Ravi Teja Pagidoju
About My Background:
I'm a Senior Software Engineer with 9+ years of experience in software engineering and AI/ML research. I pursued MS in Applied Computer Science and am also pursuing PMBA currently. My research focuses on practical applications of machine learning, optimization techniques, and generative AI models across various domains.
Published Work:
My recent publications include:
Planogram Synthesis using Diffusion Models - Published by Springer (constraint-aware generative models for spatial optimization)
LSTM Compression Techniques - Accepted at IEEE ICUIS 2025 (neural network optimization for resource-constrained deployment)
Generative AI for MES Optimization: LLM-Driven Digital Manufacturing Configuration Recommendation- Published in International Journal of Applied Mathematics (LLM-based optimization for manufacturing systems)
Comparative Analysis of Optimized GCD and Hybrid LLM-GCD Approaches for Retail Shelf Space Allocation - Published in European Journal of Information Technologies and Computer Science (hybrid approaches combining LLMs with classical optimization)
Cost-Performance Analysis of Cloud-Based Retail Point-of-Sale Systems: A Comparative Study of Google Cloud Platform and Microsoft Azure
ResearchGate: https://www.researchgate.net/profile/Ravi-Teja-Pagidoju/research
Peer review Experience:
I have reviewed papers at IEEE Transactions on Industrial Informatics Journal(Q1).
I have judged multiple hackathons, DECA startup pitches, business intelligence awards,.
I’m also a mentor at Fuel accelerator (
https://www.fuelaccelerator.com) , an active member in Retail AI Council.
My Speaking Experience:
Presented at Generative AI Expo 2026
Presented at NWA Tech Fest
Presented Keynote at SCRS ConferenceSCRS Conference
Regular knowledge sharing within engineering teams
Email: Pagidojuraviteja1@gmail.com
Session
Deploying deep learning models for time-series forecasting at retail scale presents a fundamental tension between prediction accuracy and computational cost. This talk presents a Python-based framework combining structured pruning, quantization-aware training, and knowledge distillation to compress LSTM networks for demand forecasting. Using NumPy, TensorFlow/Keras, and scikit-learn, we achieved 47% accuracy improvement over baseline models while reducing model size by 73% and inference costs by 92%. We discuss practical implementation patterns, reproducibility considerations, and how these compression techniques generalize beyond retail to any domain requiring efficient sequential prediction at scale.