Computer Engineering & Systems Group - Texas A&M University

Congratulations Dr. Sunil P. Khatri and Dr. Xi Zhang for winning the 2020 Outstanding Professor Awards!

This award was presented by the Texas A&M University Department of Electrical and Computer Engineering in recognition of teaching, research, and service excellence in the betterment of the education of our students and to promote our department.

Thank you for your hard work!

Congratulations to Dr. Nick Duffield, Royce E. Wisenbaker Professor I and Director of Texas A&M Institute of Data Science, for being selected by the Association for Computing Machinery (ACM) as a 2020 ACM Fellow.

The ACM Fellows program recognizes the top 1% of ACM Members for their outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community. Fellows are nominated by their peers, with nominations reviewed by a distinguished selection committee. The accomplishments of the 2020 ACM Fellows have driven innovations that ushered in significant improvements across many areas of technology, industry, and personal life.

Dr. Michel A. Kinsy is our new Associate Professor of Electrical Engineering and Computer Engineering. He directs the Adaptive and Secure Computing Systems (ASCS) Laboratory and serves as the Associate Director of TAMU Cybersecurity Center. He focuses his research on computer architecture, hardware-level security, and efficient hardware design and implementation of post-quantum cryptography systems.

Dr. Kinsy is an MIT Presidential Fellow and an Inaugural Skip Ellis Career Award recipient. He earned his PhD in Electrical Engineering and Computer Science in 2013 from the Massachusetts Institute of Technology (MIT). Before joining the TAMU faculty, Dr. Kinsy was an assistant professor in the Department of Electrical and Computer Engineering at Boston University (BU). Prior to BU, he was an assistant professor in the Department of Computer and Information Systems at the University of Oregon, where he directed the Computer Architecture and Embedded Systems (CAES) Laboratory. From 2013 to 2014, he was a Member of the Technical Staff at the MIT Lincoln Laboratory, where he led the Advanced Computer Architecture Concepts sub-group tasked with exploring future secure computing architectures in critical DoD systems.

Dr. Kinsy is excited and looks forward to collaborating with colleagues and mentoring students in designing secure and trustworthy computing systems.

For more information on Dr. Kinsy, please visit https://cesg.tamu.edu/faculty/michel-kinsy/.

Congratulations to Dr. Sunil Khatri!

The  Association of Former Students presented Dr. Khatri with a Distinguished Achievement Award in College-Level Teaching for the Academic Year of 2019.

The Dean of Engineering, Dr. Banks, honored four winner:
– Dr. Sunil Khatri, Electrical and Computer Engineering
– Dr. Kristi Shryock, Aerospace Engineering
– Dr. Aakash Tyagi, Computer Science and Engineering
– Dr. Victor Ugaz, Chemical Engineering

Thank you for contributing to our program Dr. Khatri!

Dr. Jiang Hu received the Texas A&M AgriLife Research 2019 Director’s Award for contribution to the research project on IoT-based intelligent irrigation. This is research in collaboration with AgriLife researchers Dr. Dana Porter and Dr. Thomas Marek.

Agriculture irrigation accounts for the use of two-thirds of the world-wide fresh water resources which is increasingly experiencing shortage due to human population growth and climate change. Technological advances have created numerous bells and whistles that can potentially improve water-use efficiency. However, there lacks a complete solution that makes use of all leverages and provides growers with peace of mind. Jiang Hu and CESG students, Justin Sun, Yanxiang Yang and Hongxin Kong, collaborate with the AgriLife research team to develop a complete solution for intelligent and automatic variable rate irrigation. It integrates spatial data from soil moisture sensors, past water applications and weather forecasts. At the same time, the system must be tolerant of faults and breakdowns. The centerpiece and brain of the system is an intelligent control algorithm. It estimates the amount of water needed for each location according to current soil moisture level and considers the probability of near future precipitation and evapotranspiration. The algorithm uses a machine learning procedure that is sensitive to crop and soil types. The proposed system can simultaneously improve crop yield and reduce water use. A prototyping system has been built in the AgriLife experiment field near Amarillo, Texas and three patents derived from the research have been filed.