Eric C. Larson

Associate Professor

Dr. Eric Larson is the Bobby B. Lyle Endowed Professor in Engineering Innovation. He is also an Associate Professor in the department of Computer Science in the Bobby B. Lyle School of Engineering, Southern Methodist University. His main research interests are in machine learning, sensing, and signal & image processing for ubiquitous computing applications, in particular, for healthcare and security applications. His work in both areas has been commercialized and he holds a variety of patents for sustainability sensing and mobile phone-based health sensing. He is active in signal processing education for computer scientists and is an active member of ACM. He received his Ph.D. in 2013 from the University of Washington, where he was co-advised by Shwetak N. Patel and Les Atlas. He received his B.S. and M.S. in Electrical Engineering in 2006 and 2008, respectively, at Oklahoma State University, where he was advised by Damon Chandler.

Computer Science
Bobby B. Lyle School of Engineering
Southern Methodist University

twitter: @ec_larson
email: eclarson@lyle.smu.edu

CS Office: 451 Caruth Hall
Lyle School of Engineering
Caruth Hall
3145 Dyer Street, Suite 445
Dallas, TX 75205

SMU UbiComp Lab:
Johnson Square 189

About Me
I am the Bobby B. Lyle Endowed Professor in Engineering Innovation. I am also an Associate Professor in Computer Science in the Bobby B. Lyle School of Engineering, Southern Methodist University. I am a fellow of the Hunt Institute for Engineering Humanity, member of the Darwin Deason Institute for Cyber-security, and member of the ATT center for virtualization.

I am a passionate teacher and researcher in Computer Science. The most important aspect of a thriving research and teaching community is culture. An open, mentoring culture is paramount to creating an environment where ideas, concerns, and critiques can contribute to student growth. I believe research culture can be shaped in three ways: (1) being highly selective of graduate students when setting up a research lab, (2) intermingling undergraduate and graduate research, providing mentoring opportunities to graduate students, and (3) teaching courses with opportunities to conduct meaningful research. I believe my research lab exemplifies this quality; the lab itself becomes a mentoring tool that exemplifies research methods to new students by example.

I received my Doctorate from the University of Washington where I was a Intel Science and Technology fellow. At UW, I was co-advised by Shwetak Patel and Les Atlas. I also have an MS in Image Processing from Oklahoma State University, where I was advised by Damon Chandler.

I am an associate editor for the ACM Journal IMWUT. My work has been published in numerous conferences and journals disseminated through many different cross-disciplinary venues: IMWUT, Pediatrics, JBHI, JCI, ICIP, UbiComp, CHI, DEV, WCCI, PerCom, PETRA, SPIE, and Pervasive, garnering numerous best paper nominations. Please see my publication page and/or Google Scholar page for more details.

When I am not working, I am spending time with my wife and three wonderful children.

Research Focus
My research explores the interdisciplinary relationship of machine learning and signal/image processing with the fields of security, psychology, mobile health, education, human-computer interaction, and ubiquitous computing. Like most academics, I have a passion for teaching and mentoring, and I view research as an ideal opportunity to instruct the next generation of computer scientists and engineers. I have positioned myself (with plenty of help from others) in a unique role, supporting cyber-security, education, healthcare, and sustainability applications via the integration of machine learning and ubiquitous sensing. I have become increasingly interested in sensing markers of health and context awareness using commonplace sensors. My research supports many healthcare, educational, and security initiatives not only through basic research but also through applied contributions and analysis.

Research Interests
•    Machine Learning and Deep Learning [Sponsored]
•    Cyber Security, Mobile Sensor Data Leakage [Sponsored], Counterfeit Detection [Sponsored]
•    Context Aware Computing/Learning in Man-Machine Interfaces [Sponsored]
•    Mobile Health Computing [Sponsored]
•    Target-Ligand Virtual Screening