About
I hold a master's degree in computational biology from the University of Stuttgart and am currently seeking research or PhD positions that combine computational biology with machine learning. My academic training has focused on building quantitative and principled models for biological systems, with a strong emphasis on methodological clarity and reproducibility.
My research centers on computational structural biology, particularly generative modeling for protein structure design, structure prediction, and molecular dynamics. I am especially interested in physics-aware and geometry-aware generative models, including energy-based and diffusion-based approaches. My goal is to develop models that respect the underlying physical constraints of biomolecular systems and contribute to a deeper, mechanistic understanding of protein folding and conformational dynamics.
Projects
CppNet: High-Performance Deep Learning
A C++ deep learning library built for performance and transparency. Features efficient tensor operations via Eigen, CPU parallelism with OpenMP, and GPU acceleration through CUDA for training neural networks at scale.
View on GitHub →TorchDiff: Diffusion Models in PyTorch
PyTorch library for building and experimenting with diffusion-based generative models. Implements DDPM, DDIM, score-based SDE models, latent diffusion, and UnCLIP with support for conditional and unconditional generation.
View on GitHub →GRN-Designer: Spatial Gene Networks
Computational framework for automated design and analysis of gene regulatory networks with spatial structure. Uses hybrid evolutionary and gradient-based optimization to generate networks reproducing predefined spatial expression patterns.
Master's Thesis Project →Deep Learning From Scratch
Implementation of core architectures from first principles, including MLPs, CNNs, RNNs with LSTMs, and Transformers. Covers optimization methods and regularization with emphasis on conceptual clarity and mathematical correctness.
View on GitHub →Biostoch: Stochastic Simulation
Python library for simulating biological systems using deterministic and stochastic formulations. Implements ODE solvers and stochastic reaction models to study cellular and biochemical dynamics under intrinsic noise.
View on GitHub →SmartSolve: ML Toolkit
Unified framework for implementing and evaluating classical machine learning models. Provides consistent workflow for preprocessing, training, and evaluation across supervised and unsupervised methods.
View on GitHub →