Academic Credentials
  • Ph.D., Physics, University of Rochester, 2013
  • M.A., Physics, University of Rochester, 2005
  • B.S., Applied Physics, University of California, Davis, 2003

As a computational physicist with over 15 years of programming experience, Dr. Groves has expertise in algorithm design, software solutions, and regulatory compliance. In the artificial intelligence sector, she has built computational systems for object detection, facial recognition, and feature extraction using image processing and machine learning algorithms. 

Specific projects include the use of artificial intelligence for the prediction of animal biometrics and automated diagnosis and quantification of defects in consumer products.

The emerging field of artificial intelligence regulation demands evaluation of which traditional computer science quality assurance practices are relevant and applicable for systems based in machine learning and deep learning methodologies. Dr. Groves has software quality assurance expertise in traditional computer science sectors, including source code review for FDA approval and other governing bodies. In addition, Dr. Groves has experience evaluating the relevance of traditional computer science quality assurance methodologies for modern autonomous vehicle systems.

Dr. Groves has worked on all aspects of the development lifecycle, including design, development, testing, and maintenance. She has extensive experience in algorithm design, and developing and maintaining accurate documentation of complex software. Dr. Groves' experience includes design and development of scalable, object-oriented software for image processing software performing filtering, edge detection, and feature extraction, for use in mobile applications. Dr. Groves also has extensive experience in data mining and statistical analysis of large datasets.

As a physicist, Dr. Groves has investigated techniques in both experimental high energy physics and theoretical quantum optics. She developed software and analytical solutions to study nonlinear partial differential equations that model ultrashort laser pulses in resonant systems. Using finite-difference techniques and soliton solutions, she derived new methods for storage of broadband optical information for next-generation photonic devices. Her experience in experimental high energy physics includes applying statistical and data mining techniques to search for supersymmetric particles and the Higgs boson.