Resume

Co-founded a neurotechnology venture building EEG-powered fatigue detection for the logistics industry. I direct research, the machine-learning pipeline, and academic and industry partnerships. The company is valued at $5M with $100k in pre-seed funding.

• Architected real-time ML pipelines that classify driver vigilance states from in-ear EEG, producing a proprietary Vigilance Score that outperforms camera-based fatigue classification by 40%.
• Built on-device inference models triggering haptic feedback within milliseconds, with encrypted local processing for data privacy.
• Recruited and supervised two interns (an EE PhD candidate and a CS undergraduate); led IRB submission for human fatigue trials at JHU Bayview.
• Co-authored a provisional patent on a DOT-compliant in-ear EEG headset; partnered with IDUN Technologies for consumer-grade hardware.
• Secured pilots with Terminal Transportation and Old Dominion Freight Line and a research partnership with the U.S. Army Aeromedical Research Laboratory (USAARL).
• Selected for Antler NYC and the JHU Pava Center Spark/Ignite/Fuel/Blaze accelerators; recognized at e-Fest, America 250, Telora, and the Carey Venture Showcase.

Independent and honors-thesis research on closed-loop neuromodulation for autonomic control after spinal cord injury. Awarded the Provost's Undergraduate Research Award (PURA) and a Neuroscience Departmental Summer Research Award.

• Engineered closed-loop epidural stimulation systems to modulate mean arterial pressure in post-spinal-cord-injury rats using multi-modal physiological signals.
• Led an independent project comparing focused ultrasound (FUS) to invasive vagus nerve stimulation — now the basis of my honors thesis and a first-author publication.
• Fabricated custom PEDOT:PSS in-ear sensors on PDMS substrates and built Python/MATLAB pipelines for R-peak detection and heart-rate-variability analysis matching clinical gold-standard electrodes.
• Performed 30+ rat laminectomies and assisted in 10+ porcine surgeries; authored an approved IACUC amendment to launch autonomic dysreflexia research.
• Contributed to 9 papers (2 first-author) and presented at the 2026 Design of Medical Devices Conference and Yale Bouchet Conference.

Developing a clinical platform to characterize pain signals through in-ear EEG, validated against a 64-lead EEG cap.

• Hand-soldering in-ear electrodes and building a clinical-trial-ready data-collection and stimulation interface.
• Integrated an Arduino-based controlled pulse generator to synchronize recording software with the raw data stream.
• Attending cardiovascular surgeries on porcine models to contextualize the physiological basis of pain signaling.

Clinical EEG research identifying early biomarkers of postoperative delirium in spine surgery patients.

• Consent patients, administer pre-operative cognitive assessments, and apply the Ceribell rapid EEG headset with signal-quality verification before surgery.
• Monitor and maintain EEG signal integrity intraoperatively while coordinating with surgical and nursing teams.
• Assess patient alertness and comfort in the post-anesthesia care unit before final data collection.

• Responded to 70+ calls across lower-income Baltimore County neighborhoods, serving patients in homelessness, substance-use, and mental-health crises.
• Participate in Baltimore's Narcan leave-behind program, distributing naloxone at no cost.
• Completed station-based field training, HAZMAT, CPR/BLS recertification, and fire-pole certification; help run firehouse community events.

• Set up and tested experimental devices and mapped circuit-level dynamics from raw data.
• Built a Raspberry Pi simulation replicating the brain’s attentional pathways.
• Mentored two high-school students in Python, machine learning, and hardware toward a conference-ready Young Engineers demonstration.

• Contributed to an Abbott Invention Disclosure for gait analysis using a spine-implanted inertial measurement unit (IMU).
• Initiated algorithm development for wearable-sensor data collection.
• Supported data processing for the REALITY study analyzing long-term outcomes of 2,000+ neurostimulation patients.

• Researched early diagnosis for non-lactating mothers using machine learning.
• Invited as a presenter at the BMES Annual Conference for a selective high-school poster competition.