About

Srinivas Rahul Sapireddy, Ph.D.

Assistant Professor, College of Engineering, Illinois State University (ISU). My research focuses on low-power, hardware-aware artificial intelligence, RF signal classification, and VLSI design, with an emphasis on edge-intelligent systems.

Hardware-Aware AI RF Signal Intelligence Edge Computing VLSI / Physical Design

INSys Lab Publications Teaching

About Me

I integrate signal processing, lightweight machine learning, and hardware-efficient architectures to enable accurate and energy-efficient computation in resource-constrained environments.

My teaching includes Logic Design and Engineering Computation, with prior instructional experience in ASIC Physical Design and Analog IC Design.

Open to Collaboration

I welcome research collaborations in efficient edge AI, signal intelligence, and hardware–software co-design. I am also interested in mentoring students and engaging in interdisciplinary research discussions.

Research and Technical Focus

Hardware-aware and low-power machine learning for edge-intelligent systems
RF signal and modulation classification using time-frequency and statistical feature analysis
Design of hardware-efficient neural networks, including custom and piecewise-linear activation functions
VLSI system design and physical implementation, including RTL-to-GDSII flow and timing analysis
Embedded AI accelerators and system-on-chip (SoC) architectures

Prior experience includes research and development roles at the Missouri Institute of Defense and Energy and industry experience as an AI Intern at SmartBridge Pvt. Ltd.

Selected Publications

IEEE SoutheastCon, Alabama, USA, 2026 (accepted): Bin-Based R-Value Statistical Envelope Analysis for RF Modulation Classification
ACM GLSVLSI, New Orleans, USA, 2025: Hardware-Efficient Custom Activation Functions for LSTM Networks
IEEE RFCoN, 2025: Statistical Envelope Analysis for Lightweight RF Signal Classification
MDPI Electronics, 2025: Early Detection of Adversarial Examples in Internet-of-Things Networks
MDPI Memories, 2024: Piecewise Linear Approximations of Activation Functions for Neural Networks

View complete list of publications

Awards and Recognitions

Best Paper Award, IEEE RFCoN 2025 (Track 2, Session II)
Dean’s International Scholar Award, University of Missouri–Kansas City
IEEE–Eta Kappa Nu (HKN) Honor Society Membership
CS Balaji Memorial Travel Grant, 2025
Second Place, Hack-A-Roo Fall 2022 (Entrepreneur Track)
Third Place, Hack-A-Roo Fall 2021 (CS/IT Track)

Professional Development and Technical Expertise

Professional training in product management and technology commercialization (CII)
Advanced training in artificial intelligence and MLOps (Duke University, CDAC)
Statistical learning and machine learning foundations (Stanford University)
Mathematics for machine learning (Imperial College London)

Technical expertise includes hardware design and analysis using Verilog, Yosys, and OpenSTA, as well as machine learning development and deployment using PyTorch, TensorFlow Lite, Docker, and Scikit-learn.

Srinivas Rahul Sapireddy