My research focuses on engineering-driven machine learning methodologies for real-world problems in manufacturing and healthcare. The objective is to develop new models that combine learning methods with domain knowledge, in order to improve (i) efficiency, performance, and interpretability of the learning models; and (ii) productivity, scalability, and security of the engineering systems. 

Research areas: Additive Manufacturing, Bio-manufacturing, Healthcare, Machine Learning, Statistics.

Location:
Jialei Chen, Ph.D.
Department of Statistics, University of Georgia 

Alex Adams’s research focuses on designing, fabricating, and implementing new ubiquitous and wearable sensing systems. In particular, he is interested in how to develop these systems using equity-driven design principles for healthcare. Alex leverages sensing, signal processing, and fabrication techniques to design, deploy, and evaluate novel sensing technologies.

Originally a musician, Alex became fascinated by how he could capture and manipulate sounds through analog hardware and digital signal processing, which led him back to his hometown (Concord, NC). Alex completed his BS at the University of North Carolina at Charlotte in 2014 and his Ph.D. at Cornell University in 2021 (advised by Professor Tanzeem Choudhury). Alex then became the resident Research Scientist for the Precision Behavioral Health Initiative at Cornell Tech (NYC) until the fall of 2022, when he joined the School of Interactive Computing at the Georgia Institute of Technology. Currently, his research focuses on the equity-driven design and the development of multi-modal sensing systems to simultaneously assess mental and physical health to enable a new class of mobile health technologies.