Jacob Marsh Email & Phone Number

Longmont, Colorado, United States

During the course of the summer, I had the opportunity to acquire a diverse range of technical and non-technical skills, achieving significant personal and professional growth while interning at Seagate Technology. Notably, my role encompassed the design and enhancement of the controller motherboard schematic for the forthcoming iteration of the redundant storage architecture, a pivotal component of multi-petabyte enterprise systems employing SAS and SATA hard drives.This undertaking entailed a comprehensive set of responsibilities, including integrating a advanced processor into a controller via PCIe Gen 5 and DDR5, updating the clock network and the sideband communication network with I2C, SPI, and Ethernet, configuring the boot Flash hardware for multiple programming paths. I collaborated with senior engineers to guide the process and we developed presentations aimed at various engineering teams and upper management, effectively communicating intricate technical details.Furthermore, I conducted thorough testing of the peripheral electronics associated with a novel chassis capable of accommodating up to 100 hard drives, contributing to the ongoing innovation within the organization. I wish to extend my heartfelt gratitude to the entire team for affording me this remarkable opportunity to contribute and grow professionally.

Philadelphia, Pennsylvania, United States

I was honored to be selected for the Penn Undergraduate Research Mentorship Program, where I dedicated my summer to pioneering research in the laboratory. During this period, I focused on the development of innovative prediction models for patients afflicted with epileptic seizures, harnessing data encompassing brainwave activity, heart rate, and phenotypical attributes.My responsibilities encompassed the creation of a Python-based data processing pipeline. This pipeline was designed to extract intricate signal features, process them, and subsequently employ Scikit-Learn and TensorFlow models to forecast the precise timing of seizures in patients. The overarching objective was to attain a predictive accuracy level of identifying seizures up to one hour in advance, aiming for a success rate of 95%.I am pleased to report that the developed pipeline demonstrated a moderate degree of success in identifying pre-ictal episodes. The pipeline emphasizes the extraordinary potential of early seizure detection system for epilepsy patients.

Master'S Degree, Electrical Engineering

Bachelor Of Engineering - Be, Electrical Engineering