I studied Computer Science (Information systems) for my Bachelor’s. Due to my keen interest in learning, and solving complex problems I started my research career early as a sophomore student. My first paper titled “Eye Detection based on SVD Transforms” submitted to and accepted in International Journal of Imaging Systems and Technology in 2004. From 2003 until 2008 I worked on Image processing, computer vision, and pattern recognition in collaboration with IPM, Iran, CVAP LAB., Sweden, and Robotics group of Cognitive Neuroscience Dept., Germany.
During my Master, I studied Neural and Behavioral Sciences and did research at International Max Planck research Institute, Germany. My goal was to have innovative work in the area of machine learning inspired by cognitive science and neuroscience, and simultaneously providing machine learning methods that can solve problems in these fields. My paper titled “Mathematical Modeling of a Biological Odometry”, published in Cogsci2012, and my Master thesis in “Neural Basis of Category Learning” (scored excellent) are examples of these researches.
During my PhD I created new family of Statistical Kernel methods besting the state-of-the-art of Machine Learning capable of handling noisy high dimensional data with nonlinear relationship and complex structures. Using these methods I solved problems such as dimensionality reduction, sparse structured regression analysis, kernel selection for structured data, statistical hypothesis testing, and model selection on challenging real world problems in computer vision, robotics, and neuroscience.
I have written and got approved SNF grants covering a total of 18 months of funding, aimed at visiting 4 top-notch labs abroad: The Gatsby Computational Neuroscience unit and CSML center (UCL), United Kingdom, COSMAL Lab. (UCSD), United States, and the Institute of Statistical Mathematics (ISM), Japan. I have collaborated with, and am endorsed by, most of the top world experts in my field.