There has been some discussion in the past about women data scientists. Things look very bleak if you ask us. For example, in the Experfy marketplace, despite our relentless attempts to recruit women data scientists, less than 5% of all providers are women. Much of this can be attributed to the STEM gap in the labor work, a subject we recently revisited on this blog.
However, we want to celebrate the women who have made it. Here are some selected profiles of women data scientists who have distinguished themselves in a field still largely dominated by men. Please leave names of others that you may know with some references in the comments section. We will try to add your suggestions to this listlets see how many more we can find!
Corinna Cortes received her M.S. degree in physics from Copenhagen University in 1989. She first joined AT&T Bell labs in a reserach position, and served in that company for a decade. Later, she received in PhD in computer science from the University of R0chester in 1993.
She is currently the Head of Google Research; and is known for her work in the field of machine learning. Cortes happens to be a recipient of the Paris Kanellakis Theory and Practice Award for her contributions in the field of support vector machines. She is an Editorial Board member of the journal Machine Learning.
At Google, she works on a broad range of machine learning problems both theoretical and applied. In 2000, she received the AT&T Science and Technology Medal for her work on data-mining in very large data sets.
Her areas of specialization are Artificial Intelligence and Machine Learning, Algorithms and Theory, and General Science.
Corinna is also a competitive runner, and a mother of two. She has multi-authored many Google publications.
Known for her wide research interests including in the various facets of data mining, she has made exceptional contributions to spatio-temporal reasoning and spatio-temporal data mining. Although she has held many management and research positions, her research projects have gained international acclaim. She has served as the coordinator for various European and national research projects; she is currently the co-coordinator of the FP6-IST project GeoPKDD: Geographic Privacy-aware Knowledge Discovery and Delivery.
She is responsible for the Working Group on Privacy and Security in Data mining of the KDUBIQ network of excellences. She has taught classes on databases and data mining at universities in Italy and abroad. Having authored close to one hundred publications, she has also served in various scientific committees related to Data Mining, Databases, and Logic Programming, In 2004, she co-chaired the European conference on Machine Learning and Knowledge Discovery in Data Bases.
Her published works include
- Mobility Profiling
- You Know Because I Know: a Multidimensional Network Approach to Human Resources Problem
- On multidimensional network measures
- Explaining the Product Range Effect in Purchase Data
- Spatial and Temporal Evaluation of Network-based Analysis of Human Mobility
Armed With a BTech in Computer Science and Engineering from the Indian institute of technology, Kharagpur, and a PhD from the Computer Science Division at the University of California, Berkeley, Sunita has served as Associate professor in IIT Bombay, and as Visiting associate professor at the Computer science department of CMU (Jan 2004 -June 2004), among others. Her PhD thesis titled Query processing in tertiary memory databases is available for viewing.
Her research interests span across several interdisciplinary fields including data mining, machine learning, and statistics. She is currently engaged in research on web information extraction, with special emphasis on data integration and graphical models.
You can follow her publications here: publications.
A couple of her specific projects are provided in the following section:
- World Wide Tables: This project focuses on finding methods to answer table queries by tapping partially structured sources like tables and lists on the web.
- ALIAS: This project focuses on creating a prototype model of an application that utilizes machine-learning techniques like Active Learning to ease the duplicate elimination task that arise in data cleaning.
Sunita has served in many professional committees, has many technical patents to her credit, and has guided many doctoral students.
With a Ph.D. in Computer Science from Carnegie Mellon, she works as Senior Research Scientist Analytics in LinkedIn, she has developed the LinkedIn product analytics team from two to 10 data scientists. She is known for spearheading many of LinkedIns key products: the Talent Match system for matching jobs with candidates; the first machine learning model for People You May Know; and the first version of Groups You May Like.
Her papers and presentations include:
- Lies, Damned Lies, and the Data Scientist
- Monica Rogati interviewed at Strata Summit 2011
- The Model and the Train Wreck A Training Data How-To;
- The Data-Driven Parent
Rebecca L Shapley
Her academic background includes a Bachelors in Ecology & Environmental Science from Bryn Mawr College (graduated in 1997), and a Masters in Information Management and Systems from UC Berkeley (Graduated in 2005). During her Masters program, her specialization was on technology needs assessment and user centered design of interactions, interfaces, and systems.
Her past positions include among othersseven years of educational experience in science museums, and four years stint in multimedia software and website production management at ScienceVIEW, LHS.
Now, she works as a User Experience Researcher at Google in Mountain View, CA. Previously, she worked with Books and Picasa. She has completed many important projects, which are listed on her website at http://www.rebeccashapley.com/
Wei Wang received her MS degree from the State University of New York at Binghamton (1995), and a PhD degree in Computer Science from the University of California at Los Angeles (1999). Her past positions include a research staff membership at the IBM T. J. Watson Research Center between 1999 and 2002, and a faculty position in Computer Science and a membership of the Carolina Center for Genomic Sciences and Lineberger Comprehensive Cancer Center at the University of North Carolina at Chapel Hill from 2002 to 2012.
