September 2018
Name: Lila Fakharzadeh
School: Brandeis University
Research Topic: Visual, Auditory, and Vestibular Control of Inverted Pendulum Dynamics in Spatial Orientation
Research Advisor: Dr. James Lackner
Lila received a BA in Neuroscience from Wellesley College in 2013 with a focus on computational and molecular neuroscience. She is currently a PhD candidate in neuroscience at Brandeis University in the Ashton Graybiel Spatial Orientation Laboratory. In college she was an undergraduate researcher (UROP) at the Kanwisher Lab at the McGovern Institute for Brain Research at MIT from summer 2011 to her graduation. There she was exposed to brain imaging techniques such as fMRI and DTI. Upon graduating from college, she joined a clinical research lab at the Brigham and Women’s hospital where she studied multiple sclerosis, a neurodegenerative disease, and acquired experience using wet lab techniques. Her current research is focused on the integration of auditory, visual, and vestibular cues in spatial orientation.
Name: Gregory Dachner
School: Brown University
Research Topic: A Vision-based Model of Crowd Navigation
Research Advisor: Dr. William H. Warren
Gregory is a fifth year doctoral candidate at Brown University, in Cognitive, Linguistic, and Psychological Sciences. His dissertation work is developing an experimentally-driven model of human crowd dynamics that simulates pedestrians based upon visual information. Recent projects have used virtual reality to better understand how people in crowds use optical variables to navigate. His research has been presented in talks at Pedestrian and Evacuation Dynamics 2016 in Hefei, China; the International Conference of Perception and Action (ICPA) 2017 in Seoul, South Korea; and the Visual Sciences Society Conference 2017 in St. Pete Beach, Florida. His published work has modeled the interactions between neighbors in crowds.
Name: Aram Kim
School: University of Southern California
Research Topic: Using immersive virtual reality to assess context-dependency of locomotor skill learning in people with Parkinson’s disease
Research Advisor: Dr. James M. Finley
Aram received a BS in Physical Therapy from Catholic University of Daegu (South Korea) in 2013, and a MS in Biokinesiology from the University of Southern California in 2016. She is currently a PhD student in the Biokinesiology in Locomotor Control Laboratory at the University of Southern California. Her research focuses on characterizing deficits in locomotor skill learning in people with neurological impairments using immersive virtual reality. Specifically, she has been developing a novel, treadmill-based locomotor skill learning task in VR to train people with Parkinson’s Disease and to probe underlying mechanisms of locomotor deficits in this population.
Name: Robert DiPietro
School: Johns Hopkins University
Research Topic: Surgeme Vectors for Context-Aware, Task-Independent Representations of Motion in a Robot Assisted Surgery
Research Advisor: Dr. Gregory D. Hager
Robert is a PhD student in the Department of Computer Science at Johns Hopkins University, where his research focuses on machine learning for complex time-series data. During his PhD, he has worked toward automated skill assessment and automated training in the domain of robot-assisted surgery, and he has worked on recurrent-neural-network architectures that are capable of learning extremely long-term dependencies. Robert also enjoys teaching. At Johns Hopkins, he has TA’d courses such as CS 475/675: Introduction to Machine Learning and has co-taught courses such as CS 482/682: Deep Learning. He also received the Excellence in Teaching Award from the Department of Computer Science in 2016. Before pursuing his PhD, Robert obtained his BS /MS in Applied Physics/Electrical Engineering at Northeastern University, and worked for 3 years as a member of the research staff at MIT Lincoln Laboratory.
If you would like to find out more about the Link Modeling, Simulation and Training Fellows and projects that have been funded in the field of Modeling, Simulation and Training by the Link Foundation, please visit the webpage at http://www.linksim.org/.