• 🎓 Education:

  • Master of Engineering in Computer Science from Cornell University
  • Bachelor of Science in Mathematics and Computer & Information Sciences from Fordham University

• 💼 Current Position:

  • Software Engineer at Salesforce Core Engineering, focusing on infrastructure and database optimization.

• 📝 Published Research:

  • First author of a paper on multi-tenant systems accepted into the IEEE Big Data Conference 2023.
  • Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System

• 🔭 Interests Right now:

  • Advance retrieval augmented generation techniques for multimodal information
  • Deep infrastructure issues like database optimization, Paxos, fault tolerance, and system consistency.
  • videography

• 🌱 Currently Learning:

  • Advanced algorithms in fault tolerance, replication, time synchronization, and multi-node system architectures.
  • Database consistency and consensus protocols.
  • daVinci resolve

• 💬 Ask me about:

  • Color-grading, Photography, Prompting, fine-tuning, Large Language Models, Fault tolerance, database optimization.

• Proficient in: Python, C++, SQL, Java, HTML/CSS
• Familiar with: Bash, JavaScript, c

• Distributed Systems: MapReduce, HDFS, Spark, Kafka, gRPC, MPI, Horovod
• Machine Learning: PyTorch, PyTorch Distributed
• Web Development: Flask, React
• Version Control: Git, GitHub, Perforce (p4v)
• DevOps: AWS (Certified Developer Associate, Certified Cloud Practitioner)

• MySQL, PostgreSQL, Supabase, Pgvector

• Summary: Authored and published a research paper accepted into IEEE Big Data Conference 2023.
• Highlights:
  • Develop the real time resource allocation algorithm based on real time sampling of worker nodes
  • Developed a metrics-driven platform with a user-friendly API for simulating multi-tenant systems.
  • Implemented architecture utilizing manager and multiple worker nodes with RPC.
  • Integrated multiple frameworks for comprehensive analysis.
• Technologies: Python, RPC, Distributed Systems Frameworks

• Summary: Developed a system to detect security vulnerabilities in web applications using NLP to analyze code snippets.
• Technologies: Python, Hugging Face Transformers, TensorFlow, Flask

• Summary: Web application for analyzing and understanding extensive documentation and audio files using embeddings.
• Technologies: React, Flask, AWS Cognito, Vector Databases, NLP

• Summary: Tool for enhancing public speaking and acting performances with AI-driven feedback from video recordings.
• Technologies: Flask, AWS S3, GPT-4 Vision, TogetherAPI, Llama 2 (70B), Whisper, JavaScript, HTML, CSS

• Summary: Simulated distributed processing using MapReduce and HDFS concepts for handling large datasets.
• Technologies: Python, Hadoop, Bash

• Fault Tolerance & Replication: Exploring advanced techniques for ensuring system reliability and data consistency.
• Distributed GPU Training: Investigating methods to scale machine learning models across multiple GPUs in distributed environments.

• The first time/the first type of coding I did was Redstone in Minecraft!
• Enjoy reading interesting articles on technical problems and how companies tackle them
• Big fan of podcasts and always eager to learn something new 🎧

I'm always excited to collaborate on challenging projects that push the boundaries of technology. Whether it's building fault-tolerant systems, optimizing databases, or resolving scaling issues in distributed GPU networks, I enjoy working with others to solve complex engineering problems. Let's build something amazing together!

Feel free to reach out, and let's make a difference together!