WiDS 2023 Fall Speakers

Central Massachusetts Women in Data Science

September 14th, 2023

Opening Remarks

Dr. Geri Louise Dimas

Dr. Geri Louise Dimas

Assistant Professor, Data Science Program, Bryant University

Geri Louise Dimas received her Ph.D. in Data Science Program from Worcester Polytechnic Institute in May 2023. She is now an Assistant professor at Bryant University in the Department of Information Systems and Analytics teaching Data Science courses.  Dr. Dimas is also the Co-Director of the Institute for the Qualitative Study of Inclusion, Diversity, and Equity (QSIDE) Stopping Trafficking And Modern-day Slavery Project (STAMP) Lab. Her research focuses on applications of applied analytics and data science at the intersection of societal issues such as immigration, anti-human trafficking, and homelessness.

Dr. Elke Rundensteiner

Dr. Elke Rundensteiner

Professor and Director / Founder, Data Science Program, WPI

As founding Director of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, new educational initiatives to our innovative Graduate Qualifying projects at the graduate level.

Having served as primary advisor and mentor of over 35 PhD students who have secured successful professional careers in computing, I’m proud of all the great accomplishments of students I have had the opportunity to collaborate with. With an h-index of 55, I have authored well over 400 publications, numerous patents, and software systems released to public domain. My research work, widely cited, has been supported by government agencies including NSF, NIH, DOE, FDA, and DARPA, and by industry including HP, IBM, Verizon Labs, GTE, NEC, AMADEUS, Charles River Analytics, and by labs such as MITRE Corporation. I’ve enjoyed holding leadership positions in the big data field, including having served as Associate Editor of prestigious journals including IEEE Transactions on Data and Knowledge Engineering and VLDB Journal and as area chair on premiere professional big data conferences, including ACM SIGMOD, VLDB, IEEE ICDE, and others

Panel: Data Science and Environmental Justice

Moderator

Dr. Geri Louise Dimas

Dr. Geri Louise Dimas

Assistant Professor, Data Science Program, Bryant University

Geri Louise Dimas received her Ph.D. in Data Science Program from Worcester Polytechnic Institute in May 2023. She is now an Assistant professor at Bryant University in the Department of Information Systems and Analytics teaching Data Science courses.  Dr. Dimas is also the Co-Director of the Institute for the Qualitative Study of Inclusion, Diversity, and Equity (QSIDE) Stopping Trafficking And Modern-day Slavery Project (STAMP) Lab. Her research focuses on applications of applied analytics and data science at the intersection of societal issues such as immigration, anti-human trafficking, and homelessness.

Panelists

Dr. Chaitra Gopalappa

Dr. Chaitra Gopalappa

Associate Professor, Mechanical and Industrial Engineering, UMass Amherst Associate Professor, Commonwealth Honors College, UMass Amherst Guest Researcher, Centers for Disease Control and Prevention (CDC/NCHHSTP)

Dr. Chaitra Gopalappa is an associate professor of industrial engineering and operations research at the Department of Mechanical and Industrial Engineering and Commonwealth Honors College at the University of Massachusetts, Amherst. She is also a guest researcher at the U.S. Centers for Disease Control and Prevention. Her work is in the area of simulation, simulation-based optimization, reinforcement learning and machine learning, and stochastic processes. Her lab focusses on developing mathematical and computational models to inform public health policies. Recent work includes use of machine learning methods to capture the interactions between interrelated sexually transmitted diseases and social determinants of health, to subsequently quantify disease risk attributed to social and economic conditions, understand social needs of persons at risk of disease, and inform structural interventions. Her lab is funded by grants from the National Institutes of Health, the National Science Foundation, the U.S. Centers for Disease Control and Prevention, and the World Health Organization.

Dynamic simulation models are a critical tool to evaluate the impact of alternative disease intervention combinations to identify the strategy that is most optimal at controlling an outbreak.  The fundamental building blocks of most infectious disease models are to simulate human behaviors such as contact networks, and testing and treatment behaviors. However, data show that disease cases are concentrated among populations in low socio economic conditions and further that social and economic factors are drivers of behaviors that increase risk of diseases. While behaviors are fundamental mathematical mechanisms for predicting the spread of diseases, modeling behaviors alone without the factors that drive them can enormously misrepresent intervention needs. These data also suggest that social factors are common risk factors of multiple diseases, and as such, the need for a method to jointly modeling related diseases to inform optimal allocation of resources through a unified approach to disease prevention. I will discuss some of our work in use of machine learning methods to address the challenges associated with building such a multi-disease model and simulating behaviors as functions of social determinants, related to sexually transmitted diseases.

Lab website: https://diseasemodeling.github.io/

Talk Title: Follow the data: social factors are among key drivers of disease risk. Building disease prediction models informed by data

Dr. Reshmi Ghosh

Dr. Reshmi Ghosh

Applied Scientist, Microsoft

Reshmi received her double master’s and doctorate from Carnegie Mellon University (CMU), where she focused on utilizing Deep Learning methods to reconstruct parts of missing data required to assess the robustness of evolving U.S. grid under inter-annual variability of climatological factors. She also researched and developed stochastic methods in conjunction with Machine Learning algorithms for analyzing various renewable energy (especially offshore wind) integration scenarios in the New England, California, and Texas region.
Currently, Reshmi is a Machine Learning Scientist at Microsoft and is leveraging her background in Computer Science and AI to help the company research integration of Large Language Models and build products/services that can elevate user-experience and aid in increasing productivity for users. She has developed intelligent features in the Azure and Office product group which are currently being used by 7 million customers worldwide.

 

Dr. Catherine Izard

Dr. Catherine Izard

Data Science Manager, National Grid

Catherine Izard, PhD is a utility analytics professional who uses the power of data to shape the energy networks of the future.  Catherine is passionate about combining interdisciplinary, systems thinking, advanced quantitative modeling and data science in a business context to solve energy and climate problems. She is currently a Data Science Manager at National Grid, where her team works on a wide variety of projects across the business to drive down ratepayer costs, support the transition to a clean energy future, and ensure the safety of gas and electric networks. Catherine holds a PhD in Engineering and Public Policy from Carnegie Mellon University.

 

Closing Remarks

Dr. Elke Rundensteiner

Dr. Elke Rundensteiner

Professor and Director / Founder, Data Science Program, WPI

As founding Director of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, new educational initiatives to our innovative Graduate Qualifying projects at the graduate level.

Having served as primary advisor and mentor of over 35 PhD students who have secured successful professional careers in computing, I’m proud of all the great accomplishments of students I have had the opportunity to collaborate with. With an h-index of 55, I have authored well over 400 publications, numerous patents, and software systems released to public domain. My research work, widely cited, has been supported by government agencies including NSF, NIH, DOE, FDA, and DARPA, and by industry including HP, IBM, Verizon Labs, GTE, NEC, AMADEUS, Charles River Analytics, and by labs such as MITRE Corporation. I’ve enjoyed holding leadership positions in the big data field, including having served as Associate Editor of prestigious journals including IEEE Transactions on Data and Knowledge Engineering and VLDB Journal and as area chair on premiere professional big data conferences, including ACM SIGMOD, VLDB, IEEE ICDE, and others