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Professor Usman A. Khan | Homepage - Tufts University Usman A Khan (bio) Amazon Scolar, Amazon Robotics Professor, Electrical and Computer Engineering, Tufts Professor, Computer Science, Secondary Appointment, Tufts Office: 318 B 574 Boston Ave Medford, MA 02155 Phone: (617) 627-5299 Email: khan AT ece DOT tufts DOT edu Education Postdoc, University of Pennsylvania, 2009-2010 PhD, Carnegie Mellon University, 2009 (graduation photo with Prof
Professor Usman A. Khan | Homepage Usman A Khan Professor, Electrical and Computer Engineering Professor, Computer Science, Secondary Appointment Tufts University Office: 135 Halligan, 161 College Ave , Medford, MA 02155 Phone: (617) 627-5299 Email: khan AT ece DOT tufts DOT edu Biography Usman A Khan is a Professor of Electrical and Computer Engineering (ECE) at Tufts University where he also holds a secondary appointment in
Professor Usman Khan | Research Team - Tufts University PhD Students Postdocs Current Mohammad Panahazari (BS Sharif), Sep 2023 to date Sief Addeen Atari (BS Univ of Bridgeport, CT), Sep 2023 to date Alumni Haroon Raja (Postdoc), Feb 2021 to Jan 2022 First or Current Job: Eli Lilly and Company Muhammad Ibrahim Qureshi (PhD), Near-shot Learning: Theorey and Applications, Dec 2023 Ran Xin (PhD), Aug 2022, PhD from CMU co-advised by S Kar
Professor Usman Khan | Teaching Completed Lecture-based Classes EE 130, Distributed Machine Learning and Control: Spring 2021, 2022, 2023 EE 130, Networked Estimation and Control: Spring 2012, 2014, 2016, 2018--2020 EE 105, Feedback Control Systems: Fall 2011--2017, Fall 2020--2023 Information Processing over Graphs: Spring 2015 at KTH, Stockholm, Sweden Special Topics Independent Study Classes Fall 2020: Measure and
High dimensional consensus in large-scale networks: Abstract In this thesis, we develop the theory of High Dimensional Consensus (HDC), a general class of distributed algorithms in large-scale networks HDC relies only on (i) local information, (ii) local communication, and (iii) low-order computation, and, hence, is ideally suited to implement network tasks under resource constraints, e g , in sparse networks with a limited computation budget