Anika Jolly is a researcher and advocate who directly addresses the clinical blind spots in women's healthcare. Her mission began with a family experience of a delayed cardiac diagnosis that exposed how medical systems systematically fail women. Now, as an ML researcher at Stevens Institute of Technology under Dr. Hao Wang, she is auditing cardiovascular diagnostics bias for women, examining specific sex-based disparities in how ECG ML models interpret women's heart health with varying features. She synthesizes PhD-level literature and authors academic research proposals on data collection and model validation, work that will reshape how these diagnostic tools are built. Beyond this core research, she explores how applied ML can address various other tangible health gaps. After being selected as the only high schooler for the 2026 Harvard Health Systems Innovation Lab Hackathon in Boston, she prototyped OsteoSense with a team of Cornell and UC Berkeley engineers, an AI model for early osteoporosis detection that she trained and benchmarked to 76% diagnostic accuracy. This project reinforced her belief that accessible prototyping and creative problem-solving can accelerate innovation in women's health. 

Her commitment to equity extends beyond the lab. As a Rutgers 4-H STEM Ambassador, she led robotics and engineering clubs across Union County libraries, growing attendance by 200% and deliberately prioritizing girls and underrepresented youth. She brought Girls Who Code speakers to her clubs because she knew firsthand what it felt like to code without seeing women in the room, and she made sure the next generation wouldn't face that gap. She has also successfully advocated for menstrual product accessibility in her school district, turning student survey data into policy affecting 2,400+ students, and earning recognition from her superintendent. Whether through computation, mentorship, or policy, Anika's work reflects a single through-line: using technical skill and her voice to make medicine and opportunity more equitable for women’s health.