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About John

John Treilhard is a technology specialist in the Washington, D.C., office of Fish & Richardson P.C. His practice focuses on patent prosecution in the software, computer, and electrical engineering technology areas, with an emphasis on machine learning (including deep learning, reinforcement learning, and explainable machine learning), neuromorphic computing, image processing, control systems, and computer architecture.

Prior to joining Fish, Mr. Treilhard completed an M.Sc. at the department of biomedical engineering at Yale University, where he developed machine learning algorithms for medical image analysis. He also completed an M.Sc. in applied mathematics at Queen’s University, where he researched the theory and applications of stochastic analysis. Mr. Treilhard has authored/co-authored five scientific publications.

Mr. Treilhard previously held research appointments at the National Institute for Research in Computer Science and Automation (INRIA) in Nice, France, and the department of mathematics at the University of Saarland, in Saarbrucken, Germany.

Limited recognition under 37 CFR 11.9(b) to represent Fish & Richardson clients in patent matters at the United States Patent and Trademark Office.

Publications

John Treilhard, Susanne Smolka, James Duncan, et. al.: “Liver tissue classification in patients with hepato-cellular carcinoma by fusing structured and rotationally invariant context representation.” 2017. Medical Image Computing and Computer-Assisted Intervention (MICCAI).

Susanne Smolka, Wilfred Manzano, Julius Chapiro, John Treilhard, et. al. “The impact of antiangiogenic therapy combined with Transarterial Chemoembolization on enhancement based quantitative tumor response assessment in patients with hepatocellular carcinoma.” 2017. Clinical Imaging.

John Treilhard, Abdol-Reza Mansouri. “Concentration inequalities via Malliavin calculus with applications.” 2015. Electronic Communications in Probability.

Serban Belinschi, Roland Speicher, John Treilhard, and Carlos Vargas. “Operator Valued Free Multiplicative Convolution: Analytic Subordination Theory and Applications to Random Matrix Theory.” 2014. International Mathematics Research Notices.

Rachid Deriche, John Treilhard. “Using Radial NMR Profiles to Characterize Pore Size Distributions.” 2012. SPIE Medical Imaging Proceedings.

Focus Areas

Services

Education

J.D. expected, Georgetown University Law Center (2023)


M.Sc., Biomedical Engineering, Yale University (2016)


M.Sc., Applied Mathematics, Queen's University (2014)


B.Eng., Mathematics and Engineering, Queen's University (2012)

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