Eric C. Larson
Assistant Professor

Eric Larson is an Assistant Professor in the department of Computer Science and Engineering 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 environmental sustainability 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 IEEE. 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.

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

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

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

SMU UbiComp Lab:
Johnson Square 189

Prospective Students
I am looking for undergraduate and graduate students passionate about investigating the role of technology in solving impactful problems. If this interests you, please contact me so that we can setup a time to chat about mutual interests and potential research projects.

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

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 grad-uate 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 CM Journal IMWUT. My work has been published in numerous conferences and journals disseminated through many different cross-disciplinary venues: IMWUT, Pediatrics, 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, mobile health, education, psycho-visual psychology, 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 by creating applications that (1) manage and diagnose many chronic/infectious ailments, (2) help learners master educational topics, and (3) investigate information leakage in pervasive and mobile devices.

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

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