Narjes Nourzad
ELECTRICAL ENGINEER
About
I am currently pursuing my Ph.D. in the Department of Electrical and Computer Engineering at the University of Southern California.
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My research is mainly focused on the practical applications of Multi-armed bandits and Reinforcement Learning models' applications in real-world problems.
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Explore a brief overview of my academic and research journey here. For detailed information, please contact me.
September 2024 - Present
ANRGLab - UNIVERSITY OF SOUTHERN CALIFORNIA
Research Assistant
ADAPTIVE UNIFIED REASONING AND AUTOMATION BASED ON LLMS AND MARL FOR NEXTG CELLULAR NETWORKS
Integrating Multi-Agent Reinforcement Learning with Large
Language Models to create a framework capable of managing the complexity and dynamism of 6G networks.
June 2024 - Present
ANRGLab - UNIVERSITY OF SOUTHERN CALIFORNIA
Research Assistant
GCNSCHEDULING BY LEVERAGING DEEP REINFORCEMENT LEARNING
Developing a deep actor-critic algorithm combined with a graph representation neural network to learn an efficient priority of tasks using twin networks.
December 2023 - May 2024
ANRGLab - UNIVERSITY OF SOUTHERN CALIFORNIA
Research Assistant
CORRELATED MULTI-ARMED BANDITS
Using the correlation between arms to lower the regret bound by performing more efficient exploration without using any/minimal prior information.
September 2023 - December 2023
ANRGLab - UNIVERSITY OF SOUTHERN CALIFORNIA
Research Assistant
CONTEXTUAL MULTI-ARMED BANDIT APPROACH FOR RECOMMENDER SYSTEMS
Using Clustering and Contextual Multi-Armed Bandit for Recommendation Systems.
June 2023 - September 2023
ANRGLab - UNIVERSITY OF SOUTHERN CALIFORNIA
Research Assistant
SEMI-COMBINATORIAL MULTI-ARMED BANDIT APPROACH FOR ANYPATH ROUTING
By coupling DSEE with Anypath routing, the algorithm optimizes packet routing through continuous learning and ensures accurate delivery probability estimation, while maintaining a near-logarithmic regret bound.
September 2022 - June 2022
UNIVERSITY OF SOUTHERN CALIFORNIA
Research Assistant
LEVERAGING REINFORCEMENT LEARNING AND PREDICTION FOR A FINANCIALLY AUTONOMOUS THERMOSTAT
By using the PPO algorithm and integrating predictions of next day temperature, the developed thermostat balances the cost of having a desired temperature as well as user satisfaction.
September 2021 - June 2022
UNIVERSITY OF TEHRAN COMPUTATIONAL AUDIO-VISION LAB
Research Assistant
SEISMIC SENSOR NETWORK SIGNAL PROCESSING
By using Deep Neural Networks, an algorithm to distinguish between earthquake signals and other signals captured on seismic sensors was developed.
June 2020 - August 2020
INTERNATIONAL INSTITUTE OF EARTHQUAKE ENGINEERING AND SEISMOLOGY
Intern
MODIFYING EARTHQUAKE SIGNALS USING SIGNAL PROCESSING ALGORITHMS
By implementing Signal Processing Algorithms, earthquake records on a given dataset were modified as part of preprocessing for further usage. After baseline adjustments, several filters were used to eliminate long-period noise.
RESEARCH EXPERIENCE
PUBLICATIONS
CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for Mental Health - MOBILESoft 2024
Smart Routing with Precise Link Estimation: DSEE-Based Anypath Routing for Reliable Wireless Networking - IEEE ICMLCN 2024
A Crystal Ball for Comfort: Leveraging Reinforcement Learning and Prediction for a Financially Autonomous Thermostat - Deployable RL workshop - RLconference 2024
Smart Crystal Ball on a Budget: Reinforcement Learning and Prediction for Budget-Friendly Comfort - IEEE ICA 2024
EDUCATION
2022 - Present
Pursuing a PhD
UNIVERSITY OF SOUTHERN CALIFORNIA
Ming Hsieh Department of Electrical and Computer Engineering​
Advisor: Professor Bhaskar Krishnamachari
2017 - 2022
Bachelor's Degree
UNIVERSITY OF TEHRAN
Department of Electrical and Computer Engineering
Program of Study: Telecommunication​
HONORS AND AWARDS
Awarded the Outstanding Poster Award at the 13th Annual Research Festival, University of Southern California
Awarded the Annenberg Fellowship top off, University of Southern California.
M.Sc. Admission from Electrical Engineering Department, University of Tehran, as an exceptional talent student.
Ranked among top 10 students in undergraduate class, Electrical Engineering Department, University of Tehran.
Ranked among top 0.5% in the nationwide university entrance exam in Mathematics and Physics fields for B.Sc. degree, 2017.
SKILLS
PROGRAMMING LANGUAGE
Python, R, MATLAB, LATEX, C++
LIBRARIES AND FRAMEWORKS
Pytorch, Gymnasium(Gym), Numpy, Pandas, Tianshou