Cynthia Rudin, PhD
Dr. Rudin is a professor at Duke University and directs the Prediction Analysis Lab, whose main focus is interpretable machine learning. She is also an associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. In 2019, she was elected as a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics “for her contributions to interpretable machine learning algorithms, prediction in large scale medical databases, and theoretical properties of ranking algorithms.” She is a three-time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the “Top 40 Under 40” by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015.
Jeannette M. Wing, PhD
Dr. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Adjunct Professor of Computer Science at Carnegie Mellon University where she has been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology. She is currently a member of the American Academy for Arts and Sciences Council; the New York State Commission on Artificial Intelligence, Robotics, and Automation; and the Advisory Board for the Association for Women in Mathematics. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).
Kristian Lum, PhD
Dr. Lum is an assistant research professor at the University of Pennsylvania. She studies and develops machine learning models to tackle problems with social impact. Her work includes statistical population estimation models to estimate the number of undocumented victims of human rights violations, “fair” algorithms for use in high-stakes decision making, and epidemiological models to study disease spread among and between marginalized populations and the broader community. Previously she served as the lead statistician at the Human Rights Data Analysis Group where she led the HRDAG project on criminal justice in the United States.
Francesca Tripodi, PhD
Dr. Tripodi is a sociologist and media scholar whose research focuses on subjects like search, political partisanship, media manipulation and inequality. In 2019, she testified before the Senate Judiciary Committee, explaining how search processes are gamed to drive ideologically based queries. She also studies patterns of gender inequality on Wikipedia, shedding light on how knowledge is contested in the 21st century. Her research has been covered by The Washington Post, The New York Times, The New Yorker, The Columbia Journalism Review, Wired, The Guardian and The Neiman Journalism Lab.
Barbara Poblete, PhD
Dr. Poblete is an associate professor of computer science at the University of Chile. Her research on the topic of “information credibility in social networks” (2010-2013), was the first on misinformation in social media (+3000 citations), and has appeared in SciAM, WSJ, Slate, The Huffington Post, BBC News and NPR. She received a Google Latin America Research Award (PI) and two “Social Media and Democracy Research Grants” (co-investigator) from SSRR to study the effects of social media on elections.
Emily Hadley
Emily Hadley is a Research Data Scientist with the RTI International Center for Data Science. Her work spans several practice areas including health, education, social policy, and criminal justice. Emily holds a Bachelor of Science in Statistics with a second major in Public Policy Studies from Duke and a Master of Science in Analytics from NC State. Emily served as an AmeriCorps college adviser in rural North Carolina, where she used data-driven techniques to build a college-bound culture. That college advising work was featured in the New York Times in May 2017.
Kelsey Campbell
Kelsey Campbell is a data scientist at Visionist, Inc. Kelsey is also the founder of Gayta Science, a project started in 2017 with the mission of exploring the LGBTQ+ experience using data science and analytics. With a growing team of volunteer analysts, designers, researchers, and developers, Gayta Science is devoted to investigating a variety of LGBTQ+ issues using data science. Kelsey holds an M.S. in Analytics from the Institute for Advanced Analytics. They previously graduated from Purdue University, Krannert School of Management, with a B.S. in Economics.
Irene Chen
Irene Chen is a computer science PhD student at MIT. Her research focuses on machine learning methods to improve clinical care and deepen our understanding of human health, with applications in areas such as heart failure and intimate partner violence. Her work has been published in both machine learning conferences (NeurIPS) and medical journals (Nature Medicine, AMA Journal of Ethics), and covered by media outlets including MIT Tech Review, NPR/WGBH, and Stat News. She has been named a Rising Star by University of California Berkeley, Harvard University, and University of Maryland. Prior to her PhD, Irene received her AB in applied math and SM in computation engineering from Harvard University.
Alina Barnett
Alina Barnett is a 3rd year PhD Student at Duke University researching interpretable machine learning and computer vision. Her work on interpretable image recognition was spotlighted by NeurIPS in 2019 and she earned a Duke Incubation Fund Award for a multi-department interdisciplinary project for superior interpretability on neural networks that analyze mammograms.
Caroline Kery
Caroline Kery is a data scientist at the Center for Data Science at RTI International. She uses her experience in natural language processing, data visualization, and machine learning to explore novel ways of answering questions through data. While at RTI, Ms. Kery worked with the team to develop SMART, an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks.
Sarah Egan Warren, PhD
Dr. Egan Warren has 25+ years of experience developing curriculum and delivering presentations, training, and instruction in undergraduate, graduate, and industry settings. She is a teaching assistant professor at the Institute for Advanced Analytics where she created and teaches the technical communication curriculum. With a focus on ethical storytelling with data, she works with upcoming and established data professionals to hone their effective communication skills.
Xin Hunt, PhD
Dr. Hunt is a senior machine learning developer at SAS, where her primary focus has been on explainable AI and model interpretability. She obtained her PhD in Electrical and Computer Engineering from Duke University in 2017.
Eugenia Anello
Eugenia Anello is currently a masters student in Data Science at the University of Padova in Italy, and a Data Scientist Intern at Statwolf where she is responsible for building and testing anomaly detection models for a manufacturing company. She is a regular contributor to Medium’s publications Towards AI and Better Programming. Her articles focus on a variety of data science concepts in a simple and understandable way.