Namaste!
I'm Deepanshu Verma and I do math.

...
Namaste!
I'm Deepanshu Verma and I do math.


Assistant Professor
Clemson University

Research Interests

  • PDE Constrained Optimization
    1. E. Gelphman, D. Verma, N.T. Yang, S. Osher and S. Wu Fung. End-to-end Training of High-Dimensional Optimal Control with Implicit Hamiltonians via Jacobian-Free Backpropagation. arXiv: .
    2. X. Li, K. Kan, D. Verma, K. Kumar, S. Osher and J. Drgoňa. Zero-Shot Transferable Solution Method for Parametric Optimal Control Problems. arXiv: .
    3. D. Verma, N. Winovich, L. Ruthotto and B. van Bloemen Waanders. Neural Network Approaches for Parameterized Optimal Control. DOI: 10.3934/fods.2024042. arXiv: .
    4. X. Li, L. Ruthotto, and D. Verma. A Neural Network approach for Stochastic Optimal Control problems. Accepted to SISC VoL 46 Iss. 5 2024. DOI: 10.1137/23M155832X. arXiv: .
    5. M. Madondo, D. Verma, L. Ruthotto, and N. A. Yong. Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics. ML4H 2023. arXiv: .
    6. B. P. Lamichhane, N. Nataraj, and D. Verma. A mixed finite element method using a biorthogonal system for optimal control problems governed by a biharmonic equation. Accepted in ANZIAMJ 2023.
    7. H. Antil, T.S. Brown, R. Löhner, F. Togashi, and D. Verma. Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows. Accepted 2021. DOI: https://doi.org/10.1007/978-3-030-90539-2_2. arXiv: .
    8. H. Antil, R. Khatri, R. Löhner and D. Verma. Fractional Deep Neural Network via Constrained Optimization. Machine Learning: Science and Technology 2020. DOI: https://doi.org/10.1088/2632-2153/aba8e7. arXiv:
    9. H. Antil, D. Verma, and M. Warma. Optimal Control of Fractional Elliptic PDEs with State Constraints and Characterization of the dual of Fractional Order Sobolev Spaces. J Optim Theory Appl 186, 1 6 (2020). DOI: https://doi.org/10.1007/s10957-020-01684-z. arXiv:
    10. H. Antil, D. Verma, and M. Warma. External Optimal Control of Space-Time Fractional Parabolic PDEs. ESAIM: COCV 26 (2020) 20. DOI: https://doi.org/10.1051/cocv/2020005. arXiv:
  • Inverse Problems
    1. M. Chung, D. Verma, M. Collins, A.N. Subrahmanya, V. Katti Sastry and V. Rao. Latent Twins. arXiv: .
    2. Z. Wang, R. Baptista, Y. Marzouk, L. Ruthotto and D. Verma. Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference. arXiv: .
    3. H. Antil, H.C. Elman, A. Onwunta, and D. Verma. Novel Deep Neural Networks for Solving Bayesian Statistical Inverse Problems. Mach. Learn.: Sci. Technol. 4 035015. DOI: 10.1088/2632-2153/ace67c. arXiv:
  • Numerical Analysis
    1. W. Akram, S. Gautam, D. Verma and M. Mohan. Error estimates for viscous Burgers' equation using deep learning method. arXiv: .
