EDUCATION BACKGROUND

Pursuing Ph.D. in Mechanical Engineering at Carnegie Mellon University  (July 2020 – Present)

Computational Bio-Modeling Lab (CBML) & Biohybrid and Organic Robotics Group (B.O.R.G.)

Advisors: Prof. Yongjie Jessica Zhang and Prof. Victoria Webster-Wood

M.S. Research in Mechanical Engineering at Carnegie Mellon University  (Aug. 2018 to May 2020)

Computational Bio-Modeling Lab (CBML) & Morphing Matter Lab at HCII

Advisors: Prof. Yongjie Jessica Zhang and Prof. Lining Yao

B.S.E in Mechanical Engineering from the University of California, San Diego (Sep. 2014 to Jun. 2018)

PROFESSIONAL EXPERIENCE AND LEADERSHIP

U.S. Association for Computational Mechanics Student Chapter Executive Member (Jan 2023 - Present)

• Serving as an executive committee member of the founding session for the USACM Student Chapter

• Organizing professional meetings and gatherings for researchers in the field of computational mechanics at the 17th U. S. National Congress on Computational Mechanics (USNCCM17)

• Working with USACM mentors to promote computational mechanics to prospective students across the U.S.

Graduate Researcher Assistant (Ph.D. student) | CMU, Pittsburgh (Jun 2019 - Present)

Neuron Growth and Neurodevelopmental Disorder Study (ADHD, autism, etc.)

• Led an NSF-funded project to develop an isogeometric analysis nonlinear solver on locally refined volumetric spline on multi-threaded high-performance computing (HPC) platforms (95.15% confidence with p = 0.336, 20~140x faster.)

• Developing a physics-informed convolutional-recurrent ensemble neural network with a preliminary CNN for multi-channel neurite growth data (<2.23% error and >7 orders of magnitude faster).

• Authored three NSF proposals involving six principal investigators across three top US institutions, securing funding of $500,000 from CBET. Managed 3 million core hours on HPC accumulatively and supported projects at the CBML.

Volumetric Spline Mesh Generation Development (Knee joints, car components for Honda, etc.)

• Developed a mesh generation method using supervised and reinforcement learning, achieving over 98% accuracy.

• Improved mesh quality (min Jacobian>0.6) with reward function optimizing abstract features (spacing and sharpness).

• Developed and delivered volumetric spline models that support multiple extraordinary points per face (>5x convergence rate, ~40-90x fewer degrees of freedom, and >50% faster).

Residual Deformation Simulation for Additive Manufacturing (Metal Heat changer for Army Research Laboratory)

• Automated ANSYS additive manufacturing analysis (5833 APDL simulations on HPC) and developed a DNN tool for mitigating residual deformations (>95% accuracy).

• Revived a stalling project, coded APDL scripts, and managed a team of 3. Securing an additional 1.5 years of funding.

Graduate Research Assistant (Master student) | CMU, Pittsburgh (Sep 2018 - Jun 2020)

Shape Memory Polymer Actuator Design & Manufacturing

• Developed bilayer actuators using various shape memory materials at CMU HCII (drug delivery applications, etc.)

• Achieved accurate 3D-printed fiber-reinforced thermoplastic composite deformation simulation with material characterization (>95% accuracy, confidence interval of 0.972 to 0.985).

• Created a rhinoceros-based, ML-driven design tool that delivers real-time iterations (>97% accuracy), significantly enhancing previous FEA capabilities (enabling real-time design iterations that were previously impossible).

Undergraduate Research Assistant | UCSD, La Jolla (Sep 2014 - Jun 2018)

• Developed gas-injection splitter for focal charge compensation on a serial block-face scanning electron microscope (SBEM) at the National Center for Microscopy and Imaging Research.

• Optimized N2 distribution in ultra-high SBEM vacuum environments using Solidworks and ANSYS Fluent. Achieved 4X better discharging effects with 30% less nitrogen gas and a 15% budget.

• Manufactured low altitude long endurance (LALE) foam-padded carbon fiber box-wing drone at AUVSI

WORK EXPERIENCE

Summer Graduate Research Assistant | CBML, CMU, Pittsburgh (June 2019 – Aug. 2019)

• Conducted compression, tensile, and flexure tests and analyzed carbon fiber embedding under 150 microns using SEM.

• Successfully designed 3D-printed carbon-fiber reinforced thermoplastic bilayer actuators (heat responsive).

