Cecilia Clementi is an Italian-American scientist who specialises in the
simulation of biomolecules. She is a Professor of Theoretical and
Computational Biophysics at the Free University of Berlin.
She was previously a Professor of Chemistry and Physics at Rice University
and co-director of the National Science Foundation Molecular Sciences
Software Institute. From 2017 to 2019, she held an
Einstein Foundation fellowship. In 2020 she was appointed Einstein Professor of Physics at Freie Universität Berlin.
She is also a scuba divemaster and underwater photographer.
Some of her photographs are shown in her Instagram page
Email:
cecilia.clementi@fu-berlin.de
Administrative assistant
About: Swantje keeps us sane.
Email:
s.hartmann-rolke@fu-berlin.de
Postdoc
About: Alessandro's research focuses on designing and developing new message-passing
architectures to incorporate long-range interactions in machine-learned potentials of
large biomolecular systems.
Email:
alessandro.caruso@fu-berlin.de
More:
Website
Dr. Raquel López-Ríos de Castro
Postdoc
About: Raquel is a Marie Skłodowska-Curie Postdoctoral Fellow. Her research focuses on developing computational and machine learning methods to enhance protein structure modeling and accelerate the discovery of new therapeutics.
Email:
raquel.lopez-rios.de.castro@fu-berlin.de
More:
Google Scholar
Postdoc
About: Lorenzo is a Marie Skłodowska-Curie Postdoctoral Fellow. His research focuses on the interpretability of Graph Neural Networks used for Machine Learning Potentials, through the lens of graph theory and statistical mechanics.
Email:
lorenzo.giambagli@fu-berlin.de
More:
https://lorenzogiambagli.com/
Postdoc
About:
Luca's research focuses on pushing the boundaries of biomolecular simulations by adapting machine-learned coarse-grained
force fields across broader chemical spaces and on multiscale modeling by bridging quantum, atomistic and CG simulations.
Email:
l.sagresti@fu-berlin.de
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Postdoc
About:
Zak is a postdoctoral researcher funded by the Einstein Foundation Berlin within the research project
"Einstein Visiting Fellow Prof. Mark E. Tuckerman". His work focuses on device scale modelling for
solid polymer electrolytes using machine-learning interatomic potentials and generative flow-based models.
Email:
zakariya.el.machachi@fu-berlin.de
More:
Google Scholar
Postdoc
About: Jorge (he/him) is a postdoctoral scholar supported by the Einstein Stiftung Berlin to develop efficient methods for modeling quantum effects during charge transport in liquid electrolytes. He is also interested in building AI-boosted tools to enable kinetically-consistent coarse-graining of molecular systems and to extract free-energy landscapes from nonequilibrium molecular data.
Email:
j.rosa-raices@fu-berlin.de
More:
LinkedIn
Postdoc
About: Gianmarco joined the lab with the goal of developing a transferable Machine Learning Force Field for membrane proteins. He studied in Pisa (Bachelor’s in Physics) and Trento (Master’s in Quantitative and Computational Biology), and achieved his PhD at the Frankfurt Institute of Advanced Studies and Goethe University Frankfurt in the Lab of Prof. Roberto Covino,
where he spearheaded the development of the AI-driven path sampling method AIMMD.
Email:
g.lazzeri@fu-berlin.de
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Google Scholar
PhD Candidate
About: Filippo is a Marie Skłodowska-Curie Doctoral Candidate as part of the doctoral network
`ELEGANCE' (https://elegance.dtu.dk/). He works on Machine Learning Force Fields for enzymes.
Email:
albani@physik.fu-berlin.de
PhD Candidate
About:
Finn's research focuses on the development of new machine
learning models for molecular dynamics simulations of large biomolecules.
He is specifically interested in incorporating long-range interactions in
inherently local message passing neural networks.
Email:
finn.tillinger@fu-berlin.de
PhD Candidate
About: Klara is a PhD Candidate of the FAIME project funded by the German Federal Ministry for Education and Research.
Her research is focussed on building machine learned coarse grained force fields for proteins and interpreting them using explainable AI tools.
Email:
klara.bonneau@fu-berlin.de
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Google Scholar
PhD Candidate
About:
Andrea is an IMPRS-BAC PhD candidate. Her research focuses on
developing machine-learned coarse-grained models to simulate protein folding, disorder, and aggregation.
Email:
aguljas@zedat.fu-berlin.de
More:
Google Scholar
PhD Candidate
About:
Aldo is a IMPRS-BAC PhD candidate. His research is centered around enhancing machine-learned coarse-grained models for biomolecules. Outside of work he enjoys music and tea.
Email:
aldo.sayeg.pasos.trejo@fu-berlin.de
More:
https://sayeg84.github.io/
PhD Candidate
About:
Edoardo's research focuses on the development and improvement of machine learning-based force fields for biomolecular systems, with an emphasis on integrating experimental data to increase accuracy and transferability.
Email:
edoardo.rolando@fu-berlin.de
More:
Google Scholar
Ana Cristina Molina Taborda
PhD Candidate
About:
Ana is a PhD candidate at Freie Universität Berlin. Her research is focused in developing machine-learned coarse-graining models for large proteins.
Email:
ana.molina.taborda@fu-berlin.de
PhD Candidate
About:
Jacopo is an IMPRS-BAC PhD candidate. He joined the group after his bachelor in physics at university of Florence through a fast track program of the Max Planck Institute. He’s work focuses on including long-range interactions and thermal dependency into coarse grained models for proteins.
Email:
jacopo@physik.fu-berlin.de
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Google Scholar
PhD Candidate
About: Ella is a Freie Universität Berlin PhD candidate.
She works on the development and application of machine learning methodologies for complex molecular systems.
Email:
ellav73@zedat.fu-berlin.de
PhD Candidate
About: Jayashree, is a Marie Skłodowska-Curie Doctoral Candidate as part of the doctoral network
Track the Twin. She works on building Machine Learning Force Fields for Perovskite Halide Quantum Dots.
Email:
jayashree.narayan@fu-berlin.de
More:
https://jayashreenarayan.github.io/
PhD Candidate
About: Hannes is a HEIBRiDS PhD candidate. His research focuses on developing automated, AI-assisted approaches to map molecular complexes in human cells in cryo-ET images.
Email:
hannes.drobek@mdc-berlin.de
More:
https://hannesdro.github.io/