Data Scientist / Machine Learning Engineer with 3+ years of experience in machine learning deployment, data modeling, ETL pipelines, data quality and issue governance, A/B testing, recommendation systems and AI applications. I specialize in Computer Vision and am actively exploring NLP and LLM-based applications, including multimodal systems. I have worked at Samsung R&D (Canada), Trip.com Group (China), Value Simplex (China), and volunteered for the non-profit organization 50/50 Leadership (USA).
Python, SQL, PySpark, PyTorch, TensorFlow, Docker, Kubernetes, AWS (S3, Redshift), GCP, Bash/Linux, CI/CD, REST API
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- An agentic operational-memory system for student orgs — real-time multi-source sync, confidence-scored cited answers, and human-in-the-loop reconciliation to keep the record trustworthy.
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- An AI-powered codebase assistant that uses LLMs, embeddings, and semantic search to help developers explore repositories, understand architecture, and answer code-related questions more efficiently.
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Deep-Learning-Based Pavement Marking Detection and Classification
- Pixel-level pavement marking segmentation and classification using Transformer-based deep learning
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Containerized and Serverless Deployment of YOLO for Cloud-Based Inference
- Deployed YOLOv8 inference as a containerized REST service on GKE and Cloud Run, and benchmarked latency, throughput, and resource utilization under real workloads
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PointNet-Attn: Attention-Driven 3D Point Cloud Learning Framework
- Enhancing PointNet with Multi-Head Self-Attention and Local Neighborhood Aggregation for 3D Object Classification
Contact : Email