Blessing Andrew Okoro
Graduate Research Assistant - Wireless Ai & Spectrum Analytics @University at Albany
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WORK HISTORY
Graduate Research Assistant - Wireless Ai & Spectrum Analytics @University at Albany
Albany, NY, US
Developing AI/ML frameworks for automated spectrum monitoring and interference detection in complex wireless environments. Research focuses on unsupervised learning methods for real-time transmitter detection, harmonic interference characterization, and distributed localization.Key Projects- SCAN INFOCOM\'25): Unsupervised multi-transmitter detection achieving 95%+ accuracy for 10 concurrent signals in low SNR environments- M-SHarC (Under Review): Multi-spectral harmonic interference detection protecting radio astronomy facilities- MDL (Under Review): Distributed detection and localization with 2x-12x improved accuracy using tensor decomposition- VIA INFOCOM\'24): Platform-agnostic spectrum data trustworthiness assessment- RadView DySPAN\'24): High-sensitivity radar detection enabling 3x reduction in CBRS protection zonesTechnologies: PyTorch, TensorFlow, MATLAB, GNU Radio, USRP, RTL-SDR, POWDER Testbed, Docker, PythonImpact: 5 publications at top-tier venues, contributed to NSF SpectrumX research infrastructure.
EDUCATION
University at Albany
Doctor of Philosophy - PhD, Computer Science
University at Albany
Master's degree, Computer Science
Federal University of Technology Owerri Nigeria
Bachelor of Engineering - BEng, Electrical and Electronics Engineering
ABOUT BLESSING ANDREW OKORO
I\'m a wireless AI researcher developing machine learning frameworks for automated spectrum monitoring and interference detection. I create unsupervised AI systems that detect, characterize, and localize wireless transmitters in complex radio frequency environments.My research bridges machine learning, signal processing, and wireless communications to solve critical challenges in spectrum management, interference mitigation, and network coexistence. I\'ve published at top-tier venues INFOCOM, DySPAN) and deployed my frameworks on advanced wireless testbeds including POWDER and Colosseum. Research Areas- AI/ML for wireless signal processing- Unsupervised learning for spectrum analytics- Real-time transmitter detection and localization- Spectrum sharing and interference mitigation- Radio frequency (RF) signal characterization Technical Skills- Machine Learning: PyTorch, TensorFlow, scikit-learn- Signal Processing: MATLAB, GNU Radio, FFT, Wavelet Analysis- Wireless Platforms: USRP, RTL-SDR, POWDER Testbed, Colosseum- Programming: Python, MATLAB, C, Bash- Networking: Wi-Fi (802.11), LTE, 5G, TCP/IP Impact- 5 peer-reviewed publications at premier venues- NSF SpectrumX Graduate Research Fellowship (20•••••53 years training next-generation spectrum researchers through SpectrumX Summer SchoolI\'m interested in industry research opportunities in wireless AI, spectrum analytics, 5G/6G systems, and ML for communications. Open to connecting with researchers and engineers working on similar challenges.
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