Research & Students

My current research projects are on: 

1. Statistical Modeling of Large-Dimensional Stochastic Processes with Heavy-Tail Distributions

2. Compressed Sensing and Sparse Signal Processing with Breakpoints

3. Applications of Reproducing Kernel Hilbert Spaces and their extensions in High-Frequency Finance

4. Image Analysis, Face Recognition and (Hidden) Emotions Detection using Spatial-Temporal Data and 3D-Convolutional Neural Networks

5. Dynamic Geometric Deep Learning on Face Manifolds

6. Analysis of the Dynamics of High Frequency Price Data and the Limit Order Book

7. Machine Learning and Deep Learning in Asset Pricing and Portfolio Optimization

8. Reinforcement Learning in Quantitative Finance

9. Multivariate Time Series Models

Compressed Sensing and Sparse Signal Processing with Breakpoints

Dynamic Geometric Deep Learning on Face Manifolds with Applications in Medicine and Deepfake Videos Detection

Machine Learning and Deep Learning in Asset Pricing and Portfolio Optimization

New Classification and Severity Progression Predictors of COVID-19 Patients Using Generalized Additive Models

Past MS and PhD students  and projects which they did with me:

• El Mehdi Ainasse (https://www.linkedin.com/in/el-mehdi-ainasse/)

• Joris van der Aa (https://www.linkedin.com/in/joris-van-der-aa-6a3a0221/), master thesis in Quant-Finance program at ETH in Zurich - Application of Non-Gaussian, Non-Elliptic GARCH Modeling to Large-Dimensional, High-Frequency Financial Assets

• Simon Hediger (https://www.bf.uzh.ch/de/persons/hediger-simon/publications  University of Zurich, Ph.D. student in the Department of Banking and Finance) 

• Manuel Kannenberg (https://www.linkedin.com/in/manuelkannenberg/), master thesis in Quant-Finance program at ETH in Zurich - Machine Learning Based Views in a Generalized Black-Litterman Framework

• Jan Krepl (https://www.linkedin.com/in/jankrepl/), master thesis in Quant-Finance program at ETH in Zurich - Supervised Learning for Financial Market Predictions

• Jeff Naef (http://jeffreynaef.crimsonsphere.ch/), master thesis in Quant-Finance program at ETH in Zurich - Tail Heterogeneous Distributions in Portfolio Optimization;  Joint paper: Naef, J., Paolella, M. S., and Polak, P. (2019). Heterogeneous Tail Generalized COMFORT Modeling via Cholesky Decomposition. Journal of Multivariate Analysis. https://doi.org/10.1016/j.jmva.2019.02.004 

• Qilin Peng (https://www.linkedin.com/in/qilinpeng/) - Summer Research Project at Columbia University - Portfolio of Pairs - Pairs Trading and Portfolio Optimization

• Teng Chen  (Stony Brook University, Ph.D. Student in the Department of Applied Mathematics and Statistics) Chen, T., Polak, P., Uryasev S. (2022). Classification and severity progression measure of COVID-19 patients using pairs of multi-omic factors. Journal of Applied Statistics (https://www.tandfonline.com/doi/full/10.1080/02664763.2022.2064975).

• Urban Ulrych  (https://www.linkedin.com/in/urban-ulrych-272387141/ PostDoc. EPFL Lausanne)

• Zexing Xu (https://www.linkedin.com/in/zexing-xu-76a9a78b/) - Summer Research Project at Columbia University - Pockets of Predictability in Fama-French Five Factor Model

• Jingwen Yin (https://www.linkedin.com/in/jingwenyin/) - Summer Research Project at Columbia University - Improved Global Portfolio and Flight to Safety Effects

• Jiaqian Yu (https://www.linkedin.com/in/jiaqianyu/) - Summer Research Project at Columbia University - High Frequency Volatility Estimation for Portfolio Optimization

• Ying Zhang, master thesis in Applied Mathematics EPFL Lausanne - Likelihood Based Hierarchical Clustering and its Application on Dissecting Financial Market