Sahand Farhoodi

I am going to work on clinet-side Internationalization project at Google. More specifically, I will be working on machine learning and statistical approaches in language processing (NLP), to find solutions for problems that exist in client-side internatiozation, such as segmentation.

I am currently in fourth year of my Ph.D. Studies in Statistics. My coursework in this program includes Linear Models, Generalized Linear Models, Estimation Theory, Hypothesis Testing, Computational Statistics, Mathematical Statistics, Nonparametric and Semiparametric Data Modeling, Probability Measure Theory, Statistical Machine Learning, Stochastic Processes, and Directed Studies in Neuroscience. I have worked on multiple research projects under the supervision of prof. Uri Eden, where in some of them we were collaborating with other institutes, including Stanford University.

My coursework in the department of Computer Engineering includes Data Structures, Algorithms, Programming, Signals and Systems, Computer Structure and Languages, Operating Systems, Computer Networks, Computer Simulations, Digital Systems Design, Computer Architectures, Database Design, IT Project Managements, and Approximation Algorithms. During this bachelor studies, I served as the teaching assistant for courses Algorithms, Probability and Statistics, and Discrete Mathematics. In addition, I worked on a lattice-based cryptography research project under the supervision of prof. Siavash Bayat Sarmadi.

My coursework in the department of Mathematics includes Linear Algebra, Graph Theory, Combinatorics, Abstract Algebra, Real Analysis, Discrete Mathematics, Mathematical Analysis, Probability, Statistics, Number Theory, Topology, Differential Equations, Stochastic Processes, Numerical Analysis, Calculus, and Complex Analysis. During this bachelor studies, I served as the teaching assistant for course Graph Theory, participated in multiple programming competitions, e.g. Sharif ACM-mathematics devision where my team ended up 2nd and 3rd two consequetive years, and conducted a research project under the supervision of prof. Saeid Akbari where we worked on multiple Graph Theory problems.

In this experience I worked on a group of combinatorical problems that fall into the category of computational geometry and have potential applications in machine learning. In this group of problems, a large set of points in a d-dimensional space is given and the objective is to compute different metrics that determine how sparse are the given points. Examples of such metrics are the largest distance between all of the points (diameter), the sum of distance between every two points, or radius of the smallest circle that can cover all of the points. These problems are specifically interesting when the data size is gigantic, and ordinary algorithms that compute the exact solution are too slow. I developed a novel epsilon-approximation algorithm that finds the diameter of a set of points in linear time. My supervisor during this internship was prof. Pan Hui.