Greg Hamerly

Greg Hamerly is a Professor and former Interim Chair (2022–2025) in the Department of Computer Science at Baylor University’s School of Engineering and Computer Science. He holds a PhD from the University of California, San Diego (2003) and specializes in machine learning, particularly unsupervised learning and data clustering, with real-world applications in medical imaging and ecological monitoring.

• PhD, University of California San Diego (2003)
• MS, University of California San Diego (2001)
• BS, California Polytechnic State University (1999)

His research centers on the development and application of efficient machine learning algorithms. Key areas include improving k-means clustering techniques, federated learning, and applying AI to early detection of retinoblastoma through leukocoria in photographs and monitoring invasive aquatic species using video data. He has also contributed to computational thinking curriculum development.

His recent publications reflect strong trends in unsupervised and self-supervised learning, with a focus on algorithmic efficiency, robustness, and real-world deployment in healthcare and environmental science. His work often combines clustering, deep learning, and computer vision for practical applications.

• Dubious awards (as humorously noted on personal website)
• Chief Judge for ICPC South Central USA regional competition (2018, 2019)
• Site Director for ICPC South Central USA regional competition (2010–2013, 2015–2019)
• Member of ICPC Live Analytics team at ICPC World Finals (2011–2015)
• Participant in ICPC World Finals as contestant (2000, 2001)

Greg Hamerly has advised numerous graduate students, including PhD candidates now at Texas A&M Kingsville and Meta. He has secured research funding from the NSF, NIH, Department of the Interior, and Texas Parks and Wildlife Department. He is also deeply involved in competitive programming, having coached students and developed the Competitive Learning course at Baylor with over 300 original problems.

He leads research projects on eye disease detection and invasive species monitoring, often in collaboration with interdisciplinary teams. His lab integrates algorithmic innovation with practical deployment, emphasizing accessibility and real-world impact.