Qiang Huang, Ph.D.
Associate Professor and Gordon S. Marshall Early Career Chair in Engineering
Epstein Department of Industrial and Systems Engineering
University of Southern California
Associate Editor, IEEE Transactions on Automation Science and Engineering
Contributing Editor, InterNano (www.internano.org)
For prospective PhD students:
Two Research Assistant Positions Available in 2014
Contact me if you have strong engineering and analytical background
My lovely daughter and son
Associate Professor, Daniel J. Epstein Department of Industrial and Systems Engineering,
University of Southern California, Los Angeles, CA, March 2012 ~ Present.
Assistant Professor, Daniel J. Epstein Department of Industrial and Systems Engineering,
University of Southern California, Los Angeles, CA, August 2009 ~ March 2012.
Associate Professor, Department of Industrial and Management Systems Engineering,
University of South Florida, Tampa, FL, June 2008 ~ August 2009.
Assistant Professor, Department of Industrial and Management Systems Engineering,
University of South Florida, Tampa, FL, August 2003 ~ June 2008.
Ph.D. Industrial & Operations Engineering, University of Michigan, 2003
M.A. Statistics, University of Michigan, 2002
Ph.D., M.S. B.S. Mechanical Engineering, Shanghai Jiao Tong University, 1998, 1996, 1993
ISE 599: Foundation of Applied Mathematics for
Engineered System Analytics (New)
Synopsis: As technologies evolve to a high degree of complexity, e.g., nanotechnology and its scale-up in nanomanufacturing, applying the principles and concepts of applied mathematics to these emerging areas faces the dilemma of whether or not to connect the generic modeling methodologies with the underlying physical mechanisms. Particularly, when we attempt to use modeling methods developed in applied mathematics to investigate complicated engineering phenomena and mechanisms, reconnecting applied mathematics to its engineering roots is essential for not only the resurgence of the classical techniques of applied mathematics, but also the development of new principles, concepts, and methods not existing in the current applied mathematics. For example, modeling of nanomanufacturing for scale-up faces the issue of insufficient measurement data and physical understandings. The uncertainties in data and physical knowledge result in a large set of candidate models and their corresponding underlying physical mechanisms equally explain the data well. Since some physical mechanisms compete with each other it makes applied mathematics hard to proceed to interpretation and predication. Reconnecting applied mathematics with complex engineering phenomena provides exciting opportunities for improved understanding of engineering problem and methodological development for applied mathematics. The improved physical understandings also lead to advanced engineered systems for varieties of application in scalable nanomanufacturing, additive manufacturing, healthcare systems, and social network.
Course objective: The objective of this course is therefore to introduce principles, concepts, and methods of applied mathematics from the perspective of modeling of engineering phenomena. We will also introduce our on-going research and results of reconnecting applied mathematics with nanomanufacturing for engineering scalable systems. The course is not intended to provide a standard procedure or method for the reconnection. Rather, it points out a viable path to discover exciting solutions for engineering complex systems.
ISE 525: Design of Experiments
ISE 426: Statistical Quality Control
Epstein Institute Seminar Series
My general area of interest is modeling and analysis of complex systems for quality and productivity improvement, in particular,
- Predictive Model-Based Quality Control for Additive Manufacturing
- Quality Engineering and Applied Statistics
1. Leading PI, ¡°Collaborative Research: Geometric Shape Error Control for High-Precision Additive Manufacturing,¡± NSF CMMI- 1333550, $285,000, 2013-2016 (Co-PI: Y. Chen at USC, PI: T. Dasgupta at Harvard).
2. PI, "CAREER: Nanomanufacturing Process Modeling and Control -- A Foundation for Large-Scale Production," NSF CMM-1055394, $400,001, 2011-2016.
3. Co-PI (50% effort), "Cyber-Enabled Manufacturing Systems (CeMS): Real-Time Shape Compensation for Accurate Direct Digital Manufacturing," Office of Naval Research, ONR Grant# N000141110671, $439,645, 2011-2014 (PI: Y. Chen at USC).
