New Fellows join Reuben College this Michaelmas Term
Ghada Alsaleh (Cellular Life)
Professor Ghada Alsaleh trained as a pharmacist before following her ambition to become a scientific researcher, undertaking a MSc and PhD at the University of Strasbourg. She moved to the University of Oxford as a post-doc in 2017 and established her own research group in 2021 at Botnar Research Centre, investigating how controlling autophagy can intervene in biological aging and age-related diseases.
More recently, Ghada achieved a significant milestone by establishing the UK’s inaugural Space Innovation Lab at the Botnar Institute for Musculoskeletal Sciences. This pioneering initiative serves as a hub for interdisciplinary collaboration, aiming to advance cellular and molecular biology research within the realm of space exploration. The lab's focus lies in enhancing our comprehension of human physiology and health by investigating the impact of microgravity on aging and age-related diseases
Philip Awadalla (Cellular Life)
Philip Awadalla is the Professor of Molecular Genetics at the Nuffield Department of Population Health, as well as the Big Data Institute at the University of Oxford. He also serves as the National Scientific Director of CanPath.
Previously, he was a Professor at the University of Toronto and the University of Montreal Ste Justine Children’s Hospital, where he was Director of the CARTaGENE cohort in Quebec.
Philip and his team have expertise in genomics, computational biology, and epidemiology. The research focus of his team is on healthy aging and resilience, early cancer, and the evolution of somatic mutations. Philip has trained over 30 graduate students and postdocs in health and computational approaches to understanding disease evolution using single-cell, organoid, imaging and population cohort data.
Anne Ferrey (Cellular Life)
Anne is a behavioural scientist with an interest in interdisciplinary working and the use of psychological insights to develop health-related interventions.
She also directs Oxford's MSc in Translational Health Sciences and jointly directs the linked DPhil programme. Anne has a strong interest in mental health, particularly around developing creative or arts-based interventions to improve health and wellbeing. She is currently collaborating with Oxford Health on a novel arts-based intervention for housebound older adults, which builds on her British Academy-funded work, “Crafting Heritage for Wellbeing in Iraq”. Other research interests include innovations for rural and remote healthcare, and the impact of disability on mental health and wellbeing.
Anne founded the “Summer Academy for the Social Science of Health Innovation” and is the faculty mentor for the Translational Health and Medicine Student Society at Oxford.
Ho-Yin Mak (Innovation & Entrepreneurship)
Ho-Yin Mak is a Professor of Management Science at Saïd Business School, University of Oxford. Previously, he served as a faculty member at Georgetown University (2022-25) and the Hong Kong University of Science & Technology (2009-15). He was also a Turing Fellow of the Alan Turing Institute.
He obtained his PhD in Operations Research from the University of California at Berkeley. Ho-Yin’s research interests cover operations research, data science, and operations management. On the methodological side, his work focuses on developing prescriptive analytics methods that convert data into business decisions. On the applications side, his research in operations management aims to develop both managerial insights and analytics solutions to complex problems arising in contexts such as supply chain management, digital retail operations, and smart cities.
Bartek Papież (Artificial Intelligence & Machine Learning)
Bartek Papież leads multidisciplinary research at the intersection of artificial intelligence, biomedical imaging, and health data science.
At Oxford’s Big Data Institute, he directs the Machine Learning & Biomedical Data Research Lab, where his team develops new algorithms for image analysis, data integration, and robust machine learning. A key focus of his work is combining medical images with other sources of information, such as genetic data, electronic health records, and natural language, to address pressing challenges in medicine and population health.
His projects span disease monitoring, the discovery of new treatment targets, and advances in cancer imaging. By uniting cutting-edge AI with real-world biomedical data, Bartek’s research aims to deepen disease understanding, enable earlier diagnosis, and support more precise treatments.
Jun Zhao (Artificial Intelligence & Machine Learning)
Jun Zhao is a Senior Researcher at the Department of Computer Science and fellow of Oxford Martin School. She leads the Oxford Child-Centred AI Design Lab.
Her research focuses on investigating the impact of algorithm-driven decision making upon our everyday lives, with a particular emphasis on families and young children. For this, she takes a human-centred approach, focusing on understanding real users' needs, in order to design technologies with tangible, real-world impacts.
She received her PhD from The University of Manchester. She is on the Advisory Board of Reuben College’s Generation AI Programme and the AI in Education at Oxford University.
Harriet Bartlett (Environmental Change)
Dr Harriet Bartlett is a Research Fellow at the University of Oxford, jointly based in the Department of Biology and the Smith School of Enterprise and the Environment.
She leads the HESTIA Farm Trials, designing large, real-world randomised trials and life-cycle assessments to identify scalable ways to cut greenhouse-gas emissions, reduce biodiversity and pesticide risks, and improve farmer livelihoods in high-impact food systems (e.g., UK pigs, Brazilian beef, Vietnamese coffee, Kenyan maize). Methodologically, she specialises in causal inference, life cycle assessment, farmer engagement and open science.
At the College, Harriet contributes to the Environmental Change theme, mentors students, and works with farmers, processors, NGOs, and policymakers to translate evidence into practice.
Jacob Blackmore (Artificial Intelligence & Machine Learning)
Dr Blackmore is an atomic and molecular physicist with a particular interest in using complex quantum systems for useful applications. Currently, he works with Prof. David Lucas in the department of physics and is specialising in expanding ion trap quantum computers by using networks of single photons. Recently, he has also studied how robust, miniature nodes can be constructed for wider-scale deployment of quantum networks, with Dr Joe Goodwin. This work led to several patents and commercial partnerships.
