Venkata Vivek Panguluri

M.S. Computer Science student at the University of Georgia, researcher, and software engineer based in Athens, Georgia.

Download Résumé

Current Role

Student Lab Assistant - Deep Learning for Statistical Downscaling

University of Georgia - GAIM Lab

Contributing to deep-learning research for statistical weather downscaling and high-resolution weather reconstruction.

August 2026 - Present
Athens, Georgia

Extending an existing PyTorch downscaling framework with a configurable Enhanced U-Net backbone.
Building Hydra-based model and experiment configuration for controlled architecture studies.
Implementing PyTorch Lightning checkpoint and training-resume workflows for interrupted research runs.
Preparing HRRR data workflows for manifest generation, normalization, and paired downscaling experiments.
Adding pytest regression coverage in a Git, Linux, and HPC-oriented research workflow.

Education

M.S. Computer Science

University of Georgia

GPA: 3.86 / 4.00

January 2025 - December 2026
Athens, Georgia

B.E. Computer Science

R.M.D Engineering College

August 2020 - May 2024
Chennai, India

Earlier Research Experience

Research Intern - IoT Security and Machine Learning

R.M.D Engineering College

Built machine-learning workflows for IoT intrusion detection, translating network telemetry into anomaly signals for operational monitoring.

2024
Chennai, India

Designed end-to-end ML pipelines for high-volume IoT network telemetry.
Engineered temporal, behavioral, and protocol-level features for multi-vector attack behavior.
Evaluated Random Forest, SVM, and ensemble models with attention to detection accuracy, computational efficiency, and real-time deployment feasibility.
Contributed to IEEE research output in IoT anomaly detection and security.

Research Intern - Supply Chain Analytics and Risk Modeling

R.M.D Engineering College

Modeled supply-chain behavior under uncertainty using probabilistic simulation, decision trees, and NPV-based risk analysis.

2024
Chennai, India

Built simulation frameworks for disruption scenarios, demand variability, and operational constraints.
Developed decision-tree and NPV-based models to compare supply-chain alternatives under financial risk.
Performed scenario-driven analysis to identify stable, cost-efficient strategies under volatility.
Co-authored IEEE-indexed research on uncertainty-aware risk modeling for complex supply systems.

Publications

IoT anomaly detection / IoT security

Utilizing Machine Learning Techniques for Detecting Anomalies in IoT Networks

P. Shobha Rani, Mohammad Vajid Ahamed, Kommi Sree Sai Chaithresh, Sakkuru Kundan Srinivas, Panguluri Venkata Vivek

2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST) - April 9, 2024 - Jamshedpur, India - pp. 105-110

IEEE research output from work on machine-learning pipelines for IoT intrusion detection, network telemetry, temporal and behavioral features, protocol-level features, and real-time deployment considerations.

IoT security Anomaly detection Machine learning Network telemetry

Paper DOI

Uncertainty-aware supply-chain risk modeling

Evaluation of Supply Chain Design Decisions under Uncertainty – A Case Study Analysis

Joe Prathap P. M., L Sherin Beevi, W Vinil Dani, Panguluri Venkata Vivek, E. Ajith Jubilson

2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE) - May 16, 2024 - Shivamogga, India - pp. 1-7

IEEE-indexed research output from work on probabilistic simulation, disruption scenarios, demand variability, operational constraints, decision trees, NPV-based modeling, and scenario analysis.

Supply chain analytics Risk modeling Probabilistic simulation Decision analysis

Paper DOI

Projects

Robust Earth Forecast

A research software pipeline for ERA5-to-PRISM temperature downscaling, focused on high-resolution spatial reconstruction, terrain-aware residual modeling, and boundary diagnostics.

Active research codebase; temporal modeling is archived while spatial reconstruction limits are being diagnosed.

Scientific Machine Learning / Climate Downscaling

Adaptive Network Security System

A Java, Spring Boot, and React research system modeling secure communication as an attack-defense-recovery workflow with adaptive security modes.

Public academic/full-stack cybersecurity project; not a third-party-certified production messenger.

Cybersecurity / Full-Stack Systems

Context-Aware Cross-Domain Recommendation System

A local React and Node.js recommendation prototype connecting food, fitness, and media with contextual signals, feedback history, and explainable adaptive scoring.

Working local full-stack prototype with optional MongoDB and local JSON fallback storage.

Recommendation Systems / Full-Stack

Skills

Programming

Python Java C SQL JavaScript TypeScript

ML / Data

PyTorch PyTorch Lightning Hydra TensorFlow Scikit-learn Pandas NumPy xarray Herbie

Software Engineering

React Node.js Express Spring Boot Spring Security REST APIs

Cloud / Infrastructure

Git Docker Google Cloud Kubernetes Terraform MongoDB MySQL

Research / Technical Domains

Machine Learning Deep Learning Scientific Computing Geospatial ML Statistical Downscaling Cybersecurity Weather and Climate Modeling Recommendation Systems Supply Chain Analytics

Certifications

Google Cloud Skill Badges

Hands-on Google Cloud training covering cloud infrastructure, networking, Terraform, GKE, IAM, VPC networking, containerized applications, and distributed systems concepts.

Cloud Skills Profile