4. Leading PI, ¡°Collaborative Research: Nanostructure Growth Process Modeling and Optimal Experimental Strategies for Repeatable Fabrication of Nanostructures for Application in Photovoltaics,¡± $300K, NSF CMMI-1000972, 2010-2013 (Co-PI: C. Zhou at USC, PI: T. Dasgupta at Harvard).
5. Co-PI, ¡°Bayesian process control for nanomanufacturing with mixed resolution information,¡± Hong Kong RGC (Research Grant Council), 2009-2011 (PI: F. Tsung, Co-PI: J. Shi).
6. PI, ¡°In Situ Nanomanufacturing Process Control Through Multiscale Nanostructure Growth Modeling,¡± $350K, NSF CMMI-0728100, 2007-2010 (Co-PI: A. Kumar).
7. PI, ¡°Analysis of Correlated Functional Variables for Manufacturing Process Diagnosis,¡± $280K, NSF CMMI-0600066, 2006-2009, (Co-PI, A. Kumar) (Including the supplement request of $15K for Cyberinfrastructure Experiences for Graduate Students (CIEG)).
8. Co-PI, ¡°Nanoengineered, Manufacturable, Ion-Implantation Seeded Silica Nanowires for Sensitive BioScreening, ¡± NSF CMMI-0700659, $289,980, 2007~2010 (PI: S. Bhansali).
9. PI, ¡°Multiscale Nanostructure Growth Modeling for Control of Nanomanufacturing,¡± Functional Multiscale Materials by Design Initiative, University of South Florida, Summer Support, 2007.
10. PI, ¡°Measurement Strategy for Serial-Parallel Reconfigurable Manufacturing Systems with Real-Time RF-Tag Information,¡± sponsored by NSF ERC-RMS at University of Michigan (Co-PI: Dr. R. Katz, Chief Engineer at ERC-RMS), 2004.
11. PI, ¡°Process Control based on Multivariate Functional Data,¡± USF Internal Award, 2004.
1. US Provisional Patent Application No. 61/712,723: Algorithm of compensating shape shrinkage for 3D printing processes, filed on October 11, 2012.
2. U.S. Patent Application No. 14/052,418: 3D Printing Shrinkage Compensation Using Radial and Angular Layer Perimeter Point Information, filed on October 11,2013.
Refereed Journals and Transactions
1. Huang, Q., Zhang, J., Sabbaghi, A., and Dasgupta, T., 2013, ¡°Optimal Offline Compensation of Shape Shrinkage for 3D Printing Processes, ¡±IIE Transactions on Quality and Reliability, Accepted.
2. Zhu, L., Dasgupta, T., and Huang, Q., 2013, ¡°A Locally D-Optimal Design for Estimation of Parameters of an Exponential-Linear Growth Curve of Nanostructures,¡± Technometrics, Accepted.
3. Wang, L., and Huang, Q., 2013, ¡°Cross-Domain Model Building and Validation (CDMV): A New Modeling Strategy to Reinforce Understanding of Nanomanufacturing Processes,¡± IEEE Trans on Automation Science and Engineering, Vol. 10(3), pp. 571–578. (Finalist of 2012 QSR Best Student Paper Competition)
4. Xu, L., and Huang, Q., 2013, ¡°EM Estimation of Nanostructure Interactions with Incomplete Feature Measurement and Its Tailored Space Filling Design,¡± IEEE Trans on Automation Science and Engineering, Vol. 10(3), pp. 579–587.
5. Xu, L., and Huang, Q., 2012, ¡° Modeling the Interactions among Neighboring Nanostructures for Local Feature Characterization and Defects Detection, "IEEE Trans on Automation Science and Engineering, Vol. 9, pp.745-754.
