The next seminar will be held on 30 January 2024, 12:00 – 13:00 at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom.
To sign up on Eventbrite click here.
This month will feature the following two talks:
Multimodal, data-efficient, and robust AI for real-world biosignals and the role of generative models – Dr Dimitris Spathis, Senior Research Scientist at Nokia Bell Labs and Visiting Researcher at University of Cambridge
The limited availability of labels for machine learning on multimodal data hampers progress in the field. In this talk, I will discuss our recent efforts to address this problem, building on the paradigms of self-supervised and multimodal learning. With models such as CroSSL, Step2Heart, and SelfHAR, we put forward principled ways to learn generalizable representations from high-resolution data through masking, knowledge distillation, and physiology-inspired pre-training. We show that these models can be applied to various clinically relevant applications to improve mental health, fitness, sleep, and voice-based diagnostics. At the same time, due to data size limitations, these models are limited in size and generalization capabilities compared to popular generative models such as GPT. What if we could use Large Language Models (LLMs) as data-agnostic pre-trained models? I will close the talk by highlighting where LLMs fail in processing sequential data as text tokens and some ideas on how to address the critical “modality gap”.
Using machine learning methods to improve classification and prediction of psychiatric conditions – Dr Katharina Zühlsdorff, Visiting Postdoctoral Fellow at Department of Psychology
Cognitive flexibility can be investigated using tests such as probabilistic reversal learning (PRL). In various neuropsychiatric conditions, including substance use disorders, gambling disorder, major depressive disorder and schizophrenia, overall impairments in PRL flexibility are observed. Using reinforcement learning (RL) models, a deeper mechanistic explanation of the latent processes underlying flexibility can be gained. I will present results from an analysis of PRL data from individuals with different psychiatric diagnoses using a hierarchical Bayesian RL approach and relate behavioural findings to the underlying neural substrates. Furthermore, I will discuss how graph neural network models can be used to incorporate cognitive and neuroimaging data to improve prediction of psychiatric conditions.
This is a hybrid event so you can also join via Zoom: Meeting ID: 990 5046 7573 and Passcode: 617729