Projects & Publications
From AI research breakthroughs to MedTech innovations, here's how I'm building the future of technology.
15+ Projects
Across AI, Tech, and social impact
3 Publications
In topics I'm passionate about
5 Startups
Founded or contributed to
Featured Work
A selection of projects that showcase my passion for AI, MedTech, and social impact
Featured Projects
AI-Powered Urban Planning Tool
CompletedAgentic workflow system using IBM Watson models and cutting-edge AI to optimize urban planning decisions, resource allocation, and infrastructure development.
Key Achievements:
- •IBM Hackathon Winner
- •Multi-agent orchestration
- •Real-time urban analytics
Generative AI Customer Support
ProductionEnterprise chatbot system built for Dell Technologies, serving 50K+ users with 60% reduction in ticket resolution time.
Key Achievements:
- •50K+ users served
- •60% faster resolution
- •Enterprise deployment
Other Projects
Skin Disease and Solution Platform (SkinSnap)
CompletedRevolutionary diagnostic tool that uses computer vision and deep learning to analyze medical images, reducing diagnostic time by 40% while improving accuracy.
Key Achievements:
- •10K+ images processed daily
- •92% diagnostic accuracy
- •Conrad Challenge Award Winner
Smart ECG Analysis System
CompletedMachine learning system for real-time ECG analysis and cardiac abnormality detection, developed during MIT Beaver Works program.
Key Achievements:
- •92% accuracy rate
- •Real-time processing
- •MIT collaboration
Violence Prevention Education Platform
DeployedInteractive digital curriculum platform that reduced reported incidents by 30% across pilot schools in the CUHSD district.
Key Achievements:
- •2000+ students reached
- •30% incident reduction
- •25 educators trained
Autonomous Vision System
PublishedComputer vision model for autonomous systems with 25% improved object detection accuracy using novel attention mechanisms.
Key Achievements:
- •25% accuracy improvement
- •Conference publication
- •MIT collaboration
Scalable Statistical Timing Engine (C++)
CompletedBuilt a high-performance C++ engine for timing analysis on large graph-based systems, incorporating probabilistic modeling to simulate variation and uncertainty. Designed incremental update algorithms to reduce re-computation by 80% on dynamic graphs.
Key Achievements:
- •80% reduction in re-computation
- •100k+ node graphs
- •Probabilistic modeling
High-Performance C++ Graph Simulator
CompletedImplemented a C++ simulator for large-scale dynamic graphs (100k+ nodes), optimizing traversal and memory usage. Achieved 4× faster execution through incremental updates and profiling-driven optimizations.
Key Achievements:
- •4× performance improvement
- •100k+ nodes supported
- •Memory optimized
AI-Based Bias Analysis in Resume Screening
PublishedDesigned and evaluated algorithms to detect and mitigate bias in automated resume screening systems, using statistical analysis to improve fairness and reliability at scale.
Key Achievements:
- •Gender bias detection
- •Fairness metrics developed
- •Journal publication
Neural Network for Prenatal Anomaly Prediction
CompletedBuilt a deep learning pipeline to predict prenatal anomalies from medical imaging data, integrating uncertainty estimation to flag high-risk cases and improve model reliability.
Key Achievements:
- •High-risk case detection
- •Uncertainty quantification
- •Medical imaging analysis
Interactive Graph Optimization Tool
CompletedDeveloped a graph-based optimization and visualization tool for large networks, enabling efficient traversal, real-time updates, and intuitive analysis. Recognized at multiple hackathons for innovation and performance.
Key Achievements:
- •Hackathon awards
- •Real-time updates
- •Large network support
Research Publications
Contributing to the academic community through peer-reviewed research in AI, ML, and ethics
Machine Learning-driven Resume Screening: Challenges and Implications for Gender Bias
FeaturedMachine Learning (ML) has revolutionized hiring processes, introducing new dynamics and challenges, especially in the realm of resume screening. This study delves into the challenges and implications of ML-driven resume screening, specifically addressing gender bias. We utilized a dataset containing resumes from different sectors in India to train models, including Random Forests and Multilayer Perceptron Classifier. These models were employed to categorize resumes into sectors such as technology, business, etc. Our results reveal gender biases, with men more frequently predicted as executives and women in technical roles, reflecting historical disparities. Furthermore, we identify limitations and ethical concerns surrounding such classifiers, emphasizing the need for responsible AI deployment in recruitment processes. By shedding light on the complexities of bias in ML-based recruitment, this research contributes to the ongoing discourse on ethical AI deployment, offering insights and recommendations for fostering fairness and accountability in hiring-based automated decision-making processes.
Using Deep Learning to Predict Mitochondrial, Multi-factorial, and Single-gene Genetic Disorders
FeaturedIn this era of abortion bans and restricted reproductive healthcare, we present an imbalance-aware deep learning framework for predicting mitochondrial, multifactorial, and single-gene genetic disorders from prenatal clinical metrics. The model uses a multi-encoder architecture with attention-based feature fusion and focal loss with adaptive class reweighting to address extreme rarity and heterogeneous inputs. The approach achieves 81% accuracy with an F1-score of 0.36, prioritizing clinically meaningful recall for early, non-invasive prenatal risk stratification while emphasizing fairness, uncertainty, and responsible deployment.