Psychiatrist | IPPRF Fellow @imperialcollege - Digital Mental Health
Joined March 2015
- Tweets 72
- Following 112
- Followers 83
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5 Photos and videos
10 Nov 2025
I've recently joined Imperial College London as an IPPRF Fellow in Psychiatry in the Department of Brain Sciences. I will be working on AI- and wearable technology-based approaches to improve care for people with mood disorders and deepen our understanding of these conditions.
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10 Nov 2025
I’ve had a wonderful five years in Edinburgh. I am grateful to everyone I met while working as a psychiatrist for NHS Lothian and during my PhD at University of Edinburgh. I look forward to building on the skills and experiences I gained there as I begin this next chapter.
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10 Nov 2025
If you work in digital mental health or wearable tech and would like to grab a coffee in London or chat online, feel free to reach out!
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8 Oct 2024
1/3 Excited to share our tell-tale heart study on Bipolar Disorder (BD), now published in @npjMentalHealth : nature.com/articles/s44184-0… @tetraduzione @bryanlimy @DHidalgoMazzei @BioMedAI_CDT @BipolarBCN
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8 Oct 2024
2/3 Heart rate variability (HRV) - variability in the time between consecutive heartbeats -reflects the health of the autonomic nervous system. Our study shows that HRV recovery correlates with symptom improvement in BD 🎭, suggesting HRV could be a potential biomarker for BD.
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8 Oct 2024
3/3 Collecting HRV data and psychometric scales over multiple time points during a BD episode is costly, limiting the sample size. We introduce a Bayesian Hierarchical Model, better suited for small samples and uncertainty quantification.
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Filippo Corponi retweeted
6 Sep 2024
and congratulations to Dr. @filippocmc who successfully passed his viva, examined by Paolo Ossola and @KiaNazarpour
Filippo's thesis is a wonderful bridge between #ML #AI and clinical practice in psychiatry for mood disorders with wearable!
24 Jul 2024
1/2 If you are working in #wearables, #AI, and #healthcare do not let small datasets stop you. mhealth.jmir.org/2024/1/e550… 👈 We share the largest publicly available data collection for #empatica #e4 and release the codebase for pre-processing and self-supervised pre-training.
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24 Jul 2024
1/2 If you are working in #wearables, #AI, and #healthcare do not let small datasets stop you. mhealth.jmir.org/2024/1/e550… 👈 We share the largest publicly available data collection for #empatica #e4 and release the codebase for pre-processing and self-supervised pre-training.
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24 Jul 2024
2/2 Repurposing unlabelled data with self-supervised learning can help with your task of interest. @tetraduzione @DHidalgoMazzei @bryanlimy @BioMedAI_CDT @ancAtEd Data: huggingface.co/datasets/FcmC…. Codebase: github.com/april-tools/e4sel….
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Filippo Corponi retweeted
17 Jul 2024
#NeuroSymbolic #AI 🤖 enforces constraints, but models can achieve high accuracy using wrong concepts.
Can we spot when a model relies on flawed concepts and ensure #trustworthiness?
Yes, with BEARS! 🐻
📢 Introducing our latest paper, accepted as a spotlight at UAI2024 !🎉📄
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Filippo Corponi retweeted
27 Jun 2024
we evaluate #continual learning models on "baby" benchmarks, where it is easy to show no catastrophic forgetting!
we propose a new simple benchmark that glues simple but still challenging tasks in a curriculum: from MNIST to Imagenet and back!
📜link.springer.com/article/10…
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Filippo Corponi retweeted
29 Apr 2024
Will be at AISTATS this week, would love to chat if you have integrals to approximate or are generally into compstat / opt. transport / estimation in causal inference.
Also we have two papers around importance sampling and variational inference.
1st: adaptive IS for heavy tails
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Filippo Corponi retweeted
16 Apr 2024
The softmax bottleneck is an interesting problem; it has many side effects which we do not yet fully understand!
If you want to build an intuition for the problem, here is an interactive visualisation I made grv.unargmaxable.ai/static/f… (best viewed on desktop).
15 Apr 2024
(6/10) This problem is in fact very much related with the softmax bottleneck issue (arxiv.org/abs/1711.03953)
Basically, we try to map "low" dimensional contextual representations to potentially high-dimensional contextual probability manifolds, using a simple linear layer:
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Filippo Corponi retweeted
16 Apr 2024
New preprint out!🚨We performed a large scale analysis of physicochemical features extracted from over half a million AlphaFold structural models, and using data-driven methods we showed a link between these features and in vivo behaviour of proteins. Find out more below⬇️1/9
14 Apr 2024
Large scale analysis of predicted protein structures links model features to in vivo behaviour biorxiv.org/cgi/content/shor… #bioRxiv
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Filippo Corponi retweeted
9 Apr 2024
Classical mixture models are limited to positive weights and this requires learning very large mixtures!
Can we learn (deep) mixtures with negative weights?
Answer in our #ICLR2024 spotlight by @loreloc_ Aleks, Martin, Stefan, Nicolas @arnosolin
📜openreview.net/forum?id=xIHi…
9 Apr 2024
After the score-based models, I decided to take a step back and cover the basics in a new blog post: Probabilistic modeling and Mixture Models. Additionally, I make a brief intro to ✨Probabilistic Circuits✨ Check:
📄Post: jmtomczak.github.io/blog/19/…
🖥️ Code: github.com/jmtomczak/intro_d…
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Filippo Corponi retweeted
1 Apr 2024
Applications are now open to join our new UKRI AI Centre for Doctoral Training in Biomedical Innovation for September 2024 entry. All the information you need to apply is ➡️ ai4biomed.io/ @UKRI_News @AI4BI_CDT
ALT An image of biomedical informatics, a DNA helix and a network of neurons
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26 Mar 2024
1/3 Our work on inferring #mooddisorder symptoms 🧠from #wearable ⌚️physiological data is out on Nature Translational Psychiatry: nature.com/articles/s41398-0… @tetraduzione @DHidalgoMazzei @bryanlimy @BioMedAI_CDT
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26 Mar 2024
2/3 Different symptom combinations, requiring different therapy and management approaches, can be seen within an acute affective episode. Thus, the reductionist binary classification (acute episode yes or no) previously pursued is limited and misses out on actionable information.
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26 Mar 2024
3/3 The task we propose, inferring what symptoms are driving an acute episode, is better aligned with the actual clinical practice and more useful towards informing clinical decision-making. This however comes with new #machinelearning challenges. Code: github.com/april-tools/wear-…
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