Chaospy - Toolbox for performing uncertainty quantification.

MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

A model free Monte Carlo approach to price and hedge American options equiped with Heston model, OHMC, and LSM

Providing reproducibility in deep learning frameworks

Riemannian stochastic optimization algorithms: Version 1.0.3

A platform for distributed optimization expriments using OpenMPI

Simple MATLAB toolbox for deep learning network: Version 1.0.3

Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)

Introduction to options pricing theory and advanced numerical methods for pricing both vanilla and exotic options.

Variance reduction in energy estimators accelerates the exponential convergence in deep learning (ICLR'21)

Pricing and Analysis of Financial Derivative by Credit Suisse using Monte Carlo, Geometric Brownian Motion, Heston Model, CIR model, estimating greeks such as delta, gamma etc, Local volatility model incorporated with variance reduction.(For MH4518 Project)

Framework to model two stage stochastic unit commitment optimization problems.

Monte Carlo used for the seminar Monte Carlo Methods in Econometrics and Finance at the university of Copenhagen

Chance-constrained control and pricing for natural gas networks using Julia/JuMP.

[AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution

In this paper, we propose Filter Gradient Decent (FGD), an efficient stochastic optimization algorithm that makes a consistent estimation of the local gradient by solving an adaptive filtering problem with different designs of filters.

This project focuses on applying advanced simulation methods for derivatives pricing. It includes Monte-Carlo, Variance Reduction Techniques, Distribution Sampling Methods, Euler Schemes, and Milstein Schemes.

Stochastic Simulation and Statistics in Tidyverse

An R Library published on CRAN for variance reduction algorithms.

Code the ICML 2024 paper: "Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models"