Pape Ndiaye

Experienced Quantitative Strategist, passionate about innovation and valorization of research as strategic asset for creation of value for Investment Management and Industrial Applications. Hands on and self-starter with entrepreneurial experience. Proficient in building strategic Infrastructure leveraging Quantitative Decision and Data, Financial Engineering, Optimization and Analytics for Portfolio Allocation, Risk, and Algorithmic Trading. Broad interest in investments topics from macroeconomic perspective and Alpha Signal generation to Portfolio Construction and Scenario Analysis to Market microstructure and Execution. Specialist Customized Risk & Allocation Solutions forInvestment Management. - Robust Portfolio Construction process for efficient frontiers combining Fundamental Views and Signals from Market data, whether for enforcing diversification or optimizing Risk and Capital allocation in Cross-Asset Class and Multi-strategy settings;; - Custom Risk and Capital Allocation platforms incorporating information from the Key players of Global Allocation or ERM framework: Risk Managers, Portfolio Managers, Portfolio Strategists and Economists. - Design and implementation of holistic allocation platforms with inputs from market data, Risk Management such as Stress-Test scores, DrawDown-to-Risk Ratios and attributes such as Team Quality or Infrastructure scores. - Reconciliation on views on Risk, Portfolio Risk Aggregation; - Systematic Global Macro and Asymmetric Allocation; - Model Reduction Methods for Large Portfolios and Global Business Risk; - Portfolio Stress-Testing.Experience with Software providers, Consulting, and as Quant Researcher in a Hedge Fund Portfolio Construction Group.Interested in Research and collaborations in the area of focus is Portfolio Theory and Applications in connection with portfolio construction, risk modeling for allocation decision, and alpha signal generation. I am specifically interested in applications of robust optimization, systems modelling and control, and connections with machine learning to leverage data for better quantitative decision and also in decision systems that use a mixture of qualitative and quantitative inputs. I am also interested in application of optimal control to Algorithmic Trading, in particular optimal execution and liquidation.