Loghman Samani

Loghman Samani

Computational Biologist

Diffusion Models • Molecular Dynamics • Machine Learning

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.

Computational Structural Biology Molecular Dynamics Generative AI Protein Modeling Statistical Physics

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.

C++ • Eigen • OpenMP • 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.

Python • PyTorch • Diffusion Models

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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.

Python • Optimization • PDEs

Master’s Thesis Project

Deep 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.

Python • Neural Networks • Optimization

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Biostoch: 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.

Python • ODEs • Stochastic Simulation

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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.

Python • Machine Learning • Model Evaluation

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Writing

Latent Diffusion Models and Their Implementation in TorchDiff
Overview of latent diffusion models and their practical implementation in PyTorch, with a focus on efficiency, scalability, and conditional generation using compressed latent spaces
Medium • 11 min read • Read on Medium →
Score-Based Generative Modeling with SDEs: Theory and Implementation in TorchDiff
Detailed exposition of score-based generative models defined via stochastic differential equations, covering VE, VP, and sub-VP formulations with practical PyTorch implementations
Medium • 17 min read • Read on Medium →
TorchDiff: A PyTorch Library for Denoising Diffusion Probabilistic Models and Beyond
Introduction to the theoretical foundations of diffusion models with practical PyTorch implementations, covering DDPM, DDIM, score-based SDE models, and latent diffusion
Medium • 17 min read • Read on Medium →
From Biology to Computation: Exploring Genetic Algorithms
Step-by-step exploration of genetic algorithms, connecting principles of biological evolution to Python-based implementations for solving optimization problems
Medium • 18 min read • Read on Medium →
Adam Optimization Algorithm: Improving MLP Performance
Detailed explanation of the Adam optimizer and its application in training multiclass MLPs, highlighting techniques for faster convergence and robust performance
Medium • 21 min read • Read on Medium →
From Theory to Practice: Building a Deep Feedforward Neural Network with Back Propagation in Python
Step-by-step guide to constructing and training a deep feedforward neural network using backpropagation, with Python implementations for binary classification
Medium • 13 min read • Read on Medium →
Introducing BioStoch: A Python Library for Simulating Biological Systems
Overview of BioStoch, a Python library for deterministic and stochastic simulation of biological systems, including Euler, Runge-Kutta, SSA, Tau-Leaping, and Chemical Langevin methods
Medium • 13 min read • Read on Medium →
Highly Accurate Protein Structure Prediction with AlphaFold
Detailed explanation of AlphaFold, a machine learning system for predicting protein structures from amino acid sequences with atomic accuracy
Medium • 13 min read • Read on Medium →
Introduction to Nuclear Magnetic Resonance (NMR)
Overview of NMR spectroscopy and its applications in structural biology, including protein structure, dynamics, interactions, and intrinsically disordered proteins
Medium • 20 min read • Read on Medium →