His Ph.D was completed at Durham University in the group of Prof Simon Cornish. During this time, he studied the limiting factor on the lifetime of ultracold molecules made from rubidium and caesium atoms, as well as developing elementary techniques for the control over the internal hyperfine and rotational state of the same molecules.
He will soon be taking up an Engineering and Physical Sciences Research Council (EPSRC) career acceleration fellowship and starting a new lab.
Sarah Briggs (Environmental Change)
Sarah Briggs is an NIHR-funded Clinical Lecturer in Nuffield Department of Medicine and a practicing clinician, working as a Medical Oncology Registrar at Oxford University Hospitals.
She completed her medical degree and subsequently her MRC-funded DPhil at the University of Oxford. Her current research focuses on the development of environmentally sustainable healthcare, with a particular focus on cancer care, and patient and public perspectives.
Her interests include environmental sustainability, planetary health, bioethics, and health policy. She is also keen to foster interdisciplinary collaborations, and is currently collaborating on an Agile Sprint Award - "NetZeroEd" - which aims to support secondary school curriculum designers to deliver high-quality Net Zero education.
Konstantin Reidl (Artificial Intelligence & Machine Learning)
Konstantin is a Postdoctoral Research Associate in Deep Learning at the Mathematical Institute of the University of Oxford. He obtained his doctoral degree (Dr. rer. nat.) in mathematics from the Technical University of Munich summa cum laude in 2024, with a dissertation on the mathematical foundations of interacting multi-particle systems for optimisation.
His research interests lie in applied mathematics with a particular focus on the design and numerical analysis of algorithms and methods used in machine learning, data analysis, signal processing, and scientific computing, as well as their interfaces. To elucidate their very often intricate behaviour, he employs tools from the analysis of partial differential equations (PDEs), probability theory, statistics, and stochastic analysis together with numerical simulations.
Jiahe Cui (Artificial Intelligence & Machine Learning)
Jiahe is an Eric and Wendy Schmidt AI in Science Fellow, working between the Dynamic Optics and Photonics Group (DOP) at Engineering Science and Oxford Perception Lab (OPL) at Experimental Psychology.
She completed a DPhil in Engineering Science from the University of Oxford in 2022 and her research focused on the design and build of a compact high-resolution neurosurgical microscope with integration of adaptive optics, remote focusing, and machine learning techniques to provide stabilised high-resolution image guidance during surgery.
Following her DPhil, she made a career shift towards high-resolution human ophthalmic imaging technology and is leading the design and development of a multi-functional adaptive optics scanning laser ophthalmoscope (AO-SLO) and a high-speed adaptive optics optical coherence tomography system (AO-OCT). The eye is a window to the brain, and these systems provide a unique set of engineering capabilities to empower ground-breaking research in visual neuroscience, experimental psychology, ophthalmology, neurodegenerative and neurodiverse conditions, and neurovascular and systemic diseases.
As a Schmidt AI in Science Fellow, her work will focus on using AI techniques to understand oculomotor control from high-resolution retinal images and how the central nervous system responds to different visual stimuli. She is also an executive committee member of the Optica Applications of Visual Science Technical Group, a Guest Editor of Photonics, a member of the Engineering Science Researcher Committee, a departmental tutor, and a College advisor. She twice received the Award for Excellence from the University of Oxford.
Auggie Marignier (Artificial Intelligence & Machine Learning)
Auggie is a Schmidt AI in Science Fellow, developing and applying AI & ML methods to the seismic imaging of the Earth's deep interior.
Auggie obtained his PhD in Data Intensive Science at University College London, studying probabilistic inversion techniques to reconstruct seismic images of the Earth’s upper mantle and, looking beyond Earth, maps of dark matter around distant galaxy clusters. These methods, while computationally expensive, provide uncertainty information that is commonly lacking in other methods but is crucial for the scientific interpretation of the images.
Following his PhD, he spent two years as a postdoc at the Australian National University developing new methods to seismically measure the thickness of sediments at continental scales, and improving the resolution of mineral deposits for geophysical exploration. He then worked with the Earth Rover Program, which is pioneering the use of seismology and geophysics to study the health of soils in agricultural settings.
Jonathan Pattrick (Artificial Intelligence & Machine Learning)
Jonathan (he/him) is a Schmidt AI in Science Fellow in the Department of Biology, based just outside the city of Oxford at the John Krebs Field Station in Wytham. His research field is within pollination biology, where he seeks to understand how and why pollinators make choices about which flowers to visit, and how this affects the success and fitness of both sides of the plant-pollinator mutualism. He is particularly interested in how the physical and chemical properties of nectar and pollen interact with bee foraging dynamics and energetics to shape their pollination behaviour.
Following a PhD at the University of Cambridge on the biomechanics of plant-pollinator interactions, Jonathan moved to Oxford as a post-doctoral researcher. Here, he worked on taste perception and floral reward evaluation in several bee species, focussing on nectar sugar composition, and the many minor and often non-nutritive secondary compounds present in nectar.
In his fellowship, Jonathan plans to apply computer vision and machine learning techniques to enhance the resolution and throughput of bee behavioural and biomechanical assays. The aim is to transfer lab-based experimental techniques to the field to improve understanding of the key factors which influence the ecosystem service provided by these pollinators in both natural and agricultural systems.
