PK Douglas, PhD

Neuroscientist and AI Specialist - UCL Faculty

Specialties & Experience of this Expert Witness

Additional Information

Academic Appointments University College London (2023-) Senior Lecturer Queen Square Institute for Neurology IST SMST (2016-2022) Assistant Professor Modeling and Simulation, Computer Science University of California, Los Angeles (2015-2022) Assistant Research Professor, Researcher Department of Psychiatry & Biobehavioral Medicine Education Johns Hopkins University B.S. Biomedical Engineering, Mathematics Concentration University of Pennsylvania M.S. Bioengineering, Summa Cum Laude University of California, Los Angeles Ph.D. Neuroengineering, 1st Person to Achieve High Pass+ on Graduate Written Qualifying Exam Teaching UCLA Neuroimaging Affinity Course (2016-2022) Graduate course in advanced neuroimaging analysis methods, coordinated by Prof. Jesse Rissman; guest lecture on topics related to machine learning, deep learning, and interpreting decoding models for neuroimaging analysis IST SMST (2016-2022) Brain Inspired AI: (formerly called Modeling Neuronal Systems) - from micro to meso to systems level, an integrated view on how computational neuroscience can inspire new deep learning architectures, and conversely how AI models can be used to model computation in the human brain. Selected student comments: “I appreciate strong interdisciplinary approach between computer science and neuroscience, allowing students from multiple backgrounds to come together and contribute. Most importantly, I appreciate the passion demonstrated during class lectures. A course having all of these elements is truly awesome.” -Student 2018 “Everything about this course was great! 11/ 10” - Student 2021 Research Methods In Modeling & Simulation: appreciating how to identify research questions of import in M&S, design experimental paradigms to answer these questions, and write an effective research paper for publication in this domain Research Practicum: a core curriculum course for SMST students, explores fundamental and applied research on contemporary issues in modeling and simulation including experimental design involving human subjects, generative modeling/simulation, best practices for reporting results in scientific literature, and how to patent and/or protect your intellectual property UCLA Computer Science Guest Lecturer (2016-2018) Decoding Brain Networks Using Deep Learning MOOC University of Waikato, Computer Science (2016-2021) (Online) Online lecturer as part of a MOOC series to educate participants about using the open source Java toolbox, WEKA, a repository for machine learning algorithms Topics include: feature selection, classifying neuroimaging data University of Queensland St. Lucia, Brisbane, Australia (2016) Taught a summer course on machine learning (ML) and multivoxel pattern analysis (MVPA) Topics included: a variety of ML algorithms, methods for dimension reduction and feature extraction, methods for classification that are interpretable, and model selection UCLA NIH-NITP Advanced Neuroimaging Course (2010-2016) Co-coordinated (with Prof. Jesse Rissman and Prof. Mark Cohen) an internationally attended and recognized course in advanced Neuroimaging Topics with live and archival online streaming (2016) Lecturer in this course. Topics included: Dimension Reduction, Machine Learning of fMRI, WEKA, Dynamic Causal Modeling of EEG data, and Independent Component Analysis (ICA) (2010-2013, 2015-2016) Invited Lectures (Selected) 2024 Keynote, IEEE Brain-Computer Interface Meeting, Korea 2023 Organization for Human Brain Mapping (OHBM), Multimodal Workshop 2023 IEEE ICASSP: Unraveling the Brain Workshop 2023 Institute of Pure & Applied Mathematics (IPAM) - Explainable AI Workshop 2022 Keynote, CoSyne MAIN Meeting