineering Science from the University of Virginia. Her Ph.D. in Materials Science and Engineering from MIT was focused on development of cathode materials for lithium ion batteries.

 

 

 

Roundtable Topics 

Topic Chair(s)
Finding mentors, networking and sustained mentorship Lisa Amini, Rania Khalef
Industry v. academic careers Laura Haas
Diversity in CS Carla Brodley
Careers at IBM Dinah Whitchurch, Yasaman Khazaeni
Starting a new research project Yara Rizk
Developing a personal brand Florence Lu
Deep learning and spatiotemporal data Rose Yu
Accessibility, HCI and AI Danielle Bragg
Natural Language Processing (NLP) Derry Wijaya
Healthcare and AI Olivia Choudhury

 

Oragnizers

Lisa Amini, Director of IBM Research Cambridge

Preethi Raghavan, Research Staff Member, IBM Research Cambridge

Kristen Severson, Postdoc, IBM Research Cambridge

 

Questions? Contact Kristen Severson at kristen.severson@ibm.com

 

Past Events

April 2019

In partnership with WiML, IBM Research Cambridge is excited to announce the first of our 2019 New England WiML-Partner Events featuring Leila Piharji, lightning talks from local researchers, and a meet and greet social afterwards.

Registration Link (Closed)

Leila Pirhaji is the founder and CEO of ReviveMed, a venture-based startup,  based in Cambridge, MA, focusing on discovering therapeutics for metabolic diseases using AI. Dr. Pirhaji received her Ph.D. in biological engineering from MIT, where she developed a pioneering artificial intelligence platform to overcome the difficulties of leveraging metabolomics for drug discovery, which was published in Nature Methods. Her innovative work has received several prestigious awards and been featured in media outlets including TechCrunch. In 2019, Dr. Pirhaji has been chosen as a TedFellow, joining a class of 20 global visionaries and delivering a talk on the TED stage.

In addition to Dr. Pirhaji's talk, we will have three 10 minute rapid fire talks on current research related to AI and health. If you are interested in giving a rapid fire talk, please indicate your interest when registering and include a brief bio and short abstract.

Please join us on Thursday April 4, 2019, 4:00pm - 5:00pm with reception immediately after the event at the IBM Research auditorium, 75 Binney St. Cambridge, MA and help us celebrate women in AI and ML! Registration and preliminaries start at 3:45pm.

June 2019

In partnership with WiML, IBM Research Cambridge is excited to announce the second of our 2019 New England WiML-Partner Events featuring Tamara Broderick and a meet and greet social afterwards. Professor Broderick will present on "Approximate Cross Validation for Large Data and High Dimensions".

Registration Link (Closed)

Abstract: The error or variability of machine learning algorithms is often assessed by repeatedly re-fitting a model with different weighted versions of the observed data. The ubiquitous tools of cross-validation (CV) and the bootstrap are examples of this technique. These methods are powerful in large part due to their model agnosticism but can be slow to run on modern, large data sets due to the need to repeatedly re-fit the model. We use a linear approximation to the dependence of the fitting procedure on the weights, producing results that can be faster than repeated re-fitting by orders of magnitude. This linear approximation is sometimes known as the "infinitesimal jackknife" (IJ) in the statistics literature, where it is mostly used as a theoretical tool to prove asymptotic results. We provide explicit finite-sample error bounds for the infinitesimal jackknife in terms of a small number of simple, verifiable assumptions. Without further modification, though, we note that the IJ deteriorates in accuracy in high dimensions and incurs a running time roughly cubic in dimension. We additionally show, then, how dimensionality reduction can be used to successfully run the IJ in high dimensions in the case of leave-one-out cross validation (LOOCV). Specifically, we consider L1 regularization for generalized linear models. We prove that, under mild conditions, the resulting LOOCV approximation exhibits computation time and accuracy that depend on the recovered support size rather than the full dimension D for D = o(e^N). Simulated and real-data experiments support our theory.

T_BroderickBio: Tamara Broderick is the ITT Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT. She is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). She completed her Ph.D. in statistics at the University of California, Berkeley in 2014. Previously, she received an AB in mathematics from Princeton University (2007), a Master of Advanced Study for completion of Part III of the Mathematical Tripos from the University of Cambridge (2008), an MPhil by research in physics from the University of Cambridge (2009), and an MS in Computer Science from the University of California, Berkeley (2013). Her recent research has focused on developing and analyzing models for scalable Bayesian machine learning.

She has been awarded an NSF CAREER Award (2018), a Sloan Research Fellowship (2018), an Army Research Office Young Investigator Program Award (2017), Google Faculty Research Awards, an Amazon Research Award, the ISBA Lifetime Members Junior Researcher Award, the Savage Award (for an outstanding doctoral dissertation in Bayesian theory and methods), the Evelyn Fix Memorial Medial and Citation (for the Ph.D. student on the Berkeley campus showing the greatest promise in statistical research), the Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton senior with the highest academic average).

Please join us on Thursday June 20, 2019, 4:00pm - 5:00pm with reception immediately after the event at the IBM Research Cafe, 75 Binney St. Cambridge, MA and help us celebrate women in AI and ML! Registration and preliminaries start at 3:45pm.