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code-krishna/README.md

Vamsi Krishna Pendyala

LinkedIn
GitHub

🏫 Education

  • Arizona State University
    Ph.D. Science - Computer Science (GPA: 3.81/4.00)
    January 2023 - Present

  • National Institute of Technology - Agartala
    Bachelor of Technology - Computer Science and Engineering (CGPA: 8.49/10)
    June 2015 - May 2019

🛠️ Technical Skills

  • Languages: Python, C++, Java, C
  • Research: Discrete Event Simulations, Machine Learning & AI (neural networks, transformers, LLMs, decision trees), Natural Language Processing, Time Series Analysis
  • Tools: Database Management Systems, Object-Oriented Design, Software Development, Networking & Routing, Big Data, LLMs, Evaluation Framework Development

💼 Experience

Graduate Research Associate

Arizona State University, Intel Corporation
August 2023 - Present

  • Modeled Discrete Event Simulations (DES) of Intel's MiniFab for semiconductor manufacturing.
  • Optimized throughput, turnaround, workflow scaling, and buffer utilization.
  • Developed AI/ML solutions (e.g., transformers, temporal convolutional networks), fine-tuned LLMs, reducing execution time by 95.4%.

Software Engineer 1

Immensitas - Lemnisk
March 2021 - March 2022

  • Developed REST APIs using Java Servlets, SpringBoot, and Spring.
  • Optimized JavaScript encryption logic, reducing response time by 20%.
  • Automated SFTP file loading with Python scripts.

Software Engineer 1

Epsilon - Conversant
July 2019 - March 2021

  • Managed data onboarding with Python, Greenplum, and PostgreSQL.
  • Designed parallel batch processing, cutting manual effort by 80%.

🔬 Projects

  • LLM Agentic Framework: Developed an LLM-based framework for Intel MiniFab, reducing execution time by 95.4% while maintaining fidelity.
  • Time Series Diffusion Model: Built generative AI models using Temporal Convolutional Networks (TCN), achieving an average MAPE of 11.81% for throughput prediction.
  • Fact Verification System: Created an NLP-based system with 64.95% accuracy for claim classification using textual sources.

📚 Publications

  • Time Series Dataset for Semiconductor Manufacturing using DES: MiniFab Model - arXiv, 2024
  • Generating TCN Models from Parallel DEVS Models: Winter Simulation Conference, 2024
  • Validation of Facts Against Textual Sources: RANLP Conference, 2019

🏆 Awards and Activities

  • Research Funding: Intel Corporation, Chandler, AZ.
  • IICDC-2017 Winner: Designed a color-sensor-based locking system.
  • Travel Grant Recipient: Arizona State University, SCAI.

Statistical Data

code-krishna

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