About me

I'm a passionate AI/ML Engineer with 4+ years of experience designing and deploying impactful AI solutions. I hold a Master's degree in Computer Science from University of Southern California (✌️) with a specialization in Artificial Intelligence, and have honed my expertise across natural language processing, computer vision, and AI infrastructure.

Currently, I work as an Applied AI Engineer at Mistral AI, where I design and deploy innovative AI solutions for customer-facing projects and real-world use cases.

My journey spans startups, research labs, and industry, where I excel at transforming complex ideas into practical, business-driven solutions. I am deeply committed to innovation and research, with 25+ publications in leading conferences and journals, and 1400+ citations on Google Scholar. I thrive on solving challenging problems at the intersection of advanced AI and real-world impact.

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Mistral AI
Conferences/Workshops: ICLR, ACL, NeurIPS, WACV, ECML, ECAI, etc. Journals: TMLR, IEEE IoT, Elsevier FGCS, etc.

I am deeply passionate about advancing the frontiers of Artificial Intelligence and Machine Learning, constantly exploring cutting-edge developments and innovative approaches. This intellectual curiosity drives me to not only master new technologies but also effectively bridge gaps between technical and business domains. My strong communication skills and collaborative mindset have enabled me to successfully lead cross-functional teams and deliver high-impact projects. I take pride in fostering an inclusive environment that encourages knowledge sharing and collective growth, having mentored junior engineers and contributed to building robust AI solutions that directly address business challenges.

I also enjoy sharing knowledge with the community. If you're interested in data science or AI, check out my articles and tutorials on Medium.


Skills

  • Programming Languages: Python, C++, R, Matlab, SQL
  • Development Tools: HTML, CSS, Javascript, Angular, NodeJS, SwiftUI
  • Machine Learning & Deep Learning Frameworks: PyTorch, Tensorflow, Keras, ONNX, HuggingFace, NLTK, OpenCV, Spacy, LangChain, Scikit-learn, Flask, PySpark, MLFlow
  • Tools: Databricks, Weights & Biases (wandb), Jenkins, Gradio, Git, Docker, OpenVINO, SonarQube, Postman, Google Analytics
  • Cloud Platforms: AWS (EC2, S3, Lambda, SageMaker, RDS) and GCP (Compute Engine, Cloud Storage, Cloud Functions, BigQuery)

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