Dr. Adolfo Perrusquía is a Lecturer in Reinforcement Learning for Engineering at the Centre for Autonomous and Cyberphysical Systems. He completed his BSc studies in Mechatronic Engineering at the National Polytechnic Institute (UPIITA-IPN), MSc and PhD studies in Automatic Control at the Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), and has been awarded by the Mexican Society of Artificial Intelligence with the third place for the best PhD Thesis in Artificial Intelligence 2021. He was a Research Fellow in Reinforcement Learning for Engineering at Cranfield University from 2021 to 2023 and a RAEng UK-IC Fellow from 2021 to 2023. He is extremely interested in system identification, nonlinear control (which includes adaptive and robust control), robotics, deep learning, and especially in reinforcement learning applications, and his current research interests include reinforcement learning, inverse reinforcement learning, system identification, machine learning, deep learning, neural networks, linear and nonlinear control, and robotics.
Prof. Weisi Guo is a Professor of
Human-Machine Intelligence at Cranfield University and was a Turing Fellow. He
completed his BSc studies in Engineering at the University of Cambridge (2005),
MA (Cantab) studies at the University of Cambridge (2011), and PhD studies in
Computer Science at the University of Cambridge (2011). He has published over
250 peer-reviewed papers. Over the years he has won numerous international
awards (IEEE, IET, Bell Labs) and he currently leads a team of 20 researchers
to achieve new breakthroughs across information engineering and social physics.
He is a fellow of the higher education academy, and also a frequent reviewer
for EPSRC (full college member, FLF), NSF, H2020, NSERC, Royal Society,
Leverhulme, USAF, and RAEng funding councils. His research expertise is in
Information, Networks, and Intelligence. In particular, he is interested in
securing and safeguarding distributed learning systems, with a focus on
primary-party design, third-party inference problems, and joint encoding for
integrated sensing communication and computing (ISACC).
