Highly accomplished AI/ML Research Engineer with a Master's degree from a top-ranked university, specializing in Multi-Agent Deep Reinforcement Learning (DRL) for complex distributed systems and Mobile Edge Computing (MEC). Proven expertise in optimizing resource utilization, enhancing load balancing, and maximizing Quality of Experience (QoE) through innovative algorithmic design and simulation. Eager to leverage advanced DRL approaches and a strong foundation in network optimization to drive cutting-edge research and development in dynamic system environments.
Research Engineer
Tehran, Tehran, Iran (Islamic Republic of)
→
Summary
Leads research and development in multi-agent DRL for collaborative computation offloading in partially observable MEC systems, driving scalable and joint decision-making.
Highlights
Developed and implemented advanced multi-agent Deep Reinforcement Learning (DRL) algorithms for collaborative computation offloading in partially observable Mobile Edge Computing (MEC) systems.
Designed scalable and joint decision-making frameworks for heterogeneous agents operating under non-stationary system dynamics, improving adaptability and performance.
Enhanced resource utilization and load balancing by enabling collaborative offloading scenarios, leading to optimized system efficiency and responsiveness.
Pioneered the application of Centralized Training with Decentralized Execution (CTDE) paradigms, fostering cooperative learning strategies among diverse agents.
Research Assistant
Tehran, Tehran, Iran (Islamic Republic of)
→
Summary
Conducted DRL-based task offloading research in highly-dynamic and energy-constrained MEC systems, optimizing distributed decision-making for time-sensitive tasks.
Highlights
Developed DRL-based task offloading algorithms for highly dynamic and energy-constrained Mobile Edge Computing (MEC) systems, achieving significant performance gains.
Optimized distributed and independent offloading decisions for time-sensitive tasks, ensuring strict processing deadlines under time-varying network conditions.
Maximized end-user Quality of Experience (QoE) by effectively balancing trade-offs between task completion, latency, and energy consumption based on individual requirements.
Designed solutions that robustly accounted for dynamic workloads at edge nodes, significantly improving system adaptability and efficiency.
Reviewer
→
Summary
Reviewed research articles for the IEEE Internet of Things Journal, contributing to the quality and integrity of published scientific work.
Highlights
Evaluated submissions for technical rigor, methodological soundness, and significant contributions to the fields of IoT and distributed systems.
Provided constructive, detailed feedback to authors, enhancing the clarity, impact, and scientific value of their research.
Reviewer, 27th International Computer Conference
→
Summary
Contributed to the peer review process for the 27th International Computer Conference, ensuring high-quality academic content.
Highlights
Assessed conference papers for scientific merit, originality, and relevance to contemporary computer science domains.
Facilitated the selection of impactful research for presentation at a prominent national academic conference.
Head Teaching Assistant, Software Defined Networking
→
Summary
Supported instruction for 'Software Defined Networking,' guiding students through advanced network architecture principles.
Highlights
Provided in-depth explanations and practical guidance on Software Defined Networking (SDN) concepts and implementations to students.
Collaborated with Professors Ali Movaghar and Dr. Mohammad Hosseini on curriculum enhancement and student assessment strategies.
Head Teaching Assistant, Performance Evaluation of Computer Systems
→
Summary
Served as Head Teaching Assistant for 'Performance Evaluation of Computer Systems,' supporting student learning and course delivery for over 50 students.
Highlights
Mentored over 50 students in complex system performance evaluation concepts, methodologies, and practical applications.
Assisted Professors Ali Movaghar and Dr. Mahdi Dolati in developing comprehensive course materials and evaluating student assignments.
Conducted supplementary sessions to clarify challenging topics and enhance student understanding of advanced concepts.
Teaching Assistant, Verification of Reactive Systems
→
Summary
Assisted in teaching 'Verification of Reactive Systems,' focusing on formal methods and system correctness under Professor Ali Movaghar.
Highlights
Facilitated student understanding of formal verification techniques and their application to reactive systems.
Supported Professor Ali Movaghar in delivering course content and evaluating student progress through assignments and exams.
Teaching Assistant, Theory of Machines and Languages
→
Summary
Provided instructional support for 'Theory of Machines and Languages,' covering foundational computer science topics under Professor Ali Movaghar.
Highlights
Guided students through complex concepts in automata theory, formal languages, and computational complexity.
Assisted Professor Ali Movaghar with lecture preparation, problem-solving sessions, and student engagement activities.
