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Alexandre Tkatchenko is a Professor of Theoretical Chemical Physics at the University of Luxembourg. He obtained his bachelor degree in Computer Science and a Ph.D. in Physical Chemistry at the Universidad Autonoma Metropolitana in Mexico City. In 2008−2010, he was an Alexander von Humboldt Fellow at the Fritz Haber Institute of the Max Planck Society in Berlin. Between 2011 and 2016, he led an independent research group at the same institute. Tkatchenko has given more than 250 invited talks, seminars and colloquia worldwide, published more than 170 articles in peer-reviewed academic journals (h-index=67), and serves on the editorial boards of Physical Review Letters (APS) and Science Advances (AAAS). He received a number of awards, including elected Fellow of the American Physical Society, the 2021 van der Waals Prize from ICNI, the 2020 Dirac Medal from the World Association of Theoretical and Computational Chemists, the 2011 Gerhard Ertl Young Investigator Award of the German Physical Society, and three flagship grants from the European Research Council: a Starting Grant in 2011, a Consolidator Grant in 2017, and a Proof-of-Concept Grant in 2020. His group pushes the boundaries of quantum mechanics, statistical mechanics, and machine learning to develop efficient methods to enable accurate modeling and new insights into complex materials.

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Research Interests: Statistical physics, imaginary-time path integral methods, nuclear quantum effects, ab initio simulations

Working Experience:

Senior Researcher January, 2016 - present University of Luxembourg

Postdoctoral Fellow December, 2013 - January, 2016 Fritz Haber Institute of the Max Planck Society

Postdoctoral Fellow July, 2012 - December, 2013 Institute of Theoretical and Computational Chemistry at POSTECH 

Junior Research Associate April, 2009 - July, 2012 B. Verkin Institute for Low Temperature Physics & Engineering, National Academy of Sciences of Ukraine

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Current interests:

Coarse-grained many-body methods based on coupled quantum harmonic oscillators as well as first-principles approaches to study molecules and materials.

Expertise:  

Computational condensed matter physics, in particular, first-principles calculations of the electronic structure of three- and two-dimensional crystals as well as description of their transport properties for various phenomena in the field of spintronics and spin caloritronics (like the spin relaxation, the spin and anomalous Hall effect, the spin Nernst effect), including phenomena caused by the geometric phase.

Positions:

•  2012-2016   PostDoc, Max Planck Institute of Microstructure Physics, Halle, Germany

• 2006-2011   PostDoc, Martin Luther University Halle-Wittenberg, Halle, Germany Development and employment of program packages for ab initio calculation of electronic structure and transport properties of solid states, description of physical phenomena related to the field of spintronics and spin caloritronics

• 1999-2005   Physical-Technical Institute, Ural Branch of Russian Academy of Sciences, Izhevsk, Russia

             2004-2005: Senior scientist
             2000-2004: Researcher
             1999-2000: Junior research scientist

Development and employment of program packages for calculation of the electronic structure and magnetic properties of solid states, theoretical investigations of their surfaces and electronic response to an applied electric field, transport properties of ultrathin films

Education:

• 1996-1999   Physical-Technical Institute, Izhevsk, Russia, Ph.D.: Solid State Theory (1999)

• 1991-1996   Udmurt State University, Izhevsk, Russia, M.S.: Theoretical Physics (1996) [with honors]

Research:

I am interested in  biomolecule mechanics and self assembly, in particular I like to observe  protein and DNA in action and to understand these versatile molecules  in their operation as nanomachines.

Expertise:

Computer simulation, statistical mechanics, thermodynamics, kinetics, rare event statistics and machine learning.

History:

2010 -> present :  University of Luxembourg, Research Scientist and Lecturer.

2009 -> 2010      :  University of Mainz, Germany.  Postdoctoral researcher.

2006 -> 2010      :  University of Leeds, UK: PhD student (Physics and Life Sciences).

2001 -> 2006      :  Imagination Technologies, UK: Graduate design engineer.

2000 -> 2001      : University of Edinburgh, School of Informatics: MSc student.

1996 -> 2000      :  University of Edinburgh, School of Maths and Physical Sciences: BSc student.

Cross-Affiliations: Centre for complex living systems https://cls.uni.lu/

Scholar: https://scholar.google.com/citations?user=quu2fm8AAAAJ&hl=en

University staff directory: https://wwwen.uni.lu/research/fstm/dphyms/people/josh_berryman

Personal Homepage: http://berrymanscience.com

Github:  https://github.com/tojb

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- Current interests: 

Study of strongly correlated materials, where the physics is dominated by the interplay between electronic, spin and vibrational degrees of freedom. The goal is to dominate theoretical laws in order to investigate the exotic phases of matter that might emerge in these materials and predict the collective properties of very large numbers of electrons, atoms or molecules to discover new principles and phenomena at the multidisciplinary interfaces of microscopic and macroscopic models.

- Academic History:

2011–2013 - Master Degree in Theoretical Physics (110/110 L), University of Bari, Italy.

