Summer Camps

Over Summer 2026, I worked with Dr. Lee in the Ji group at Stanford Medicine, under the Stanford Compression Forum's Science, Humanities, Technology, Engineering, Mathematics (SHTEM) internship program at Stanford for high school students.

Motivation: Gastric cancer is the fourth leading cause of cancer-related deaths worldwide. If the gastric cancer is Epstein-Barr Virus (EBV) Associated gastric cancer, patients have better prognosis and those with EBV-associated gastric cancer respond better to immunotherapy. It is therefore pivotal to be able to identify EBV in pathology whole slide images (WSIs) of gastric cancer patients.

Goal: Using a foundation machine learning model to determine whether a WSI of a gastric cancer patient has EBV or not. Our cohort worked with Dr. Lee in the Ji research group for this task.

Result: Click on "Learn More" and then at "Using a Pathology Foundation Model to Predict Epstein-Barr Virus Positive or Negative in Gastric Cancer Whole Slide Images" to view our final research report. Learn More

Over Summer 2025, I worked with Dr. Akbari in the Digital Signal Processing Lab at UC Irvine, under the University of California Irvine × Gifted And Talented Institute (UCI × GATI) Bio Engineering AI Medicine (BEAM) program for high school students.

Motivation: Cardiac arrest affects almost half a million people in the U.S. per year. The brain is highly vulnerable during cardiac arrest and early resuscitation. Multimodal tools (ECoG, optical imaging, vitals) can help monitor patients and improve outcomes.

Goal: Optimizing brain recovery to improve neurological outcome post–cardiac arrest. Our cohort worked with Dr. Akbari's DSP team to interpret signals collected during surgeries.

Result: Click here to watch our final research presentation

About the Camp: Undergraduate Research Experience in Machine-Learning based Cybersecurity NSF REU, SJSU Award; <4% acceptance rate.

I participated in the 2025 summer NSF-supported Research Experience for Undergraduates (REU) program at San Jose State University. The program aims to train students in applying machine learning and deep learning to cybersecurity. We as participants worked full-time over 10 weeks on state-of-the-art projects such as intrusion detection, malware analysis, malicious network traffic modeling, bot detection, and LLM-based security solutions. Students gain hands-on experience with professional tools, close mentorship from faculty, and the opportunity to present their research through posters and papers. The program also provides a stipend, housing and food support, and valuable networking in the cybersecurity research community.

Although designed for undergraduate students, I was selected due to prior research in cybersecurity for over a year, including multiple IEEE conference papers. I was nominated by my faculty mentor.

Motivation to join: Explore how AI strengthens cybersecurity, understand real-world threats (malware, network intrusions), and learn how modern tools secure systems.

What I learned: Practical experience with hardware and coding; research-driven projects in intrusion detection and malware analysis; cutting-edge ML/AI applications for cyber defense; emphasis on ethics and responsible innovation.

This was a life-changing experience—it deepened my knowledge, expanded my professional network, and motivated me to pursue further research in this field.

About the Camp: In July 2024 I attended CyberGuardians: Navigating the AI-Enhanced Future — WITH (Workforce Innovation Technology Hubs) at San José State University. Supported by SJSU's School of Information, Department of Computer Engineering, and Department of Computer Science, the camp provided hands-on learning with Raspberry Pi and applied cybersecurity/AI activities.

Motivation to join: Deepen my understanding of the intersection between AI and cybersecurity, and how it shapes the future of safe, resilient systems.

What I learned: Raspberry Pi 5 hardware workflows, core cybersecurity concepts, and applied AI exercises—reinforcing the importance of ethical, responsible technology use.