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 in C++
CppNet is a C++ deep learning library for building and training neural networks with a focus on performance and transparency. It supports efficient tensor operations via Eigen, CPU parallelism with OpenMP, and GPU acceleration using CUDA.
View on GitHub →TorchDiff: Diffusion Models in PyTorch
A 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 both conditional and unconditional generation.
View on GitHub →GRN-Designer: Spatial Gene Regulatory Networks
Computational framework for automated design and analysis of gene regulatory networks with spatial structure. Uses hybrid evolutionary and gradient-based optimization to generate GRNs that reproduce predefined spatial expression patterns.
Master’s Thesis ProjectDeep Learning Models From Scratch
Implementation of core deep learning architectures from first principles, including MLPs, CNNs, RNNs with LSTMs, and Transformers. Covers optimization methods and regularization techniques with a focus on conceptual clarity and correctness.
Coursework and ImplementationsBiostoch: Stochastic Biological 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: Classical Machine Learning Toolkit
Python library for implementing and evaluating classical machine learning models. Provides a unified workflow for preprocessing, training, and evaluation across a range of supervised and unsupervised methods.
View on GitHub →