Education

S.M. Electrical Engineering and Computer Science, Massachusetts Institute of Technology (2015)
  • Irwin Mark Jacobs and Joan Klein Jacobs Presidential Fellowship (2013)
B.S. Computer Science and Engineering, The Ohio State University (2012)
Graduated Summa Cum Laude with Honors Research Distinction in Electrical and Computer Engineering
  • Undergraduate Research Scholarship (2012)
  • Warren G. and James M. Elliott Engineering Scholarship (2011 & 2012)
  • Crowe Horwath Scholarship (2011)
  • TechTomorrow Scholarship (2010)

Research

My research interests include machine learning, probabilistic graphical models, Bayesian non-parametric inference, information-driven path planning, and sensor allocation problems.


Industry Experience

Apple Inc., Cupertino, CA
  • Siri, Intern (Summer 2017)
Google Inc., Mountain View, CA
  • Google Photos, Software Engineering Intern (Summer 2016)
Systems & Technology Research, Woburn, MA
  • Control and Estimation Systems Group, Intern (Summer 2015)
MIT Lincoln Laboratory, Lexington, MA
  • Surveillance Systems Group, Co-op (Spring & Summer 2013)
  • Embedded and Open Systems Group, Intern (Summer 2011)

List of Publications

Efficient MCMC Inference for Remote Sensing of Emission Sources

S.M. Thesis, Massachusetts Institute of Technology (2015)

This thesis presents and analyzes an non-parametric MCMC-based inference procedure for identifying the number and properties of gaseous emission sources from measurements taken downwind of the emitters, subject to a specific atmospheric dispersion model. Advised by John W. Fisher III.

The Value of Delayed Information in Tracking with Distributed Sensor Networks

B.S. Honors Thesis, The Ohio State University (2012)

This thesis considers the value of "delayed" observations for object tracking with a variant of the extended Kalman filter where at each time-step a network of communication-constrained sensors can only integrate either one current measurement or one old measurement. Advised by Emre Ertin.

Student Perspectives on Learning Through Developing Software for the Real World

Christopher L. Dean, Thomas D. Lynch, Rajiv Ramnath
Proceedings of 2011 IEEE Frontiers in Education Conference

We examine several sources of experiential learning for their ability to bridge the gap between the computer science classroom and professional practice. In particular, we compare internships and co-ops, senior capstone projects, and participation in long-term, real-world, student-led software development projects.