2014–2015 - Holder of a Research Grant in Ghost Imaging and Plenoptic Correlation, University of Bari, Italy.

2015–2018 PhD in Physics, IMPMC, Sorbonne Université, Paris, France.
Thesis: "Exotic phenomena in the new frustrated spin ladder Li2O(CuSO4)2" (Supervisors: Guillaume Radtke and Gwenaelle Rousse)

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Education:

-2018-2019: Research assistant at the Center for Advancing Electronics Dresden, Dresden, Germany.

-2014-2018: Doctor of Engineering, Chair of Materials Science and Nanotechnology, Dresden University of Technology, Dresden, Germany. 

-2010-2012: Master in Physics, Condensed matter group, Universidad Nacional Mayor de San Marcos, Lima, Peru.

Research interests:

- Machine learning methods for understanding the chemical space of molecular systems.

- Quantum and classical picture of thermal transport phenomena at the nanoscale.

- Thermoelectric properties of low-dimensional materials.

- Software development for research.

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Current interests:
- Development of a quantum Monte Carlo code with geminal and Pfaffian wave functions with GPU acceleration
- Contribution to the project of electron-positron interactions in molecular systems
- Contribution to the project of multiscale approaches for the study of long range interactions

Expertise:

- Quantum Monte Carlo methods
- Density functional theory
- Ab initio wave function based quantum chemistry methods

- Strongly correlated systems.

Academic history:

2017 - 2019 Postdoctoral researcher at the University of Luxembourg
2014 - 2016 Research assistant at the  National Research Council, Institute of Nanoscience (Modena)
2015 - PhD in ‘Engineering and Physical-Mathematical Modelling', University of L'Aquila
2011 - MSc in Physics, `Sapienza' - University of Rome
2006 - BSc in Physics, `Sapienza' - University of Rome

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Current interests

My research interests lie in the study of correlated atomic systems in out-of-equilibrium conditions. My current work focuses on the interplay between many-body dispersion interactions and an external driving in simple model systems.

Expertise

— Density functional theory and Green's functions methods for many-body systems
— First principles calculations of electronic structure and spin transport properties of magnetic nanostructures
— Finite temperature atomistic spin dynamics modelling of ferromagnetic and antiferromagnetic materials

Education

[2015-2019] PhD in Theoretical and Computational Physics at Trinity College Dublin :
                     “Multi-scale analysis of current-driven spin dynamics in magnetic tunnel junctions”

[2013-2015] MSc in Theoretical and Model Physics, Università di Padova :
                     “Development of a smart BSE approach for the calculation of optical properties of complex systems”
[2010-2013] BSc in Physics, Università di Padova :
                     “Quantum entanglement for ultra-cold atoms in optical traps”

Current interests

The main current research interests concern the role eventually played by classical and quantum electrodynamics interactions in the dynamical organization of biomolecular systems. In this framework, I recently focused my attention on the possible role played by many-body van-der-Waals interactions in the biomolecular allosteric pathways and on how such interactions may contribute to affect the mechanical response of biomolecules in THz and far-IR domain out-of-thermal equilibrium in an aqueous environment. Moreover, I am interested to apply the existing geometrical formulations of quantum mechanics to characterize van-der-Waals dispersive interactions in many-body quantum systems.

Expertise

Modellization of dynamics and statistical properties of biomolecular systems out-of-thermal equilibrium. Description of quantum optical effects in biomolecular systems. Application of differential geometrical and topological methods for the description of classical Hamiltonian dynamics and phase transitions in classical systems at the thermodynamical equilibrium.

Education

2020 PostDoc, Quantum Biology Laboratory, Howard University: Study of quantum electrodynamics and dispersive interactions in biomolecular complexes

2018-2019 PostDoc, Centre de Physique Theorique, Universite' Aix-Marseille

2016 Ph.D. [Theoretical and Mathematical Physics] Centre de Physique Theorique, Universite' Aix-Marseille:  Phase transition theory with applications to BiophysicsImprovement of a Necessity Theorem on the topological origin of phase transitions at thermal equilibrium. Theoretical contributions to fix the problem due to a "counterexample". Theoretical study of Fröhlich-like out-of-equilibrium phase transition in classical systems. Interpretation of THz spectroscopy experiments in biophysics. Numerical investigations on systems of mutually interacting and diffusing biomolecules for the validation of experimental methods

2013 M.Sc. [Theoretical and Mathematical Physics] University of Florence: Theoretical and numerical investigation on diffusion properties of biomolecules for experimental testing of long-range interactions between them

2010 B.Sc. [Physics] Univerisity of Florence: Geometrical description of Hamiltonian Dynamics. Description of classical Hamiltonian dynamics in terms of trajectories in configuration space endowed with Jacobi metric

Background:

2017-2021 Postdoc in Korea Institute for Advanced Study (KIAS)

2014-2017 PhD. in String Theory at Université Pierre et Marie Curie

2011-2014 Undergrad and Master degree in Ecole Normale Supérieure (Paris)

Current interests:

-Machine Learning for the study of physico-chemical properties of molecules

-Geometrical and Quantum Field Theory approaches to the study of molecular interactions.

