AI Researcher
B.Sc. in Computer Engineering
Sharif University of Technology
Tehran, Iran
radinch1783@gmail.comI recently graduated with a B.Sc. in Computer Engineering from Sharif University of Technology. I am interested in information retrieval, retrieval-augmented generation, and reasoning and reliability in language models.
I am a remote research assistant at LS3 Lab, University of Toronto & Reviewerly, advised by Prof. Ebrahim Bagheri and Dr. Negar Arabzadeh. Previously, I was a summer research intern at the University of Hong Kong’s STAR Lab, advised by Prof. Reynold Cheng.
My current work focuses on deep research agents: how to help agents find, recognize, and use the evidence needed for well-supported answers.
B.Sc. in Computer Engineering · Tehran, Iran
GPA: 19.70/20 · Class rank: 6/201
Selected coursework: Advanced Information Retrieval; Machine Learning; Artificial Intelligence; System 2 (Reasoning) AI; Reinforcement Learning; Probability & Statistics; Linear Algebra.
Under review at WWW 2027
Unpublished manuscript
arXiv preprint, 2025
Earlier preprint ↗A substantially revised and extended version is under review at WWW 2027.
Research Assistant · Remote
Advisors: Prof. Ebrahim Bagheri & Dr. Negar Arabzadeh
Peer-review datasets and AI-text detection; retrieval-augmented verification of peer-review claims; question decomposition and evidence retrieval for deep research agents.
Research Assistant
Advisor: Prof. Mahdieh Soleymani Baghshah
Evaluating LLM deception through benchmarks and interactive games, examining reasoning, planning, value priorities, and contextual triggers.
Research Assistant
Advisor: Prof. Maryam Ramezani
Neural extensions of mechanistic diffusion models for interpretable forecasting of information cascades.
Summer Research Intern
Advisor: Prof. Reynold Cheng
Query decomposition and knowledge graphs for multi-hop question answering, with bandit-based optimization of evidence retrieval.
Research Assistant
Advisors: Prof. Hamid R. Rabiee & Prof. Maryam Ramezani
Bayesian collaborative filtering using uncertainty-aware user–item representations, learned matching, and ensemble predictions.
A text–image search system combining dense and sparse retrieval, rank fusion, and cross-encoder reranking.
Neuro-symbolic reasoning, inference-time search, LLM post-training with GRPO, and graph-based retrieval-augmented generation.
Value-based and policy-gradient methods, with extensions to offline, multi-agent, and meta-reinforcement learning.
Teaching Assistant, Sharif University of Technology
System 2 (Reasoning) AI; Artificial Intelligence; Intelligent Analysis of Biomedical Images; Linear Algebra; Engineering Probability and Statistics; Data Structures and Algorithms; Computer Structure and Machine Language; Fundamentals of Programming (Python).