Currently, she is a professor in the Department of Computer Science at University of California at Los Angeles and the director of the Scalable Analytics Institute (ScAI). Some of the recent courses she taught are CS229 Computational Biology: Next Gen Sequence Analysis, (Spring 2014); and CS249 Big Data Analytics (Winter 2014).
Dr. Wangs research interests span big data, data mining, computational biology, among others. She has several patents and more than 100 research publications in peer-reviewed journals to her credit. She has also published one monograph.
Dr. Wang received the IBM Invention Achievement Awards in 2000 and 2001. She has a list of awards and in her name, which are listed in her website at http://www.cs.ucla.edu/~weiwang/
Dr. Wang has either been an editor or an editorial board member in the following publications:
- IEEE Transactions on Knowledge and Data Engineering
- ACM Transactions on Knowledge Discovery in Data
- Journal of Knowledge and Information Systems
- International Journal of Knowledge Discovery in Bioinformatics
- The International Journal of Data Mining and Bioinformatics and the Open Artificial Intelligence Journal.
After her PhD, which overlapped the fields of machine learning, data analysis and numerical engineering), she entered The National Center for Scientific Research (CNRS), France as a research fellow in 1991.
From a hardcore math background, Michèle Sebag went entered the industry to learn computer science, artificial intelligence, and also project management. She soon directed her interests to machine learning; and received an opportunity to pursue research on applied machine learning in numerical engineering at the Laboratoire de Mécanique des Solides at Ecole Polytechnique.
In 2001, she led the Inference and ML group, now ML & Optimization group, at LRI, Université Paris-Sud. In 2003 she co-founded the TAO (ML & Optimization) INRIA project.
Her research interests are widespreadcovering several learning theories, and information theory for robotics and surrogate optimization. She has given many talks and tutorials, and guided many PhD students throughout her career. Among her many responsibilities, two that deserve special mention are her position as President of the French Association for Artificial Intelligence (2003-2010) and as a Member of the board of Machine Learning Journal.
Rachel Haot (née Sterne)
A veteran New Yorker, Rachel Sterne Haot attended Dobbs Ferry High School; and later graduated from the New York University with a bachelors degree in History. Right from the beginning of her professional career, she has proved herself as an entrepreneur over and again.
Rachel currently serves as the Chief Digital Officer and Deputy Secretary of Technology for New York State. Before this, she was the Chief Digital Officer for the City of New York for three years under Mayor Michael Bloomberg. Much later in her life, she also taught as an adjunct professor of Social Media and Entrepreneurship at the Columbia Business School.
From 2006 to 2010, Haot founded and served as Chief Executive Officer of Ground Report, a crowd-sourced news startup. In 2008, Haot founded another digital consulting firm named Upward.
In 2011, Bloomberg named Haot for the post of Chief Digital Officer. Her responsibilities encompassed the development and execution of New York Citys Digital Roadmap, which broadly focused on digitalized Government departments, growth of online engagement, STEM education, and technology-enablement of industry. In 2012, the World Economic Forum recognized her as a Young Global Leader. In 2013, Haot and Mayor Bloomberg jointly launched We Are Made in NYan initiative to promote the growth of the tech sector in New York City.
During superstorm Sandy, Haot was interviewed by WNYC, where she talked at length about the efforts made by her team in rehabilitating the city and the citizens with the support of advanced digital infrastructure. Rachel has been featured on Forbes, Vogue, and Crains magazines.
Priyanka describes herself as an Analytics thought leader, keynoting at conferences about data-driven decision making in Business and so she proved to be.
My main research interest lies in extracting and employing actionable insights from data to enable gainful decision making. She is gifted in discovering patterns and driving decision-centric change in an organization.
At CEO of Aryng, she offers data-driven solutions to business problems; and does this by developing people, process and tools. Priyanka also likes to believe that her problem-solving tools and skills will empower businesses to create better products and better customer experience. Before founding Aryng, She served in analytics roles in organizations like PayPal and Adobe.
Her specialties include:
More than 15 years of analytics and entrepreneurial experience
10 years of people advocacy and management experience
Keynoter/Speaker at Business and Analytics conferences
You will find her bestseller here: Behind Every Good Decision
Having served as Chief Scientist at bitly for four years, she now serves as Scientist Emeritusguiding a team of data scientists dedicated to real-time internet study through a combination of research, exploration, and engineering. Hilary co-founded HackNY to support upcoming engineering students make an entry into the startup community of technologists in New York City.
She serves as a Mentor and Advisor to many organizations like Mortar, knod.es; and serves as a member of Mayor Bloombergs Technology and Innovation Advisory Council. She has been featured in TechFellows Engineering Leadership, Forbes 40 under 40 Ones to Watch list, and Crains New York 40 under Forty, Glamour, Fast Company, and Scientific American magazines.
Hilary has made major presentations, and has shared her thoughts on topics like how to replace yourself with a very small shell script, machine learning: a love story, and more.
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