Experience
Research Intern · Lotfollahi Lab
Self-supervised transcriptomic foundation modeling and diffusion-based single-cell graph generation to improve clustering, trajectory inference, and cross-modal alignment.
- Transcriptomics
- Self-Supervised
- Transformers
Undergraduate Research Intern · SMART Lab
Anomaly-aware diffusion for counterfactual medical imaging, plus diffusion-based reconstruction for undersampled MRI targeting faster scans with robust image quality.
- MRI Reconstruction
- Anomaly Diffusion
- Counterfactuals
Research Intern · AIDAM Group
Latent-space diffusion for aerodynamic shape generation, paired with large-batch neural multi-objective Bayesian optimization to discover diverse 2D airfoils that balance lift–drag, satisfy constraints, and expand the Pareto front.
- Generative Design
- Diffusion Models
- Multi-Objective BO
See recommendation
Navid Ansari
Insight AI Researcher @ Huawei
I supervised Mahan Veisi during his research internship in the Artificial Intelligence Design and Manufacturing (AIDAM) Group at the Max Planck Institute for Informatics. We collaborated on developing a creative generative model to explore innovative high-performing designs within a system.
I appreciated Mahan’s dedication, creativity, problem-solving skills, and strong teamwork throughout the project. He is an excellent fit for machine learning roles, particularly those involving generative modelling. I highly recommend him.
Undergraduate Researcher
Developed a cascaded, frequency-mining transformer U-Net for k-space cardiac MRI reconstruction across multi-contrast scans. Also: FinBERT-PPO-LSTM stock-trader and a SincNet gait model for Parkinson’s detection.
- Med-AI
- Multi-Contrast MRI Reconstruction
Armin Salimi-Badr
Assistant Professor @ SBU
I am writing to strongly recommend Mr. Mahan Veisi, an exceptional student with whom I have worked closely over the past few years. Mahan first demonstrated his academic talents in my undergraduate “Signals and Systems” and “Artificial Intelligence” courses, earning top grades (A+). His ability to engage with complex ideas and propose innovative solutions made him stand out in both courses.
Mahan joined my research group, contributing significantly to our RoboCup IranOpen 2023 team, where we achieved second place in the 2D Soccer Simulation category. His work on developing intelligent agents and optimized strategies was instrumental in securing a spot at the World RoboCup in France in 2023, a major achievement.
He voluntarily enrolled in my graduate “Reinforcement Learning” course, scoring 19.63/20 and delivering the best project of the class, creatively integrating financial market data with RL algorithms. This work led to a co-authored paper presented as an oral at IEEE ICCKE 2024.
Moreover, Mahan worked with me on applying SincNet layers for Parkinson’s Disease detection, achieving superior accuracy to prior benchmarks and improving explainability and efficiency via advanced pruning and clustering. This work is under review in a top-tier journal; the preprint is available on arXiv.
As a Teaching Assistant for my “Signals and Systems” (Spring 2024), he managed assignments and taught/mentored image-processing projects. In “Artificial Intelligence” (Fall 2024), he oversaw assignments and designed engaging projects (minimax, evolutionary algorithms), a board game with an intelligent agent that competed with students’ agents, and the league platform. In Spring 2025, he TA’d my graduate “Deep Reinforcement Learning,” helping design a DRL stock-trading project.
Recently, I examined his B.Sc. thesis, “Improving Cardiovascular MRI Reconstruction via Frequency Mining and Modulation.” It was excellent and well above typical B.Sc. standards.
In conclusion, Mahan possesses the dedication, creativity, and technical depth to thrive in any program he chooses. I offer my highest recommendation and encourage you to consider his application favorably.
Shahabedin Nabavi
Lecturer in Computer Science
It is my pleasure to strongly recommend Mr. Mahan Veisi, one of my undergraduate students, for his exceptional academic performance, research engagement, and outstanding motivation for learning. I have known Mahan since 2022 and have had the opportunity to teach and supervise him in several capacities.
He took my “Operating Systems Lab” course and achieved a perfect score of 20/20. His final project focused on optimizing the read-ahead feature using machine learning models, applying regression techniques to predict optimal read-ahead sizes, which significantly improved disk prefetching efficiency. In my “Introduction to Computer Vision with Deep Learning” course, he went beyond course requirements by initiating and leading a team project to create a unique Rock-Paper-Scissors dataset using YOLOv11. The model achieved the highest accuracy among all projects and was integrated into an interactive video-processing game. He also earned a 20/20 in this course.
In research, Mahan has been an active collaborator on a Cardiac MRI (CMRI) reconstruction project utilizing prompt-in-prompt transformers. The project is now in its final stage, and we are preparing a manuscript for submission to a leading journal. Notably, this project won the Best Bachelor Project Award in our faculty, reflecting both the innovation and technical depth of his work. In addition, Mahan joined our team for the CMRxRecon 2025 Challenge at MICCAI 2025, where our team achieved third place award — a remarkable international accomplishment demonstrating his ability to translate research into high-impact results.
Beyond research, Mahan has also contributed as a Teaching Assistant for my “Artificial Intelligence and Machine Learning” (Spring 2025) course. He has been responsible for managing assignments, conducting tutorial sessions, and assisting students, showing both reliability and strong communication skills.
In summary, Mahan is a talented and motivated student with an impressive combination of technical expertise, research curiosity, and teamwork. I am confident that he will excel in any advanced academic or research environment he joins.
Undergraduate Teaching Assistant (Part-time)
Managed assignments, ran recitations, and mentored projects across core AI/ML coursework.
- Teaching
- RL/ML/AI
Course list
- Reinforcement Learning (Graduate) — Spring 2025, Dr. Armin Salimi-Badr
- Machine Learning — Spring 2025, Dr. Hamed Malek
- Artificial Intelligence — Spring 2025, Dr. Shahabedin Nabavi
- Artificial Intelligence — Fall 2024, Dr. Armin Salimi-Badr
- Machine Learning — Fall 2024, Dr. Hamed Malek
- Compiler Design — Fall 2024, Dr. Mehran Alidoost-Nia
- Compiler Design — Spring 2024, Dr. Mehran Alidoost-Nia
- Signals and Systems — Spring 2024, Dr. Armin Salimi-Badr
- Signals & Systems — Fall 2023, Dr. Yasser Shekofteh
- Advanced Programming — Spring 2023, Dr. Mojtaba Vahidi
- Advanced Programming — Spring 2023, Dr. Sadegh AliAkbari
- Formal Languages & Automata Theory — Spring 2023, Dr. Ramak Ghavamizadeh
- Advanced Programming — Fall 2022, Dr. Mojtaba Vahidi
- Introduction to Programming — Fall 2022, Dr. Sadegh AliAkbari