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.
Research Interests
Protein Structure & Design
Generative models for protein structure prediction, design, and conformational sampling.
Molecular Dynamics
Physics-informed approaches to simulating and learning biomolecular dynamics at scale.
Diffusion & Score-Based Models
Theory and application of diffusion processes and score matching to generative modeling in biology.
Statistical Physics & ML
Energy-based models, Boltzmann distributions, and the intersection of statistical mechanics with deep learning.
Gene Regulatory Networks
Computational design of spatial gene networks using hybrid optimization methods.
Scientific Computing
High-performance implementations for numerical simulation and deep learning in C++ and Python.
Education
University of Stuttgart, Germany
University of Kurdistan, Iran
Projects
CppNet: High-Performance Deep Learning
C++ · Eigen · OpenMP · CUDAA 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.
GitHub →TorchDiff: Diffusion Models in PyTorch
Python · PyTorchPyTorch 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.
GitHub →GRN-Designer: Spatial Gene Regulatory Networks
Python · Optimization · PDEsComputational 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 →Deep Learning From Scratch
Python · NumPyImplementation 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.
GitHub →BioStoch: Stochastic Simulation Library
Python · ODEs · SSAPython 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.
GitHub →SmartSolve: Classical ML Toolkit
Python · scikit-learnUnified framework for implementing and evaluating classical machine learning models. Provides consistent workflow for preprocessing, training, and evaluation across supervised and unsupervised methods.
GitHub →