Sidney Bonke Wafula

Dedicated and detail-oriented Mathematician with a strong analytical background and extensive experience in quantitative analysis, data modeling, and statistical research. Passionate about leveraging mathematical principles to solve real-world problems and optimize decision-making processes. Proven ability to apply mathematical theories in finance, technology, and research fields.

• Certified Data Analyst (CDA)
• Machine Learning Certification (Coursera/Stanford)
• Financial Risk Management (FRM) – Level 1

• American Mathematical Society (AMS)
• Society for Industrial and Applied Mathematics (SIAM)
• Association for Women in Mathematics (AWM)

• Robust Convex Optimization, A. Ben-Tal, A. Nemirovski, Mathematics of Operations Research, 1998, This paper explores robust approaches to convex optimization problems, providing solutions resilient to data uncertainty.
• Optimization of Algorithmic Trading Strategies Using Machine Learning, SIAM Journal on Optimization, 2020, This research integrates machine learning techniques with traditional optimization methods to enhance the performance of algorithmic trading systems.
• Applied Cryptography for Data Security in National Defense Systems, ACM Conference on Computer and Communications Security (CCS), 2021, The paper discusses the implementation of advanced cryptographic protocols to secure sensitive defense communications.
• Enhanced Security of Symmetric Encryption Using Combination of Steganography with Visual Cryptography, S.H. Murad, A.M. Gody, T.M. Barakat, arXiv, 2019, This study proposes a novel method of combining steganography and visual cryptography to bolster the security of symmetric encryption schemes.
• Research Trends in Combinatorial Optimization, J.M. Weinand, K. Sörensen, P. San Segundo, M. Kleinebrahm, R. McKenna, arXiv, 2020, This bibliometric analysis examines the evolution and current trends in combinatorial optimization research, highlighting key areas of focus and development.