KDD will be hosting a panel discussion during the Women’s Lunch. The panelists are:
- Calandra Moore (Data Scientist, Department of Defense)
- Elaine O. Nsoesie (Assistant Professor, Boston University School of Public Health)
- Karin Kimbrough (Chief Economist, LinkedIn)
- Sihem Amer-Yahia (Research Director, CNRS)
- Subarna Sinha (Engineering Leader, Machine Learning, 23andMe)
- Vanessa Murdock (Applied Science Manager, Amazon)
Dr. Calandra Tate Moore is a Data Scientist at the U.S. Department of Defense. She received her M.S. and Ph. D. in Applied Mathematics from the University of Maryland College Park and a B.S. in Mathematics from Xavier University of Louisiana. Previously, she spent a number of years in academia, as a mathematics professor in the Department of Mathematics at City University of New York's College of Staten Island, and prior federal service, as a mathematician on the Multilingual Research Team at the U.S. Army Research Laboratory and Visiting Scientist Chair at the U.S. Military Academy. She has conducted research on a wide range of mathematical and statistical applications, but currently her work is focused on human language technology as an evaluation lead in video, image, speech, and text analytics research.
Dr. Nsoesie is an Assistant Professor of Global Health at Boston University (BU) School of Public Health. She has a PhD in Computational Epidemiology from the Genetics, Bioinformatics and Computational Biology program at Virginia Tech. She also has an MS in Statistics and a BS in Mathematics. Her research is focused on the use of digital data and technology for public health surveillance. She is the founder of Rethé - an initiative focused on providing scientific writing tools and resources to student communities in Africa in order to increase representation in scientific publications. She has written for NPR, The Conversation, Public Health Post, Think Global Health and Quartz. Dr. Nsoesie was born and raised in Cameroon.
Karin joined LinkedIn as Chief Economist in January 2020. She joined from Google where she previously was an Assistant Treasurer. Prior to Google she was a Managing Director and Head of Macroeconomic Policy at Bank of America, and Vice President at the Federal Reserve Bank of New York during the financial crisis. Karin holds a doctorate in Economics from Oxford University, a masters in Public Policy from the Harvard Kennedy School, and an undergraduate degree in Economics from Stanford. In 2017 Karin was recognized by Black Enterprise as one of the most powerful Black Women in Business. She is a member of the Fannie Mae Board of Directors and the Chicago Fed's Academic Advisory Council.
Sihem Amer-Yahia is a Research Director at LIG in Grenoble where she leads the SLIDE team. Her interests are exploratory data analysis and fairness in job marketplaces. Before joining CNRS, she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem served on the SIGMOD Executive Board, the VLDB Endowment, and the EDBT Board. She is the Editor-in-Chief of the VLDB Journal for Europe and Africa and has been on the editorial boards of TODS and the Information Systems Journal. She was PC chair of VLDB 2018. Sihem received her Ph.D. in CS from Paris-Orsay and INRIA in 1999, and her Diplôme d’Ingénieur from INI, Algeria.
Subarna Sinha currently leads the Machine Learning Engineering team at 23andMe. After getting her PhD, she worked in research groups at Intel and Synopsys developing novel, cutting-edge solutions for automating semiconductor chip design. She then joined Stanford University where she pivoted her research to computational biology. Following Stanford, she moved to SRI International, where she was a NIH-funded Principle Investigator with a focus on developing novel computational tools for precision medicine. Her current position at 23andMe enables her to build solutions at the intersection of engineering and computational biology. With 50+ peer-reviewed publications in leading research journals and patents, she is also the recipient of numerous awards, including the Donald O. Pederson/IEEE best paper award and the Synopsys Inventor of the Year award. She holds a B.Tech. in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, and a Ph.D. in electrical engineering and computer sciences from the University of California, Berkeley.
Vanessa Murdock leads a research group in Alexa Shopping at Amazon, whose focus is search and recommender systems. Previously, she worked at Microsoft as a Principal Scientist, leading a research team that developed ML techniques for account attribution, advertising, and device fingerprinting, as well as working on location inference and notifications at Bing and Cortana. Prior to Microsoft, Murdock led the Geographic Context and Experience Group at Yahoo! Research in Barcelona, doing research on topics related to geographic information retrieval and user-generated content. She has been awarded 18 patents, and has more than 20 patent applications pending, resulting in a Master Inventor Award from Yahoo! (2012). She received the OAA Award for Outstanding Achievement by a Young Alum from the University of Massachusetts in 2014. She has more than 50 publications in the area of Information Retrieval. Murdock received a Ph.D. in Computer Science from the University of Massachusetts Amherst in 2006.
Prakruthi Prabhakar currently works as a Senior Software Engineer, Machine Learning in the notifications and communications AI team at LinkedIn. Prior to this role, she worked as a Research Engineer in the Big Data Labs at American Express. She holds a B.Tech. and M.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Madras, and a Masters degree in Computer Science from Carnegie Mellon University, Pittsburgh.
How can we assist you?
We'll be updating the website as information becomes available. If you have a question that requires immediate attention, please feel free to contact us. Thank you!
Please enter the word you see in the image below: