Dr Frank Langbein

Biography

Frank C. Langbein received his Mathematics degree from Stuttgart University, Germany, in 1998 and a PhD on “Beautification of Reverse Engineered Geometric Models” from Cardiff University, Wales, UK, in 2003. He is currently a senior lecturer at the School of Computer Science and Informatics, Cardiff University, where he is a member of the Visual Computing Research Section and co-leads the Qyber\black international research network in quantum control and the Healthcare Technologies Research Group. He led the research programme on vision-based geometric modelling and the interface with science at the One Wales Research Institute for Visual Computing (RIVIC). His research interests include control, machine learning and geometry applied in quantum technologies, visual computing, geometric modelling and healthcare. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the American Mathematical Society (AMS).

Education

• 2003: PhD (Computer Science, Geometric Modelling), Cardiff University, UK.
• 1998: Diploma in Mathematics (comparable to MSc), Stuttgart University, Germany.

Career Overview

• 1999 - present: Cardiff University School of Computer Science and Informatics
• 1994 - 1999: Stuttgart University, Mathematical Institute A & B
• 1991 - 1996: Owner of a small computer business

Professional memberships

• American Mathematical Society
• IEEE

Publications

We are currently unable to retrieve the list of publications. Visit our institutional repository.

Teaching

I am currently coordinating undergraduate individual and group projects and provide support to students on these projects. I teach a range of programming techniques, from functional programming, including analog and differentiable approaches, to quantum computing. I have also been teaching imperative and object-oriented languages and can cover anything from machine code, backend, frontend to architecture and operations. I have also been teaching graphics and artificial intelligence.

Teaching material and further details are available on my website Ex Tenebris Scientia and the Qyber\black international research network in quantum control.

My research interests lie in control, machine learning and geometry applied in quantum technologies, geometric modelling, computer graphics, computer vision and healthcare.

My current work is focused on robust quantum control, developing a theory of robust, controlled quantum processes and associated algorithms to find and analyse such controls. It includes work on optimisation and reinforcement learning approaches as well as process tomography and simulation techniques. Initially focused on quantum information transfer, it has been expanded to quantum simulation and computing. The approaches are relevant to a wide range of hardware platforms, e.g., ultra-cold atoms, ion traps and superconducting circuits.

I also work on robust quantum control and machine learning techniques to improve magnetic resonance imaging and spectroscopy. A specific aim is to improve the accuracy of in-vivo quantification of metabolites by developing algorithms to find chemically specific pulse sequences and quantify metabolites in the resulting spectra. I am further interested in learning physical laws and constructing control models from empirical data for biochemical and quantum processes.

Moreover, my work encompasses geometric and solid modelling, processing of meshes, curves and surfaces, pattern recognition, graphics, and computational geometry. Special interests include point-based modelling, design intent of geometric models, approximate symmetries and reverse engineering geometric models.

Further details on my work are available on my website Ex Tenebris Scientia and the Qyber\black international research network in quantum control.

Supervision

I am interested in supervising PhD students on topics in quantum control and magnetic resonance imaging and spectroscopy:

• Quantum control for quantum technologies:
  • Robust quantum control
  • Quantum reinforcement learning
  • Analog quantum computing
  • Learning physical laws and control models
• Magnetic resonance imaging and spectroscopy (MRI/S)
  • Pulse sequence programming and quantum control
  • Metabolite quantification, spectroscopy
  • Early-stage cancer and dementia diagnosis
  • Biochemical mechanisms and control models

Current supervision

Past projects

• Co-Supervisor (70%) for Max Stewart Chandler - New Methods in Quantification and RF Pulse Optimisation for Magnetic Resonance Spectroscopy (awarded 2019).
• Co-Supervisor (50%) for Amir (Navid) Rahimi - Error Estimation for Simplifications of Electrostatic Models (awarded 2016).
• Co-Supervisor (40%) for Zhibin Niu - Declarative CAD Feature Recognition - An Efficient Approach (awarded 2015).
• Co-Supervisor (50%) for Jonathan Quinn - Low-Discrepancy Point Sampling of 2D Manifolds for Visual Computing (awarded 2009).