Women in Machine Learning and AI @Cambridge       


Lisa Amini  PhD photoRania Khalaf photo Amanda Papp photo Preethi Raghavan photo

Women in Machine Learning and AI @Cambridge - overview

IBM Research Cambridge and WiML are excited to announce a Women in Machine Learning and AI community for the Boston-Cambridge area! Our goal is to encourage and support local women, especially students, post-docs, or early career researchers and engineers, in machine learning and AI to build and navigate successful careers by offering seminars from thought-leading women researchers and engineers, and opportunities to present their own research, to meet and interact with other women in the field, and to find mentors, role models and colleagues.
Please join our kickoff event Thursday Oct 4, 2018, 3:00pm-6:00pm at the IBM Research auditorium, 75 Binney, Cambridge, MA and help us form this new community!

Date: Thursday, October 4th, 2018

Time: 03:00 PM - 06:00 PM

Location: @ IBM Cambridge, 75 Binney Street, First Floor Auditorium, Cambridge, MA

The event is OPEN and FREE - but please register!

02.30 pm

Registration and preliminaries


Registration desk Binney Auditorium
03.00 pm

Welcome and Introductions


Lisa Amini Binney Auditorium
03.10 pm

Keynote: AI for Good


Aleksandra Mojsilovic Binney Auditorium
03.35 pm

Keynote: Ways to represent your research and technical work


Katherine Gorman Binney Auditorium
04.00 pm Panel: Next Steps and Great Leaps for AI and Us, with Moderator Katherine Gorman
Jennifer G. Dy,
Yiling Chen,
Vivienne Sze, Kate Saenko,
Tina Eliassi - Rad,
Eni Mustafaraj
Janet Slifka,
Sravana Reddy
Binney Auditorium
05.00 pm

Snacks and coffee


5th floor cafeteria
05.15 pm

Round table discussions


Technical and career topics. See agenda below.
5th floor cafeteria
Aleksandra (Saška) Mojsilović is a scientist, Head of AI Foundations at IBM Research, Co-Director of IBM Science for Social Good, and IBM Fellow. She is a Fellow of the IEEE and a member of the IBM Academy of Technology. Saška received the Ph.D. degree in electrical engineering from the University of Belgrade, Belgrade, Serbia in 1997. She was a Member of Technical Staff at the Bell Laboratories, Murray Hill, New Jersey (1998-2000), and then joined IBM Research. Over the last 20 years, Saška has applied her skills to problems in computer vision, healthcare, multimedia, finance, HR, public affairs, economics, AI ethics and social good. Saška is the author of over 100 publications and holds 16 patents. Her work has been recognized with several awards including IEEE Signal Processing Society Young Author Best Paper Award, INFORMS Wagner Prize, IBM Extraordinary Accomplishment Award, and IBM Gerstner Prize. Saška also serves on the board of directors for Neighborhood Trust Financial Partners, which provides financial literacy and economic empowerment training to low-income individuals.

