CV

Ph.D. in Mechanical & Computer Engineering (Co-major)

• GPA: 3.72/4.00
• Dissertation: Deep Learning & GPU algorithms for Computer Aided Engineering
• Advisors: Prof. Adarsh Krishnamurthy & Prof. Soumik Sarkar

Bachelor of Engineering (Hons.) in Mechanical Engineering

• GPA: 3.68/4.00 (9.02/10.0)

Data Scientist

Postdoctoral Research Associate

Machine Learning Co-op

Design Engineer - I

Summer Intern

Instructor

• ME 170 – Engineering Graphics and SolidWorks (Spring 2022)
• CPS 364X – Cyber-Physical Systems Applications (Spring 2023)

Teaching Assistant

• ME 592X – Data Analytics and Machine Learning for Cyber-Physical Systems (Spring 2018 & Spring 2019)
• ME 570X – Solid Modeling and GPU Computing (Spring 2019)
• ME 324 – Manufacturing Processes (Fall 2016)

• {"Finalist, NSF NAMRC Bluesky Manufacturing Competition, \"Beyond the blueprint"=>"conversational AI as a game-changer in manufacturing\" Rutgers University, NJ"}

• NVIDIA Deep Learning Institute Ambassador, Iowa State University
• ACCESS-CI Campus Champion, Iowa State University

• Finalist, NSF NAMRC Bluesky Manufacturing Competition, "Physics-aware machine learning surrogates for real-time digital twins in additive manufacturing," St. Louis, MO

• Research excellence award, Iowa State University

• Travel grant award for Neural Information Processing Systems (NIPS)

CPS: Medium: Artificial-intelligence-enabled Atomic Force Microscopy

• Role: Co-PI
• Co-Investigators: Juan Ren, Adarsh Krishnamurthy, Soumik Sarkar, Aditya Balu
• Amount: $1,000,000

Collaborative Research: EAGER: An LLM-Powered Framework for G-Code Comprehension and Retrieval

• Role: Co-PI
• Co-Investigators: Adarsh Krishnamurthy, Chinmay Hegde, Aditya Balu, Baskar Ganapathysubramanian
• Amount: $200,000

• Saadati, N., Pham, M., Saleem, N., Waite, J.R., Balu, A., Jiang, Z., Hegde, C., & Sarkar, S. (2024). DIMAT: Decentralized Iterative Merging-and-Training for Deep Learning Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 27517-27527.
• Arshad, M.A., Jubery, T., Afful, J., Jignasu, A., Balu, A., Ganapathysubramanian, B., Sarkar, S., & Krishnamurthy, A. (2024). Evaluating neural radiance fields (NeRFs) for 3D plant geometry reconstruction in field conditions. Plant Phenomics, 6(0235), 1-17.
• Herron, E., Rade, J., Jignasu, A., Ganapathysubramanian, B., Balu, A., Sarkar, S., & Krishnamurthy, A. (2024). Latent diffusion models for structural component design. Computer-Aided Design, 171, 103707.
• Khara, B., Balu, A., Joshi, A., Sarkar, S., Hegde, C., Krishnamurthy, A., & Ganapathysubramanian, B. (2024). NeuFENet: Neural finite element solutions with theoretical bounds for parametric PDEs. Engineering with Computers, 1-23.
• Khara, B., Herron, E., Jiang, Z., Balu, A., Yang, C.H., Saurabh, K., Jignasu, A., Sarkar, S., Hegde, C., Ganapathysubramanian, B., & Krishnamurthy, A. (2024). Neural PDE solvers for irregular domains. Computer-Aided Design, 172, 103709.

• Rade, J., Jignasu, A., Herron, E., Corpuz, A., Ganapathysubramanian, B., Sarkar, S., Balu, A., & Krishnamurthy, A. (2023). Deep learning-based 3D multigrid topology optimization of manufacturable designs. Engineering Applications of Artificial Intelligence.
• Balu, A., Rajanna, M.R., Khristy, J., Xu, F., Krishnamurthy, A., & Hsu, M.C. (2023). Direct immersogeometric fluid flow and heat transfer analysis of objects represented by point clouds. Computer Methods in Applied Mechanics and Engineering, 404(115742).
• Feng, J., Saadati, M., Jubery, T., Jignasu, A., Balu, A., Li, Y., Attigala, L., Schnable, P., Sarkar, S., Ganapathysubramanian, B., & Krishnamurthy, A. (2023). 3D reconstruction of plants using probabilistic voxel carving. Computers and Electronics in Agriculture, 213, 108248.

• Jiang, Z., Lee, X.Y., Tan, S.Y., Tan, K.L., Balu, A., Lee, Y.M., Hegde, C., & Sarkar, S. (2022). MDPGT: Momentum-based Decentralized Policy Gradient Tracking. Proceeding of AAAI Conference on Artificial Intelligence.

• Cho, M., Balu, A., Joshi, A., Prasad, A.D., Khara, B., Sarkar, S., Ganapathysubramanian, B., Krishnamurthy, A., & Hegde, C. (2021). Differentiable Spline Approximations. Proceedings of the Neural Information Processing Systems.
• Esfandiari, Y., Tan, S.Y., Jiang, Z., Balu, A., Herron, E., Hegde, C., & Sarkar, S. (2021). Cross-gradient Aggregation for Decentralized Learning from Non-IID Data. Proceedings of the 38th International Conference on Machine Learning, 3036-3046.
• Balu, A., Ghadai, S., Young, G., Sarkar, S., & Krishnamurthy, A. (2021). A machine learning framework for decision support in design and manufacturing. Advances in Computing and Information in Engineering (ACIER), 2, 479-498.

Enhancing AI with Domain Knowledge: Lessons from Some Anecdotal Science and Engineering Applications

Communication Efficient Decentralized Deep Learning for Agricultural Applications

Enhancing Deep Learning Models with Geometry and Physics Priors

AI in Mechanical Engineering