FACULTY PROFILE

Dr Saurabh Bansal earned his PhD in Mathematics from IIT Guwahati, where his thesis received the Best Thesis Award. He completed his MSc at IIT Bombay and his BSc at the Central University of Rajasthan. He has also qualified GATE (AIR 91), CSIR-JRF (AIR 20), and IIT-JAM (AIR 27), and is an INSPIRE Scholar.

Before joining RGIPT, he was a postdoctoral fellow at the Indian Institute of Science (IISc), Bengaluru, and earlier served as an Assistant Professor at UPES, Dehradun.

His research lies at the intersection of computational finance, scientific machine learning, and numerical methods. He has published 11 research papers in reputed international journals and conference proceedings.

Education
Ph D 2020-2025 Indian Institute of Technology Guwahati
MSc 2018-2020 Indian Institute of Technology Bombay
BSc 2015-2018 Central University of Rajasthan 
Experience
August 2026 – till date Assistant Professor, Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi
January 2026 - July 2026 Postdoctoral Fellow, Indian Institute of Science (IISc), Bengaluru
August 2025 - January 2026 Assistant Professor, UPES, Dehradun

 

Odd Semester 2026–27

MA111 - Real Analysis and Calculus
B.Tech. 1st Year, Section C 
Lectures: Monday 14:00–14:55, Tuesday 10:00–10:55, Thursday 10:00–10:55

MA123 - Applied Mathematics I
B.Tech. 1st Year, Section D (Shared course with Dr. Sudeep Kundu)
Lectures: Tuesday 11:00–11:55, Wednesday 11:00–11:55, Thursday 12:00–12:55

Research Interest
  • Computational and Mathematical Finance
  • Numerical Analysis and Differential Equations
  • Numerical Methods for PDEs and Partial Integro-Differential Equations
  • Physics-Informed Neural Networks and Scientific Machine Learning
  • Option Pricing, Calibration and Inverse Problems
  • Data-Driven Modeling


Published Articles

1. S. Badireddi, S. Bansal, and S. Natesan, “Numerical solution of passport option pricing problem with polynomial neural networks,” Computational Economics, vol. 66, pp. 4695–4726, 2025.

2. S. Bansal, P. Boro, and S. Natesan, “Application of physics informed neural networks to partial integro-differential equations in financial modeling and decision making,” Applied Soft Computing, p. 114208, 2025.

3. S. Bansal, P. Boro, and S. Natesan, “Physics-informed neural network for option pricing weather derivatives model,” Computers & Mathematics with Applications, vol. 200, pp. 1–21, 2025.

4. S. Bansal and S. Natesan, “A novel higher-order efficient computational method for pricing European and Asian options,” Numerical Algorithms, vol. 99, pp. 1127–1159, 2025.

5. S. Bansal and S. Natesan, “A robust and effective numerical technique for solving Black–Scholes PDEs,” Current Progress in Interdisciplinary Research, vol. 3, pp. 361–376, 2025.

6. S. Bansal and S. Natesan, “A stabilized finite element method for solving Black–Scholes PDEs with applications to lookback options,” Indian Journal of Pure and Applied Mathematics, pp. 1–19, 2025.

7. S. Bansal and S. Natesan, “An accurate and stable numerical method for pricing Asian options,” Methodology and Computing in Applied Probability, vol. 27, no. 2, p. 50, 2025.

8. S. Bansal and S. Natesan, “An efficient and robust computational approach to passport option pricing PDEs,” Decisions in Economics and Finance, vol. 48, pp. 1931–1956, 2025.

9. S. Bansal and S. Natesan, “An efficient robust computational method for solving Black–Scholes PDEs,” Mathematical Communications, vol. 30, no. 2, pp. 191–205, 2025.

10. S. Bansal and S. Natesan, “An efficient fourth-order numerical scheme for nonlinear multi-asset option pricing problems,” Mediterranean Journal of Mathematics, vol. 21, no. 7, p. 194, 2024.

11. S. Bansal and S. Natesan, “Richardson extrapolation technique for generalized Black–Scholes PDEs for European options,” Computational and Applied Mathematics, vol. 42, no. 5, p. 238, 2023.

 

Articles Under Review

1. S. Bansal and S. Natesan, “Physics-informed neural networks for accurate pricing of American options under jump-diffusion models,” under review, 2025.

2. S. Bansal and S. Natesan, “Physics-informed neural networks: A new frontier in option pricing,” under review, 2025.

  • Best Thesis Award, Department of Mathematics, IIT Guwahati, 2026

  • GATE – All India Rank 91 in Mathematics, 2020

  • CSIR-JRF – All India Rank 20 in Mathematical Sciences, 2019

  • IIT-JAM – All India Rank 27 in Mathematics, 2018

  • University Topper (2015 Batch), B.Sc., Central University of Rajasthan

  • INSPIRE Scholar, top 1% of students, Rajasthan Senior Secondary Board, 2015 – 2020

Opportunities for Research Students and Interns

I am building a research group at RGIPT and welcome enquiries from students interested in numerical methods for differential equations, computational finance, and scientific machine learning.

How to Apply

Please write to me at sbansal@rgipt.ac.in (or saurabhbansal039@gmail.com) with a short statement of your research interests and your CV.

Dr Saurabh Bansal

Assistant Professor
Mathematics
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