Dr. Pamela Douglas

Professor Computational Neuroscience

Dr. Pamela Douglas stands out as a distinguished figure in neuroscience, armed with a remarkable resume brimming with groundbreaking research. Her relentless pursuit of knowledge and unrivaled dedication to unraveling the complexities of the human brain and its development has propelled her to the forefront of her field. Dr. Douglas’s groundbreaking research challenges conventional wisdom, fostering more effective medical interventions, academic strategies, and developmental assistance. Dr. Douglas embarked on her academic journey at Johns Hopkins University, where she laid the foundation for her illustrious career. Here, she earned a Bachelor of Science in Biomedical Engineering & Math, equipping her with a multidisciplinary perspective. Building upon this strong base, she pursued further academic excellence at the University of Pennsylvania, obtaining a Master of Science in Bioengineering. This program nurtured her research capabilities and prepared her to delve deeper into the intersection of engineering and biological sciences. Ultimately, she completed her Doctor of Philosophy program in Neuroengineering at UCLA, cementing her status as a computational neuroscientist. Throughout her career, Dr. Douglas has honed her expertise in neuro-engineering, an intricately specialized field that demands a profound understanding of brain development. Driven by her analytical prowess, she has collaborated with prestigious institutions to explore computer simulations, mathematical models, and theoretical analysis as tools to unravel the mysteries of the brain. Her notable positions include serving as a Computational Neuroscientist at the David Geffen School of Medicine at UCLA, a Post Doctoral Fellow at UCL at the Wellcome Trust Centre for Neuroscience, a Klingenstein Third Generation Fellow at UCLA, and a National Space Biomedical Research Fellow at NASA’s Johnson Space Center. Her research has focused on developing brain computational models, exploring neuroimaging techniques for attention analysis, and employing transcranial ultrasound to investigate 1/f spectral patterns.

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