Thermal Mechanical Intern | Huayu Automotive Systems Co., Ltd., Shanghai (July 2018 – Aug. 2018)

• Delivered high-quality all-hexahedral mesh (Jacobian > 0.7 using Hypermesh) for 52Kw rotor thermal analysis.

• Validated heat dissipation analysis with experiments for 52Kw, 85Kw, and 105Kw motors for production.

Mechanical Engineering Intern | Siemens High Voltage Switchgear Co., Ltd., Hangzhou (July 2015 – Aug. 2015)

• Assisted factory chief production engineer in China with English communications.

• Processed engineering drawings of silver-plated graphite high-voltage switchgear for factory productions.

PROJECTS

DNN-driven ML Model of Metal Printability Evaluation for Heat Exchangers (Jan. 2023 – Present)

• Developed an APDL residual deformation automation script for enhanced additive manufacturing (AM) design risk assessments (geometric compensation-based) with arbitrary geometry handling capability.

• Delivered the DNN-based surrogate model (> 95% accuracy) for heat exchanger residual deformation mitigation (based on 5833 APDL simulations on HPC) tool for novice AM users at the U.S. Army Research Laboratory.        

Low-cost, Portable Breast Tumor Detection (Sep. 2020 – Present)

• Developed optical imaging methods to detect tumors using spatial frequency domain imaging technique (accurate with R2 = 0.992), significantly improving early-stage diagnosis for breast tumors.

• Performed parametric simulations on ANSYS Workbench to model large deformations in soft tissues, effectively simulating tissue mechanics (Young’s modulus as low as 4 kPa).

Hexahedral-dominant Mesh Generation for Automotive Applications (Oct. 2019 - Present)

• Developed a semi-automatic, polycube-based all-hex mesh generation (min Jacob > 0.2) from CAD models, enhancing hybrid IGA-FEA analysis with a 30%~80% reduction in degrees of freedom, increasing computational efficiency.

• Provided Honda R&D with engineered volumetric spline models of engine mounts, directly contributing to up to 50% improvement in the computation of simulations used in safety evaluations and design optimizations. 

Supervised and Reinforcement Learning-assisted Mesh Generation (June. 2023 – Sep. 2023)

• Engineered an advanced mesh generation method using supervised and reinforcement learning (> 98% accuracy) to replace the advancing front method and improve meshing quality (min Jacobian >0.6) with abstract targets as rewards functions (EP spacing, element sizing, sharp feature preservations).

All-Hexahedral Mesh Construction for Heterogeneous Domains (Jun. 2023 – Aug. 2023)

• Engineered advanced octree-based iso-contouring hexahedral mesh generation code, incorporating local optimization techniques that successfully eliminated negative Jacobians, enhancing mesh quality and analysis stability.

• Provided the Cao Research Lab at Northwestern University with optimized mesh models featuring pillowed layers, enabling ABAQUS analysis of heterogeneous microstructures comprising of more than 60 grains. This collaboration significantly improved the accuracy and reliability of material behavior simulations.

Analysis-suitable T-spline for Honda Automobile Applications (July. 2020 – Mar. 2022)

• Developed a T-spline that allows multiple extraordinary points per face (~40-90x less DOFs than FEA).

• Delivered B-pillar and car side-panel models to Honda R&D Co., Ltd. (> 5 times convergence rate improvements).

Performance Racing Horseshoe using Metal Additive Manufacturing (Jan. 2019 – May. 2019)

• Conducted topology optimization simulations and validated structure integrity (deformation < 31 microns).

• Manufactured topology-optimized hollow horseshoes (58% weight reduction) for racing horses with Ti64 using EOS LPBF at a fraction of the cost compared to commercial high-performance footwear.

REFERRED PUBLICATIONS (Google Scholar)

1. K. Qian, V. A. Webster-Wood, Y. J. Zhang. 3D Neuron Growth Model using Truncated Hierarchical B-splines with Multi-level Local Refinements. In preparation.

2. K. Qian, G. O. Suarez, T. Nambara, T. Kanekiyo, A. S. Liao, V. A. Webster-Wood, Y. J. Zhang. Neurodevelopmental Disorders Modeling using Isogeometric Analysis, Dynamic Domain Expansion and Local Refinement. Under review.

3. C. M. Robbins, K Qian, Y. J. Zhang, J. M. Kainerstorfer. Monte Carlo simulation of spatial frequency domain imaging for breast tumors during compression. Under review.

