Fedor Velikonivtsev

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Official repository of our NeurIPS-2024 publication

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Clustering Hi-C contact map using graph neural networks. Utilities and data pipelines. Created as part of Bioinformatics institute spring 2022 project

My algorithms & data structures toolbox implemented in Python while prepairing for Bioinformatics Institute amdission Algo program based on the MIPT Python DSA course and extended by myself.

My hobby repo for automated posting of the solutions for data structures and algorithms related problems from different resources, written in Python/C++

Solve puzzles. Learn CUDA.

Repository containing DMoN from Google research with modified workflow for handling contact map clusterization

A course in reinforcement learning in the wild

Course about deep learning for computer vision and graphics co-developed by YSDA and Skoltech.

Temporary repo for modified for Hi-C GNN clustering by GraphMB

Repo for VAMB for GraphMB

[ICLR2025, ICML2025, NeurIPS2025 Spotlight] Quantized Attention achieves speedup of 2-5x compared to FlashAttention, without losing end-to-end metrics across language, image, and video models.

Materials for the VisualGenAI course at YSDA2026

Anthropic's original performance take-home, now open for you to try!

Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)

This is a collection of tutorials for learning how to use Docker with various tools. Contributions welcome.

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Spatiotemporal forecasting with PyTorch and DGL.

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Efficient Deep Learning Systems course materials (HSE, YSDA)

An annotated implementation of the Transformer paper.

tsl: a PyTorch library for processing spatiotemporal data.

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Homeworks, ML course

Course "Machine learning" in Bioinformatics Institute

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Maria Maria

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This repository contains homework from the Bioinformatics Institute's machine learning course

Repository is for ML homework in Bioinformatics Institute

My homeworks for ML course of Bioinformatics Institute, 2024

MRI-semantic-segmentation

DL course co-developed by YSDA, HSE and Skoltech

Python package built to ease deep learning on graph, on top of existing DL frameworks.

A benchmark for the next generation of data-driven global weather models.

Open Machine Learning course

YSDA course in Natural Language Processing

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Result of the beginning task for students

🐍 🐍 🐍 Repository where I learn FastAPI, Django, python and etc

Shortcuts & pipelines for ML projects. May the Force be with them!

Basic-Operational De-Bruijn graph Builder (BODBuilder) is automatic program building De-Bruijn graph from given set of sequences. Self-pruning builder and errors estimator.

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DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

Leetcode solutions

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HiCBin: binning metagenomic contigs and recovering metagenome-assembled genomes using Hi-C contact maps

Extract metagenome-assembled genomes (MAGs) from metagenomic data using Hi-C.

My CV for any interested

📝 [Tutorial] RepSeq data mining basics in R

Repo for statistics homeworks

Repository for Introduction to Machine Learning course

My bioinformatics

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A helpful 4-page data science cheatsheet to assist with exam reviews, interview prep, and anything in-between.

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

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Official Kaggle API

Open source code for AlphaFold.

Rep-Seq data analysis tutorial

Outdated binary tree package, please switch to sortedcontainers

terminal game to test git skills

Variant calling and somatic mutation/CNV detection for next-generation sequencing data

Keep your application settings in sync (OS X/Linux)

A python port of the glmnet package for fitting generalized linear models via penalized maximum likelihood.

Coursera Machine Learning - Python code

Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.