Ms. Divyanee Garg | Mathematics | Research Excellence Award

Ms. Divyanee Garg | Mathematics | Research Excellence Award | PhD Scholar | Indian Institute of Technology in Delhi | India

Ms. Divyanee Garg is an emerging researcher in quantitative finance and mathematical optimization, currently pursuing her Ph.D. in Mathematics at IIT Delhi, where she works on portfolio optimization, behavioural finance, robust allocation models, and data-driven decision techniques. Her academic journey reflects exceptional consistency, beginning with a strong foundation in Mathematics through her B.Sc. from S. S. Jain Subodh College, Jaipur, followed by an M.Sc. in Mathematics from IIT Roorkee, and culminating in her doctoral research supported by prestigious recognitions. Ms. Divyanee Garg has demonstrated outstanding academic excellence through multiple national-level achievements, including selection under the Prime Minister’s Research Fellows (PMRF) scheme and securing AIR 119 in CSIR-UGC NET (JRF), AIR 210 in GATE Mathematics, AIR 155 in JAM, and receiving the INSPIRE Scholarship from DST for five consecutive years. Professionally, she has contributed significantly as a teaching assistant in diverse mathematical domains such as Financial Mathematics, Fuzzy Sets, Optimization Methods, Econometrics, and Machine Learning, handling both undergraduate and postgraduate teaching responsibilities at IIT Delhi. Her research interests include portfolio optimization under risk measures like Expectile VaR and CVaR, cumulative prospect theory, robust optimization with neural networks, numerical optimization, and large-scale computational methods. Research skills demonstrated by Ms. Divyanee Garg include expertise in Python, R, MATLAB, LaTeX, MS Excel, and the formulation of optimization models using advanced mathematical programming techniques. She has published impactful research in reputed international journals such as Computational and Applied Mathematics and Omega, with additional manuscripts under revision. Her work has been showcased at major academic platforms, including the International Symposium at ISI Delhi, the International Conference on Computations and Data Science at IIT Roorkee, the Annual Convention of ORSI at IIT Bombay, and the EURO Conference in the UK. She has also engaged in summer schools and workshops related to large-scale optimization, strengthening her methodological foundations and collaborative experience. Her academic distinctions include district-level awards and formal recognition for academic excellence. In conclusion, Ms. Divyanee Garg exemplifies a strong blend of analytical capability, high-quality research output, and dedicated academic service, making her a promising researcher in quantitative finance and optimization. Her continuous contributions through publications, teaching, international presentations, and interdisciplinary problem-solving reflect her commitment to advancing scientific knowledge, while her growing expertise positions her for impactful leadership roles in research, innovation, and academic communities.

Profile: ORCID | Scopus | Google Scholar

Featured Publications 

1. Garg, D., & Mehra, A. (2026). Portfolio optimization with expectile value at risk and conditional value at risk: Deviation measure and robust allocation. Computational and Applied Mathematics.

2. Garg, D., Khan, A. Z., & Mehra, A. (2026). Enhanced indexing using cumulative prospect theory utility function with expectile risk.

3. Garg, D., Sehgal, R., & Mehra, A. (n.d.). Data-driven approach to robust portfolio optimization using deep neural networks. Manuscript under revision.

4. Garg, D., & Swaminathan, A. (n.d.). Numerical improvement of Gauss–Chebyshev quadrature rule. Unpublished research study.

5. Garg, D., & Gupta, S. K. (n.d.). Optimality and duality conditions for semi-infinite programming problems. Project report.

6. Garg, D. (n.d.). Robust allocation models using behaviour-driven portfolio optimization. Working paper.

7. Garg, D. (n.d.). Machine learning-assisted optimisation frameworks for financial decision making. Working paper.