4. K. Qian, A. Liao, S. Gu, V. Webster-Wood, Y. J. Zhang. Biomimetic IGA Neuron Growth Modeling with Neurite Morphometric Features and CNN-Based Prediction. Computer Methods in Applied Mechanics and Engineering, A Special Issue in Honor of the Lifetime Achievements of T. J. R. Hughes, 116213, 2023.

5. H. Tong, K. Qian, E. Halilaj, Y. J. Zhang. SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method. Journal of Computational Science, 72:102109, 2023.

6. K. Qian, A. Pawar,  A. Liao, C. Anitesco, V. Webster-Wood, A. W. Feinberg, T. Rabczuk, Y. J. Zhang. Modeling Neuron Growth Using Isogeometric Collocation Based Phase Field Method. Scientific Reports, 12:8120, 2022.

7. Y. Yu, K. Qian, H. Yang, L. Yao, Y. J. Zhang. Hybrid IGA-FEA of Fiber Reinforced Thermoplastic Composites for Forward Design of AI-Enabled 4D Printing. Journal of Materials Processing Technology, Special Issue on Artificial Intelligence in Advanced Manufacturing Processes, 302:117497, 2022.

8. X. Wei, X. Li, K. Qian, T. J.R. Hughes, Y. J. Zhang, H. Casquero. Analysis-suitable Unstructured T-splines: Multiple Extraordinary Points Per Face. Computer Methods in Applied Mechanics and Engineering, 391:114494, 2022.

9. H. Yang, D. Luo, K. Qian, L. Yao. Freeform Fabrication of Fluidic Edible Materials. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1-10, 2021.

10. H. Yang, K. Qian, H. Liu, Y. Yu, J. Gu, M. McGehee, Y. J. Zhang, L. Yao. SimuLearn: Fast and Accurate Simulator to Support Morphing Materials Design and Workflows. Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, 71-84, 2020.

11. Y. Yu, H. Liu, K. Qian, H. Yang, M. McGehee, J. Gu, D. Luo, L. Yao, Y. J. Zhang. Material Characterization and Precise Finite Element Analysis of Fiber Reinforced Thermoplastic Composites for 4D Printing. Computer-Aided Design, 122:102817, 2020.

12. K. Qian, Z. Wen, M. Wu, S. Singh, A. Sinha. Computational Fluid Dynamics Studies of Slag Entrapment in Continuous Casting Process. Proceedings of the 2020 Iron & Steel Technology Conference, 1004-1017, 2020.

CONFERENCE ABSTRACTS AND PRESENTATIONS

1. K. Qian, V. Webster-Wood, Y. J. Zhang. Investigating neurodevelopmental disorders using innovative IGA, dynamic domain expansion, local refinement and deep learning: Part I. Under review for 12th International Conference on IsoGeometric Analysis 2024, St. Augustine, Florida. October 27-30, 2024.

2. K. Qian, V. Webster-Wood, Y. J. Zhang. Investigating neurodevelopmental disorders using innovative IGA, dynamic domain expansion, local refinement and deep learning: Part II. Under review for 12th International Conference on IsoGeometric Analysis 2024, St. Augustine, Florida. October 27-30, 2024.

3. K. Qian, A. Liao, S. Gu, V. Webster-Wood, Y. J. Zhang. Biomimetic IGA neuron growth modeling with neurite morphometric features and CNN-based prediction. 17th U.S. National Congress on Computational Mechanics. Albuquerque, New Mexico. July 23-27, 2023.

4. H. Tong, K. Qian, E. Halilaj, Y. J. Zhang. SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method. 17th U.S. National Congress on Computational Mechanics. Albuquerque, New Mexico. July 23-27, 2023.

5. K. Qian, A. Pawar, A. S. Liao, C. Anitescu, V. A. Webster-Wood, A. W. Feinberg, T. Rabczuk, and Y. J. Zhang. Modeling Neuron Growth Using Isogeometric Collocation Based Phase Field Method. 15th World Congress on Computational Mechanics (WCCM-XV) & 8th Asian Pacific Congress on Computational Mechanics (APCOM-VIII), Yokohama, Japan, July 31 – August 5, 2022.

6. D. Luo, H. Yang, M. Khurana, K. Qian, L. Yao. Demonstrating Freeform Fabrication of Fluidic Edible Materials. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 1-4, 2021.

