2026 Diversity Project Presentation
On April 9, 2026, from 1:30 p.m. to 3:30 p.m. at the Big Data Hub Presentation Studio, we showcased the amazing projects our students are working on to advance Diversity, Inclusion, Equity, and Justice in Computing Science Research and Professional Practices.
The event began with a brief introduction to the CSDC Diversity Awards, followed by project presentations. We continued with a break for networking and gourmet snacks, and then introduce the winning project.
Our shortlisted projects for 2026 CS Diversity Award were:
Winner of the 2026 CS Diversity Award
Project Title: PLANET Hacker: Autonomous and Transparent Penetration Testing for Broader Access
Team Members: Alyssa Rusk, Nina Qiu, Wania Imran
Advisor: Mohammad Tayebi
Description: PLANET Hacker is an autonomous penetration-testing system that integrates large language models, MCP tools, and an offensive knowledge base to perform end-to-end security assessments, from reconnaissance and attack generation to exploit execution and transparent reporting. The system can identify vulnerabilities, execute exploits, and adapt when initial attempts fail, demonstrating its value as both an offensive security tool and a decision-support system for secure software development.
Beyond its technical contributions, PLANET Hacker advances EDIJ by making penetration testing more transparent, scalable, and accessible, lowering barriers to entry and enabling broader participation in advanced cybersecurity practices. Led by the only three women in the Professional Master’s Cybersecurity cohort, the project also strengthens representation and highlights the impact of diverse teams in an underrepresented field.
Project Title: Crowd Disaster Prevention Using Hybrid Vision Models: Real-Time Adaptive Crowd Intelligence with YOLOv5, CSRNet, Temporal Surge Detection, and Dynamic Zoom
Team Members: Sukanya Sen
Advisors: Professor Ali Mahdavi-Amiri
Description: This project presents a real-time hybrid vision system for crowd disaster prevention by combining object detection (YOLOv5) and density estimation (CSRNet). The system adaptively switches models based on crowd density, integrates temporal surge detection to identify dangerous crowd compression, and uses dynamic zoom to highlight high-risk regions. By enabling early detection of hazardous crowd conditions, this work aims to reduce risks in large public gatherings such as religious events, concerts, and protests. From an EDIJ perspective, the system promotes equitable public safety by supporting scalable, automated monitoring in under-resourced or high-density settings where manual oversight is limited. This contributes toward inclusive safety infrastructure that protects vulnerable populations and improves emergency response effectiveness.
Project Title: LearnVerse: A Skill-Based Marketplace for Accessible and Inclusive Education
Team Members: Jasleen Kaur, Daniel Li, Rodrigo Añasco
Advisors: N/A (independently developed as part of a GDSC semester-long hackathon)
Description: Access to quality education remains uneven, with financial constraints and language barriers preventing many individuals from learning or sharing their skills. LearnVerse addresses these challenges by reimagining education as a skill-based exchange marketplace, where users can trade knowledge instead of money. This approach empowers individuals from underrepresented and low-income communities to both teach and learn without financial barriers.
Built using React and Firebase, LearnVerse enables users to create skill listings, connect through real-time communication, and build trust through reputation systems. To further support inclusivity, we are integrating translations to make cross-language learning seamless and accessible. By combining transparent reputation systems, real-time chat, and community-centered design, LearnVerse creates an inclusive digital environment where people can teach, learn, and build trust across diverse backgrounds. The project advances EDIJ principles by expanding access to quality education and promoting equitable knowledge exchange on a global scale.
Project Title: Critical Technical Practice to Strengthen the “Immune System” in Technical Development
Team members: Weina Jin
Advisors: Ghassan Hamarneh
Description: When developing a technique, computer scientists are usually motivated by good intentions, such as solving real-world problems and benefiting society. We show, however, that having good intentions cannot necessarily guarantee good outcomes: from our technical development experience of explainable AI (XAI) — the technical operationalization of the AI ethics principle of explainability, we found that the current research agenda of XAI rarely centralizes end users, and the prevailing evaluation metric in XAI can even be misleading and manipulative for end users. So how can we make our ethical and responsible pursuit in technical development more effective and robust?
This project provides computer science students and practitioners a comprehensive methodology, named critical technical practice, to integrate justice, ethics, and responsibility from the outset and in the entire technical development life cycle. Critical technical practice contributes to just, ethical, and responsible technical development in computer science education, research, and professional practices by strengthening our “immune system” in technical practice: it supports the identification of complex root causes in unethical and unscientific technical practices, and critically reflects the underlying assumptions that may be taken for granted in technical problem identification, design, evaluation, and deployment.
Project Title: Exploring Diminished Reality for Attention Support: A Co-Design Study with Students with ADHD
Team members: Alex Noh, Audrey Safikhani, Anjun Zhu
Advisors: Wolfgang Stuerzlinger, Lawrence Kim
Description: Our project explores how Diminished Reality (DR), technology that modifies or removes elements of the perceived environment, can be designed to support attention for people with ADHD. Through diary studies, interviews, and speculative co-design sessions with 15 university students with ADHD, we provide rich qualitative accounts of the challenges students with ADHD face and a mismatch between what DR research assumes and what the participants envisioned. From these insights, we contribute the concept of Attentional Diminishment, reframing DR design away from visual outcomes and toward attentional effects, centering the lived experiences of neurodivergent users. This work advances EDIJ in HCI research by ensuring that emerging interactive technologies are shaped by, and for, the communities they aim to serve.