My name is Concha Bielza.
I am professor in the Department of Artificial Intelligence
at the School of Computer Science of UPM.
I co-lead the Computational Intelligence Group.
This is a research group devoted to modelization
from statistical and machine learning perspectives
and also devoted to heuristic optimization.
I am also co-director of the Ellis Unit Madrid,
an important application area since 2009 is Data-Driven Neuroscience,
where we build models from data to answer different
research questions.
A major line of research
has been in neuroanatomy with different contributions.
As the classification
of cortical and gabaergic interneurons according to their morphology.
This is a hard problem since neuroanatomists dont agree on the nomenclature.
Hence, this supervised classification problem
doesn't have instances with clear labels, which is very,
very challenging from the machine learning point of view.
We have tried to identify similar groups of dendritic spines.
The spatial distribution of
synopsis along dendritic networks has been also studied
the simulation of dendritic trees
of pyramidal cells, or the wiring economy.
Also for this kind of cells testing the hypothesis that our arborizations
optimize brain connectivity in terms of total wiring length.
The second main line of research is in neurological diseases
where we have tried to search for
genetic biomarkers in Alzheimer disease
or the classification of task related
mental activity from Magnetoencephalography data,
which is very relevant in brain computer interface and in Parkinson's disease.
We have identified different subtypes with motor, and non-motor symptoms
or the prediction of the dementia development.
And also we have tried to predict genetic health related quality of life
measure with five dimensions mobility,
self-care, usual activities,
pain, discomfort and anxiety, depression by using the very,
very specific quality of life Parkinson Disease Questionnaire.
In epilepsy, we have tried
to predict the outcome of temporal lobe epilepsy surgery
to see whether the patient will fully recover from epilepsy or not
by using clinical variables
and also pathological on neuropsychological evaluations.
We have also implemented an open machine
learning and a statistical web framework
with neuroscience applications, and this is called NeuroSuites
Some of the works have been published in top journals
like Nature Reviews Neuroscience or Nature Neuroscience.
And also always
we have preferred models that are called Bayesian networks
because they are interpretable, and this is very important
in building unexplainable artificial intelligence
where humans can trust on intelligent systems and avoid biases
and discriminative and opaque outcomes.
Our work in neuroscience has been framed
within two major projects of ten years duration.
one starting in 2009, the Cajal
Blue Brain Project, funded by the Spanish Ministry
on the second from 2013
to this year 2023, The Human Brain Project.
This is a FET flagship of the European Commission
where we have been involved from the very beginning in the ramp
up phase and in the three specific grant agreements
In this projetcs, currently we are developing a genetic addressing toolbox
with a web interface and a plugin
for the Brain Atlas view of EBRAINS.
EBRAINS is the digital brain research infrastructure created
by the Human Brain Project in this case, and massive Bayesian networks are used
for interactively learning gene regulatory networks from gene expression.
Data extracted from the Atlas.
I think it students have
a great opportunity to gain insights from the application of machine learning
to all kinds of data that are available nowadays
generated by different neural technologies like morphological,
electrophysiological, molecular clinical data, etc.
I think that we have done our bit in a student education by offering to book
data driven computational neuroscience, machine learning and statistical models.
This book was published in 2020 by Cambridge
University Press and has more than 700 pages.