Katherine Gorman is the founder and co-host of the machine intelligence podcast, Talking Machines and a former public radio producer. In 2017 Katherine helped the Neural Information Processing Systems conference to develop and implement their media strategy. In 2018 she will serve as press co-chair for both the Neural Information Processing Systems Conference and ICML. Katherine is also the head of productions for Collective Next where she helps groups to communicate about issues that matter to them, and to find the heart at the center of their work and community. In 2016 and 2017 she served as a curator and host for TedXBoston which focused on issues around and research in machine learning and artificial intelligence. 
Jennifer G. Dy is a Professor at the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, where she first joined the faculty in 2002. She received her M.S. and Ph.D. in 1997 and 2001 respectively from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, and her B.S. degree (Magna Cum Laude) from the Department of Electrical Engineering, University of the Philippines, in 1993. Her research is in machine learning, data mining and their application to biomedical imaging, health, science and engineering, with a particular focus on clustering, multiple clusterings, dimensionality reduction, feature selection and sparse methods, large margin classifiers, learning from the crowds and Bayesian nonparametric models.  She received an NSF Career award in 2004. She has served as an associate editor for Machine Learning and Data Mining and Knowledge Discovery, an editorial board member for JMLR, organizing/senior/program committee member for ICML, ACM SIGKDD, AAAI, IJCAI, AISTATS and SIAM SDM, and was program chair for SIAM SDM 2013.
Yiling Chen is a Gordon McKay Professor of Computer Science at Harvard John A. Paulson School of Engineering and Applied Sciences. She is a member of the EconCS and AI research groups, and a faculty affiliate of the Center for Research on Computation and Society (CRCS). Prior to Harvard, she spent about two years at Yahoo! Research in New York City. She obtained her Ph.D. from the College of Information Sciences and Technology at The Pennsylvania State University. She is a recipient of an NSF CAREER Award and The Penn State Alumni Association Early Career Award, and was recognized by IEEE Intelligent Systems as one of AI’s 10 to Watch in 2011.
Vivienne Sze is an Associate Professor at MIT in the Electrical Engineering and Computer Science Department.  Her research interests include energy-aware signal processing algorithms, and low-power circuit and system design for portable multimedia applications, including computer vision, deep learning, autonomous navigation, and video process/coding. Prior to joining MIT, she was a Member of Technical Staff in the R&D Center at TI, where she designed low-power algorithms and architectures for video coding. She also represented TI in the JCT-VC committee of ITU-T and ISO/IEC standards body during the development of High Efficiency Video Coding (HEVC), which received a Primetime Emmy Engineering Award.  Prof. Sze received the B.A.Sc. degree from the University of Toronto in 2004, and the S.M. and Ph.D. degree from MIT in 2006 and 2010, respectively. In 2011, she received the Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering at MIT.  She is a recipient of the 2018 Facebook Faculty Award, the 2017 Qualcomm Faculty Award, the 2016 Google Faculty Research Award, the 2016 AFOSR Young Investigator Research Program (YIP) Award, the 2016 3M Non-Tenured Faculty Award, the 2014 DARPA Young Faculty Award, the 2007 DAC/ISSCC Student Design Contest Award, and a co-recipient of the 2017 CICC Outstanding Invited Paper Award, the 2016 IEEE Micro Top Picks Award and the 2008 A-SSCC Outstanding Design Award.  
Eni Mustafaraj is an Assistant Professor of Computer Science at Wellesley College in Wellesley, MA, USA, who received a M. Eng. in Computer Engineering from the Polytechnic University of Tirana (Albania) and a Ph.D. in Computer Science from the Philipps University of Marburg (Germany). She studies web-based, socio-technical systems, especially platforms such as Google, Twitter, and Wikipedia, and is currently interested in the problem of assessing the credibility of online sources, using human-centered machine learning algorithms. For this research she received an NSF CAREER in 2018. Please visit her research page to learn more. She is also in the editorial board of The Spoke, an Albright Institute faculty initiative, and blogs about her research on Medium.
Janet Slifka joined Amazon in 2012 as part of the science team that built and brought Amazon Alexa to market. Janet started out in a scientist role and quickly moved into a manager role -- first building the data collection team and strategies for developing ground truth resources for machine learning, then building the data services team for transcription and annotation, and in 2016 founding the Applied Modeling and Data Science org with Alexa AI. She currently manages globally distributed and cross-functional teams to enable a continually improving experience for Alexa Customers. Janet holds a PhD in Health Sciences and Technology from MIT as part of joint program between Harvard and MIT. She has experience in start-ups, in the health sciences field as a Principal Investigator for NIH and as a scientist for Eliza Corporation, in academia as a Research Scientist as MIT, and in industry as an acoustics engineer at Bose Corporation.
Kate Saenko is an Associate Professor of Computer Science at Boston University and director of the  Computer Vision and Learning Group. She is also a member of the IVC research group. Her past academic positions include: Assistant Professor at the Computer Science Department at UMass Lowell, Postdoctoral Researcher at the International Computer Science Institute, Visiting Scholar at UC Berkeley EECS and a Visiting Postdoctoral Fellow in the School of Engineering and Applied Science at Harvard University. Her research interests are in the broad area of Artificial Intelligence with a focus on Adaptive Machine Learning, Learning for Vision and Language Understanding, and Deep Learning. 
Tina Eliassi-Rad is an Associate Professor of Computer Science at Northeastern University in Boston, MA. She is also on the faculty of Northeastern's Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of massive data from networked representations of physical and social phenomena. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, and cyber situational awareness. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2010, she received an Outstanding Mentor Award from the Office of Science at the US Department of Energy.
Sravana Reddy is a researcher at Spotify in Boston, where she works on projects related to natural language processing and machine learning. She also holds a courtesy appointment at Dartmouth College. She got her PhD in Computer Science from the University of Chicago, and has spent time at USC ISI, Dartmouth and Wellesley. Her work spans NLP, speech, machine learning, and linguistics. Most of her academic research centers around language variation: both dealing with it in practical systems, and analyzing it using large corpora. She is also interested in the applications of computation to literature and writing. She has developed and maintains DARLA, a web application for automating sociophonetics. 

Roundtable Topics

Topic Chair

Ethics and fairness in AI


Aleksandra (Saška) Mojsilović

Cognitive Science and Computer Vision


Aude Oliva

Natural language processing, applied AI


Sravana Reddy

Building ML Infrastructure (Systems + ML)


Rania Khalaf

AI on the Web (human-centered AI that works on behalf of users and their informational needs)


Eni Mustafaraj

Technical career development and Recruiting (Academia vs. industry, Work life balance, Careers@IBM)


Sophie Vandebroek, Dinah Whitchurch

Deep learning for medical applications


Jennifer G. Dy

Explainability and bias in AI


Kate Saenko

Adversarial attacks in machine learning


Tina Eliassi-Rad

Finding mentors, networking and sustained mentorship


Lisa Amini
If you'd like to tell us more about your career interests, please complete this online form prior to our event.
Lisa Amini, Director of IBM Research Cambridge
Preethi Raghavan, Research Staff Member, IBM Research Cambridge
Amanda Papp, Senior Principal Talent Acquisition Partner, IBM
Ehimwenma Nosakhare, PhD student at MIT EECS

Questions? Contact Preethi Raghavan at praghav@us.ibm.com.