Where Are They Now?
Clarissa Shephard (currently working in Dr. Stanley's lab at Gatech)
The computational neuroscience training grant gave me the foundation I needed to pursue my thesis project. Every week, I am able to participate in weekly computational neuroscience meetings where the local neuroscience community comes together to discuss journal articles and methods. These interactions allowed me to meet the faculty working in this field while also gaining exposure to a variety of techniques/methods. I also took a few courses based in computational neuroscience over my first two years on the training grant, which only helped to reinforce the lessons learned during our weekly computational neuroscience journal clubs and methods clinics. During my first year of the training grant, I had the opportunity to participate in rotations in the labs of Robert Liu, Garrett Stanley, and Robert Butera - which was great. I gained first-hand experience in a large number of new modeling techniques and learned about the types of problems that benefit from computational techniques (all of them!). Through the training grant, I was also able to attend the Okinawa Computational Neuroscience Course and host an invited speaker! In my second year, I joined Garrett Stanley's lab working on somatosensory neural coding (which is where I work now!). My project focuses on the role of state in the encoding of somatsensory information in the thalamocortical circuit. Computationally, my work aims to identify feature selectivity (spike triggered analyses) and create informative models about the encoding mechanism of the thalamus (linear-nonlinear-Poisson models, generalized linear models, etc). Ultimately, I have received a lot of opportunities through the training grant over the years that continue to impact my project to this day.
Charles Zhao (currently working in Dr. Lu's lab at Gatech)
I continue to work at the Hang Lu Lab at Georgia tech, studying synaptic formation in the motor neurons of C. elegans. The Computational Neuroscience Training Grant has provided me with access to a variety of neuroscience training I would have not have otherwise received, as well as teaching me useful computation tools. Just as importantly, the program gave me the opportunity to rotate in labs and meet professors and people I would have not otherwise met. In the course of this, I gained valuable experience, and I even have a paper in preparation with one of my rotation labs.
Seth Koenig (currently working in Dr. Buffalo's lab at University of Washington)
I transferred to the University of Washington with my advisor Elizabeth Buffalo where I am continuing my PhD in Neurobiology and Behavior. We are continuing to study and model viewing behavior in primates. I am also learning electrophysiology in the hippocampus of awake, behaving primates.
The Computational Neuroscience Training Grant helped me learn a variety of computational techniques that have greatly advanced our modeling and data analysis work. Without this background we would never have been able to publish ¿A nonparametric method for detecting fixations and saccades using cluster analysis: Removing the need for arbitrary thresholds¿ in the Journal of Neuroscience Methods. Additionally, we are currently working on 2 modeling papers!
A really awesome aspect of the training grant was the opportunity to present your work at conferences around the world. At conferences like SFN you get to see a lot of these people again. You get really excited about hearing how far their research has come since you last saw them. The connections you form are extremely valuable. One of these connections even started an official collaboration with a lab in Germany!
Allie Del Giorno (currently a first-year CS graduate student at Carnegie Mellon University)
Allie is working on her Ph.D. at Carnegie Mellon University's School of Computer Science. Her current research focus is on advances in machine learning with applications to computer vision. She is an active member the Carnegie Mellon / University of Pittsburgh Center for Neural Basis of Cognition and hopes to work with both computer vision and human vision scientists to improve our understanding of visual perception and computation.