6. Chang, C.J., Xu, L., Huang, Q., and Shi, J., 2011, ¡°Quantitative Characterization and Modeling Strategy of Nanoparticle Dispersion in Polymer Composites, "IIE Transactions, Special Issue on Quality, Sensing and Prognostics Issues in Nanomanufacturing, Volume 44, Issue 7, pp. 523-533.
7. Huang, Q., Wang, L., Dasgupta, T., Zhu, L., Sekhar, P.K., and, Bhansali, S., An, Y., 2011, ¡°Statistical Weight Kinetics Modeling for Silica Nanowires Growth Catalyzed by Pd Thin Film,¡± IEEE Trans on Automation Science and Engineering, Vol. 8, pp.303-310.
8. Huang, Q., 2011, ¡°Physics-Driven Bayesian Hierarchical Modeling of Nanowire Growth Process at Each Scale," IIE Transactions, IIE Transactions on Quality and Reliability, Vol. 43, pp. 1-11.
9. Zhang, X., Huang, Q., 2010, ¡°Analysis of Interaction Structure Among Multiple Functional Process Variables for Process Monitoring in Semiconductor Manufacturing,¡± IEEE Transactions on Semiconductor Manufacturing, DOI: 10.1109/TSM.2010.2041580.
10. Chen, S., Wang, H., and Huang, Q., 2010, ¡°Diagnosis of Multiple Error Sources Under Variation Equivalence," Accepted to NAMRI/SME Transactions, Vol. 38.
11. Zhang, X., Wang, H., Huang, Q., Kumar, A., and Zhai, J., 2009,¡°Statistical and Experimental Analysis of Correlated Time-varying Process Variables for Condition Diagnosis in Chemical-Mechanical Planarization,¡± IEEE Transactions on Semiconductor Manufacturing, Vol.22 (3), pp. 512-521.
12. Wang, H., Zhang, X., Kumar, A., Huang, Q., 2009, ¡°Nonlinear Dynamics Modeling of Correlated Functional Process Variables for Condition Monitoring in Chemical-Mechanical Planarization¡±, IEEE Transactions on Semiconductor Manufacturing, Vol. 22, pp.188-195.
13. Wang, H., Kababji, H., and Huang, Q., 2009, ¡°Monitoring Global and Local Variations in Multichannel Functional Data for Manufacturing Processes,¡± SME Transactions, Journal of Manufacturing Systems, doi:10.1016/j.jmsy.2009.03.001.
14. Wang, H., and Huang, Q., 2007, ¡°Using Error Equivalence Concept to Automatically Adjust Discrete Manufacturing Processes for Dimensional Variation Reduction,¡± ASME Transactions, Journal of Manufacturing Science and Engineering, 129, pp. 644—652.
15. Kim, J., Huang, Q., Shi, J., 2007, ¡°Latent Variable-based Key Process Variable Identification and Process Monitoring for Forging,¡± SME Transactions Journal of Manufacturing Systems, Vol. 26, pp. 53-61.
16. Wang, H., and Huang, Q., 2006, ¡°Error Cancellation Modeling and Its Application in Machining Process Control,¡± IIE Transactions on Quality and Reliability, 38, pp.379-388.
17. Wang, H., and Huang, Q., Yang, H., 2006, ¡°In-Line Statistical Monitoring of Machine Tool Thermal Error Through Latent Variable Modeling,¡± SME Transactions Journal of Manufacturing Systems, Vol. 25, No.4, pp. 279-292.
18. Kim, J., Huang, Q., Shi, J., and Chang, T.-S., 2006, ¡°Online Multi-Channel Forging Tonnage Monitoring and Fault Pattern Discrimination Using Principal Curve,¡± ASME Transactions, Journal of Manufacturing Science and Engineering, 128, pp. 944--950.
19. Wang, H., Huang, Q., Katz, R., 2005, ¡°Multi-Operational Machining Processes Modeling for Sequential Root Cause Identification and Measurement Reduction,¡± ASME Transactions, Journal of Manufacturing Science and Engineering, 127, pp. 512-521.