    2. H. Antil and D. Verma. Randomized Matrix Sketching for Efficient Neural Network Training and Gradient Monitoring. arXiv: .
    3. T.S Brown, H. Antil, R. Löhner, F. Togashi, and D. Verma. Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows. DOI: https://doi.org/10.1007/978-3-030-90539-2_2. arXiv:
    4. H. Antil, R. Arndt, C.N. Rautenberg, and D. Verma. Non-Diffusive Variational Problems with Distributional and Weak Gradient Constraints. Advances in Nonlinear Analysis 2022. DOI: 10.1515/anona-2022-0227.
  • Machine Learning
    1. M. Chung, D. Verma, M. Collins, A.N. Subrahmanya, V. Katti Sastry and V. Rao. Latent Twins. arXiv: .
    2. H. Antil and D. Verma. Randomized Matrix Sketching for Efficient Neural Network Training and Gradient Monitoring. arXiv: .
    3. W. Akram, S. Gautam, D. Verma and M. Mohan. Error estimates for viscous Burgers' equation using deep learning method. arXiv: .
    4. E. Gelphman, D. Verma, N.T. Yang, S. Osher and S. Wu Fung. End-to-end Training of High-Dimensional Optimal Control with Implicit Hamiltonians via Jacobian-Free Backpropagation. arXiv: .
    5. Z. Wang, R. Baptista, Y. Marzouk, L. Ruthotto and D. Verma. Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference. arXiv: .
    6. D. Verma, N. Winovich, L. Ruthotto and B. van Bloemen Waanders. Neural Network Approaches for Parameterized Optimal Control. DOI: 10.3934/fods.2024042. arXiv: .
    7. X. Li, L. Ruthotto, and D. Verma. A Neural Network approach for Stochastic Optimal Control problems. Accepted to SISC VoL 46 Iss. 5 2024. DOI: 10.1137/23M155832X. arXiv: .
    8. M. Madondo, D. Verma, L. Ruthotto, and N. A. Yong. Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics. ML4H 2023. arXiv: .
    9. H. Antil, H.C. Elman, A. Onwunta, and D. Verma. Novel Deep Neural Networks for Solving Bayesian Statistical Inverse Problems. Mach. Learn.: Sci. Technol. 4 035015. DOI: 10.1088/2632-2153/ace67c. arXiv:
    10. T.S. Brown, H. Antil, R. Löhner, F. Togashi, and D. Verma. Parallel Deep ResNets for Chemically Reacting Flows. AIAA SciTech Forum 2022-1076. 2022. DOI: 10.2514/6.2022-1076.
    11. H. Antil, T.S Brown, R. Khatri, A. Onwunta, D. Verma, and M. Warma. Deep Neural Nets with Fixed Bias Configuration. Numer. Algebra Control Optim. (NACO) 2022. DOI: 10.3934/naco.2022016.
    12. T.S Brown, H. Antil, R. Löhner, F. Togashi, and D. Verma. Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows. Accepted in Computational Fluid Dynamics Simulations and Analysis (CFDML) 2021. DOI: https://doi.org/10.1007/978-3-030-90539-2_2. arXiv:
    13. H. Antil, R. Khatri, R. Löhner and D. Verma. Fractional Deep Neural Network via Constrained Optimization. Machine Learning: Science and Technology 2020. DOI: https://doi.org/10.1088/2632-2153/aba8e7. arXiv:

