Goker Erdoganname surname [at] google.com
I am passionate about taking insights from how we humans learn so much about the world from so little supervision and endowing machines with this remarkable ability.
Previously, I was a research scientist at Cogitai building AI/machine learning solutions to make continual learning practical in a broad range of applications.
Between 2012-2017, I was a graduate student in Dept. of Brain and Cognitive Sciences at University of Rochester and a member of Computational Cognition and Perception lab. My research focus was shape perception, and I used behavioral, neuroimaging and computational methods to understand the nature of the representations and algorithms involved in shape perception. [thesis][cv]
Erdogan G., Jacobs R. A. (2017) Visual Shape Perception as Bayesian Inference of 3D Object-centered Shape Representations. Psychological Review. webpage pdf code supplementary materials
Erdogan G., Jacobs R. A. (2016) A 3D shape Inference Model Matches Human Visual Object Similarity Judgments Better Than Deep Convolutional Neural Networks. In Papafragou, A., Grodner, D., Mirman, D., & Trueswell, J.C. (Eds.) Proceedings of the 38th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. webpage code supplementary materials
Erdogan G., Chen, Q., Garcea F. E., Mahon B. Z., Jacobs R. A. (2016) Multisensory Part-Based Representations of Objects in Human Lateral Occipital Complex. Journal of Cognitive Neuroscience. Vol. 28, No. 6, Pages 869-881. webpage pdf
Erdogan G., Yildirim I., Jacobs R. A. (2015) From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach. PLoS Comput Biol 11(11): e1004610. doi: 10.1371/journal.pcbi.1004610 webpage pdf
Erdogan G., Yildirim I., Jacobs R. A. (2015). An Analysis-by-synthesis Approach to Multisensory Object Shape Perception. Multimodal Machine Learning Workshop. NIPS 2015. webpage
Erdogan G., Yildirim I., Jacobs R. A. (2014). Transfer of Object Shape Knowledge across Visual and Haptic Modalities. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. webpage
TeachingNovember 2018 - Introduction to Machine Learning lecture notes
Spring 2014 - BCS111 Foundations of Cognitive Science
Spring 2015 - BCS153 Cognition
Fall 2015 - BCS183 Animal Minds
A word game based on BERT - "What would BERT say?"
On Conant and Ashby's "Good Regulator Theorem"
Variational Autoencoder Explained
Multiple Kernel Learning for Extracting Protein-protein Interactions
Outlier Detection Toolbox in MATLAB
Mixture of Experts - C Implementation and Sparse Input Data
Mixture of Experts
Informed Search Methods
Uninformed Search and Constraint Satisfaction Problems
Bias Variance Dilemma