20. Huang, Q., and Shi, J., 2004, ¡° Stream of Variation Modeling of Serial-Parallel Multistage Manufacturing Systems with Coupled Process Routes,¡± ASME Transactions, Journal of Manufacturing Science and Engineering, 126, pp.611-618.
21. Huang, Q., and Shi, J., 2004, ¡°Variation Transmission Analysis and Diagnosis of Multi-Operational Machining Processes,¡± IIE Transactions on Quality and Reliability, 36, pp. 807-815.
22. Zhou, S., Huang, Q., and Shi, J., 2003,"State Space Modeling for Dimensional Monitoring of Multistage Machining Process Using Differential Motion Vector," IEEE Transactions on Robotics and Automation, 19, 296-309.
23. Huang, Q., Shi, J., 2003, ¡°Simultaneous Tolerance Synthesis through Variation Propagation Modeling of Multistage Manufacturing Processes,¡± NAMRI/SME Transactions, 31, pp. 515-522.
24. Huang, Q., Shi, J., and Yuan, J., 2003, ¡°Part Dimensional Error and Its Propagation Modeling in Multi-Operational Machining Processes,¡± ASME Transactions, Journal of Manufacturing Science and Engineering, 125, 255-262.
25. Huang, Q., Zhou, S., and Shi, J., 2002, ¡°Diagnosis of Multi-Operational Machining Processes through Variation Propagation Analysis,¡± Robotics and Computer-Integrated Manufacturing, 18, 233-239.
Referred Conference Proceedings
1. Huang, Q., Zhou, N., and Shi, J., 2000, ¡°Stream of Variation Modeling and Diagnosis of Multi-Station Machining Processes,¡± Proc. 2000 ASME Int. Mech. Eng. Congress & Exposition, MED-Vol. 11, pp.81-88, November 5-10, Orlando, FL., 2000.
2. Huang, Q., Zhou, S., and Shi, J., 2001, ¡°Diagnosis of Multi-Operational Machining Processes By Using Virtual Machining,¡± Int. Conf. on Flexible Automation & Intelligent Manufacturing, pp. 804-813, July 16th - 18th, Dublin, IRELAND.
3. Huang, Q., and Shi, J., 2001, ¡°Stream of Variation Analysis and Root Cause Diagnosis for Multi-Operational Machining Processes,¡± 2002 Japan-USA Symposium on Flexible Automation, July 15-17, 2002, Hiroshima, Japan.
4. Kim, J., Huang, Q., Shi, J., and Chang, T.-S., 2004, ¡°Online Multi-Channel Forging Tonnage Monitoring and Fault Pattern Discrimination Using Principal Curve,¡± ASME IMECE 04.
5. Wang, H., Huang, Q., Katz, R., 2004, ¡°Multi-Operational Machining Processes Modeling for Sequential Root Cause Identification and Measurement Reduction,¡± ASME IMECE 2004.
6. Wang, H., Huang, Q., 2005, ¡°Automatic Process Adjustment for Reducing Dimensional Variation in Discrete Part Machining Processes,¡± ASME IMECE 2005.
7. Wang, H., Chen, S., and Huang, Q., 2009, ¡°Multistage Machining Process Design and Optimization Using Error Equivalence Method ", 2009 ASME International Manufacturing Science and Engineering Conference (MSEC), October 4-7, 2009, West Lafayette, IN.
8. Huang, Q., 2011, ¡°Integrated Nanomanufacturing and Nanoinformatics for Quality Improvement ¡±, 44th CIRP International Conference on Manufacturing Systems, June 1-3, 2011, Madison, Wisconsin (Invited).
9. Xu, L., Huang, Q., Sabbaghi, A., and Dasgupta, T., 2013 ¡°Shape Deviation Modeling for Dimensional Quality Control in Additive Manufacturing, ¡±Proceedings of the ASME 2013 International Mechanical Engineering Congress & Exposition, November 15-21, 2013, San Diego, USA.
