Team

Simon was an undergraduate researcher at University of Copenhagen with the late Flemming M. Poulsen working on protein folding and NMR Spectroscopy in SBiN Lab. He continued his MSc research in SBiN Lab with Kaare Telium working on ensemble modeling and NMR spectroscopy of the intrinsically disordered peptide IAPP.

Simon moved on to do a PhD with Thomas Hamelryck in statistical bioinformatics with applications in structural biology at SCARB.

In 2014, Simon was awarded an independent postdoctoral fellowship from the Danish Council for Independent Research to move to Switzerland and take up a postdoc with Andrea Cavalli and Roland Riek jointly at the IRB in Bellinzona and ETH Zurich. Here, Simon worked on developing methods for combining experimental biophysical data with molecular simulations.

In 2016, Simon was awarded the DRS postdoctoral fellowship and an Alexander von Humboldt postdoctoral fellowship to join Frank Noé at FU Berlin to work on integrative methods for structural biology and artificial intelligence methods for the sciences.

Since October 2020, Simon is a WASP AI-MLX Professor for Artificial Intelligence in the Natural Sciences, at Chalmers, where he leads the Artificial Intelligence in the Natural Sciences (AIMLeNS) group.

Christopher works on generative models for protein structure and experimental data integration. He obtained his Bachelor’s degree in Molecular Biology from the University of Basel (Switzerland). He continued to do his Master’s in Structural Biology and Biophysics at the Biozentrum in the lab of Sebastian Hiller working on simulations of NMR experiments.

Juan works on deep generative models for sampling of 3D configurations of small, drug-like molecules, and molecular property prediction (Industry advisors: Ola Engkvist and Atanas Patronov). Before starting as a PhD student, Juan worked in the group as a MSc student with Sara to build deep generative models for molecular design with desired property profiles and 3D conformer distributions. That project was co-supervised by Dr. Rocío Mercado (AstraZeneca).

Flemming is working on generative models for molecular applications. Before joining the group, he obtained a Master degree in Theoretical Physics from the University of Göttingen.

Hang engages in research at the intersection of AI, quantum computing, and quantum chemistry. Before starting his PhD, he obtained a Master degree in Nanotechnology from Chalmers.

Ross works on deep generative models for molecular design (Industry advisors: Alessandro Tibo and Jon-Paul Janet). Before starting as a PhD student, Ross was part of the Graduate Programme at AstraZeneca. Before that he graduated with 1st class honors with a MEng degree in Computing (AI and Machine Learning) from Imperial College London.

Mathias has a Ph.D. in machine learning for molecular science from DTU, and a background in both physics and AI. He is working on generative models for molecular applications.

Selma is working on generative models for inverse molecular design. Before starting as a PhD student, Selma was a research project student working on generalizing generative models across thermodynamic states together with Weilong.

Weilong is a research project student working on generalizing generative models across thermodynamic states together with Selma.

Berta’s research focuses on branch chewing methodology, and squirrel chasing strategies.

Sara works with Juan to build deep generative models for molecular design with desired property profiles and 3D conformer distributions. Co-supervised by Dr. Rocío Mercado (AstraZeneca).

Azadeh works with Dipti to build deep generative models to accelerate molecular conformer generation by incorporating new Normalizing Flows that respect data topology and are more efficient molecular convolutional models. She is a MSc student in Applied Data Science and is co-supervised by Juan.

David works on a master thesis together with Sara and is supervised by Professor Simon Olsson. The objective is to detect meta-stable states in protein folding using VAMPNets and training it with an (E3) equivariant neural network. He is a MSc student in Engineering Mathematics and Computational Science.

Dipti works with Azadeh to build deep generative models to accelerate molecular conformer generation by incorporating new Normalizing Flows that respect data topology and are more efficient molecular convolutional models. She is a MSc student in Data Science and AI and is co-supervised by Juan.

Enmin works with Wenli on deep dynamic graphical models for molecular kinetics.

Guillem is doing a Ph.D. focused on developing computational protocols for enzyme design under the supervision of Prof. Sílvia Osuna and co-supervision of Dr. Javier Iglesias. He obtained a Bachelor’s degree in Biotechnology and a Master’s degree in Advanced Catalysis and Molecular Modeling from the Univesity of Girona.

Tobias works on generative, probabilistic models for protein sequences with application in protein and biologics design. Before joining the lab, Tobias did his MSc thesis with Prof. Claes Strannegård on Multi-Agent Deep Reinforcement Learning in a Three-Species Predator-Prey Ecosystem.

Gustav worked with Agnes to use multi-label classification methods to predict contexts for chemical reactions. Co-supervised by Dr. Samuel Genheden and Dr. Thierry Kogej (AstraZeneca).

David worked with Julia on deep generative models for near-term quantum device control with applications in chemistry. Co-supervised by Assistant Professor Morten Kjaergaard (NBI, University of Copenhagen).

Agnes worked with Gustav to use multi-label classification methods to predict contexts for chemical reactions. Co-supervised by Dr. Samuel Genheden and Dr. Thierry Kogej (AstraZeneca).

Julio worked on learning inter-molecular potentials using deep energy-based models via score-based estimators for drug-discovery. Co-supervised by Dr. Rocío Mercado (AstraZeneca).

Riccardo works on score-based methods for density estimation with applications in molecular systems.

Sara works on a master thesis together with David and is supervised by Professor Simon Olsson. The objective is to detect meta-stable states in protein folding using VAMPNets and training it with an (E3) equivariant neural network. Sara is last year MSc student in Data Science & AI.

Julia worked with David on deep generative models for near-term quantum device control with applications in chemistry. Co-supervised by Assistant Professor Morten Kjaergaard (NBI, University of Copenhagen).

Wenli works with Enmin on deep dynamic graphical models for molecular kinetics. She is a MSc Student in Data Science and AI.