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CCAIM AI and Machine Learning in Healthcare Summer School

Monday, 5 September 2022, 9.00am to Friday, 9 September 2022, 5.00pm
Location: Online

Organiser: Cambridge Centre for AI in Medicine

The Cambridge Centre for AI in Medicine are delighted to announce the world’s first Summer School exclusively focused on AI and machine learning for healthcare! From 5 to 9 September 2022, students, researchers, clinicians, and industry experts will join for this unique opportunity to dive into the future of machine learning in healthcare. Over 5 days a range of exciting topics will be covered, including data interpretation, clinical trials, graph neural nets, synthetic data, and many more. Bringing in world-leading experts, there will be engaging talks, a mentorship program and accompanying mini-project for students. Roundtables, networking, and a special online exhibition complete this exciting and productive week. Have a look at the CCAIM website for more details and to sign up.

Forthcoming talks

TBC

Thursday, 7 July 2022, 4.00pm to 5.00pm
Speaker: Heidi Howard, Microsoft Research
Venue: FW11 and https://cl-cam-ac-uk.zoom.us/j/97216272378?pwd=M2diTFhMTnppckJtNWhFVTBKK0REZz09

TBC

Synthetics with Digital Humans

Friday, 8 July 2022, 12.00pm to 1.00pm
Speaker: Dr. Erroll Wood (Staff Software Engineer at Google)
Venue: https://zoom.us/j/6492509351?pwd=U0hoSzJ0anlhRGhzYVFmTzltNk9wZz09 (meeting ID: 649 250 9351 / passcode: 7mu5ZJ)

*Abstract*

Nowadays, collecting the right dataset for machine learning is often more challenging than choosing the model. We address this with photorealistic synthetic training data – labelled images of humans made using computer graphics. With synthetics we can generate clean labels without annotation noise or error, produce labels otherwise impossible to annotate by hand, and easily control variation and diversity in our datasets. I will show you how synthetics underpins our work on understanding humans, including how it enables fast and accurate 3D face reconstruction, in the wild.

*Bio*

Dr. Erroll Wood is a Staff Software Engineer at Google, working on Digital Humans. Previously, he was a member of Microsoft's Mixed Reality AI Lab, where he worked on hand tracking for HoloLens 2, avatars for Microsoft Mesh, synthetic data for face tracking, and Holoportation. He did his PhD at the University of Cambridge, working on gaze estimation.

Google Calendar for Future Seminars: https://calendar.google.com/calendar/u/0?cid=c2pjcHN0YXM2N3QyMWU3c2FqNjB...

Combining multi-omics and biological knowledge to extract disease mechanisms

Monday, 11 July 2022, 3.00pm to 4.00pm
Speaker: Julio Saez-Rodriguez, Faculty of Medicine of Heidelberg University, Director of the Institute of Computational Biomedicine and Group Leader at the EMBL- Heidelberg University Molecular Medicine Partnership Unit (MMPU)
Venue: CRUK CI Lecture Theatre

Multi-omics technologies, and in particular those with single-cell and spatial resolution, provide unique opportunities to study deregulation of intra- and inter-cellular processes in cancer and other diseases. In this talk I will present recent methods and applications from our group towards this aim, with a focus is on computational approaches that combine data with biological knowledge within statistical and machine learning methods. This combination allows us to increase both the statistical power of our approaches and the mechanistic interpretability of the results. I will also discuss the value to perform perturbation studies, combined with mathematical modeling, to increase our understanding and therapeutic opportunities. Finally, I will show how, using novel microfluidics-based technologies, this approach can also be applied directly to biopsies, allowing to build mechanistic models for individual cancer patients, and use these models to propose new therapies.

Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction

Tuesday, 12 July 2022, 3.00pm to 4.00pm
Speaker: Martin Fajčík ( Brno University of Technology )
Venue: Computer Lab, FW26

Abstract:

We present Claim-Dissector: a novel latent variable model for fact-checking and fact-analysis, which given a claim and a set of retrieved provenances allows learning jointly (i) what are the provenances relevant to this claim (ii) what is the veracity of this claim. We show that our system achieves state-of-the-art results on FEVER comparable to two-stage systems often used in traditional fact-checking pipelines, while using significantly less parameters and computation.
Our analysis shows that proposed approach further allows to learn not just which provenances are relevant, but also which provenances lead to supporting and which toward denying the claim, without direct supervision. This not only adds interpretability, but also allows to detect claims with conflicting evidence automatically. Furthermore, we study whether our model can learn fine-grained relevance cues while using coarse-grained supervision. We show that our model can achieve competitive sentence-recall while using only paragraph-level relevance supervision. Finally, traversing towards the finest granularity of relevance, we show that our framework is capable of achieving strong token-level interpretability. To do this, we present a new benchmark focusing on token-level interpretability ― humans annotate tokens in relevant provenances they considered essential when making their judgement. Then we measure how similar are these annotations to tokens our model is focusing on. Our code, dataset and demo will be released online.

Bio:

Martin Fajčík (read as Fay-Cheek) is a PhD candidate in Natural Language Processing from Knowledge Technology Research Group active at FIT-BUT in Brno, Czech Republic, advised by prof. Pavel Smrž (ž is read like j in french "Jean"). From 2021, he also works as a research assistant in IDIAP research institute based in Martigny, Switzerland. His PhD work is focusing on open-domain knowledge processing, mainly in question answering and fact-checking. He enjoys a good hikes and an informal discussions over tea.

Statistics Clinic Summer 2022 I

Wednesday, 13 July 2022, 5.30pm to 7.00pm
Speaker: Speaker to be confirmed
Venue: Venue to be confirmed

If you would like to participate, please fill in the following "form":https://forms.gle/J6kRGdFeUG8dYqYW8. The deadline for signing up for a session is 12pm on Monday the 11th of July. Subject to availability of members of the Statistics Clinic team, we will confirm your in-person or remote appointment.

This event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.