Engineering Cognition: New Frontiers in Complex Systems Science
Applications to Precision Medicine and Engineering
This talk aims at showing how apparently simple ideas borrowed from engineering can have strong applications in precision medicine and how our understanding of human cognition can impact the way we model the world around us to better simulate and understand it.
I will briefly discuss some of the recent knowledge on how humans learn to explain how machines can learn. I will also showcase how traditional approaches to solving engineering problems differ from those required in personalised medicine and then illustrate how the needs for patient-specificity transfers to engineering problems where data is assimilated on the fly.
I will provide examples such as wind energy harvesting, building information models, structural health monitoring and computational archaeology. By examining these diverse applications, I aim to inspire cross-disciplinary collaborations and push the boundaries of data-driven computational modeling of complex systems. Prof. Stéphane Bordas Stéphane is a multi-disciplinary computational and data science researcher, educator, mentor and coach. He was trained as an engineer and applied mathematician and has been teaching and researching in computational sciences since year 1999, in various capacities. He has been in the top 1% most cited in his field, worldwide since year 2015 (ISI Clarivate).
Stéphane leads the Legato Team (legato-team.eu), a multi-disciplinary team of about 30 researchers of a dozen nationalities. He is focusing on bringing the rigour of mathematics to create intuition into the behaviour of complex systems. In particular, he pioneered new approaches to guarantee the accuracy of complex system simulations.
The methodologies he creates translate across discipline boundaries. For example, the methodological backbone of his PhD thesis supports applications in fracture mechanics, nanoscale heterogeneities, biofilm growth, cancer growth, astrocytic metabolism and many others. Recently, his team has become involved, through the Institute of Advanced Studies of the University of Luxembourg in the nascent field of Computational Archaeology.
Currently, one of the main focus points of his Team is to bring machine learning tools to bear on mathematical models of complex physical systems. In particular, his group develops adaptive data assimilation, model selection and discretisation optimisation schemes for the deformation of soft matter under large deformation with applications to surgical simulations and robotics. His team has been applying such ideas to programmable matter, multi-scale material modelling, wind energy harvesting, chemical engineering process optimisation, among others.
Stéphane has taught over 5,000 students directly and given short courses and research seminars reaching thousands of attendees. He has extensive experience in one-to-one tutoring, mentoring and coaching across various disciplines. He has directly worked with over four hundred collaborators and over fifty different companies, worldwide, as an R&D consultant. Stéphane and his students and collaborators received multiple international prizes for their research and mentorship. He has raised over 28 million euros in research funding from the private and public sector alike. He is Fellow of the Learned Society of Wales, and recipient of the 2022 Eugenio Beltrami Senior Scientist Prize. He is Editor in Chief of Advances in Applied Mechanics, Executive Editor of Data-Centric Engineering, and Subject Editor for Applied Mathematical Modelling.
Table of Contents:
00:00 - Marker
00:58 -
02:08 - Legato-team Luxembourg
02:55 - Quantify the quality of the simulation
08:30 - Quantify the quality of the simulation
08:38 - Quantify the quality of the simulation
09:17 -
10:08 -
10:44 -
10:47 -
11:13 -
11:27 -
11:31 -
11:37 -
11:55 -
12:40 -
12:42 -
12:49 -
12:55 -
13:09 -
13:13 -
13:31 -
13:55 -
14:47 -
15:30 -
15:38 -
15:40 -
15:48 -
16:42 -
17:48 -
18:04 -
18:06 -
18:27 -
18:31 -
19:25 -
19:33 -
19:35 - A baby is a scientist in the crib (Gopnik)
22:55 - The bayesian brain
23:04 -
25:09 -
25:13 -
25:16 -
25:34 -
25:37 -
25:39 -
25:43 -
25:48 -
25:52 -
25:57 -
25:59 - The bayesian brain and the bayesian computer
26:16 -
29:09 -
29:39 -
30:37 - Interlude
30:38 -
31:33 - Interlude
32:07 -
33:34 -
33:35 -
33:47 -
34:07 - inCERT: Computer assisted surgery with confidence
39:57 -
39:57 -
40:50 - How important is astrocyte morphology for neural metabolism?
41:32 - Metabolism and calcium signalling cross-talk in spatially resolved cellular domains
41:32 - Introduction I Astrocytes
41:35 - Introduction I Astrocytes as Metabolic Mediator
41:38 - Introduction I Reactive Astrocytes
42:54 - Model I Metabolic Model
43:41 - Model I Metabolism and Calcium Model
43:43 - Results I Geometries and  dynamics impact on metabolites
43:44 -
5 авг 2024
