James Davis

James Davis


Vice Provost Emeritus
Special Advisor on Smart Manufacturing and Data Science Office of Research and Creative Activities

5907 Math Sciences

Email: jfdavis@ucla.edu
Phone: (310) 922-6327


In stepping down from Vice Provost and CIO positions at UCLA after 23 years, Jim has returned to his faculty appointment as Special Advisor on Smart Manufacturing and Data Science to the Vice Chancellor of Research and Creative Activities. In this role, Jim remains the Principal Investigator with oversight of the Clean Energy Smart Manufacturing Innovation Institute (CESMII), the national Manufacturing USA Institute sponsored by the Department of Energy. He is also emphasizing the expansion of industry deployment activities with CESMII, CESMII’s Innovation Centers, private and public partners, and other Institutes, and is continuing to define, build, and shape Smart Manufacturing as it continues to expand as a U.S. initiative.         As a Professor in the Department of Chemical and Biomolecular Engineering, Jim does research and has consulted extensively across diverse manufacturing industries including chemicals, refining, oil and gas, paper, steel, packaging, metals fabrication and glass. Research interests include:
      • Smart Manufacturing, Industrial Internet of Things (IIoT), Industry 4.0
      • AI, Machine Learning, Intelligent Systems
      • Data Analytics, Data Centric Modeling
      • Cyber-Physical Systems
      • Process Monitoring, Diagnostics and Control
      • Cybersecurity of Interconnected Systems

Selected Publications on Smart Manufacturing, Control and Operations and AI, Machine Learning and Data Analysis

Smart Manufacturing, Control and Operations

    ·         R. Rathinasabapathy, M. Elsass, J. Davis, "Smart Manufacturing and Real-Time Chemical Process Health Monitoring and Diagnostic Localization," Artificial Intelligence in Process Fault Diagnosis: Methods for Diligent Plant Surveillance,  Editor Richard Fickelscher, Wiley, ISBN 9781119825890, 2023 (in print)
  • Richard, D., J. Jang, B. Cıtmacı, J. Luo, V. Canuso, P. Korambath, O. Morales-Leslie, J. Davis, H. Malkani, P. Christofides, C. Morales-Guio, “Smart Manufacturing Inspired Approach to Research, Development, and Scale-up of Electrified Chemical Manufacturing Systems, iScience, CELLPress, (2023)
·         Davis, J. Biller, S. , St Pierre, J., Jahanmir, S., “Towards Resilient Manufacturing Ecosystems Through Artificial Intelligence – Symposium Report, Advanced Manufacturing Series (NIST AMS), National Institute of Standards and Technology, Gaithersburg, MD, https://doi.org/10.6028/NIST.AMS.100-47 (2022) ·         Citmaci, B., J. Luo, J. Jang, P. Korambath, C. Morales-Guio, J. Davis, P. Christofides, “Digitalization of an Experimental Electrochemical Reactor via Smart Manufacturing Innovation Platform,” Digital Chemical Engineering, Vol. 5, December (2022) ·         Davis, J., H. Malkani, J. Dyck, P. Korambath, J. Wise, “Cyberinfrastructure for the Democratization of Smart Manufacturing,” Smart Manufacturing: Concepts and Methods, Editors: Masoud Soroush, Michael Baldea, Thomas F. Edgar, Elsevier ISBN 9780128200278, (2020) ·         Davis, J., “Smart Manufacturing,” Encyclopedia of Sustainable Technologies, Pages, 417-427, (2017) ·         Davis, J. and T. Mahoney, “Cybersecurity for Manufacturers: Securing the Digitized and Connected Factory,” MForesight, https://deepblue.lib.umich.edu/handle/2027.42/145442 (2017) ·         R. Rathinasabapathy, M. Elsass, J. Josephson, J. Davis, “A smart manufacturing methodology for real time chemical process diagnosis using causal link assessment,” AIChE J. 62 (9), 3420-3431, (2016)
  • Frank Riddick, Evan Wallace, and Jim Davis, “Managing Risks Due to Ingredient Variability in Food Production,” Journal of Research of the National Institute of Standards and Technology, Vol. 121, pgs 17-32, (2016)
  • Korambath, P, J. Wang, A. Kumar, J. Davis, R. Graybill, B. Schott and M. Baldea, “A Smart Manufacturing Use Case: Furnace Temperature Balancing in Steam Methane Reforming Process via Kepler workflows,” Procedia Computer Science 80, 680 (2016)
  • Davis, J., T. Edgar, R. Graybill, P. Korambath, B. Schott, D. Swink, J. Wang, J. Wetzel, “Smart Manufacturing,” Annual Review of Chemical and Biomolecular Engineering, (6) (2015)
  • Davis, J.F., T. Edgar, J. Porter, J. Bernaden, M. Sarli, “Smart Manufacturing, Manufacturing Intelligence and Demand-Dynamic Performance,” Computers & Chemical Engineering, Elsevier Science, 2012
  • Edgar, T.F. and J.F. Davis, “Smart Process Manufacturing: A Vision of the Future,” Industrial & Engineering and Chemistry Research, 100th Anniversary Commemorative Issue (2008)

