This page is my interactive CV.

Education

[2022 - 2024] Master of Science in Advanced Mathematics @ SPbU Thesis: Efficient neural architectures for NP-hard problems and recommender systems [2018 - 2022] Bachelor of Science in Math and Computer Science @ SPbU Thesis: A strengthening of the concurrent normals theorem for polytopes

Work Experience

Deep Learning Researcher @ HUAWEI

• [2024 - ongoing] LLM inference acceleration
• [2024 - ongoing] LLM interpretability and uncertainty quantification

Machine Learning Researcher @ PDMI AI lab, led by S. Nikolenko

• [2023 - 2024] Research DL-based simulators for recommendation systems @ PDMI
• [2022 - 2023] Research DL-based heuristics for SAT-solving @ HUAWEI x PDMI

Math Optimization Specialist

• [2023 - 2024] MILP solvers for logistic optimization @ Zyfra

CNC Programmer | Entrepreneurship

• [2019 - 2022] Modernization and repair of CNC machines @ Izhevsk. Developing field-level link modules and their drivers, minor repairs and CNC integration

Teaching

• [2023 - 2025] Teaching assistant on Machine Learning @ CS HSE. Awarded Best Teacher 2024
• [2023 - 2024] Teaching assistant on Deep Learning @ Math & CS SPbU
• [2018 - 2020] Grading regional competitions in Math, teaching and preparing school students for math competitions

Publications

• Neural Click Models for Recommender Systems M. Shirokikh, I. Shenbin, A. Alekseev, A. Volodkevich, A. Vasilev, A. V. Savchenko, S. Nikolenko, ACM SIGIR, 2024.
• Sparse Prefix Caching for Hybrid and Recurrent LLM Serving M. Shirokikh, S. Nikolenko, arXiv preprint, 2026.
• Nabla2-DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials K. Khrabrov, A. Ber, A. Tsypin, K. Ushenin, E. Rumiantsev, A. Telepov, D. Protasov, I. Shenbin, A. Alekseev, M. Shirokikh, S. Nikolenko, E. Tutubalina, A. Kadurin, NeurIPS, 2024.
• User Response Modelling In Recommender Systems: A Survey M. Shirokikh, I. Shenbin, A. Alekseev, A. Volodkevich, A. Vasilev, S. I. Nikolenko, accepted by the Journal of Mathematical Sciences, Springer.
• Machine Learning for SAT: Restricted Heuristics and New Graph Representations M. Shirokikh, I. Shenbin, A. Alekseev, S. Nikolenko, arXiv preprint, 2023.