Ali Rehan AI & Big Data Analytics MSc Candidate in CFD/Active Flow
Resume:
Ali Rehan
*.******@*.***.** +7-903-***-**-** LinkedIn Github Novosibirsk 630090
Education
Novosibirsk State University, Russia, Master’s in Artificial Intelligence and Big Data Analytics (continue)
2024 – May 2026
• CGPA: 4.86/5.0 (till 3rd Semester)
• Thesis: Application of machine learning algorithms for flow control in wakes and jets
• Coursework: Machine Learning, Deep Learning, Data Storage Technology, Medical Imaging Processing, Business Analysis, Quantum Machine Learning, Distributed Computer Systems (DCS) etc. University Of Education, Lahore, Bachelor’s in Mathematics 2019 – 2023
• CGPA: 3.24/4.0
• Coursework: Numerical Analysis, Linear Algebra, Complex Analysis, PDE, ODE, Mathematical Statistics, Graph Theory, Fluid Dynamics, C++, Matlab etc.
Research Experience
Topic: Application of machine learning algorithms for flow control in wakes and jets Research Advisor: Dr. Sergey N. Yakovenko, Head of Laboratory, ITAM SB RAS
[Oct 2024] – Present
• Built and validated CFD cases in OpenFOAM for canonical wake/jet configurations across multiple Reynolds numbers.
• Implemented and optimized deep reinforcement learning for active flow control using DRLinFluids; reproduced key results for 2D cylinder wake control.
• Ran structured experiments varying Reynolds number (75,100,150, & 200) and training budget; analyzed learning stability, drag reduction, and generalization across test cases.
• Prepared the workflow for extension toward more complex regimes (e.g., 3D / higher-Re configurations) and alternative learning strategies.
Internship Experience
Research Intern, Chair of Aerophysics and Gas Dynamics (Physics Dept.) Novosibirsk State University, Russia
Aug 2025 – Dec 2025
• Contributed to AI model development for CFD-oriented flow-control studies (wakes and jets), targeting drag reduction and vortex-shedding suppression.
• Implemented DRL components within DRLinFluids and executed experiments on NSU HPC resources (HP servers / NSU cluster).
• Supported performance evaluation across multiple environments and Reynolds-number settings; helped improve runtime efficiency and parallel scalability.
Publications
• Ali Rehan, S. N. Yakovenko. Application of Machine Learning Algorithms in Flow Control Problems. Accepted; in press. Presented at the 41st Siberian Thermophysical Seminar (STS41), Novosibirsk, Russia, 7–10 Oct 2025.
To be published in Russian in PMiTPh (Siberian Branch of RAS) and subsequently translated into English for Springer (Journal of Applied Mechanics and Technical Physics). Projects
End-to-End MLOps Workflow for Diabetes Prediction
• Designed and deployed an end-to-end MLOps pipeline including data preprocessing, model training (LR, RF, KNN, Gradient Boosting), experiment tracking, and containerized API deployment.
• Tools Used: Python, MLflow, DagsHub, FastAPI, Docker KonturTalk To VK
• Developed an online meeting system for the AI department of our university using KonturTalk, automating meeting recordings and distributing them to a VK Private Group via API for streamlined collaboration.
• Tools Used: Python
OCR-Based Verification System for Handwritten Bibliographic Cards
• Developed a Python script to extract text from handwritten bibliographic card images using OCR, and compared the extracted data with corresponding RUSMARC records, achieving over 80% similarity accuracy to validate record correctness.
• Tools Used: Python, pytesseract
3-D Spleen Segmentation with Deep Learning
• Developed automated spleen segmentation from CT scans using 3D U-Net through MONAI framework. Implemented comprehensive evaluation metrics including Hausdorff Distance for clinical validation.
• Tools used: Python, OpenCV
Predictive Modeling of Teacher Performance Using Clustered Data
• Applied linear regression and K-means clustering on a synthetic dataset to model and analyze the complex, non-linear factors influencing teacher evaluation scores. Identified distinct performance profiles, providing a nuanced understanding beyond traditional regression analysis.
• Tools Used: Python
Mesh Refinement Prediction in Square Ducts Using Graph Neural Networks
• Developed a GNN-based model to predict localized mesh refinement needs in square duct simulations, outputting binary labels (1 = refine, 0 = no refine). Achieved targeted computational efficiency by focusing mesh adaptation only where accuracy gains are critical.
• Tools used: Python, PyTorch Geometric
Conferences
ITS AWESOME 2025 Conference, Novosibirsk State University, Russia Application of Machine Learning Algorithms for Flow Control in Wakes & Jets
(Research Presentation)
20 Dec 2025
41st Siberian Thermophysical Seminar (STS41), Novosibirsk, Russia Application of Machine Learning Algorithms in Flow Control Problems (Poster) 7–10 Oct 2025
Research Areas
Computational Fluid Dynamics (incompressible Navier–Stokes), Data-driven flow control,
Deep Reinforcement Learning for dynamical systems
High-performance computing
Online Courses
The Nuts and Bolts of Machine Learning
Python for Data Science, AI & Development
Introduction to Artificial Intelligence
The Power of Statistics
Introduction to Neural Network
Natural Language Processing (NLP) and Text Mining Tutorial for Beginners Honors and Awards
Scholarship Winner’s Diploma in Computer & Data Science 2024
(Open Doors Russian Scholarship Project Association) Scholarship Prize-Winner’s Diploma in Applied Mathematics & Artificial Intelligence
2024
(Open Doors Russian Scholarship Project Association) GAT-General 2023
(National Testing Services Pakistan)
Certificate of Achievement (The Mathletes) 2023
(University of Education, Lahore)
Ehsaas Undergraduate Scholarship 2020
(Higher Education Commission of Pakistan)
Volunteer Participation
Division President 2022
(Imamia Students Organization, Pakistan)
District Khushab Coordinator 2021
(Professional Guiding Society Pakistan)
Student Brand Ambassador at University of Education, Lahore 2019
(State Bank of Pakistan)
Language Proficiency
British Council English Certificate (CEFR B2), English Proficiency Certificate Technologies
Languages: C++, Python, JavaScript, Matlab
Skills: Machine Learning, Deep Learning, TensorFlow, Computer Vision, NLP, PostgreSQL, Scikit-learn, Model optimization, OpenFOAM, OCR, Fast-API, Git, HPC, Ml-flow, Dags-hub, DRLinFluids, Linux References
Dr. Sergey N. Yakovenko
Professor of Chair of Aerophysics and Gas
Dynamics
Physics Department
Novosibirsk State University, Russia
Dr. Muhammad Naveed
Associate Professor
(Head of Department of Mathematics)
University Of Education, Pakistan
Contact this candidate