Multi-sector specialist building mathematical & computational systems at scale in finance & technology with billion-dollar impact teams (C-suite driven initiatives with multimodal data). Scientific & engineering advisory, consulting & contracting. Previous CTO & Founder roles. Independent and proprietary software systems (incubated at Harvard, MIT, Oxford). Pro-bono volunteering, teaching & mentoring experience.
Legal Disclaimer: The content and work presented in this portfolio are intended solely for academic and demonstrative purposes. The opinions and ideas expressed herein are exclusively those of Philip Ndikum and do not represent the views or opinions of any affiliated institutions or employers, whether past, present, or future. Unless explicitly stated otherwise, no portion of this portfolio includes open-source software or implies a license for its use. All software associated with the research papers and projects presented is proprietary and remains unavailable for public use.
Academic and industrial career spanning mathematical and computational sciences, with a specialized focus on mathematical finance and mathematical physics (ODEs, PDEs, SDEs), numerical analysis, and stochastic simulations. Leading & supporting the development of end-to-end systems in both academic and industrial greenfield projects, employing Python and C++ supplemented by Julia for stochastic simulations and MATLAB for numerical linear algebra. Education is complemented by time series analysis and cross-disciplinary projects, integrating core business school modules. Extensive teaching experience at the graduate level at institutions such as Harvard (Mathematical Physics) and Oxford (Finance & MBA), combined with roles as a lead instructor in AI & Data Science software bootcamps. Other: Pro-bono volunteering for charities & mentoring, recreational powerlifting, former student-athlete (Rugby).
| Degree | Awarding Institution | Academic Modules |
|---|---|---|
| Master of Engineering in Computational Science & Engineering (AI Research at Harvard & MIT) | Harvard University | AI Research at Harvard & MIT. One of ten graduates from MEng Program. Stochastic Optimization, Probability Theory, Machine Learning & Data Science (multi-modal data including LLMs and Computer Vision), Reinforcement Learning (Robotics & Classical Control Theory). Additional cross-registration at HBS (Finance) & HKS (Energy). Tech: Python, C++, Julia. |
| Postgraduate Degree, Financial Strategy (Finance with AI Research) | University of Oxford | Traditional Accounting & Finance, Corporate Valuation. Strategy & Management with concurrent research at the University of Cambridge, and Oxford Machine Learning summer school. Tech: Python, Julia. |
| Master of Science (MSc), Applied Mathematics (with AI Research). | University of Manchester1 | Numerical Linear Algebra & Optimization with world-renown leaders including Professor Nicholas Higham (former President of the US SIAM) with applications to Mathematical Finance & Classical Computer Vision. Tech: Python, Julia, C++, R, MATLAB. |
| Bachelor of Science (BSc), Mathematics & Computer Science (Dual Honours) | University of Manchester1 | Courses: Pure Mathematics, Linear Algebra, Stochastic Calculus, Applied Mathematics modules applied to Physics, Finance & Engineering. Tech: Python, Java, MATLAB, R. |
This section exclusively features publicly disclosed academic works and coursework. It is important to clarify that while the academic content and coursework are openly presented herein, all underlying software artifacts associated with the independent research projects remain proprietary and are the exclusive intellectual property of Philip Ndikum. The coursework listed is purely academic, completed as part of formal education programs, and does not include any proprietary software. The independent research projects, though publicly disclosed, incorporate proprietary methodologies and software, which are not available for public use. None of the work presented is affiliated with any past or present employers, institutions, or contracting agencies.
| Type | Project | Details | Link |
|---|---|---|---|
| Research Paper | 2024 Deep Reinforcement Learning Master Thesis (Harvard & MIT) | Master's Thesis co-supervised at Harvard & MIT exploring DRL in Finance from a robotics and signal processing perspective. | N/A |
| Research Paper | 2024 Deep Reinforcement Learning Portfolio Optimization | Deep Reinforcement Learning applied to portfolio optimization, focusing on Recommender Systems and Tech Perspective with discussions on regulation and ethics. | View PDF |
| Research Paper | 2020 AI for Asset Price Forecasting | A study on AI techniques for asset price forecasting, asset-class agnostic, including discussions on regulation. | View PDF |
| Coursework | Harvard ENGSCI 201 - Decision Theory | Audited course on decision theory from signal processing and neuroscience perspectives. | View PDF |
| Coursework | MIT RL 6.7920 - Advanced Reinforcement Learning Submission 3 | MIT graduate-level course covering classical optimization and control theory leading to a DRL research paper. | View PDF |
| Coursework | MIT RL 6.7920 - Advanced Reinforcement Learning Submission 4 | MIT graduate-level course covering classical optimization and control theory leading to a DRL research paper. | View PDF |