Chris R. Sims, Ph.D.
Chris received a B.S. in computer science from Cornell University (2003), followed by a Ph.D. in Cognitive Science from Rensselaer Polytechnic Institute in 2009. After completing his Ph.D., Dr. Sims held a postdoctoral research position at the University of Rochester (in the Center for Visual Science, and Department of Brain & Cognitive Sciences), and joined the faculty at Drexel University in 2013. Dr. Sims's primary research interest lies in understanding how cognitive, perceptual, and motor resources are organized and coordinated towards the efficient achievement of goals in the world. Dr. Sims pursues this research using a multidisciplinary approach, combining computational modeling and behavioral experiments.
Rachel Lerch (graduate student)
Rachel obtained her B.S. in Psychology from Kutztown University (2012), and joined the Applied Cognitive & Brain Sciences Ph.D. program in the fall of 2014. Rachel is interested in understanding the memory, learning, and decision processes that enable people to carry out intelligent adaptive behavior. These interests have influenced specific questions in recent projects such as: How might the costs of misremembering influence how and what we remember? How does motor expertise influence perceptual memory for human motion? As well as, what information sources do people rely upon in the assessment of skill and expertise in human movement? One current project that reflects some of these questions is entitled "Movement in Mind: Perception of Motor Skill and Aesthetics in the Minds of Experts”. It is a collaborative STEAM (Science Technology Engineering Arts Mathematics) project funded by the Drexel ExCite Center ’s 2015 seed grants which explores human performance, perception and memory of expressive and skilled movement in the domains of martial arts and Pilates.
Peter Hitchcock (graduate student)
Peter is a PhD student in the clinical psychology program at Drexel. He is interested in computational psychiatry, and is working with the Sims lab during his final years of graduate school while building computational skills. He is interested in how clinical theory can enhance computational accounts of mental health problems and, conversely, how computational neuroscience can inform mental health treatment, diagnosis, and practice. His current research focuses on how to develop reinforcement learning tasks into sensitive assays for use in clinical science.