Aishwarya Srinivasan

Aishwarya Srinivasan is working as a Data Scientist in the Google Cloud AI Services team to build machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and AI Platform. Previously, she was working as an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team, and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She is an advocate for open-source technologies; previously a developer advocate for PyTorch Lightning and a contributor to Scikit Learn. She holds a post-graduate in Data Science from Columbia University.

Dr. Cecilia Aragon

Dr. Cecilia Aragon is an award-winning author, airshow pilot, and the first Latina full professor in the College of Engineering at the University of Washington in Seattle. She’s worked with Nobel Prize winners, taught astronauts to fly, and created musical simulations of the universe with rock stars. Her major awards for research, and a stint at NASA designing software for Mars missions, led President Obama to call her “one of the top scientists and engineers in the country.” Dr. Aragon holds her PhD in Computer Science from UC Berkeley and B.S. in Mathematics from California Institute of Technology.

Dr. Adèle Helena Ribeiro

Dr. Adèle Helena Ribeiro is a highlighted DAAD Postdoc-NeT-AI Fellowship recipient and Postdoctoral Research Scientist in the Causal Artificial Intelligence (Causal AI) Laboratory. Her research lies at the intersection of Computer Science, Statistics, and Artificial Intelligence in Healthcare. Her efforts are focused on advancing the theory of causal inference and learning for discovering, generalizing, and personalizing cause-effect relationships from multiple observational and experimental data collections. She received her PhD, M.S., and B.S. degrees all from the Institute of Mathematics and Statistics of the University of Sao Paulo (USP), Brazil.

Kade Crockford

Kade Crockford is the director of the Technology for Liberty Program at the ACLU of Massachusetts. Recently, their work has expanded to include open data projects that aim to harness the power of digital technologies to reform the criminal legal system in Massachusetts, towards the end of shifting resources from police and prisons to social services and public health. Kade's writing on digital security, surveillance, and state power has appeared in outlets including the Nation, the Guardian, the Boston Globe, New Inquiry, and the Baffler.

Dr. Hala Mostafa

Dr. Hala Mostafa is the Senior Manager/Lead of Advanced Learning and Analytics Team at Raytheon Technologies Research Center. For the past 7 years, she has been leading ML/data analytics projects where she applies machine learning to help her corporation's diverse business units gain insights and solve problems in areas like contract pricing, manufacturing, operations and predictive maintenance. Dr. Mostafa earned her PhD in Computer Science from the University of Massachusetts Amherst in 2011 and her BSc in Computer Science from Cairo University in 2001.

Dr. Lisa Friedland

Dr. Lisa Friedland is a computer scientist who has used methods from data mining, machine learning, network science, and computational social science to analyze large data sets. After leaving UMass, she worked as a researcher at Northeastern University's Network Scientist Institute on projects involving human mobility patterns during Covid-19, detection of malware from network traffic, and the role of misinformation in the American political discussion on Twitter. She has also published in areas including anomaly and fraud detection, near-duplicate detection, information retrieval, relational learning and bioinformatics. She received her PhD from the University of Massachusetts Amherst in 2016 and her B.A. from Harvard University in 1998.


Shruti Jadon

Shruti Jadon is currently leading ML research at CTO Org of Juniper Networks on meta-learning and time series forecasting. She has also worked as Visiting Researcher at Radiology lab of Rhode Island Hospital and BIOS Lab of John Hopkins University for research on medical image segmentation. Her research interests include multi-task learning, few-shot learning, and optimization algorithms. She has earned her M.S. in Computer Science from UMass Amherst in May 2018.

Purva Pruthi

Purva Pruthi is currently an AI resident at Google X, The Moonshot Factory where she is working on applying causal inference techniques in solving the most challenging real-world problems. She is a fourth-year Ph.D. Candidate at the University of Massachusetts, Amherst. Her research interests are in causal inference, reinforcement learning, and probabilistic approaches to machine learning. She has also worked with Amazon Research in Cambridge, UK in 2019 where she worked on designing transfer learning methods for reinforcement learning. She has previously worked as a quantitative analyst at Goldman Sachs for three years. She earned her Bachelors's Degree in Computer Science and Engineering from the Indian Institute of Technology Roorkee in 2015.