Teaching Assistant, Wireless Networking
→
Summary
Supported the 'Wireless Networking' course, helping students grasp principles of wireless communication under Professor Ali Mohammad Afshin Hemmatyar.
Highlights
Assisted Professor Ali Mohammad Afshin Hemmatyar in teaching wireless network protocols, architectures, and emerging technologies.
Provided technical support and clarification on challenging topics, improving student comprehension and problem-solving skills.
→
Master of Science
Computer Engineering/Computer Networks
Grade: 6.08/7
Courses
Thesis: Distributed Computation Offloading based on Deep RL (DRL) in Resource-Constrained MEC
→
Bachelor of Science
Industrial Engineering
Awarded By
Sharif University of Technology (SUT)
Recognized as a top-performing student, ranking within the top 10% of the Master of Science program in Computer Engineering.
Awarded By
National Organization for Educational Testing, Iran
Achieved a top 0.09% ranking (55th out of 60,000 participants) in the highly competitive nationwide Master of Science entrance examination for Computer Engineering.
Awarded By
National Organization for Educational Testing, Iran
Secured a top 1% ranking (among 180,000 participants) in the nationwide Bachelor of Science entrance examination for Mathematics and Physics.
Awarded By
RoboCup IranOpen Organizing Committee
Awarded third place in the national RoboCup competition, demonstrating strong robotics and AI application skills.
Published by
IEEE Transactions on Network Science and Engineering
Summary
Authored a peer-reviewed paper detailing a novel DRL-based algorithm for optimizing computation offloading in MEC, focusing on maximizing Quality of Experience (QoE).
Published by
Preprint / Under Review
Summary
Developed a collaborative computation offloading framework utilizing multi-agent DRL to enhance performance in partially observable MEC environments, currently in progress.
Issued By
Udemy
Issued By
Tose'e Higher Education Institute
Issued By
Remis Arjang Institute
Issued By
Anisa Iran Linux House
Stochastic Network Modeling, Network Simulation, Performance Evaluation, Wireless Network Optimization, Distributed Network Optimization, RL-driven Decision-Making, Uncertainty Management, Limited Information Processing, Dynamic System Optimization, Multi-Agent Reinforcement Learning (MARL), Mobile Edge Computing (MEC), Computation Offloading, Resource Utilization, Load Balancing, QoE Maximization, Federated Learning, Collaborative Learning.
Python, R, Bash, C++, SQL.
TensorFlow, PyTorch, Keras, Scikit-learn, Deep Reinforcement Learning (DRL), Dueling Double Deep Q-Network (D3QN), Long Short-Term Memory (LSTM), Actor-Critic Methods.
Pandas, NumPy, Matplotlib, Jupyter Notebooks.
Linux, Mininet, Ns-3, Git, LaTeX, Vim, Flask, Visio.
Distributed Systems, Wireless Communication, Internet of Things (IoT), Mobile Edge Computing (MEC), Performance Optimization, Multi-Agent Reinforcement Learning (RL), Federated Learning.
Professor of Computer Engineering, Sharif University of Technology (SUT), Tehran, Iran. Email: movaghar@sharif.edu
Assistant Professor of Electrical Engineering, Sharif University of Technology (SUT), Tehran, Iran. Email: hamedsh@sharif.edu
Assistant Professor of Computer Engineering, Shahid Beheshti University, Tehran, Iran. Email: m-hosseini@sbu.ac.ir
Professor of Computer Engineering, Sharif University of Technology (SUT), Tehran, Iran. Email: hemmatyar@sharif.edu
→
Summary
Developed a hierarchical multi-agent DRL algorithm for collaborative decision-making in non-stationary environments, based on decentralized partially observable Markov decision processes (Dec-POMDPs).
→
Summary
Conducted decision-driven simulations of communication-assisted MEC systems, evaluating collaborative computation offloading across cloud servers, edge nodes, and end devices.
→
Summary
Developed a D3QN algorithm for distributed decision-making under uncertainty, leveraging both double Q-learning and dueling network architectures. Designed for personalized QoE maximization in dynamic MEC environments based on Markov Decision Processes (MDPs).
→
Summary
Designed an LSTM model to continuously estimate dynamic workloads at edge servers, dealing with limited global information and uncertainty in Q-learning-based decision-making in MEC.
→
Summary
Conducted time-driven simulation of computation and communication processes in queuing-based MEC systems, modeling stochastic system conditions and enabling evaluation of task offloading strategies.
→
Summary
Performed event-driven simulation and performance evaluation of M/M/1/K queues under FCFS, processor sharing (PS), and discriminatory PS service disciplines.