Majid Sohrabi

Lecturer,

Junior Research Fellow,

HSE University (click here )

Bio: Majid Sohrabi is a Lecturer and Junior Research Fellow at the Faculty of Computer Science in the School of Data Analysis and Artificial Intelligence and Laboratory for Models and Methods of Computational Pragmatics at HSE University, respectively. He received his M.Sc. in Data Science at the School of Data Analysis and Artificial Intelligence at the National Research University Higher School of Economics, Moscow in 2022. And he received his B.Sc. in Computer Science at the Department of Computer Science at the University of Science and Technology of Mazandaran in 2017.

Research Areas: Machine Learning, Deep Learning, Genetic Algorithm, Medical Data Analysis, LLMs.

Education.

2022 - Present

PhD in Computer Science, National Research University Higher School of Economics

- Thesis: "Speciation in Genetic Algorithm with Interplay Among Species", Supervisor: Prof. Vasilii Gromov

2020 - 2022

MSc in Data Science, National Research University Higher School of Economics

- Term Paper: "Globally Optimal Decision Algorithms for Machine Learning", Supervisor: Prof. Oleg Pianykh

- Thesis: "Physics-Informed Neural Networks for Medical Diagnostics", Supervisor: Prof. Vasilii Gromov

2013 - 2017

BSc in Computer Science, University of Science and Technology of Mazandaran

Research.

Liquid Neural Networks: Medical Applications

Multi-Model Methods for Drug-Drug Interaction Extraction using Textual and Structural Data

Genetic Engineering Algorithm (GEA), a Modified Version of Genetic Algorithm

Selected Publication.

[1] Sohrabi, M., Fathollahi-Fard, A.M., Gromov, V.A. et al. A genetic engineering algorithm for the generalized quadratic assignment problem. Neural Comput & Applic 37, 12253–12279 (2025). https://doi.org/10.1007/s00521-025-11155-z

[2] Sohrabi, M., Fathollahi-Fard, A.M. & Gromov, V.A. Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems. Autom Remote Control 85, 252–262 (2024). https://doi.org/10.1134/S000511792403007X

[3] Gromov, V.A., Zvorykina, E.I., Beschastnov, Y.N., Sohrabi, M. (2024). Date-Driven Approach for Identifying State of Hemodialysis Fistulas: Entropy-Complexity and Formal Concept Analysis. In: Ignatov, D.I., et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2023. Communications in Computer and Information Science, vol 1905. Springer, Cham. https://doi.org/10.1007/978-3-031-67008-4_19