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Ricky J. Sethi is currently an NSF Computing Innovation Fellow at UCLA/USC Information Sciences Institute, working with Yolanda Gil. Ricky is also a Director of the Madsci Network. Prior to being chosen as an NSF Computing Innovation Fellow (CIFellow) by the CCC and the CRA, he was a Post-Doctoral Scholar with Amit K. Roy-Chowdhury at UCR, where he was the Lead Integration Scientist for the WASA project and participated in ONR's Empire Challenge 10.

Ricky has authored or co-authored over 30 peer-reviewed papers, book chapters, and reports and made numerous presentations on his research in machine learning, computer vision, social computing, and science learning. He has taught various courses in Computer Science, Physics, and General Science as a GSI and Visiting Professor. Ricky has also supervised/mentored undergraduate students, graduate students, and postdoctoral students at UCLA, USC, and DeVry University.

Ricky has served as a Panelist for the NSF Cyberlearning program, as an Editorial Board Member for the International Journal of Computer Vision & Signal Processing, and a Program Committee member for various conferences. In addition, he is a member of the YSP/Madsci Financial Board, a member of IEEE, and a member of the American Institute of Physics.

Education

Ricky received his B.A. in Molecular and Cellular Biology, Neurobiology and Physics from the University of California, Berkeley, his M.S. in Physics/Business (Information Systems) from the University of Southern California, and his Ph.D. in Artificial Intelligence from the University of California, Riverside.

Selected Publications

Ricky's Google Scholar Profile

  1. Ayelet Baram-Tsabari, Ricky J. Sethi, Lynn Bry, and Anat Yarden (2006).
    Using questions sent to an Ask-A-Scientist site to identify childrens interests in science. Science Education. PDF
  2. Ayelet Baram-Tsabari, Ricky J. Sethi, Lynn Bry, and Anat Yarden (2009).
    Asking scientists: A decade of questions analyzed by age, gender, and country. Science Education. PDF
  3. Ricky J. Sethi, and Amit K. Roy-Chowdhury (2010).
    The Human Action Image. Intl. Conf. on Pattern Recognition (ICPR). PDF
  4. Ricky J. Sethi, and Amit K. Roy-Chowdhury (2010).
    A Neurobiologically Motivated Stochastic Method for Analysis of Human Activities in Video. Intl. Conf. on Pattern Recognition (ICPR). PDF
  5. Nandita M. Nayak *, Ricky J. Sethi *, Bi Song, and Amit K. Roy-Chowdhury (2011).
    Motion Pattern Analysis for Event and Behavior Recognition. In T. B. Moeslund, L. Sigal, V. Kruger, and A. Hilton (Eds.), Visual Analysis of Humans (pp. 289-309). Springer-Verlag. PDF
    (* Both first authors listed in alphabetical order)
  6. Bi Song, Ricky J. Sethi, and Amit K. Roy-Chowdhury (2011).
    Robust Wide Area Tracking in Single and Multiple Views. In T. B. Moeslund, L. Sigal, V. Kruger, and A. Hilton (Eds.), Visual Analysis of Humans (pp. 1-18). Springer-Verlag. PDF
  7. Ricky J. Sethi and Yolanda Gil (2011).
    A Social Collaboration Argumentation System for Generating Multi-Faceted Answers in Question and Answer Communities. In Proceedings of the AAAI Workshop on Computational Models of Natural Argument (CMNA). PDF
  8. Matheus Hauder, Yolanda Gil, Ricky J. Sethi, and Yan Liu, and Hyunjoon Jo (2011).
    Making Data Analysis Expertise Broadly Accessible through Workflows. In Proceedings of the Sixth Workshop on Workflows in Support of Large-Scale Science (WORKS), held in conjunction with SC 2011. PDF

Profile

Ricky J. Sethi

Ricky J. Sethi is currently an NSF Computing Innovation Fellow at UCLA/USC ISI, Director of Research of The Madsci Network, and a Visiting Professor at DeVry University Online.

  • Affiliations:
    • University of Southern California, Information Sciences Institute
    • University of California, Los Angeles
    • University of California, Riverside
    • DeVry University
    • The Madsci Network
  • Education:
    • University of California, Berkeley
    • University of Southern California
    • University of California, Riverside
  • Field of Research:
    • Machine Learning
    • Computer Vision
    • Social Computing
    • Science Learning/Social eLearning
    • Financial Modelling