About

Assistant Professor · Electrical Engineering

Srinivas Rahul Sapireddy, Ph.D.

College of Engineering · Illinois State University

My work connects hardware-aware artificial intelligence, RF signal intelligence, edge computing, and VLSI system design. I focus on building efficient learning and signal-processing methods that can operate reliably on resource-constrained computing platforms.

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

INSys Lab Publications Teaching Resume

AI

Hardware-Aware ML

Efficient inference for edge systems

RF

Signal Intelligence

Modulation classification and sensing

VLSI

Physical Design

RTL-to-GDSII and hardware systems

Edge

Low-Power Computing

Deployment on constrained devices

🧠

Efficient AI

I design lightweight models, custom activations, and deployment-aware learning methods for hardware-constrained environments.

📡

RF Signal Intelligence

I develop statistical, time-frequency, and cyclostationary feature methods for RF signal classification and analysis.

⚙️

VLSI and Edge Systems

I work on physical design, hardware acceleration, RTL-to-GDSII flow, and efficient embedded computing systems.

👋 About Me

I am an Assistant Professor in Electrical Engineering at Illinois State University. My research integrates signal processing, lightweight machine learning, and hardware-efficient architectures to enable accurate and energy-efficient computation in resource-constrained environments.

My teaching interests include logic design, engineering computation, ASIC physical design, analog integrated circuits, embedded systems, and hardware-aware machine learning.

🤝 Open to Collaboration

I welcome research collaborations in efficient edge AI, RF signal intelligence, hardware-software co-design, VLSI systems, and applied engineering education.

I am also interested in mentoring undergraduate and graduate students through research projects, technical writing, and hands-on engineering design activities.

🔬 Research and Technical Focus

Hardware-Aware Machine Learning Low-Power Edge AI RF Modulation Classification Cyclostationary Signal Processing Statistical Feature Engineering Custom Activation Functions VLSI Physical Design RTL-to-GDSII Flow Timing Analysis Embedded AI Accelerators System-on-Chip Architectures Hardware-Software Co-Design

📍 Academic Path

2026

Assistant Professor, Illinois State University

College of Engineering, Electrical Engineering.

2025

Ph.D., Electrical and Computer Engineering

University of Missouri-Kansas City.

Prior

Graduate Training in EE, CS, and AI

Electrical engineering, computer science, and artificial intelligence foundations.

🏆 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 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

📝 Selected Publications

IEEE SoutheastCon 2026

Bin-Based R-Value Statistical Envelope Analysis for RF Modulation Classification

ACM GLSVLSI 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

Elsevier Memories 2024

Piecewise Linear Approximations of Activation Functions for Neural Networks

View complete list of publications

🛠️ Technical Expertise

Python C/C++ Verilog TCL PyTorch TensorFlow Lite Scikit-learn Docker Yosys OpenSTA Cadence Synopsys

My technical work spans machine learning development, RF signal analysis, digital hardware design, physical implementation, and deployment-oriented system optimization.

🌐 Professional Profiles

GitHub Google Scholar ORCID OpenReview Medium