Divyanee Garg | Mathematics | Research Excellence Award

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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.

Yuanheng Wang | Mathematics | Outstanding Scientist Award

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Prof. Yuanheng Wang | Mathematics | Outstanding Scientist Award

Prof. Yuanheng Wang | Mathematics | Outstanding Scientist Award | Professor | Zhejiang Normal University | China

Prof. Yuanheng Wang is a distinguished Chinese mathematician and professor at the School of Mathematical Sciences, Zhejiang Normal University (formerly Zhejiang where he has combined teaching, research, and academic administration for many years. He earned his doctoral degree in Mathematics from Shanghai Normal University, having earlier studied functional analysis and Banach space theory, and also spent time as a visiting scholar at the University of New South Wales in Australia. Over his long and productive career, he has taught a broad range of undergraduate and graduate courses — including real analysis, functional analysis, variational inequality theory, and the geometry of Banach spaces — and has supervised many master’s and doctoral students. As a researcher, Prof. Wang’s principal interests lie in nonlinear functional analysis, fixed-point theory, Banach space geometry, variational inequalities, and non-linear optimization, where he has developed and analyzed iterative methods, projection algorithms, and convergence theorems. His research skills include deep theoretical analysis, convergence proof techniques, hybrid iterative schemes, and the design of projection-based algorithms. Throughout his career, he has held leadership and service roles: he has served as a reviewer for Mathematical Reviews (USA) and contributed peer review for dozens of international journals, acted as vice-dean of the mathematics/information faculty at his university, and participated in minority-staff associations. He has received several honors, including the “First Ten Teachers in Students’ Minds” award at Zhejiang Normal University, the Zheng Xiaocang Prize, and membership in the 10th Zhejiang Provincial Committee of the Chinese People’s Political Consultative Conference. He has successfully led and completed multiple research grants at national and provincial levels, and his more than seventy published papers include over thirty indexed by SCI and Mathematical Reviews. Prof. Wang is also active in community engagement, mentoring students and promoting participation of ethnic minority faculty in academic life. In sum, Prof. Yuanheng Wang represents a rare blend of scholarly excellence, pedagogical dedication, and institutional leadership, deeply committed to advancing mathematical theory and nurturing future generations of researchers.

Profile:  ORCID 

Featured Publications

  1. Wang, Y. (2012). Strong Convergence Theorems for Asymptotically Weak G-Pseudo-Ψ-Contractive Non-Self-Mappings with the Generalized Projection in Banach Spaces. Abstract and Applied Analysis, 2012, Article ID 651304.

  2. Wang, Y. (2014). Strong Convergence Theorems for Common Fixed Points of an Infinite Family of Asymptotically Nonexpansive Mappings. Abstract and Applied Analysis, 2014, Article ID 809528.

  3. Wang, Y. (2016). Complement Theorem for Solutions of Two Congruence Systems .2016.

  4. Wang, Y. (2016). Convergence of Mann Iteration for Solutions of a Strongly Proliferating Operator Equation. 2016.

  5. Wang, Y. (2017). Approximation by an Iterative Algorithm for Generalized Equilibrium and Fixed Points of Asymptotically Nonexpansive Mappings].

  6. Wang, Y. (2017).Irrationality of the Square Root and a Rational Iteration Algorithm. 2017.

  7. Wang, Y. (2016). Convergence of a Layered Fixed-Point Viscous Continuous Generalized Approximation Scheme in a Banach Space.2016.

