research tab

Author: byungjunkim12

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 About me
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-Byungjun Kim is a postdoctoral researcher at Rutgers University (PI: Prof. Minsung Kim). His research interests focus on the application of **reinforcement learning and quantum computing--inspired algorithms to wireless communications and sensing systems**.
+I am a postdoctoral researcher in the Department of Computer Science and WINLAB at Rutgers University (PI: Prof. Minsung Kim). Before joining Rutgers, I received my Ph.D. in Electrical and Computer Engineering from University of California, San Diego.
+
+My research interest focuses computing systems including machine learning for wireless communications and sensing. My research interests focus on the application of **reinforcement learning and quantum computing--inspired algorithms to wireless communications and sensing systems**.
 
 News
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 **[May 2025]** Joined Rutgers university as a postdoctoral researhcer. \
-**[March 2025]** I finally defended!
+**[March 2025]** Finally defended and submitted dissertation!
 
 Education
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+* Application of Quantum-inspired algorithm to massive MIMO detection
+  * Implemented the optimization algorithm inspired by **quantum computing for large MIMO detection**. Developed GNU Radio block based on this algorithm and evaluated using software-defined radios (SDRs).
+
+* Reinforcement Learning-based Adversarial Attack on Neural Networks for Wireless Human Activity Recognition
+  * Developed **an attack scheme to degrade deep learning-based human activity recognition (HAR) using Wi-Fi signals** by manipulating pilot signals from user devices. To overcome the limitation of the gradient-based attack method, which requires knowledge of future channel information, proposed a **generative adversarial imitation learning (GAIL)**-based algorithm that imitates the gradient-based attack without these assumptions, successfully degrading HAR model performance.
+
+* Modulation Classification for Intelligent spectrum sensing
+  * Participated in the **IARPA-funded intelligent spectrum sensing project, leading a modulation classification part for Wi-Fi 6 and 5G signals**. The project aims to identify anomaly signals  Developed a preprocessing algorithm for deep learning-based classification without preamble or control channel information. Implemented **PHY layer channels for 5G and Wi-Fi 6** using MATLAB toolboxes and demonstrated that our proposed algorithm reliably identifies modulation of practical OFDM signals using **SDR**.

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 <!-- Work experience
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-* **Intern** at Samsung Semiconductor RF system team, June 2024 - Sept. 2024
+* **Intern at Samsung Semiconductor RF system team** (2024 Summer)
   * Developed a human gesture recognition system using **mmWave FMCW SISO radar**. To overcome SISO limitations, proposed to use a range-Doppler map as input to an LSTM-based classifier, demonstrating accurate detection of **microgestures including finger tap**.
   * Supervisor: Dr. John Kim and Dr. Dongwoo Kim
 
-* **Research Intern** at Intel Labs Wireless AI team, June 2022 - Sept. 2022
+* **Research Intern at Intel Labs Wireless AI team** (2022 Summer)
   * Applied adaptive learning algorithms for **user selection in massive MU-MIMO systems** to enhance adaptability to environmental variations. Developed **transfer learning and meta-learning algorithms** to minimize the data required for model adaptation in new environments.
   * Supervisor: Dr. Hosein Nikopour and Dr. Oner Orhan