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DERI Seminar with Haitham Ammar from Huawei technologies Research & Development UK

When: Thursday, April 20, 2023, 9:30 AM - 10:30 AM
Where: Zoom

Speaker: Haitham Ammar, Huawei technologies Research & Development UK

Zoom link: https://qmul-ac-uk.zoom.us/j/81148100921

Title: Some Applications of Bayesian Optimisation in Industry

Abstract: Bayesian optimisation (BO) is an effective tool for solving expensive-to-evaluate black-box optimisation problems. Such settings are widespread in the industry, with applications ranging from drug design to physics and robotics. This talk will survey recent combinatorial and high-dimensional BO advancements and reflect upon two essential applications. First, we will discuss how we used BO to enable the practical design of CDR3 regions of antibodies. Second, we will detail an exciting application of combinatorial BO to logic synthesis, an essential step in the EDA pipeline. In both cases, we will demonstrate the BO attains state-of-the-art results while outperforming other methods from genetic algorithms and reinforcement learning.

Bio: Haitham leads the reinforcement learning team at Huawei technologies Research & Development UK and is an Honorary Lecturer at UCL. Prior to Huawei, Haitham led the reinforcement learning and tuneable AI team at PROWLER.io, where he contributed to their technology in finance and logistics. Prior to joining PROWLER.io, Haitham was an Assistant Professor in the Computer Science Department at the American University of Beirut (AUB). Before joining the AUB, Haitham was a postdoctoral research associate in the Department of Operational Research and Financial Engineering (ORFE) at Princeton University. Prior to Princeton, he conducted researcher in lifelong machine learning while being employed as a postdoctoral researcher at the University of Pennsylvania. Being a former member of the General Robotics Automation Sensing and Perception (GRASP) lab, he also contributed to the application of machine learning to robotics. His primary research interests lie in the field of statistical machine learning and artificial intelligence, focusing on Bayesian optimisation, probabilistic modelling and reinforcement learning. He is also interested in learning using massive amounts of data over extended time horizons – a property common to "Big-Data" problems. His research also spans different areas of control theory and nonlinear dynamical systems, as well as social networks and distributed optimization.

 

 

 

 

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