In-Ho Cho


Associate Professor [CCE E]

Civil, Construction and Environmental Engineering


494 Town Engr
813 Bissell Rd
Ames, IA 50011-1066


In Ho Cho is an associate professor at Iowa State University’s Department of Civil, Construction and Environmental Engineering. He joined Iowa State University in 2014. His research focuses on novel data-driven, computational science and engineering. Dr. Cho pursues a fusion of computational statistics, machine learning (ML), computational mechanics, and physics principles to tackle unresolved problems including data science for robust ML, large complex incomplete data curing, data- and ML-driven physics in nano/micro materials and structures, infrastructure engineering, and engineering seismology.

Honors and Awards

  • Finalist of the 2015 World Technology Network Award, New York, NY, 2015
  • Black & Veatch Building a World of Difference Faculty Fellowships in Engineering, ISU, 2014-2017
  • Willis Research Network Postdoctoral Fellowship, University of Colorado, 2012-2014
  • G. W. Housner Fellowship, California Institute Technology, 2007-2012
  • SEAOSC (Structural Engineers Association of Southern California) Scholarship, 2009

Research Grant

  • NSF, CMMI-Advanced Manufacturing: Light- and Machine Learning-Controlled Nano Patterns
  • NSF, OAC-CSSI: General-Purpose Incomplete Data Curing for Statistical- and Machine-Learning
  • NSF, CBET: Micro-Soft Robotics using Machine Learning
  • MTC Project: Big Data-Driven Infrastructure Engineering
  • Minnesota DOT: Data-driven Approach to the Climate Change on Pavement Damages
  • Iowa DOT: Advanced Computational Simulation Tool for Iowa Pavement System
  • Iowa DOT: Development of Approaches to Quantify Superloads
  • ISU Presidential Initiative for Interdisciplinary Research in Data-Driven Science

News Articles



  • Ph.D. Civil Engineering/Minor in Computational Science and Engineering, California Institute of Technology-Pasadena, 2012
  • M.S. Civil, Urban and Geosystem Engineering, Seoul National University, 2003
  • B.S. Engineering, Seoul National University, 2001

Interest Areas

  • Data-Driven Science and Engineering
  • Computational Statistics and Machine Learning
  • Mechanics-Infused Machine Learning for Nano Science and Micro-Soft Robotics
  • High-Performance Computing
  • Parallel Multi-Scale Computational Mechanics
  • Engineering Mechanics/Structural Engineering/Engineering Seismology


         Selected recent papers; (*) graduate student.

  1. Cho, I., 2023. Sharpen Data-Driven Prediction Rules of Individual Large Earthquakes with Aid of Fourier and Gauss, Nature, Scientific Reports, Article Number 13:16009 [].
  2. Cho, I., Ji*, M-G, and Kim, J., 2023. Pursuit of Hidden Rules Behind the Irregularity of Nano Capillary Lithography by Hybrid Intelligence, Nature, Scientific Reports, Article Number 13:13649 [].
  3. Yang, Y.*, Y. Kwon, J. Kim, and I. Cho, 2023. Ultra Data-Oriented Parallel Fractional Hot-Deck Imputation with Efficient Linearized Variance Estimation, IEEE, Transactions on Knowledge and Data Engineering, 35(9) [].
  4. Cho, I., 2022. Gauss Curvature-Based Unique Signatures of Individual Large Earthquakes and Its Implications for Customized Data-Driven Prediction, Nature, Scientific Reports, Article Number 12:8669 [DOI: 10.1038/s41598-022-12575-w].
  5. Cho, I., Yeom, S., Sarkar, T.*, and Oh, T-S., 2022. Unraveling Hidden Rules behind the Wet-to-Dry Transition of Bubble Array by Glass-Box Physics Rule Learner, Nature, Scientific Reports, Article Number 12:3191 [DOI: 10.1038/s41598-022-07170-y]. 
  6. Bazroun, M.*, Yang, Y.*, and Cho, I., 2021. Flexible and Interpretable Generalization of Self-Evolving Computational Materials Framework, Computers and Structure, 260(106706)[]. 
  7. Cho, I., Li, Q.*, Biswas, R., and Kim, J., 2020. A Framework for Glass-Box Physics Rule Learner and Its Application to Nano-Scale Phenomena, Nature, Communications Physics 3, Article Number 78 [DOI:10.1038/s42005-020-0339-x].
  8. Yicheng Yang*, Jae-Kwang Kim, and In Ho Cho, 2020. Parallel Fractional Hot Deck Imputation and Variance Estimation for Big Incomplete Data Curing, IEEE, Transactions on Knowledge and Data Engineering, 34(8), 3912-3926 [].
  9. Jahani, E.*, Cetin, K.S., Cho, I., 2020. City-scale Single Family Residential Building Energy Consumption Prediction using Genetic Algorithm-Based Numerical Moment Matching Technique. Building and Environment, 172 [].  
  10. Yogiraj Sargam*, Kejin Wang, In Ho Cho, 2020. Machine Learning Based Prediction Model for Thermal Conductivity of Concrete, Journal of Building Engineering, 34(101956) [].
  11. In Ho Cho, 2019. A Framework for Self-Evolving Computational Material Models Inspired by Deep Learning, International Journal for Numerical Methods in Engineering 120(10), 1202-1226.  [].
  12. Qiang Li*, In Ho Cho, Rana Biswas, and Jaeyoun Kim, 2019. Nanoscale Modulation of Friction and Contact Electrification via Surface Nanotexturing, Nano Letters, 19, 850-856. [10.1021/acs.nanolett.8b04038].
  13. Ikkyun Song*, Yicheng Yang*, Jongho Im, Tong Tong*, Halil Ceylan, and In Ho Cho, 2019. Impacts of Fractional Hot-Deck Imputation on Learning and Prediction of Engineering Data, IEEE, Transactions on Knowledge and Data Engineering, 32(12) [10.1109/TKDE.2019.2922638].
  14. Myung-Gi Ji*, Qiang Li*, In Ho Cho, and Jaeyoun Kim, 2019. Rapid Design and Analysis of Microtube Pneumatic Actuators using Line-Segment and Multi-Segment Euler-Bernoulli Beam Models. Micromachines, 10(11).
  15. Jongho Im, In Ho Cho, and Jaekwang Kim, 2018, FHDI: An R Package for Fractional Hot-Deck Imputation for multivariate missing data, The R Journal, Vol. 10(1), 140-154. [].
  16. Qiang Li*, Akshit Peer*, In Ho Cho, Rana Biswas, and Jaeyoun Kim, 2018. Observation of nanopatterned triboelectric charges on elastomer surfaces induced by replica molding. Nature Communications, Vol. 9, Ariticle Number: 974. [DOI: 10.1038/s41467-018-03319-4].
  17. In Ho Cho, Ikkyun Song*, and Ya Lu Teng*, 2018, Numerical Moment Matching Stabilized by a Genetic Algorithm for Engineering Data Squashing and Fast Uncertainty Quantification, Computers and Structures Vol. 204, 31-47. [].