Research Intern

• Worked under Dr. Arif Ahmed Sekh 
• Developed FairMed, a novel framework integrating Bayesian-optimized multi-metric fairness and SHAP explainability to reduce gender bias in clinical ML models. Applied to MIMIC-IV and eICU datasets, it significantly reduced prediction disparities while maintaining performance, providing clinicians with actionable insights and promoting fair, transparent decision-making in healthcare. 

Research Intern

• Worked under Dr. Ayantika Chatterjee 
• Developed advanced optimization techniques, including quantization and bit-depth reduction, to enhance the computational efficiency of KNN and SVM models on medical datasets, achieving substantial performance gains with less than 1% deviation in model accuracy. 

AI/ML Lead 

Student Member

Letter of Appreciation

• Recognized by NASA’s Vulnerability Disclosure Program for responsibly identifying and reporting a critical security vulnerability as an independent researcher. 
• Commended by NASA OCIO and SAISO for enhancing cybersecurity awareness and contributing to the protection of NASA’s information infrastructure. 

Delegate