Design, build, and test a new process and measurement equipment for NAVAIR to use for certifying air transport worthiness of cargo and vehicles.

Rational design of a LiMn2O4 composite cathode through investigation of the impact of catholyte mechanical properties, cathode active material particle size distribution, and cathode active material content on ionic transport, stress evolution, capacity retention, and mechanical degradation.

EmpowerHer Network is a robust mobile application designed to provide a secure, women‑centric platform for professional networking and personal engagement. It merges career-development tools with customizable visual profiles to foster meaningful connections. Core features include AI‑driven mentor‑mentee matching, comprehensive career progression tracking, and a gamified system of professional challenges that incentivizes skill‑building. Users can create and share posts, participate in industry and interest‑based communities, and communicate via in‑app messaging and live video sessions. The app’s freemium model offers essential networking tools at no cost, with premium access to advanced mentorship and job‑boost features. Built on modern Android components—Firebase for data storage and authentication, TensorFlow for intelligent recommendations, WebRTC for live video interactions, and OAuth‑secured sign‑in—EmpowerHer ensures high performance, scalability, and security. With its minimalist design, intuitive navigation, and focus on inclusivity, EmpowerHer Network empowers women at every career stage to advance professionally and connect authentically and achieve long‑term career success.

This project showcases the functional verification and live demonstration of a custom silicon chip implementing a Recurrent Spiking Neural Network (RSNN)—a bio-inspired architecture designed entirely through natural language prompts to a Large Language Model (LLM), specifically ChatGPT-4.
The RSNN was synthesized from Verilog code generated by ChatGPT, validated on tasks like XOR, IRIS classification, and MNIST, and ultimately fabricated through the open-source TinyTapeout ASIC shuttle using SkyWater’s 130nm process.
To verify the fabricated chip, our team developed a complete hardware-in-the-loop test framework using microcotb, a Python-based cocotb-like environment adapted for embedded systems. We also created custom MicroPython firmware for the Raspberry Pi Pico to enable direct communication with the chip for streaming neuron parameters, injecting spike inputs, load test scripts and reading output spike trains in real time.