Amy McGovern is a Lloyd G. and Joyce Austin Presidential Professor in the School of Computer Science and School of Meteorology at the University of Oklahoma. She is also the director for the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography. She earned her PhD in 2002 from UMass Amherst and her BS from Carnegie Mellon University in 1996.
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Susan Dumais is a Technical Fellow and Director of the Microsoft Research Labs in New England, New York City and Montréal, and an adjunct professor at the University of Washington. Prior to joining Microsoft, she was a Member of Technical Staff at Bell Labs and Bellcore. Her research is at the intersection of human-computer interaction, information retrieval, and web and data science. A common theme that runs through her work is the importance of understanding and improving information systems from an interdisciplinary and user-centered perspective. She is a co-inventor of Latent Semantic Analysis, a well-known word embedding technique, which was designed to mitigate the disagreement between the words that authors use writing and those that searchers use to find information. Her research spans a wide range of topics in information systems, including email spam filtering, user modeling and personalization, context-aware information systems, temporal dynamics of information, and large-scale behavioral interactions. She has worked closely with several Microsoft product teams (Bing, Windows Search, SharePoint, and Office Help) on search-related innovations, and has published widely in the fields of information retrieval, human-computer interaction, and cognitive science.
She is an ACM Fellow, was elected to the ACM SIGCHI Academy, the National Academy of Engineering (NAE), and the American Academy of Arts and Sciences (AAAS). She received the ACM Athena Lecturer Award for fundamental contributions to computer science, the SIGIR Gerard Salton Award for lifetime achievement in information retrieval, the Tony Kent Strix Award for outstanding contributions to information science, the ACM SIGCHI Research Award for lifetime achievement in human-computer interaction, and the lifetime achievement award from Indiana University Department of Psychological and Brain Science.
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Dr. Rumman Chowdhury’s passion lies at the intersection of artificial intelligence and humanity. She is a pioneer in the field of applied algorithmic ethics, working with C-suite clients to create cutting-edge technical solutions for ethical, explainable and transparent AI since 2017.
She is currently the CEO and founder of Parity, an enterprise algorithmic audit platform company. She formerly served as Global Lead for Responsible AI at Accenture Applied Intelligence.
Rumman has been featured in international media, including the Financial Times, Harvard Business Review, NPR, MIT Sloan Magazine, MIT Technology Review, BBC, Axios, Cheddar TV, CRN, The Verge, Fast Company, Quartz, Corrierre Della Serra, Optio, Australian Broadcasting Channel and Nikkei Business Times. She is a member of BrainTrust, a community of experts for Protocol, a tech magazine by Politico.
Amy Beth Prager
Ms. Amy Beth Prager is an applied mathematician and computational scientist, researcher, and passionate advocate for diversity and inclusion, encouraging participation of young women and underrepresented groups in STEM. Amy completed her undergraduate studies at Johns Hopkins University in applied mathematics and theoretical chemistry. She then pursued a postgraduate education in quantum chemistry for 1.5 years, while working on scientific projects centered in North America and Europe. In the past two decades, her research has begun to focus more on the disproportionate barriers and systemic income inequality in countries with high education costs such as the USA and UK, particularly on women and underrepresented groups. She entered the doctoral programs in math education and mathematics at Columbia University, featuring additional coursework at Princeton University and IAS, and after becoming ABD at Columbia, completed over a calendar year of post-ABD study at MIT. Currently, Amy serves on the NCWIT Aspirations Team in her native Massachusetts, is a math lecturer at Cornell University (and other universities in the New England area). She is an active member and participant of SACNAS and the Mathematics Alliance, having served on and led panels, discussions, and keynotes at SWE conference. Further, Amy has contributed to significant to books on encouragement of women and underrepresented groups in STEM.
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Hima Lakkaraju is an Assistant Professor at Harvard University focusing on explainability, fairness, and robustness of machine learning models. She has also been working with various domain experts in criminal justice and healthcare to understand the real world implications of explainable and fair ML. Hima has recently been named one of the 35 innovators under 35 by MIT Tech Review, and has received best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS. She has given invited workshop talks at ICML, NeurIPS, AAAI, and CVPR, and her research has also been covered by various popular media outlets including the New York Times, MIT Tech Review, TIME, and Forbes.
Laura Balzer is an Assistant Professor of Biostatistics at UMass-Amherst. She is the Director of the UMass Causality Lab, and her areas of expertise include Causal Inference and Machine Learning. These disciplines are integral to developing, evaluating, and implementing data-driven solutions in Public Health and Medicine. Dr. Balzer is the Primary Statistician for four cluster randomized trials in East Africa. Her work is supported by the National Institutes of Health (NIH) and has been recognized with the ASA’s Causality in Statistics Education Award.
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Kasthuri is a Research Fellow and Adjunct Faculty at the Singapore Management University (SMU). She completed her doctoral research in the areas of pervasive computing and socio-physical sensing. Her work explores solutions to real-world urban problems cutting across human mobility, applied machine/deep learning, edge computing, network science, urban science and marketing science, and has featured at several top-tier conferences and journals (UbiComp/IMWUT, MobiSys, SenSys, CIKM, ACM TOIT and the Journal of Business Research). Recently, she was named a Rising Star in EECS by UC Berkeley (2020) and a finalist for the Gaetano Borriello Outstanding Student Award (presented at UbiComp 2019). She is also a recipient of the Google Women Techmakers Scholarship (APAC, 2017), the SMU Multidisciplinary Doctoral Fellowship (2018), SMU-CMU LARC Exchange Fellowship (2016) and the A*STAR Graduate Scholarship (2014-2018).
