Promoting a cross-disciplinary machine learning community to share, learn and connect
OxfordXML brings together researchers across different disciplines to share and learn about the impact of machine learning in their respective fields, such as physics, finance, social sciences, engineering, healthcare, and psychology.
“What we want is a machine that can learn from experience”
Alan Turing
our mission
To provide a cross-disciplinary platform for Machine Learning researchers to share, learn and connect with other researchers across different disciplines
our community
We bring together researchers from various disciplines to build a community that promotes knowledge transfer, networking and interdisciplinary collaboration.
our activities
Termtime hybrid talks, held at Wolfson College and online, discussing different applications of Machine Learning in an informal and supportive setting.
our audience
We welcome anyone interested in learning more about Machine Learning applications across different fields. Past attendees of our events include postgraduate students, early career researchers and professors.
upcoming events
what's coming up
To receive notifications, please join mailing list.
May 2023
Diversified Dynamic Routing for Vision Tasks
  • Speakers: Csaba Botos & Ashkan Khakzar
    Time: 14:30-16:00
    Seminar Room 2, Wolfson College & Online (Teams link)
  • There are two consequite talks in this seminar - Csaba's & Ashkan's:
    1) Csaba is a 4th year DPhil student working in the Torr Vision
    Group, focusing on self-supervision and continual learning.
    He earned his degree in computer science and bionic engineering
    at Pázmány Péter Catholic University and has been awarded best
    student paper two times. Recently he presented his work on Dynamic
    Routing models at ECCV 2022, on the 3rd Visual Inductive Priors for
    Data-Efficient Deep Learning Workshop. Currently he is collaborating
    with Intel Lab's Embodied AI team to understand model fine-tuning
    under evolving data distributions.
    2) Additionally, Ashkan's talk will explore feature attribution for
    interpreting vision neural networks and their black box mechanism.
    We will cover the main approaches to feature attribution and the
    cases when they could fail and when the explanations could lie.
    Additionally, the talk will delve into several approaches beyond
    attribution to provide even deeper insights into how vision neural
    networks operate.All concepts will be presented in an accessible and
    intuitive manner for a general audience.

May 2023
Towards Safe & Robust AI for
Image-guided Diagnosis
& Intervention
  • Speaker: Dr Mobarakol Islam
    Time: 14:30-16:00
    Levett Room, Wolfson College & Online (Teams link)
  • There will be cake and coffee/tea.
  • Although AI has enormous potential to accelerate
    healthcare, there are very few examples of AI-based
    medical systems to translate into clinical practice
    due to the concerns of AI algorithmic trust, safety,
    and transparency. The key limitations of current
    AI-enabled systems, incl. recent foundation models,
    are reliability, technical robustness, fairness,
    and transparency. In particular, AI models are (i)
    poorly robust: the performance drops significantly
    on data variation; (ii) unreliable: overly confident
    in prediction and unable to provide feedback when a
    prediction is wrong or confusing; (iii) fairness and
    bias: underdiagnosis towards a certain population;
    (iv) catastrophic forgetting: disruption of previously
    learned tasks with the training of novel tasks in a
    constantly changing environment. In this talk, I will
    discuss some of my works toward safe and reliable AI
    in the applications of image-guided diagnosis and
    intervention. More specifically, the novel methods
    on uncertainty and confidence calibration, perturbation,
    computational stress testing, feature-level
    regularization, curriculum Fourier domain adaptation,
    and synthetic continual learning with vision-language modeling.

Jun 2023
Innovating in Medical Device Development
  • Speaker: Dr Miguel Xochicale
    Time: 14:30-16:00
    Seminar Room 2, Wolfson College & Online (Teams link)
  • Miguel is a Research Engineer at University College London within
    the Advanced Research Computing Centre and WEISS where he is
    advancing AI-based Surgical Navigation tools. Previously, he was
    a Research Associate at King's College London where he advanced
    research in Ultrasound-Guidance Procedures and AI-enabled
    echocardiography pipelines. In 2019, he was awarded a Ph.D.
    degree in Computer Engineering from the University of Birmingham,
    researching "Nonlinear Analysis to Quantify Movement Variability in
    Human-Humanoid Interaction". His primary research interests are in
    developing data-centric AI algorithms for Medical Imaging, MedTech,
    SurgTech, Biomechanics and clinical translation. Additionally, his
    work includes generative models for fetal imaging, sensor fusion
    data from time-series and medical imaging, real-time AI for
    echocardiography, image-guided procedures, AI-based surgical
    navigation tools, and child-robot interaction in low-resource countries.

    There will be cake and coffee/tea.
Past events (2022/2023)
Events held and scheduled by us
Mar 2023
Dr. Martin Frasch
Recording of the seminar can be viewed here.
More information:
  • 02:30 PM - 04:00 PM
  • Wolfson College
Feb 2023
Mr. Valentin Hofmann
Recording of the seminar can be viewed here.
  • 02:30 PM - 04:00 PM
  • Wolfson College
Feb 2023
Dr. Nicolay Rusnachenko
To download the presentation slides: click here.
  • 02:30 PM - 04:00 PM
  • Wolfson College
Jan 2023
Dr Youcheng Sun
  • 02:30 PM - 04:00 PM
  • Wolfson College
Jan 2023
Dr Taha Ceritli
  • 02:30 PM - 04:00 PM
  • Wolfson College
Dec 2022
Ms Helen Theissen
  • 02:30 PM - 04:00 PM
  • Wolfson College
Jun 2022
Dr Daniel Asfaw
  • 03:30 PM - 05:00 PM
  • Florey Room, Wolfson College
jun 2022
Dr Yi Yin
  • 1:00 PM - 2:30 PM
  • Florey Room, Wolfson College
jul 2022
Ms Andrea Rodriguez Delherbe
  • 3:00 PM - 4:30 PM
jul 2022
About Us
A vision for an open, cross-disciplinary machine learning network
Founded in 2019 by Dr Stephen Suryasentana (now at University of Strathclyde) and Dr Yaling Hsiao, OxfordXML (where XML stands for Cross-disciplinary Machine Learning) is a research cluster based in Wolfson College that aims to provide a cross-disciplinary platform for researchers across different disciplines to come and share their experience on how machine learning or artificial intelligence has impacted on their research. Furthermore, it hopes to provide an open community for researchers across different departments to connect and initiate interdisciplinary collaboration.
  • Dr Yaling Hsiao
  • Dr Yi Yin
  • Prof Antoniya Georgieva
To join our mailing list: CLICK HERE
Contact us below if you are interested in speaking at our events