Sombit Mishra, CEO
A tech startup veteran, Sombit brings 7+ years of product management, marketing, business development, and fundraising experience to EveryFit. His relevant expertise in user interface design and analytics were key drivers in the conception and launch of EveryFit. At MIT, Sombit served as Managing Director of the $100K Competition, for which he received the 2009 Patrick J. McGovern Entrepreneurship Award. Sombit completed his MBA at the MIT Sloan School of Management, his MSc from the London School of Economics, and his Bachelors in Economics and History from Northwestern University.
View Sombit's LinkedIn profile
Dave Nelson, President
Dave brings 7+ years of financial analysis, general management, and marketing experience to EveryFit. He previously worked at Prudential Capital Group as a senior investment analyst, achieving the CFA designation, and with two startup companies in marketing roles. While at MIT, Dave was the co-chair of the MIT Venture Capital Conference. He also received the Ronald I. Heller Award in 2010 for his contributions to entrepreneurship at MIT. Dave completed his MBA at the MIT Sloan School of Management and his Bachelors in Finance at the University of Michigan. He currently serves on the board of Support for International Change, an organization working to limit the impact of HIV/AIDS in Tanzania.
Fahd Albinali, CTO
Fahd was previously a Research Scientist at the House_n Consortium at the MIT Department of Architecture. His research has focused on building and studying interactive technologies that are context-aware. His work has been published in academic venues including UbiComp, AAAI, CHI and PerCom and has received one best paper award. Fahd received his Ph.D. from the University of Arizona in 2008 working on activity recognition in domestic environments, an MSc from the University of Arizona in 2002, and a BSc degree in Computer Science from the American University in Cairo in 1999.
Recent Publications:
Automated detection of stereotypical motor movements
Using wearable activity type detection to improve physical activity energy expenditure estimation
Printer-friendly version
Send to friend