Simone Nsutezo Fobi
Simone Nsutezo Fobi is a 5th year Mechanical Engineering PhD student, in the Quadracci Sustainable Engineering Lab (QSEL) at Columbia University. Her research focuses on Electricity Demand Analytics & Prediction,and Energy Infrastructure Planning. Some of her work includesusing deep learning to understand relevant features in satellite images that are predictive of latent electricity demand for yet-to-be connected households. This is of interest to energy providers who can leverage such models to design energy systems required to meet the latent demand upon electrification. Concurrently, she applies network optimization to design the placement of energy infrastructure (transformers, low and medium voltage lines), given anticipated demand. Combining both approaches, her research will inform the design and operation of more cost-effective energy systems, thereby contributing towards affordable, reliable, sustainable and modern energy for all.
Priya L Donti
Priya Donti is a Ph.D. student in Computer Science and Public Policy at Carnegie Mellon University, and a U.S. Department of Energy Computational Science Graduate Fellow. She is also a co-founder and chair of Climate Change AI, an initiative to catalyze impactful work in climate change and machine learning. Her work lies at the intersection of machine learning, electric power systems, and climate change mitigation. Specifically, her research explores ways to incorporate domain knowledge (such as power system physics) into machine learning models.
Laure Thompson is an Assistant Professor in the College of Information and Computer Sciences at UMass Amherst. Her work bridges machine learning and natural language processing with humanistic scholarship. She builds tools and creates methodologies that enable scholars to use machine learning and statistical methods for studying cultural collections at scale. Her research focuses on understanding what computational models actually learn and how we can intentionally change what they learn. She recently received her Ph.D. from Cornell University in 2020 and received her B.S. from the University of Washington in 2013.
Su Lin Blodgett
Su Lin Blodgett is a postdoctoral researcher in the Fairness, Accountability, Transparency, and Ethics (FATE) group at Microsoft Research Montréal. She is broadly interested in examining the social implications of natural language processing technologies, and in using NLP approaches to examine language variation and change (computational sociolinguistics). She has worked on developing models to identify language variation on social media, on applying lessons from sociolinguistics and linguistic anthropology to understanding harms arising from NLP systems, and on using the language of measurement modeling to rigorously analyze approaches to quantifying bias in NLP.
Previously, she completed my Ph.D. in computer science at the University of Massachusetts Amherst working in the Statistical Social Language Analysis Lab under the guidance of Brendan O’Connor, where she was also supported by the NSF Graduate Research Fellowship. She received B.A. in mathematics from Wellesley College. She also interned at Microsoft Research New York in summer 2019, where she was very fortunate to work with Hanna Wallach, Hal Daumé III, and Solon Barocas.
Ivana Williams is a Staff Research Scientist at the Chan Zuckerberg Initiative. She is passionate about delivering state of the art machine learning and data science solutions in support of accelerating scientific discovery, unlocking insights from scientific publications, and delivering personalized content. Her recent research focuses on novel approaches to data representation and automated knowledge base construction.
Jigyasa Grover is a Machine Learning Engineer at Twitter and the author of the book ‘Sculpting Data for ML’. She has a myriad of experiences from her brief stints at Facebook, Inc., National Research Council of Canada, and Institute of Research & Development France involving Data Science, mathematical modeling, and software engineering. Having graduated from the University of California, San Diego, with a Master’s degree in Computer Science with an Artificial Intelligence specialization, she is presently plying her past experiences and knowledge towards Applied Machine Learning in the online advertisements prediction and ranking domain.
Red Hat ‘Women in Open Source’ Academic Award Winner and Google Summer of Code alumna, Jigyasa is an ardent open-source contributor as well. She served as the Director of Women Who Code and Lead of Women Techmakers for a handful of years to help bridge the gender gap in technology. In her quest to build a powerful community of girls and boys alike, and believing in “we rise by lifting others,” she mentors aspiring developers and Machine Learning enthusiasts in various global programs. She also has many international conference keynotes, technical talks, panels, workshops, blogs, and podcasts to her name. Apart from her technological ventures, she enjoys exploring new places, hanging out with friends and family, and has been recently having fun with baking.
Katie House is a Junior Data Scientist at MassMutual living in Amherst. She earned her bachelor’s in industrial engineering from Northeastern University and master’s in computer science from UMass Amherst. At UMass, Katie was involved in the hackathon community founding and directing HackHer413. After graduating, Katie worked at the Reich Lab on COVID-19 forecasting with the CDC. At MassMutual, she currently works in the customer journey domain developing models to improve marketing. She enjoys baking (particularly cookies) and making latte art at home.
Maryclare Griffin has been an assistant professor at the Department of Mathematics and Statistics at the University of Massachusetts Amherst since Fall, 2019. She received her Ph. D. In Statistics from the University of Washington in 2018, and completed a postdoc at Cornell Cornell in the Center for Applied Mathematics. Her research focuses on problems that arise in the context of high dimensional regression and dependent data.