I’m a Machine Learning Engineer with experience in search, recommendation, and AI systems.
My work spans machine learning, NLP, graph neural networks, generative AI, and scalable backend engineering.
I enjoy building end-to-end ML systems — from data pipelines to production deployment — with a focus on high-performance, real-world impact.

Programming Languages:

• Python (Data, ML, Backend)
• Golang (Backend, Web Services)
• R (Data Analytics)
• Java (Backend, Data)
• Scala (Data Pipelines)

Machine Learning & AI:

• BERT, Fairseq, GPT, Transformer Models
• DALL·E, Stable Diffusion, CLIP, OpenCV
• PyTorch, Sklearn, XGBoost, LightGBM, CatBoost, StatModels
• NLP, Knowledge Graphs, Multimodal AI, Graph Neural Networks

Cloud & Infrastructure (AWS):

• EC2, EKS, ECS, S3, RDS, Lambda, Kinesis, Firehose, CloudWatch
• SageMaker, MWAA (Airflow), Athena, CloudFormation, Lake Formation, Bedrock

Databases:

• Relational: Postgres, MySQL, MariaDB, OracleDB, Greenplum, Teradata
• Vector: ElasticSearch, Qdrant, Weaviate
• NoSQL: Redis, MongoDB, HBase, Cassandra, InfluxDB, Kylin

Data Visualization:

• Tableau, Altair, Matplotlib, Seaborn, ggplot, D3.js

DevOps & Tools:

• Git, Docker, CI/CD, YAML, ProtoBuf, Conda

• M.Sc. Data Science — University of British Columbia
• M.Sc. Information Systems Management — Carnegie Mellon University
• B.Sc. Industrial Management (MIS) — Purdue University

I’ve published on recommendation systems, NLP, sentiment analysis, graph neural networks, and data privacy/security in AI.
👉 Google Scholar Profile

• LinkedIn
• GitHub
• Google Scholar

✨ Always exploring the intersection of AI research and production engineering.