Engineering order from chaos, finding simplicity in complex data. Driven by the philosophy of finding beauty in raw, imperfect, and evolving systems.
I am a Computer Science graduate specializing in Artificial Intelligence and Machine Learning, with hands-on experience in deep learning, computer vision, and full-stack software development. My approach combines rigorous mathematics and core CS foundations with a designer's eye for clean, reliable system architecture.
Just as the *wabi-sabi* philosophy appreciates worn surfaces and asymmetric lines, I build AI systems that embrace noisy, real-world logs, shifting user patterns, and unstructured text documents. I thrive on translating chaotic datasets into predictive models and automated pipelines that deliver tangible insights.
Selected works bridging intelligence, threat analysis, and platform architecture.
A production-ready Q&A engine built for private, sensitive documents. Utilizes LangChain and FastAPI to index, retrieve, and generate contextually accurate answers from massive text files.
Enterprise documents contain private, highly sensitive information that cannot be shared with public APIs, preventing the use of standard search tools.
Engineered a robust retrieval-augmented generation (RAG) system utilizing LangChain for semantic indexing and vector search, built on FastAPI.
Delivered a secure, self-hosted search engine that retrieves precise context and answers queries from massive unstructured file logs in milliseconds.
An end-to-end AI security system that uses a hybrid modeling approach (combining Isolation Forest and XGBoost) for real-time behavioral anomaly detection in network and server logs.
Static firewalls and basic log filters fail to catch sophisticated, shifting behavioral attack patterns in server network logs.
Built a dual-stage model combining Isolation Forest for unsupervised anomaly search and XGBoost for structured behavior classification.
Created an API capable of processing thousands of incoming server events per second with high classification accuracy and low warning latency.
A full-stack social support portal built with the MERN stack. Designed to streamline campus communications, help students post doubts, track events, report lost & found items, and submit complaints.
Developed predictive data models to analyze churn risk and customer behavior. Leveraged advanced data analytics and predictive modeling techniques to extract actionable retention strategies.
An efficient cybersecurity detection filter that implements custom Hash Tables for high-speed, low-latency checking of log entries against threat signatures.
An ADMET prediction platform leveraging Chemprop-RDKit machine learning models trained on Therapeutics Data Commons (TDC) datasets. Evaluates large-scale chemical libraries for drug discovery.
Like stones carefully selected and placed in a Zen garden, each tool serves a precise, mindful purpose in my development workflow.
A log of professional experience, academic foundations, and certified capabilities.
Developed enterprise applications following SDLC principles and implemented RESTful APIs using ASP.NET and SQL Server. Collaborated with Agile teams to debug software issues, perform testing, and improve performance.
Developed Python-based data preprocessing workflows and analytical dashboards to support healthcare research. Performed feature engineering and statistical analysis on large datasets for decision support.
Led developer teams to build high-performance software models under constrained timelines.
Awarded for engineering a functional machine learning deployment at the final year project gallery.
Selected out of 100+ applicants for technical proficiency during the Princeton Smart Engineers assessment.
Interested in AI development, ML model engineering, or data automation pipelines? Let's connect and build systems that last.