Sarah Maston is a senior solution architect at Microsoft. She works with our partner ecosystem to build global scalable IoT solutions on Azure. She specializes in process architecture and data solution architecture.

She is the founder of Project 15 from Microsoft (http://aka.ms/Project15) and the inventor of the Project Edison, a safety notification and response 3rd party solution accelerator for safe schools and workplaces, for which she won the Microsoft Polaris Award. In her 20 year career, she has architected many of the data warehouses out in the world including those at Monster.com, CouchSurfing.com, and Boston Medical Center. Prior to joining Microsoft, she won an Excellence & Eminence Award at IBM for the invention of the Nutrition Graph and an Outstanding Innovation Award for the invention of Simple Data Pipe.

Tomás Palacios is a Professor in the Department of Electrical Engineering and Computer Science at MIT. He received his PhD from the University of California - Santa Barbara in 2006, and his undergraduate degree in Telecommunication Engineering from the Universidad Politécnica de Madrid (Spain). His current research focuses on demonstrating new electronic devices and applications for novel semiconductor materials such as graphene and gallium nitride. His work has been recognized with multiple awards including the Presidential Early Career Award for Scientists and Engineers, the 2012 and 2019 IEEE George Smith Award, and the NSF, ONR, and DARPA Young Faculty Awards, among many others. Prof. Palacios is the founder and director of the MIT MTL Center for Graphene Devices and 2D Systems, as well as the co-founder of Cambridge Electronics, Inc. He is a Fellow of IEEE.

As clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology, and as a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center, Leo brings together clinicians and data scientists to support research using data routinely collected in the process of care. His group built and maintains the publicly-available Medical Information Mart for Intensive Care (MIMIC) database and the Philips-MIT eICU Collaborative Research Database, with more than 25,000 users from around the world. In addition, Leo is one of the course directors for HST.936 – global health informatics to improve quality of care, and HST.953 – collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. "Secondary Analysis of Electronic Health Records" has been downloaded more than a million times, and has been translated to Mandarin, Spanish, Korean and Portuguese. He is the inaugural editor of PLOS Digital Health.

Divya is a 4th year PhD student in the Clinical and Applied Machine Learning Group at MIT. Her research focuses on building machine learning models that are robust to data characteristic to clinical settings and draws motivation from applications in women's health, including ovulatory disorders and intimate partner violence. She graduated from MIT in 2017, with a concentration in South Asian Studies.

Mike Chapman lives in Manchester, MA and holds Engineering degrees from Dartmouth College and the Thayer School of Engineering at Dartmouth. A gear-head for as long as he can remember, Mike spent his career in project and program management and has been a Formula Hybrid volunteer since the competition started in 2006. He briefly enjoyed retirement for two days before taking over as Director of Formula Hybrid from founder, Doug Fraser, to work with Jess Kinzie on the 2019 competition.

Jess lives in Wilder, VT and spent many years as a High School English teacher before bringing her educational background to Formula Hybrid in 2018. She earned her master’s degree in English Literature from Middlebury College, and has found quite the niche in incorporating Shakespeare into the engineering challenges. Though Jess has helped coordinate three competitions, only one of those has been at the actual track, and she’s looking forward to returning to the New Hampshire Motor Speedway.

Stefanie Jegelka is an Associate Professor in the Department of EECS at MIT. She is a member of the Computer Science and AI Lab (CSAIL), the Center for Statistics and an affiliate of IDSS and ORC. Before joining MIT, she was a postdoctoral researcher at UC Berkeley, and obtained her PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems. Stefanie has received a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, Google research awards, a Two Sigma faculty research award, the German Pattern Recognition Award and a Best Paper Award at the International Conference for Machine Learning (ICML). Her research interests span the theory and practice of algorithmic machine learning, in particular for discrete data.

Caroline Uhler joined the MIT faculty in 2015 as an assistant professor in EECS and IDSS. She holds an MSc in Mathematics, a BSc in Biology, and an MEd in High School Mathematics Education from the University of Zurich. She obtained her PhD in Statistics from UC Berkeley in 2011. Before joining MIT, she spent short postdoctoral positions at the Institute for Mathematics and its Applications at the University of Minnesota and at ETH Zurich, and 3 years as an assistant professor at IST Austria. She is also the co-director of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard. Her research focuses on mathematical statistics and computational biology, in particular on graphical models, causal inference and algebraic statistics, and on applications to learning gene regulatory networks and the development of geometric models for the organization of chromosomes. She is an elected member of the International Statistical Institute and she is the recipient of a Sloan Research Fellowship, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Fund.

Savannah started at Lockheed Martin RMS in Moorestown, NJ, in 2015, as a systems engineer working on the Aegis Command and Decision Team. She then moved to LM Space in Valley Forge, PA, as a software engineer on AEHF communication satellites, and then to a Top Secret Program called Diamondback, working as a software test engineer. She was selected to participate in the Leadership Development program and completed multiple rotations, including Glacier (cyber security) and Nightwatch (image analysis). Last year, Savannah moved to Lockheed Martin Space Headquarters in Littleton, CO, as a technical advisor to the Vice President of the Space Centers of Excellence (COEs), focused on producing satellite bus, optical payloads and RF payloads. Savannah is no stranger to the value (and challenges) of IEEE Student Branch activities. She spent 4 years (2013-17) in leadership roles at the 400+-member Drexel University IEEE Student Branch. While Chair, the Branch won the IEEE Exemplary Student Branch Award for Region 2, Gold in the Darrel Chong Student Activity Award, and the Larry K. Wilson Award. She was a two-time recipient of the IEEE Philadelphia Section’s Student Leadership Scholarship, won the Section’s Alan L. Kirsch Award, and spent two years as a Representative for national student initiatives with IEEE-USA. She has a BS in Electrical and Electronics Engineering from Drexel, and an MS in Systems Engineering from Cornell University.

Francisca Vasconcelos is a Rhodes Scholar currently pursuing an MSt in Philosophy of Physics at the University of Oxford. She received a BS in Electrical Engineering, Computer Science, and Physics from MIT in 2020. As an undergraduate, Francisca performed research at the MIT CSAIL, NASA JPL, the MIT Media Lab, Rigetti Computing, the MIT RLE, and Microsoft Research Quantum. She also served as a Paper/Posters Chair, Conference Chair, and Conference Advisor for the MIT IEEE URTC over three years. In the first year of her Rhodes Scholarship, Francisca received an MSc in Statistical Science from the University of Oxford. Following her time at Oxford, Francisca will pursue a PhD in quantum machine learning at Berkeley EECS, as an NSF Graduate Research fellow.