Shervin Ghasemlou
Machine Learning Scientist
Roboticist
I am a graduate research assistant in the Department of Computer Science and Engineering at University of South Carolina, where I am a member of the SCARR lab. My main research interests include: Machine Learning, SLAM, Machine Vision, Automatic Robot Design, and Algorithms.
You can find my C.V. here.
Objective
Goals and Interests
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The broad goal of my research is to design and implement highly scalable algorithms that enable complex systems, particularly the ones that utilize machine learning and machine vision, to operate autonomously, robustly, and inexpensively.
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My main interests include, but are not limited to Machine/Robot Learning(Deep Learning, interpretable ML models), Robot Design(Minimality in design), Machine/Robot Vision(visual based SLAM), Planning(Path/Motion Planning), and Algorithms in general.
Education
PH.D., COMPUTER SCIENCE
2015 -2019 (expected)
University of South Carolina
Dissertation Title: Algorithmic Robot Design: Label Maps, Procrustean Graphs, and the Boundary
of Non-destructiveness
Supervisor: Jason O’Kane
M.SC., ROBOTICS ENGINEERING
2011-2013
Thesis Title: Homecoming: a multi-robot exploration method for conjunct environments with a systematic return procedure
Supervisor: Ali Mohades
Amirkabir University of Technology
B.SC., COMPUTER ENGINEERING
2006-2011
Project Title: The Problem of Finding Scalar Generators of a Natural Number
Supervisor: Majid Namazi
Urmia University
MY RESEARCH
My current research is focused on Machine Learning, Vision based SLAM and Multi-robot Systems. To See my publications please refer to my Google Scholar page.
Simultaneous Localization and Mapping (SLAM)
Aqua is an under water robot. Here it collects some visual data sets for a project of underwater mapping. See here.
Machine Learning based Robot Design
Should your robot abort its hundreds of million of dollars mission to Mars because one of it's sensors or actuators has gone bad? See this paper.
Multi-Robot Exploration
Sometimes the team of robots exploring an environment need to get back to the base station, for a variety of reasons, including charging and delivery. This should be done preferrably with an equilibrium in task allocation(see this paper).
