Our mission is to advance artificial intelligence by exploring the interplay between neuroscience, machine learning, and emerging hardware.
Deep learning's appetite for energy is now the limiting factor almost everywhere it matters: wearables, robots, drones, satellites, implants. Biology solved the same problem on roughly twenty watts. We work backwards from that fact — reading the neuroscience for mechanisms worth borrowing, designing algorithms that survive the translation into analog silicon, and building circuits where memory and computation sit in the same place.
The lab spans fundamental and applied work, from SPICE-level device models to training algorithms and security analysis, with impacts across healthcare, transportation, and defense.
News
RIT researchers build funding momentum
A new cohort of faculty researchers reflects how the university keeps growing its research ecosystem, supported by proposal-development workshops and seed funding.
→ 30 May 2025Machine learning predicts where the HHL quantum algorithm will pay off
Quantum Zeitgeist covers work by Sonia Lopez Alarcon and Cory Merkel, both associate professors of computer engineering, with Mark Danza '25 MS.
→ 22 Jan 2025AI systems edge closer to processing information the way people do
Cory Merkel joined more than a dozen neuromorphic computing researchers on findings published in Nature.
→Read the work.
Papers on memristive learning circuits, spiking network hardware, reservoir computing, and the adversarial robustness of neuromorphic systems.
Browse publicationsWe're looking for people who like hard, unglamorous problems.
Openings for PhD, MS, and undergraduate researchers in computer engineering, electrical engineering, and adjacent fields. Circuit designers, algorithm people, and anyone who wants to work between the two are all welcome. Email a CV and a paragraph on what you'd want to build.
Email the lab