Research

EASY Lab

The Evolutionary and Adaptive SYstems Lab (EASY Lab) is my undergraduate research group at Rose-Hulman. We investigate questions about evolution, development, learning, and adaptation using computational models. Students join through the Genetic Algorithm Research Project in CSSE220 or through direct recruitment, and they contribute as genuine co-authors on peer-reviewed publications.

Since 2021, the lab has produced 13+ student co-authors and 10+ publications.

Research Interests

Neuromodulation, artificial neural networks, neuroevolution, artificial life, evolutionary computation, affective computing, cognitive development, philosophy of mind, minimally cognitive agents, evolvable hardware, embodied/embedded cognition, evolutionary development, origins of life, origins of learning, origins of consciousness.

Active Projects

Modeling Evolutionary Development (EvoDevo)

2019–2024 – We build minimal models of development in tunably rugged NK fitness landscapes to understand how developmental processes interact with evolution. This project has been a core thread of the lab, producing three ALIFE publications.

Students: Jacob Ashworth, Yujung Lee, Jackson Shen, Edward Kim, Zach Decker, Julian Fiorito, Alexa Renner, Emile Marois

Evolvable Hardware

2018–present – In collaboration with Derek Whitley (IU), we evolve analog circuits using FPGAs through intrinsic evolution. Published in ALIFE 2021 and invited to the Artificial Life journal.

Project website: evolvablehardware.org

Students: Nicklas Carpenter, Allyn Loyd, Daniel Gaull, Logan Manthey, ____

Biologically Plausible Reinforcement Learning with CTRNNs

In collaboration with Eduardo Izquierdo (IU), funded by an NSF Research Opportunity Award (~$24,000, Grant 1845322). Published in Frontiers in Computational Neuroscience (2022).

Students: Cooper Anderson, Adrian Wang

Developmental Artificial Neural Networks

Growing networks, brain-body co-development, and activity-dependent development. This line of work spans GECCO 2022, ALIFE 2024, and the Artificial Life journal.

Students: Adrian Wang, Cullen LaKemper, Yingtong Zhang, David Gottlieb, Julian Fiorito, Dylan Luttrell, Helen Wang

Quality Diversity for GANs

Student Mingyang Cai demonstrated that quality-diversity approaches provide robustness to mode collapse in generative adversarial networks.

Education Research

• Exam wrappers: Analyzed ~1,000 student exam scores (IEEE CSEE&T 2020, with Steve Chenoweth)
• Incentive points system: Published in SIGCSE 2025 (with Aaron Wilkin, Mitchel Daniel)