Early AI, Machine Learning, Data Analysis

  • Aradhye, H. B., B. R. Bakshi, J. F. Davis, and S. C. Ahalt, “Clustering in Wavelet Domain: A Multiresolution ART Network for Anomaly Detection,” AIChE Journal, 50, 10, 2455-2466 (2004)
  • Miller, D.C. and J.F. Davis, “A Process Design Decision Support System for Developing Process Chemistry,” Industrial Engineering Chemistry Research. 39(8), 2954-2969 (2000)
  • Davis, J.F., M.L. Piovoso, K. Kosanovich, B. Bakshi “Process Data Analysis and Interpretation,” book chapter, Advances in Chemical Engineering, Academic Press, vol. 25, (1999) Invited
  • Prasad, P.R., J.F. Davis, Y. Jirapinyo, M. Bhalodia, J.R. Josephson, “Structuring Diagnostic Knowledge for Large-Scale Process Systems,” Computers and Chemical Engineering, 22(22), 1897-1905 (1999)
  • Whiteley, J.R., J.F. Davis, M. Ahmet and S.C. Ahalt, “Observations and Problems Applying ART2 for Dynamic Sensor Pattern Interpretation,” IEEE Transactions on Systems, Man, and Cybernetics, 26(4), 423 – 437, July (1996)
  • Davis, J.F. and C.M. Wang, “Pattern-Based Interpretation of On-Line Process Data, Neural Networks for Chemical Engineers,” book chapter, A. Bulsari, ed., 443-470, Elsevier Science (1995) Invited
  • Rollins, D.R. and J.F. Davis, “Unbiased Estimation of Gross Errors When the Covariance Matrix is Unknown,” AICHE J., 39(8), 1335-13341, (1993)
  • McDowell, J.K. and J.F. Davis, “Problem Solver Integration Based on Generic Task Architectures,” book chapter, Intelligent Modeling, Diagnosis and Control of Manufacturing Processes, B.B. Chu and S. Chen, Ed., World Scientific Publishing, 61-82, (1992). Invited
  • Ramesh. T.S., J.F. Davis and G.M. Schwenzer, “Knowledge-Based Diagnostic Systems for Continuous Process Operations Based Upon the Task Framework,” Computers and Chemical Engineering, 16(2), 109-127 (1992)
  • Myers, D.R., C.E. Hurley and J.F. Davis, “A Diagnostic Expert System for a Sequential, PLC Controlled Operation,” book chapter, Artificial Intelligence Applications in Process Engineering, Academic Press (1990), Invited
  • B.S., Chemical Engineering, University of Illinois, 1974
  • Amoco Chemicals Corporation, 1974-1976
  • M.S., Chemical Engineering, Northwestern University, 1978
  • Ph.D., Chemical Engineering, Northwestern University, 1981
  • Lecturer, Department of Mechanical Engineering, University of Wisconsin, Madison, 1981-1983
  • Professor, Department of Chemical Engineering, Ohio State University, 1983-2000