Chih-Ming Ho

Chih-Ming Ho (何志明) is an engineering professor in interdisciplinary fields, which span from aerodynamics to AI-medicine[1]. He received a B.S. in Mechanical Engineering from National Taiwan University in 1967 and a Ph.D. in Mechanics and Material Sciences from Johns Hopkins University in 1974.

Chih-Ming Ho
Born1945
NationalityAmerican
EducationNational Taiwan University, Johns Hopkins University
OccupationEngineer
Engineering career
DisciplineAI-Medicine,

Microfluidics,

Turbulence
InstitutionsUniversity of California, Los Angeles

Academic career

Dr. Chih-Ming Ho started his career at the University of Southern California (USC) in 1975 and rose to the rank of full professor. In 1991, he moved to the University of California, Los Angeles to lead the university's establishment of the micro-electro-mechanical-system (MEMS) field, while serving as the founding Director of the Center for Micro Systems. He held the Ben Rich-Lockheed Martin Professor Chair until he retired in 2016, and currently is a UCLA Distinguished Research Professor. Ho was the Director of the NASA supported Institute for Cell Mimetic Space Exploration and the Center for Cell Control at the UCLA Henry Samueli School of Engineering and Applied Science. He served as UCLA Associate Vice Chancellor for Research from 2001 to 2005.

Research accomplishments

Control of turbulent flows

Ho was the first to introduce the idea of actively perturbing the free shear layer with subharmonics of its Kelvin-Helmholtz instability frequency for increasing the entrainment of the ambient fluid into the jet stream[2,3]. Furthermore, with an elliptic jet of small aspect ratio, he found that the entrainment of the elliptic jet can be up to five times higher than that of a round jet at a passive control mode[4]. Ho applied micro shear stress sensor arrays to detect the turbulent separation line at the leading edge of airfoil and used micro actuators to produce asymmetric separation vortices, such that the aircraft can be maneuvered in rolling, pitching and yawing modes[5,6]. These innovative flow control technologies made him a global front-runner in aerodynamics during the 1980s.

Microfluidics

In early 1990s, Ho was among the pioneers of studying flows inside microfluidic channels[7,8] and micro bio-molecular sensors[9,10]. Microfluidic devices are in the dimension of microns, which match the cell sizes, such that only a minute amount of bio sample is needed for analysis. With surface molecular modifications, amperometric sensors can detect DNA/RNA even without PCR amplification in 2000s[9]. In addition, because the electrokinetic forces also work in the micro/nano scale range, it became possible to detect single molecules in microfluidic device[10]. These bio-marker sensors can have ultrasensitivities in body fluids, blood, saliva and urine[11].

AI personalized medicine

Almost all diseases are treated by combinatorial drugs. However, M drugs with N doses for each drug constitute a huge search space of NM possible combinations. In addition, the interactions among drug molecules and omics mechanisms are an insurmountable maze. Around 2010, Ho applied the mechanism independent artificial intelligence analysis and discovered that drug-dose inputs are correlated with phenotypic outputs with a Phenotypic Response Surface (PRS)[12,13,14], which is governed by a second order algebraic equation. The coefficients of the second order algebraic equation can be determined by a small number of calibration tests. Hence, the AI-PRS equation in turn eliminates the need for big data training set for AI analysis, which is not feasible in in vivo tests, especially in clinical setting. AI-PRS is an indication agnostic and mechanism free platform technology, which has been successfully demonstrated in about 30 diseases, including cancers[15,16], infectious diseases[17,18] and organ transplants[19]. The AI-PRS platform can realize unprecedented levels of adaptability to identify the optimized drug combination for a specific patient, even if dynamic changes to the regimen and dose/drug optimization are needed on a continuous basis[15,19].

Honors and awards

Ho was ranked by Thomson Reuters ISI as one of the top 250 most cited researchers in all engineering categories (2001-2014). In 1997, Dr. Ho was inducted as a member of the National Academy of Engineering. In the next year, he was elected as an Academician of Academia Sinica. Ho has received a Doctor of Engineering Honoris Causa from Hong Kong University of Science and Technology and he holds ten honorary professorships, including the Einstein Professorship from the Chinese Academy of Sciences. Ho was elected a Fellow of American Physics Society, American Association for the Advancement of Science, American Institute for Medical and Biological Engineering and American Institute of Aeronautics and Astronautics.

Services in Professional Communities

In services to professional societies. Ho was a Chair of the Division of Fluid Dynamics (DFD) for the American Physical Society, which is the platform in the United States for scientists interested in fundamental fluid dynamics. He was on the advisory board for the AIAA Journal and is a member of the IEEE/ASME coordinating Committee of Journal of MEMS. He was an Associate Editor of the ASME Journal of Fluids Engineering and an Associate Editor of the AIAA Journal. He also has served as a Guest Editor for the Annual Review of Fluid Dynamics. He also has chaired or served on many advisory or organizing committees of international conferences on high technology topics.

Ho has served on advisory panels to provide assistance to China, France, Hong Kong, Israel, Japan, Korea, Switzerland, Taiwan, Thailand, and the United Kingdom, on the developments of nano/micro technologies.

Industrial participation

Ho is a co-founder of GeneFluidics, which specializes in rapid PCR-less molecular based identification of pathogen-specific sequence. He is also a co-founder of Kyan Therapeutics, which specializes in AI driven drug development/dosage optimization.