 

Edwiga Renald | Mathematics | Best Researcher Award

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Dr. Edwiga Renald | Mathematics | Best Researcher Award

Lecturer and researcher | Nelson Mandela African Institution of Science and Technology | Tanzania

Dr. Edwiga Renald is a distinguished Tanzanian researcher and academic currently serving as a Lecturer and Researcher at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. With an exceptional academic background in Applied Mathematics and Computational Science, Dr. Renald has established herself as a leading figure in the integration of mathematical modelling and artificial intelligence within epidemiological research. She earned her Ph.D. in Mathematical and Computer Science and Engineering (Applied Mathematics and Computational Science) from NM-AIST, where her doctoral research, “Control of Lumpy Skin Disease of Cattle: A Mathematical Modelling Approach Coupled with Deep Learning,” combined deep learning frameworks with mathematical models to enhance disease control and prediction accuracy. She previously obtained both her MSc in the same field and a BSc in Mathematics and Statistics from Mwenge Catholic University, where she also began her academic career as a Tutorial Assistant before advancing to Assistant Lecturer and later joining NM-AIST. Dr. Renald’s professional experience spans teaching, supervising postgraduate research, and conducting high-impact studies addressing real-world epidemiological and computational challenges. Her research interests include mathematical modelling of infectious diseases, deep learning applications in epidemiology, Bayesian statistics, data assimilation, and computational biology. She has been actively involved in international research programs, including the WOLIMODs Research Program at Lappeenranta University of Technology in Finland and the TWAS-SISSA Lincei Fellowship at the Scuola Internazionale Superiore di Studi Avanzati (SISSA) in Italy, where she gained expertise in Bayesian parameter estimation and Kalman filtering techniques. Dr. Renald’s scholarly output includes multiple Scopus-indexed publications in high-impact journals such as Computer Methods and Programs in Biomedicine Update and Modeling Earth Systems and Environment. Her work demonstrates a deep commitment to improving computational epidemiology and promoting evidence-based policy decisions for disease control in Africa. She is also an active member of several professional organizations, including the African Society for Biomathematics and Tanzania Women in Mathematics, advocating for gender equity in STEM fields. In recognition of her excellence, Dr. Renald received the Professor Verdiana Grace Masanja Award for the best female graduand in Applied Mathematics and Computational Science at NM-AIST. Her research skills encompass MATLAB, Python, Java, LaTeX, and GUI development, reflecting strong computational and analytical proficiency. Driven by academic excellence, mentorship, and innovation, Dr. Edwiga Renald continues to advance interdisciplinary research at the intersection of mathematics, data science, and health, inspiring a new generation of researchers in Africa and beyond.

Profile: Scopus | ORCID | Google Scholar

Featured Publications

  1. Renald, E., Amadi, M., Haario, H., Buza, J., Tchuenche, J. M., & Masanja, V. G. (2025). A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model. Computer Methods and Programs in Biomedicine Update.

  2. Renald, E., Tchuenche, J. M., Buza, J., & Masanja, V. G. (2025). Extinction and persistence of lumpy skin disease: A deep learning framework for parameter estimation and model simulation. Modeling Earth Systems and Environment.

  3. Renald, E., Masanja, V. G., Tchuenche, J. M., & Buza, J. (2024). A deterministic mathematical model with non-linear least squares method for investigating the transmission dynamics of lumpy skin disease. Healthcare Analytics, 5, 100343.

  4. Renald, E., Buza, J., Tchuenche, J. M., & Masanja, V. G. (2023). The role of modeling in the epidemiology and control of lumpy skin disease: A systematic review. Bulletin of the National Research Centre, 47(1), 141.

  5. Renald, E. K., Kuznetsov, D., & Kreppel, K. (2020). Desirable dog-rabies control methods in an urban setting in Africa – A mathematical model. International Journal of Mathematical Sciences and Computing, 6(1), 38–45.

  6. Renald, E., Kuznetsov, D., & Kreppel, K. (2019). Sensitivity analysis and numerical simulation of a SEIV basic dog-rabies mathematical model with control. International Journal of Advanced Scientific Research and Engineering, 5(4), 27–34.

  7. Renald, E. (2025). A comparative analysis of data-driven modeling techniques for epidemiological systems using AI-assisted frameworks. Under Review in Applied Computational Mathematics.