About Me
I am a PhD candidate, expected to graduate in early 2026, with experience and interest in artificial intelligence for robotics and autonomous vehicles. I have hands-on experience with multi-agent reinforcement learning, foundation models, and world models for learning and verification, especially in complex and uncertain environments. My research focuses on developing AI systems for autonomous vehicles in mixed-autonomy traffic and safety and efficiency verification of automated vehicles in simulation.
News
- Apr. 1, 2026Gave a talk at Waymo UK about NOMAD.
- Mar. 18, 2026New preprint: DiscoGen, a procedural generator of algorithm discovery tasks for machine learning. arXiv.
- Mar. 15, 2026Made the code of NOMAD public. Check it out on GitHub.
- Mar. 10, 2026Gave a talk at Motional about NOMAD.
- Feb. 06, 2026Gave a talk at EMERGE lab about NOMAD.
- Oct. 2025Started as a Research Intern at Toyota Motors Europe, Belgium, working on VLMs and Transformers for anomaly detection in autonomous driving.
- Aug. 2025Joined Oxford University as an Exchange PhD student, working with Prof. Shimon Whiteson and Prof. Jakob Foerster on multi-agent reinforcement learning.
Experience
Oxford University, UK- Developed a Self-play MARL framework for improving the performance of agents transferred from one environment to another, achieving 80-120% success rate improvement
- Implemented behavior cloning (BC) policies in complex and uncertain environments with more than 60 agents in each scenario
Toyota Motors Europe, Belgium- Training transformer-based vision models for predicting abnormal scenarios from dashcam videos
- Conducting a comparative analysis of rule-based vs. AI-based anomaly detection methods in autonomous systems
EU Horizon Project Hi-Drive, TU Delft, Netherlands- Contributed to the development of Hi-Drive automated driving function (ADF), a simulation model for longitudinal and lateral control of sim agents in both motorways and urban areas
- Led the working group on simulation parameterization for virtual verification of AVs; calibrated 30+ parameters
- Developed methods to extract edge cases and safety-critical scenarios from Hi-Drive, Waymo, and Lyft datasets
- Collaborated with a team of 50+ people from 30+ car manufacturers and institutions in Europe; 7 deliverables
Delft University of Technology, Netherlands- Developed an award-winning real-time MPC-based simulation framework for autonomous vehicles (IEEE ITSC 2023 Award Winner)
- Leveraged Foundation Models (VLMs) for critical trajectory failure detection with 90%+ accuracy on zero-shot learning
- Developed a hybrid MPC-RL framework for motion planning with 50% higher efficiency
- Co-designed simAgent algorithms for the EU Horizon 2020 flagship Project Hi-Drive
Publications
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Google Scholar profile
Projects
NOMAD: Driving in New Cities Without Demos
RL and imitation learning framework for training autonomous driving agents that generalize to new cities without human demonstrations.
DiscoGen: Algorithm Discovery Benchmark
Procedural generator of algorithm discovery tasks across ML domains including RL, vision, language modeling, and trajectory prediction.
Heterogeneous Multi-Agent Driving
VAE-based heterogeneous agent modeling in PufferDrive for realistic multi-agent driving simulation with diverse behavior styles.
MPC*RL: Hybrid Planning for AVs
Integrating Model Predictive Control with Deep RL for safe and efficient autonomous vehicle control at unsignalized intersections.
Simulation Framework for Mixed-Autonomy Traffic
Real-time simulation framework for evaluating autonomous vehicles in mixed traffic scenarios with human-driven vehicles.
Human-AV Interaction at Intersections
Data-driven analysis of autonomous and human-driven vehicle interactions at unsignalized intersections using Waymo and Lyft datasets.
Interactive MPC Learning Tool
Educational GUI application for exploring Model Predictive Control with interactive trajectory design and real-time parameter tuning.
Imitation Learning for AVs Inside World Models
RNN-based World Model for training autonomous vehicles through imitation learning in complex driving scenarios.
Edge Case Detection for Autonomous Driving
Foundation model-based system for detecting critical edge cases in autonomous driving systems (Hi-Drive Project).
Education
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- Research Topic: Multi-agent self-play reinforcement learning for generalizable agents
- Advisors: Prof. Shimon Whiteson, Prof. Jakob Foerster
- Advisors: Prof. Bart van Arem, Dr. Simeon C. Calvert
- IES IROS SYPA Award 2025
- TU Delft TMI Grant for Collaborative Research 2025
- GPA: 3.94/4.00
- Graduate Student Dean's Award
- Elite Graduate Student Award – Iran's National Elites Foundation
- GPA: 3.67/4.00