Transforming complex data into actionable insights. Passionate about machine learning, deep learning, and making data-driven decisions.
Currently working on
AI for Healthcare
I'm a passionate data scientist with 5+ years of experience in machine learning, statistical modeling, and data visualization. I hold a Ph.D. in Computer Science with a specialization in Artificial Intelligence from Stanford University.
My expertise lies in developing end-to-end machine learning solutions, from data collection and preprocessing to model deployment and monitoring. I'm particularly interested in applications of AI in healthcare, finance, and climate science.
Education
Ph.D. in Computer Science
Experience
5+ Years
Projects
30+ Completed
Publications
15+ Papers
Python
TensorFlow
PyTorch
Pandas
NumPy
Docker
Kubernetes
Spark
PostgreSQL
Git
Tech Innovations Inc.
Leading a team of data scientists to develop machine learning models for predictive analytics. Implemented a novel deep learning architecture that improved recommendation accuracy by 23%.
Data Insights Corp.
Developed and deployed machine learning models for customer segmentation and churn prediction. Created automated data pipelines that reduced processing time by 40%.
Stanford University
Conducted research on deep learning applications in medical imaging. Published 5 papers in top-tier conferences and developed novel algorithms for tumor detection in MRI scans.
Developed a deep learning model for early detection of lung cancer from CT scans with 94% accuracy.
Built an LSTM-based model to predict stock prices with 85% directional accuracy using historical data.
Created a hybrid recommendation system combining collaborative and content-based filtering for an e-commerce platform.
Fine-tuned BERT model for sentiment analysis on product reviews achieving 92% accuracy.
Developed an anomaly detection system that reduced false positives by 35% while maintaining 98% recall.
Designed an interactive dashboard for executive decision-making with real-time data visualization.
Nature Scientific Reports, 2023
Proposed a novel attention-based CNN architecture for improved segmentation of tumors in MRI scans, achieving state-of-the-art results on the BraTS dataset.
NeurIPS, 2022
Introduced a contrastive learning framework for time series data that outperforms supervised approaches in low-data regimes across multiple domains.
JAMA Network Open, 2021
Developed an interpretable model for predicting patient outcomes with clinical validation from domain experts, bridging the gap between AI and medical practice.
ICML, 2020
Proposed a novel aggregation method for federated learning that improves model performance while maintaining differential privacy guarantees.
Have a data science challenge you're trying to solve? Interested in collaborating on research? Feel free to reach out—I'm always open to discussing new projects, creative ideas, or opportunities.
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