Sunil Reddy Aramreddys

Sunil Reddy Aramreddys

Graduate Research Assistant @Umkc School Of Science And Engineering

Kansas City, MO, US
MOBILE NUMBERS
+18•••••••00

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WORK HISTORY

Jan 2026 — Present

Graduate Research Assistant @Umkc School Of Science And Engineering

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Kansas City, MO, US

Project: Robustness Analysis of AI Inference under RF/EMI Interference• Built a complete end-to-end AI inference pipeline on NVIDIA Jetson using ImageNet models (ResNet-18/50, MobileNet), including dataset preparation, deterministic preprocessing, and PyTorch → ONNX → • TensorRT (FP16/INT8) deployment.• Designed controlled evaluation workflows to measure latency, accuracy, and numerical stability under fixed power, clock, and runtime conditions.• Implemented large-scale bit-flip fault-injection experiments on both weights and activations (sign, exponent, mantissa) across multiple ResNet-18 layers to quantify robustness.• Developed fixed-input vs per-image evaluation modes to separate deterministic repeatability from input-dependent vulnerability.• Built an automated fault-injection pipeline with per-layer parameter sweeps, CSV logging, and aggregation scripts, enabling analysis over thousands of inferences.• Characterized gradual degradation and catastrophic failure regimes using multiple metrics (top-1 accuracy change, NaN/all-NaN rates, L1 and max-absolute output drift).• Designed a deterministic RF/EMI experiment timeline separating warm-up, synthetic workload, and DNN inference for clean RF alignment.• Implemented GPIO-based LED timing markers to synchronize AI inference phases with external RF measurement equipment.• Added a hardware-level anomaly indicator (second LED) using MSE-based classification-head monitoring to detect silent output drift beyond top-1 accuracy changes.• Built GPU-only, CPU-only, and mixed-workload baselines to isolate electromagnetic emissions from individual compute subsystems.• Collaborating with an interdisciplinary EMI lab to correlate RF measurements with AI inference behavior and reviewing Winograd convolution as a potential algorithm-level factor in EMI susceptibility.

EDUCATION

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University of Missouri-Kansas City

Doctor of Philosophy - PhD, Computer Science

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MADANAPALLE INSTITUTE OF TECHNOLOGY & SCIENCE

Bachelor's degree, Computer Science

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University of Missouri-Kansas City

Master's degree, Computer Science

ABOUT SUNIL REDDY ARAMREDDYS

Graduate student passionate about turning AI and biometric innovations into real-world security solutions. Experienced in deep learning, computer vision, and full-stack deployment (Java, AWS, Docker). Focused on building secure and scalable technologies for the future of identity and trust.

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