Avani Goyal
Undergraduate Researcher @ Berkeley Artificial Intelligence Research
About
EECS student at UC Berkeley focused on AI-driven security and systems research. Currently working on MobileCybench, a Berkeley–Stanford project benchmarking AI agents against real mobile app vulnerabilities. Interested in building and evaluating AI systems in security-critical, real-world environments.
United States
Berkeley
Computer Software
Artificial Intelligence (AI), Cybersecurity, Python (Programming Language), C++, Java, Machine Learning, Natural Language Processing (NLP)
Experience

Undergraduate Researcher
• Developing MobileCybench, a mobile app cybersecurity benchmarking framework, in a joint initiative between Berkeley AI Research (BAIR) Lab and Stanford AI Lab (SAIL). Designing realistic attack scenarios and evaluation environments to quantify and improve AI agent robustness. • Building on the group’s benchmarks like Cybench, which have been adopted in U.S./U.K. AISI pre-deployment evaluations, Anthropic system cards, Amazon Nova Premier documentation, and the OWASP LLM Exploit Generation Whitepaper.

Undergraduate Research Apprentice: CoRE Lab
• Built a Python-based pipeline using BeautifulSoup, spaCy, and SentenceTransformers to extract and analyze 100+ faculty websites and course syllabi for equity-related content in CS teacher credentialing programs. • Crawled and evaluated 2,000+ links from U.S. universities using Google Search, applying NLP similarity scoring to surface justice-related material and support SIGCSE-bound research. • Mentored by Brendan Henrique, Ph.D. student

Research Intern
• Built a predictive model in Python using scikit-learn’s Random Forest Classifier on 68 behavioral and demographic features from U.S. Census data, achieving F1 scores of 0.67–0.75 for ADHD, depression, anxiety, developmental delay, intellectual disability, and autism, using as few as 17 features. • Demonstrated that general disability presence can be predicted from non-clinical data with an F1 score of 0.74, showing potential for scalable early screening. • Supervised by Prof. Peter Washington
Research Intern: People, AI and Robotics (PAIR) Lab
• Collaborated with Ph.D. researchers on natural language interfaces for robotics, focusing on instruction generation for assistive tasks. • Leveraged the GPT-4 API to translate user prompts into robot-executable actions, contributing to NLP-to-robot control pipelines for home-assistive systems. • Supervised by Prof. Animesh Garg

AI Research Intern: MIT Lincoln Laboratory
• Developed a pandemic simulation game in Python using object-oriented design, and trained an AI agent with OpenAI Gym and reinforcement learning to optimize gameplay strategies through reward-based learning. • Collected and analyzed human-AI gameplay data with pandas and NumPy to compare decision patterns, supporting evaluation of agent performance and strategic alignment. • MIT Beaver Works Summer Institute; Program: Serious Game Design and Development with AI

Intern
• Built a Flask-based REST API that analyzed readability of text inputs using the Textatistic library and NLTK for tokenization and POS tagging, providing real-time linguistic feedback via Chrome extension. • Designed NLP heuristics to detect difficult vocabulary, long sentences, and noun overuse, using metrics like Dale-Chall, Flesch, and SMOG scores to guide language simplification for Uber copywriters in non-English markets.

Intern
• Led a student development team to design a personalized content-blocking mobile app supporting PTSD recovery, with features tailored through user input and cognitive therapy guidelines. • Received the Presidential Service Gold Award for community impact; organized virtual hackathons and mentored incoming interns as part of the Youth Executive Committee.
Education

B.S. in Electrical Engineering and Computer Sciences, Minor in Data Science
Claude Campus Ambassador · Fall 2025 Selected to partner with Anthropic to support student engagement around responsible AI tools and practices. Director of Videography, FAST (Fashion and Student Trends) · Fall 2025 – Present Lead videography initiatives for student-led film projects, coordinating production planning and creative execution. Coordinate thematic visual content for the Bay Area’s largest student-run runway show. CS 61A – Program Design CS 61B – Data Structures & Algorithms CS 61C – Computer Architecture CS 70 – Discrete Math & Probability CS 161 – Computer Security CS 170 – Algorithms & Complexity CS 186 – Database Systems CS 195 – Technology & Society EECS 16A/16B – Systems & Circuits EECS 127 – Optimization DATA 8 – Data Science Foundations DATA 100 – Applied Data Science MATH 53 – Multivariable Calculus PHILOS 148 – Probability & Induction
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