Current Research Focus Area
My primary research interest is the application of neuromorphic (or brain-like) computing for Machine Learning (ML). Recently I participated in the Intel Neuromorphic Denoising (N-DNS) Challenge, where I earned an honorable mention for my work in encouraging and exploiting synaptic sparsity of Spiking Neural Networks (SNNs) for low power and latency computing on the Loihi2.
In the future, I would like to study brain inspired continual learning mechanisms as well as few-shot learning techniques for SNNs, among other neuromorphic related topics.
Education:
- Master’s in ML, Georgia Institute of Technology, May 2022
- Bachelor’s in CS, Georgia Institute of Technology, May 2020
Publications:
- Co-author of “LLM Guided Evolution — The Automation of Models Advancing Models“, GECCO 2024
- Navy Applications for Machine Learning (NAML) 2024
- Military Sensing Symposium (MSS) 2023
- Lead Author of the publication: “Spikemoid: Updated Spike-based Loss Methods for Classification”, IJCNN 2023
Projects:
- Honorable Mention, Intel N-DNS (Neuromorphic Denoising) Challenge: Encouraged and exploited sparsity in an SNN to achieve low-power, low-latency denoising on neuromorphic hardware.
- Open Source Contributor to Lava-dl, Lava, and Nengo: I believe strongly in open source collaboration and therefore regularly contribute to open-source neuromorphics research.
University Projects
- Implemented a Conditionally Invertible Neural Network (cINN) in Julia for a graduate school project in 2022.
- Led a team to implement an GUI based application to automatically scrape imagery from google to fine tune neural networks in 2022.
- Implemented a novel Neural Architecture Search (NAS) method adding quantization as an additional search dimension of DARTS (Differentiable Architecture Search) in 2021 for a graduate class team project. This method called QDARTS is currently in review for KDD 2025.
- Built and coded a Face Tracking Pan-Tilt Camera using a raspberry-pi and Intel’s Neural Compute Stick (NCS) 2 for an undergraduate prototyping class in 2020
Educational Outreach
- Writing a neuromorphics tutorial series for ML engineers
- Presented my solution for the Intel N-DNS challenge to the Intel Neuromorphics Research Community (INRC) in 2023 and for an undergraduate research class at Georgia Tech.
- Writing multilingual subtitles for opera productions
Professional Experience
- Research Engineer at Georgia Tech Research Institute (GTRI): August 2022 – Present
- Graduate Research Assistant (GRA) at GTRI: August 2020 – May 2022
- Student Assistant at GTRI: 2019 – May 2020
Languages
- English (Native)
- Italian (Advanced)
- French (Intermediate)
Social Media Presence
- Github: https://github.com/Michaeljurado42
- LinkedIn: michael-jurado-691b72101
Graduate AI Coursework
- CS 8803: Imaging: data-driven models (Spring 2022)
- CS 8803: Systems for Machine Learning (Fall 2021)
- CS 7650: Natural Language Processing (Spring 2021)
- CS 7648: Interactive Robot Learning (Spring 2021)
- CS 7649: Robot Intelligent Planning (Fall 2020)
- CS 7632: Game AI (Fall 2020)
- CS 6476: Computer Vision (Fall 2020)
Undergraduate AI Coursework
- CS 2110: Computer Organization and Programing (Fall 2016)
- ECE 2031: Digital Design Laboratory (Fall 2017)
- CS 3651: Prototyping Intelligent Applications (Fall 2019)
- CS 3601: Automated Algorithms Design (Fall 2018, Spring 2019, Fall 2019, Spring 2020)
- CS 4803: Deep Learning (Spring 2020)
- CS 4651: Machine Learning (Spring 2019)
- CS 3630: Introduction to Robotics and Perception (Fall 2018)
- CS 3600: Introduction to Artificial Intelligence (Summer 2018)