Teaching

Clemson University (2024–Present)

  • Fall 2025: Math 8710 (Machine Learning I), Math 8600 (Intro to Scientific Computing)
  • Spring 2025: Math 4820 (Undergrad Research), Math 8600 (Intro to Scientific Computing)
  • Fall 2024: Math 3650 (Numerical Methods for Engineers)

Emory University (2021–2024)

  • Spring 2024: Math 221 (Linear Algebra)
  • Fall 2023: Math 485 (Convex Optimization)
  • Spring 2023: Math 221 (Linear Algebra)
  • Fall 2022, Spring 2022: Math 221 (Linear Algebra)
  • Fall 2021: Math 111 (Calculus I)

Publications


Submitted
  1. M. Chung, D. Verma, M. Collins, A.N. Subrahmanya, V. Katti Sastry and V. Rao. Latent Twins. arXiv: .
  2. H. Antil and D. Verma. Randomized Matrix Sketching for Efficient Neural Network Training and Gradient Monitoring. arXiv: .
  3. E. Gelphman, D. Verma, N.T. Yang, S. Osher and S. Wu Fung. End-to-end Training of High-Dimensional Optimal Control with Implicit Hamiltonians via Jacobian-Free Backpropagation. arXiv: .
  4. X. Li, K. Kan, D. Verma, K. Kumar, S. Osher and J. Drgoňa. Zero-Shot Transferable Solution Method for Parametric Optimal Control Problems. arXiv: .
  5. W. Akram, S. Gautam, D. Verma and M. Mohan. Error estimates for viscous Burgers' equation using deep learning method. arXiv: .
Published/Accepted
  1. D. Verma, N. Winovich, L. Ruthotto and B. van Bloemen Waanders. Neural Network Approaches for Parameterized Optimal Control. DOI: 10.3934/fods.2024042. arXiv: .
  2. X. Li, L. Ruthotto, and D. Verma. A Neural Network approach for Stochastic Optimal Control problems. Accepted to SISC VoL 46 Iss. 5 2024. DOI: 10.1137/23M155832X. arXiv: .
  3. M. Madondo, D. Verma, L. Ruthotto, and N. A. Yong. Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics. ML4H 2023. arXiv: .
  4. B. P. Lamichhane, N. Nataraj, and D. Verma. A mixed finite element method using a biorthogonal system for optimal control problems governed by a biharmonic equation. Accepted in ANZIAMJ 2023.
  5. H. Antil, H.C. Elman, A. Onwunta, and D. Verma. Novel Deep Neural Networks for Solving Bayesian Statistical Inverse Problems. Mach. Learn.: Sci. Technol. 4 035015. DOI: 10.1088/2632-2153/ace67c. arXiv:
  6. H. Antil, T.S Brown, R. Löhner, F. Togashi, and D. Verma. Deep Neural Nets with Fixed Bias Configuration. Numer. Algebra Control Optim. (NACO) 2022. DOI: 10.3934/naco.2022016.
  7. H. Antil, R. Arndt, C.N. Rautenberg, and D. Verma. Non-Diffusive Variational Problems with Distributional and Weak Gradient Constraints. Advances in Nonlinear Analysis 2022. DOI: 10.1515/anona-2022-0227.
  8. T.S. Brown, H. Antil, R. Löhner, F. Togashi, and D. Verma. Parallel Deep ResNets for Chemically Reacting Flows. AIAA SciTech Forum 2022-1076. 2022. DOI: 10.2514/6.2022-1076.
  9. H. Antil, T.S. Brown, R. Khatri, A. Onwunta, D. Verma, and M. Warma. Optimal Control, Numerics, and Applications of Fractional PDEs. Handbook of Numerical Analysis, Volume 23, 2022, Pages 87-114.DOI: 10.1016/bs.hna.2021.12.003.arXiv:
  10. H. Antil, T.S. Brown, D. Verma, and M. Warma. Optimal Control of Fractional PDEs with State and Control Constraints. Accepted in Pure and Applied Functional Analysis 2021.
  11. T.S Brown, H. Antil, R. Löhner, F. Togashi, and D. Verma. Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows. Accepted in Computational Fluid Dynamics Simulations and Analysis (CFDML) 2021. DOI: https://doi.org/10.1007/978-3-030-90539-2_2. arXiv:
  12. H. Antil, R. Khatri, R. Löhner and D. Verma. Fractional Deep Neural Network via Constrained Optimization. Machine Learning: Science and Technology 2020. DOI: https://doi.org/10.1088/2632-2153/aba8e7. arXiv:
  13. H. Antil, D. Verma, and M. Warma. Optimal Control of Fractional Elliptic PDEs with State Constraints and Characterization of the dual of Fractional Order Sobolev Spaces. J Optim Theory Appl 186, 1–23 (2020). DOI: https://doi.org/10.1007/s10957-020-01684-z. arXiv:
  14. H. Antil, D. Verma, and M. Warma. External Optimal Control of Space-Time Fractional Parabolic PDEs. ESAIM: COCV 26 (2020) 20. DOI: https://doi.org/10.1051/cocv/2020005. arXiv:

Awards & Honours

  1. Dean's Graduate Award for Excellence for 2019-2020, George Mason University.
  2. Presidential Merit Fellowship, George Mason University.
  3. Presidential Scholar Summer 2020 Research Fellowship, George Mason University.
  4. Achievements in Analysis Award, Department of Mathematics, George Mason University.
  5. Institute Silver Medal for academic excellence, M.Sc. Mathematics, Indian Institute of Technology.
  6. 1st rank holder, B.Sc.(H) Mathematics, Shri Guru Teg Bahadur Khalsa College, Delhi University, India.

Curriculum Vitae


Download CV (PDF)

Contact


  • Email: dverma@clemson.edu
  • Office: O-224 Martin Hall
  • Mailing Address:
    School of Mathematical and Statistical Sciences
    Clemson University
    O-224 Martin Hall
    220 Parkway Drive
    Clemson, SC 29634