7. K. Qian, A. Pawar, A. S. Liao, C. Anitescu, T. Rabczuk, V. A. Webster-Wood, Y. J. Zhang. Modeling Multi-Neuron Biomimetic Growth Stages using Isogeometric Collocation and Phase Field Model. Virtual International Conference on Isogeometric Analysis 2021. Lyon, France. Sep 26-29, 2021.

8. K. Qian, A. Pawar, A. S. Liao, C. Anitescu, T. Rabczuk, V. A. Webster-Wood, Y. J. Zhang. Modeling Multi-Neuron Biomimetic Growth Stages using Isogeometric Collocation and Phase Field Model. 16th U.S. National Congress on Computational Mechanics. Chicago, Illinois. June 25-29, 2021.

POSTERS

1. K. Qian, V. Webster-Wood, Y. J. Zhang. Isogeometric Analysis with Dynamic Domain Expansion and Truncated Hierarchical B-splines to Model Neurodevelopmental Atrophy. 2024 Carnegie Mellon University Mechanical Engineering Ph.D. Graduate Research Symposium, Mar 1, 2024.

2. K. Qian, X. Liang, L. White, C. Oh, M. Chen, G. Zhang, J. Cagan, A. D. Rollett, Y. J. Zhang. Residual Deformation Learning and Mitigation by Design for Metal Component Printability Enhancement. 17th U.S. National Congress on Computational Mechanics. Albuquerque, New Mexico. Jul 25, 2023.

3. H. Tong, K. Qian, E. Halilaj, Y. J. Zhang. SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method. 17th U.S. National Congress on Computational Mechanics. Albuquerque, New Mexico. Jul 25, 2023.

4. Y. Yu, H. Liu, K. Qian, H. Yang, M. McGehee, J. Gu, D. Luo, L. Yao, Y. J. Zhang. Material Characterization and Precise Finite Element Analysis of Fiber Reinforced Thermoplastic Composites for 4D Printing. NextManufacturing Center Expo, Pittsburgh, Pennsylvania, Jan 18, 2020.

5. H, Yang, K. Qian, H. Liu, Y. Yu, J. Gu, M. McGehee, Y. J. Zhang. SimuLearn: Machine Learning-Empowered Simulation and Forward-Design Tool for Morphing Material. Manufacturing Future Initiative Forum, Pittsburgh, Pennsylvania, Apr 29, 2019.

TEACHING AND MENTORSHIP EXPERIENCES         

Guest Lecturer (Fall 2021 & Fall 2022)

• 24658 Image-based Computational Modeling and Analysis, CMU

• Presented neuron growth modeling work to students interested in geometry-related research          

Project Mentorship (Summer 2021 & Summer 2022)

• Summer Geometry Institute (SGI) program, MIT            

• Led a team of 3 students each year to explore and learn geometry research work in our lab

Teaching Assistant (Fall 2021 & Fall 2022)

• 24658 Image-Based Computational Modeling and Analysis, CMU                  

• Managed class discussions and office hours, hosted guest lecturers

HONORS AND AWARDS

Bradford and Diane Smith Graduate Fellowship 2023 (Feb 2023)

• Fellowship established to help support the graduate studies of highly deserving students in the College of Engineering at Carnegie Mellon University

WCCM-APCOM Travel Award 2022 (July 2022)

• Travel Award for the 15th World Congress on Computation Mechanics & 8th Asian Pacific Congress on Computation Mechanics

NSF MMLDT-CSET 2021 Fellowship (July 2021)

• NSF Fellowship for attending Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology

Ronald F. and Janice A. Zollo Fellowship (Jan 2021)

• Fellowship established to help support the graduate studies of highly deserving College of Engineering graduate students whose research is related to neuroscience or neuroengineering

ENGINEERING WORKSHOP

ACCESS HPC Workshop: MPI (Oct 19 - 20, 2023)

• Topics including MPI programming – the standard programming tool of scalable parallel computing.

XSEDE HPC Workshop - Big Data and Machine Learning (Aug 30 - 31, 2022)

• Topics including big data analytics, machine learning with Spark, and deep learning using TensorFlow

MMLDT-CSET 2021 short course (Sep 26, 2021)

• Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology

Next Seminars and Workshops: Scientific Machine Learning Mini-Course (Oct 2020 – Jan 2021)

• Organized by Keith Phuthi, Varun Shankar, and Venkat Viswanathan with the goal of cross-pollinating ideas between the various emerging methods at the intersection of physics and machine learning