The University of Birmingham welcomes new Turing Fellows

The University of Birmingham has welcomed 38 new Turing Fellows, appointed by the Alan Turing Institute as part of its 2021/22 cohort.

The 400 fellows announced by the Alan Turing Institute are drawn from across its 13 partner universities and are established scholars with proven research excellence in data science, artificial intelligence, or a related field. They contribute to new ideas, drive collaborative projects that deliver impact, and help to grow the institute’s research capacity and its diverse network of partner organisations.

The wide-ranging research expertise of the new Fellows ranges across all five of the University’s Colleges, covering areas such as chemistry, engineering, astrophysics, linguistics, law, economics, management climate resilience, brain imaging, cancer and genomic sciences, applied health research, immunology and microbiology.

Turing Fellows are scholars with proven research excellence in data science, artificial intelligence (AI) or a related field whose research would be significantly enhanced through active involvement with the Turing network of universities and partners.

Turing Institute Director and Chief Executive Adrian Smith said, “It gives me great pleasure to welcome this new group of Fellows. This cohort is incredibly multidisciplinary and diverse. They will bring a rich range of expertise and ensure we continue to do world-leading, impactful research.”

The full list of Fellows at the University of Birmingham is as follows:

• Kit Windows-Yule (dynamics of particulate systems)
• Grant Wilson (energy data)

• Tim Albrecht (machine learning for single molecule science)

• Max Little (causal reasoning, machine learning for signal processing, smartphone sensing)
• David Parker (verification of AI systems)
• Miqing Li (optimization)
• Ata Kaban (machine learning theory)
• Per Kristian Lehre (optimization and theory)
• Hyung Jin Chang (computer vision and deep learning)
• Jinming Duan (medical image analysis and deep learning)

• John Easton (railway information systems)

• Xiaocheng Shang (sampling in high dimensional spaces)
• Fabian Spill (mathematical modelling)
• Samuel Johnson (complex systems and networks)
• Jinglai Li (computational statistics and inverse problems)

• Guy Davies (Astrophysical data science, Bayesian inference)
• Matt Nicholl (Astrophysical data science, Bayesian inference)
• Alberto Vecchio  (Astrophysical data science, Bayesian inference)

• Jack Grieve (corpus linguistics)
• Michaela Mahlberg (corpus linguistics)

• Hazel Wilkinson (digital humanities, computer vision)

• Sylvie Delacroix (AI regulation and ethics)

• Petar Milin (computational linguistics)

• Colin Rowat (game theory, explainable AI)

• Yufeng Zhang (operations management)

• Lee Chapman (climate resilience)
• Laura Graham

• Andrew Bagshaw (brain imaging and neuroscience)
• Dietmar Heinke  (human behaviour modelling)
• Ole Jensen (brain imaging and neuroscience)
• Max di Luca (human behaviour modelling)

• Andrew Beggs (Health Data)
• Jean-Baptiste Cazier (Health Data)
• Georgios Gkoutos (Health Data

• Krish Niranthirakumar (Health Data)

• Dylan Owen (Single molecule microscopy and pattern analysis)

• Nicole Wheeler (high-throughput genomic analysis for threat analysis)