In-Ho Cho

Title(s):

Associate Professor [CCE E]

Civil, Construction and Environmental Engineering

Office

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

Information

  • Dr. Cho’s group is recruiting enthusiastic high school students who are interested in data-driven science and engineering and getting involved in a National Science Foundation-supported research project.  
  • Detailed information is available on [https://iastate.box.com/s/qqfnwl8hz3qqccqhexq83a67faasjycg]
  • Send an email expressing your intentions and motivations to ICHO@IASTATE.EDU by May 8th.
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

 

Education

  • 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

Publications

         Selected recent papers; (*) graduate student.

  1. 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].
  2. 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]. 
  3. Bazroun, M.*, Yang, Y.*, and Cho, I., 2021. Flexible and Interpretable Generalization of Self-Evolving Computational Materials Framework, Computers and Structure (in-press). 
  4. 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].
  5. 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 (accepted).
  6. 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 (in press).
  7. Yogiraj Sargam*, Kejin Wang, In Ho Cho, 2020. Machine Learning Based Prediction Model for Thermal Conductivity of Concrete, Journal of Building Engineering (Accepted).
  8. Tong Tong*, Mohammed Bazroun*, In Ho Cho, and Keith Porter, 2020, Multiscale Investigations of RC Shear Wall Buildings, Journal of Earthquake Engineering, (Accepted).
  9. 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.
  10. Qiang Li*, In Ho Cho, Rana Biswas, and Jaeyoun Kim, 2019. Nanoscale Modulation of Friction and Contact Electrification via Surface Nanotexturing, Nano Letters, Vol. 19, 850-856. [10.1021/acs.nanolett.8b04038].
  11. 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 (in-press). [10.1109/TKDE.2019.2922638].
  12. 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 (in-press).
  13. 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. [https://journal.r-project.org/archive/2018/RJ-2018-020/index.html].
  14. 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].
  15. 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. [doi.org/10.1016/j.compstruc.2018.04.002].
  16. Ikkyun Song*, In Ho Cho, and Raymond K. W. Wong, 2018, An Advanced Statistical Approach to Data-Driven Earthquake Engineering, Journal of Earthquake Engineering (in press). [doi.org/10.1080/13632469.2018.1461713]
  17. Yicheng Yang*, Sai Yemmaleni*, Ikkyun Song*, and In Ho Cho, 2018, Cell Network-Based Formulas for Fast Determination of Stiffness Reduction of Non-Rectangular Core Shear Walls, Earthquake Spectra, (in-press) [doi.org/10.1193/010317EQS003M].
  18. Ikkyun Song*, In Ho Cho, Tom Tessitore, Tony Gurcsik, and Halil Ceylan, 2018, Data-Driven Prediction of Runway Incursions with Uncertainty Quantification, ASCE, Journal of Computing in Civil Engineering, Vol. 32 (2) [doi.org/10.1061/(ASCE)CP.1943-5487.0000733].
  19. In Ho Cho, 2018, Deformation Gradient-Based Remedy for Mesh Objective Three-Dimensional Interlocking Mechanism, ASCE, Journal of Engineering Mechanics, Vol. 144(1) [doi.org/10.1061/(ASCE)EM.1943-7889.0001369].
  20. In Ho Cho and Keith Porter, 2016. Modeling Building Classes using Moment Matching, Earthquake Spectra, 32(1), 285-301. [doi: http://dx.doi.org/10.1193/071712EQS239M].
  21. Jungwook Paek*, Qiang Li*, In Ho Cho, Jaeyoun Kim, 2016. Minimally Intrusive Optical Micro-Strain Sensing in Bulk Elastomer using Embedded Fabry-Perot Etalon, Micromachines, 7(4), 61 [doi:10.3390/mi7040061].
  22. Jungwook Paek*, In Ho Cho, and Jaeyoun Kim, 2015. Microrobotic tentacles with spiral bending capability based on shape-engineered elastomeric microtubes, Nature, Scientific Reports, Article number: 10768 [DOI:10.1038/srep10768].
  23. In Ho Cho and Keith Porter, 2015. Three-Stage Multiscale Nonlinear Dynamic Analysis Platform for Tackling Building Classes, Earthquake Spectra 31(2), pp. 1021-1042 [doi: 10.1193/092712EQS293M].
  24. In Ho Cho and Keith Porter, 2014. Multilayered Grouping Parallel Algorithm for Multiple-level Multiscale Analyses, International Journal for Numerical Methods in Engineering, vol. 100(12), pp. 914-932. [DOI: 10.1002/nme.4791].
  25. In Ho Cho and Keith Porter, 2014. Structure-Independent Parallel Platform for Nonlinear Analyses of General Real-Scale RC Structures under Cyclic Loading, ASCE, Journal of Structural Engineering 140(8), SPECIAL ISSUE: Computational Simulation in Structural Engineering, A4013001. [http://dx.doi.org/10.1061/(ASCE)ST.1943-541X.0000871].
  26. In Ho Cho and John F. Hall, 2014. General Confinement Model based on Nonlocal Information, ASCE, Journal of Engineering Mechanics 140(6). [10.1061/(ASCE)EM.1943-7889.0000724].
  27. In Ho Cho, 2013. Virtual Earthquake Engineering Laboratory Capturing Nonlinear Shear, Localized Damage and Progressive Buckling of Bar, Earthquake Spectra 29(1), 103-126. [doi: http://dx.doi.org/10.1193/1.4000095].
  28. In Ho Cho and John F. Hall, 2012. Parallelized Implicit Nonlinear FEA Program for Real Scale RC Structures under Cyclic Loading, ASCE, Journal of Computing in Civil Engineering 26(3), 356-365. [http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000138].

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