References
  1. “Predictable response: Finding optimal drugs and doses using artificial intelligence”, by Chakradhar, S., Nature Medicine, V.23, pages 1244–1247 (2017.)
  2. Ho, C.M. and Huang, L.S., "Subharmonics and Vortex Merging in Mixing Layers", Journal of Fluid Mechanics, Vol. 119, pp. 443–473, 1982.
  3. Ho, C.M. and Huerre, P., "Perturbed Free Shear Layers", Ann. Rev. of Fluid Mech., Vol. 16, pp. 365–424, 1984.
  4. Ho, C.M. and Gutmark, E., "Vortex Induction and Mass Entrainment in a Small Aspect Ratio Elliptic Jet", Journal of Fluid Mechanics, Vol. 179, pp. 383–405, 1987.
  5. Lee, G.B., Chiang, S., Tai, Y.C., Tsao, T., Liu, C., Huang, P.H. and Ho, C.M., "Robust Vortex Control of a Delta Wing Using Distributed MEMS Actuators" Journal of Aircraft, 37(4):697-706, 2000.
  6. “Micro machines help solve intractable problem of turbulence”, by Browne, M.W., New York Times, Jan. 3, 1995,
  7. Liu, J., Tai, Y.C., Pong, K., and Ho, C.M., "Micromachined Channel/Pressure Sensor Systems for Micro Flow Studies," Tech. Digest, 1993 International Conference on Solid State Sensors and Actuators (TRANSDUCERS’93), Yokohama, Japan, pp. 995–999, June 1993.
  8. Pong, K.C., Ho, C. M., Liu, J. and Tai, Y.C., "Nonlinear Pressure Distribution in Uniform Microchannels," Application of Microfabrication to Fluid Mechanics, FED-Vol. 197, pp. 51–56, ASME, 1994.
  9. Gau, J.J., Lan, E. H., Dunn, B., Ho, C.M., "A MEMS-based Amperometric Detector for E. Coli Bacteria - Using Self-Assembled Monolayers", Journal of Biosensor and Bioelectronics, Volume 9, Number 12, pp. 745–755, 2001.
  10. Wang, T.H., Peng, Y., Zhang, C., Wong, P.K. and Ho, C.M., “Single-Molecule Tracing on a Fluidic Microchip for Quantitative Detection of Low-Abundance Nucleic Acids”, Journal of the American Chemical Society 127, 5354-5359, 2005.
  11. “20 New Biotech Breakthroughs that Will Change Medicine”, by Wenner, M., Popular Mechanics, Dec 9, 2009
  12. Al-Shyoukh, I., Yu, F., Feng, J., Yan, K., Dubinett, S., Ho, C. M., Shamma, J.S. and Sun R., “Systematic quantitative characterization of cellular responses induced by multiple signals”, BMC Systems Biology, Vol. 5, pp. 88, 2011.
  13. Wong, P.K, Yu, F., Shahangian A., Cheng, G., Sun, R. and Ho, C.M., “Closed-Loop Control of Cellular Functions Using Combinatory Drugs Guided by a Stochastic Search Algorithm”, Proceeding of National Academy of Science, Vol. 105, No.13 pp. 5105–5110, 2008
  14. Patrycja Nowak-Sliwinska, Andrea Weiss, Xianting Ding, Paul J Dyson, Hubert van den Bergh, Arjan W Griffioen & Chih-Ming Ho, “Optimization of drug combinations using Feedback System Control”, Nature Protocols, VOL.11 NO.2, pp. 302–315, 2016
  15. Pantuck,* A.J., Lee, D.K., Kee, T., Wang, P., Lakhotia, S., Silverman, M.H., Mathis, C., Drakaki, A., Belldegrun, A.S., Ho, C.M., and Ho, D., “Modulating BET Bromodomain Inhibitor ZEN-3694 and Enzalutamide Combination Dosing in a Metastatic Prostate Cancer Patient Using CUREATE.AI, an Artificial Intelligence Platform”, Advanced Therapeutics, DOI: 10.1002/adtp.201800104, 2018.
  16. Rashid, M. B. M. A., Toh, T. B., Hooi, L., Silva, A., Zhang, Y., Tan, P. F., Teh, A. L., Karnani, N., Jha, S., Ho, C. M., Chng, W. J., Ho, D., Chow, E. K. H., “Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP)”. Sci. Transl. Med. 10, eaan0941 2018.
  17. Silva, A., Lee, B.Y., Clemens, D.L., Kee, T., Ding, X., Ho, C.M. and Horwitz, M.A., “Output-driven feedback system control platform optimizes combinatorial therapy of tuberculosis using A macrophage cell culture model”, PNAS, Vol. 113, No. 15, 2016.
  18. Lee, B.Y., Clemens, D.L., Silva, A., Dillon, B.J., Sasˇa Maslesˇa-Galic´, Nava, S., Ding, X., Ho, C.M., and Horwitz, M.A., “Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time”, Nat. Commun. 8, 14183 doi: 10.1038/ncomms14183, 2017.
  19. Zarrinpar, A., Lee, D.-K., Silva, A., Datta, N., Kee, T., Eriksen, C., Weigle, K., Agopian, V., Kaldas, F., Farmer, D., Wang, S.E., Busuttil, R., Ho, C.M., “Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform”, Sci. Transl. Med. 8, 333ra49, 2016.
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