10. Sabbaghi A., Dasgupta T., Zhang J., Huang Q. ¡°Inference with Interference and Interference for Inference: Modeling Potential Outcomes and the Structure of Interference in a 3D Printing Experiment, ¡±2013 Joint Statistical Meetings, August 2013.
Nano-QCLab is established to enhance research and teaching infrastructures on nanomanufacturing process control for process yield and repeatability improvement.
2. Featured article IIE Transactions on Quality and Reliability
Huang, Q., 2011, ¡°Physics-Driven Bayesian Hierarchical Modeling of Nanowire Growth Process at Each Scale," IIE Transactions on Quality and Reliability, Vol. 43, pp. 1-11.
3. Featured article in IIE Transactions on Quality and Reliability
Wang, H., and Huang, Q., 2006, ¡°Error Cancellation Modeling and Its Application in Machining Process Control," IIE Transactions on Quality and Reliability, Vol.38, pp.379-388.
4. 2002-2003 Student of the Year at NSF Engineering Research Center for Reconfigurable Machining Systems (ERC-RMS) at University of Michigan, May 2003.
5. Graduate Student Research Assistantship at ERC-RMS of University of Michigan, 1999-2003.
1. Hui Wang, PhD, 2007, USF, ¡°Error Equivalence Theory for Manufacturing Process Control¡± .
Current position: Assistant Research Scientist at University of Michigan, Ann Arbor, 01/08 ~ present. Will join Florida State University as Assistant Professor in spring 2014.
2. Xi Zhang, PhD, December, 2009, USF, ¡°Statistical and Physical Analysis of Functional Process Variables for Process Control in Semiconductor Manufacturing¡±.
Current position: Assistant Professor, Industrial Engineering & Management Department, Peking University, China, 09/2010 ~ present.
3. Lijuan Xu, PhD, December 2013, ¡°Nanostructure Interaction Modeling and Estimation for Scalable Nanomanufacturing.¡±
Current position: Senior Analyst, Climate Corporation, San Francisco, CA. 2014 ~ Present.
4. Li Wang, PhD, December 2013, ¡°Modeling and Analysis of Nanostructure Growth Process Kinetics and Variations for Scalable Nanomanufacturing.¡±
Current position: Senior Analyst at Liberty Mutual Insurance, Boston, MA. 06/2013 ~ present.
Current Ph.D. Students
Industrial and Systems Engineering, Ph.D. student,
BS (2013): Industrial Engineering, Sharif University, Iran.
Email: hnouri at usc.edu
1. Member of scientific committee (Editorial Board) for the North American Manufacturing Research Institution (NAMRI) of SME, 2013-2015.
2. Associate Editor, IEEE Transactions on Automation Science and Engineering, 01/2012-12/2014.
3. Contributing Editor, InterNano (www.internano.org), online resource of the National Nanomanufacturing Network (NNN), 04/2012-04/2013.
4. Associate Editor (Quality, Micro and Nano Manufacturing Systems), SME Journal of Manufacturing Systems, 2008-2011.
5. Special Issue Editor, ¡°Quality, Sensing and Prognostics Issues in Nanomanufacturing¡±, Special Issue of the IIE Transactions on Quality and Reliability Engineering/Manufacturing and Design.
6. Member of scientific committee (Editorial Board) for the North American Manufacturing Research Institution (NAMRI) of SME, 2009-2011
7. IEEE CASE 2010 Program Committee for Track Automation in Meso, Micro and Nano-Scale.
8. Council member of QSR (Quality, Statistics, and Reliability) section at INFORMS, 2010-2012.
9. Associate Editor (Automation in Meso, Micro and Nano-Scale), 2009 IEEE Conference on Automation Science and Engineering (CASE 2009).
10. Member of INFORMS, SME, IIE, ASME, IEEE.
Last updated on 10/2013