Projects
Research code, coursework and engineering projects. Peer-reviewed work lives on the publications page.
Optimal Transport in Linear ICA
Implementation of OT-ICA, which measures non-Gaussianity by the squared Wasserstein distance to a standard Gaussian rather than by proxy contrast functions. Published at the TPM workshop at UAI 2026; evaluated on simulated data, EEG artifact removal, and econometric price discovery.
Supervisors: Dr. Simon Buchholz, Prof. Dr. Michel Besserve, Prof. Dr. Joachim Grammig
Supervisors: Dr. Simon Buchholz, Prof. Dr. Michel Besserve, Prof. Dr. Joachim Grammig
NICE Toolbox (Computer Vision & DL)
Core contributor to the Nonverbal Interpersonal Communication Exploration (NICE) Toolbox. Implemented Deep Learning models for Pose Estimation, Emotion Detection, and Head Orientation. Designed the asset manager for all algorithms network weights and structured the testing and Docker containerization pipelines.
Structured Product Design, Pricing & Hedging (Financial Engineering)
Designed, priced, and hedged a Bonus Certificate linked to Costco (COST). Used the Bates Model (stochastic volatility + jumps) with a two-stage calibration and Monte Carlo simulation for path-dependent payoff pricing.
Probabilistic Asset Pricing
Developed a Bayesian framework for estimating stock risk premia. Integrated the hybrid model of Grammig et al. (2024) with forward-looking measures from Martin & Wagner (2019) using Kalman Filtering and the EM algorithm.
Supervisor: Prof. Dr. Joachim Grammig
Supervisor: Prof. Dr. Joachim Grammig
A Comparative Study of CBM and UCCAPM
Comparative analysis of Consumption-Based (CBM) and Ultimate Consumption (UCCAPM) models on 25 Fama-French portfolios. Investigated model performance during COVID-19 and the impact of lagged consumption adjustment.
ARMA Process Analysis & Estimation (Time Series Analysis)
Detailed analysis of Conditional Maximum Likelihood (CML) vs Quasi-Maximum Likelihood (QML). Performed simulation studies to test stationarity, efficiency, and robustness of confidence intervals.
TuebiFit
Co-developed VisionLLM based Mobile fitness companion: MediaPipe rep counting and form cues (CLI/video pipeline and in-app RepCount), an LLM agent (Featherless API) with LangChain / LangGraph calling Exercise DB and OpenNutrition MCP servers for workout and meal plans, and a React + Vite SPA. Full stack containerized and deployable to Google Cloud Run.
Ripple Down Rules Simulation (Tree-based Algorithm)
Implemented a Python simulation for Ripple Down Rules (RDR), a tree-based incremental learning approach.
Supervisor: Prof. Ashwin Srinivasan
Supervisor: Prof. Ashwin Srinivasan