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Harini Suresh is PhD student in computer science at MIT. She is particularly interested in studying how complications arise when machine learning is used with imperfect real-world data, and building tools that surface possible concerns to end users. She did her undergrad and M.Eng. at MIT as well, and worked on developing interpretable methods for predicting onset and weaning of invasive interventions for patients in Intensive Care Units. She has spent summers working at Google Brain, Jawbone, and Zephyr Health. Outside of research, she loves doing aerial arts, baking all kinds of things, and reading.
Marla Evelyn is an undergraduate studying computer science, economics, and math at MIT. She is a researcher at the MIT Computer Science and Artificial Intelligence Laboratory studying interpretable and explainable machine learning algorithms. Previously, she has done research at the Brookings Institution and the MIT Media Lab. Outside of class and research, Marla enjoys rowing, baking, and playing board games.
Riana Shah is a concurrent degree student at MIT and Harvard studying business and public policy. With her background in tech and sociology, she built an Ed tech startup that has done work in over 19 cities across 11 countries. She is now building Ethix.AI that combats algorithmic bias to fight systematic racism in tech.
Sarah Cen is a PhD student in Computer Science at MIT. She studies machine learning theory through the lens of social good. Her current research shows that state-of-the-art algorithms need not sacrifice performance in order to be socially responsible, using these results to construct mechanisms that align public and private interests. Her recent projects focus on social media regulation and feedback loops in prediction systems. Sarah has previously worked on autonomous vehicles, communication networks, reinforcement learning, and robotics.
Irene Chen is a PhD student in computer science at MIT, advised by David Sontag in the Clinical Machine Learning group. She works on machine learning methods to advance understanding of health and reduce inequality. Prior to MIT, she completed a joint AB/SM degree at Harvard. She also worked at Dropbox as a data scientist, machine learning engineer, and chief of staff.
Leilani H. Gilpin is a PhD student in Electrical Engineering and Computer Science at MIT. Her area of focus is explanatory artificial intelligence, which enables autonomous vehicles, and other autonomous machines to explain themselves. She is currently working on a Toyota Research Initiative Project, “The Car Can Explain!” in the Computer Science and Artificial Intelligence Lab under the supervision of Professor Gerald Jay Sussman. Before returning to academia, Leilani worked at Palo Alto Research Center (PARC) and earned a M.S. from Stanford University and a B.S. in Mathematics (with honors), B.S. in Computer Science (with highest honors), and a music minor from UC San Diego in 2011.
Milo Phillips-Brown is a Postdoctoral Associate in the Ethics of Technology at MIT Philosophy and a Research Fellow in Digital Ethics at the Jain Family Institute. He studies the ethical and social justice implications of technology and teaches ethical engineering practices to computer scientists.