-Machine Learning approach to the study of phase transitions in Statistical Physics models.

Expertise:

-String Theory

-Supersymmetric quantum field theories in various dimensions

-Conformal Field Theories in two dimensions

Expertise

- Geometric description of equilibrium phase transitions and Hamiltonian chaos

- Applications of Quantum Field Theory-based methods to condensed matter

- Theoretical methods for studying the emergence of stochastic behaviors in interacting Hamiltonian systems

- Molecular Dynamics simulations of proteins diffusion in lipid membrane

Education

[2018-2021]  Ph.D. in Theoretical and Computational Physics, RWTH Aachen University  and Forschungszentrum Jülich: Generalized Langevin Equation-based  approach for investigating Complex Biological Systems.

[2015-2018]  M.Sc., Theoretical Physics, University of Rome, Tor Vergata: Geometric  Approach to Nonlinear Hamiltonian systems and Chaos.

[2011-2015] B.Sc. in Physics, University of Rome, Tor Vergata: Tangent Gruppoid and Strict Quantization Deformation.

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Current interests:

Development and application  of atomistic modeling methods as well as machine learning techniques for  an efficient and accurate modeling of terahertz (THz) spectra of  molecular crystal polymorphs.

Expertise:

- Density Functional Theory (DFT) & Time-Dependent Density Functional Theory (TDDFT).

- Ab-initio molecular dynamics (MD) (Born-Oppenheimer MD & Car-Parrinello MD).

- Restricted Open-Shell Kohn Sham (ROKS).

- Classical MD and Monte Carlo simulations.

- Force-Field development.

- Machine learning for predicting proteins' properties.

- Computational studies of the structural, optical, excited-state and mechanical properties of lead halide perovskites.

- Characterisation of the dynamics of proteins as a way to propose therapeutic routes for diseases that are caused by mutations.

-  Characterisation of polymer/solid interfaces.

Professional Experience:

2021-2021: Scientific Research Associate, Swiss Institute of Bioinformatics, University of Basel, Switzerland.

2019-2020: Postdoctoral researcher, Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, (EPFL), Switzerland.

Education:

2014-2019: PhD, EPFL, Switzerland.

2009-2014: Diploma of Chemical Engineering, National Technical University of Athens, Greece.

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Current interests:

  - Development of interatomic potentials for large systems using neural networks combined with simple models of long-range physics, such as the many-body dispersion (MBD) method

  - Predicting the structure and stability of molecular crystals

Expertise:

  - Density functional theory (DFT)

  - Time-dependent density functional theory (TDDFT)

  - Device physics of organic solar cells

Education:

  - 2021 Ph.D. in Chemistry from Weizmann Institute of Science, Israel:

“Extending the Screened Range-Separated Hybrid functional approach to inorganic crystalline  materials”

  - 2014 M.Sc. in Nanotechnology from the Technion, Israel:

“Transparent conducting oxides as electrodes in polymer solar cells”

  - 2009 B.Sc. in Physics from Caltech, USA

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Personal Website

LinkedIn

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Current Interests
________________
My research is dedicated to the development and application of a  systematic hierarchy of efficient machine learning based methods for  predicting reaction properties and changes in vibrational and electronic  absorption spectra upon (photo-)isomerization.

Computational Chemistry • Machine Learning • Molecular Dynamics • Isomerization

Short Curriculum Vitae
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| since 06/2022 |  Feodor Lynen Postdoctoral Researcher, University of Luxemburg, Luxemburg
| 06/2021-05/2022  | Postdoctorate, Friedrich Schiller University  Jena, Germany . Workgroup: Prof. B. Dietzek-Ivanšić
| 10/2018-06/2021  | Dissertation (Dr. rer. nat) Friedrich Schiller  University Jena, Germany.Scholarship: Kekulé Fellowship. Thesis Title: Towards  Operando Spectroscopy of Supramolecular Photocatalysts – A Case Study on Ru-dppz-derived Systems. Supervisor: Prof. B. Dietzek-Ivanšić. Grade: summa cum laude
| 10/2016-09/2018 | M.Sc. in Chemistry Friedrich Schiller  University Jena, Germany. Thesis Title: Photoactive Proton  Pumping at a Microcavity Supported Lipid Bilayer Supervisors: Prof. T. Keyes and  Prof. B. Dietzek-Ivanšić (formal) Grade: 1.1 (Thesis: 1.0) |
| 03/2018-09/2018 | Research Stay, Dublin City University, Ireland. Workgroup: Prof. T. Keyes. Master Thesis (6 months) Scholarship: PROMOS DAAD |
| 10/2013-09/2016 | B. Sc. in Chemistry, Friedrich Schiller  University Jena, Germany. Thesis Title: Spectroscopic  Investigations of Electronic States of Copper(I)–*4H*-Imidazolate  Coordination Compounds. Supervisors: Dr. Martin Schulz and Prof. B. Dietzek-Ivanšić (formal). Grade: 1.3 (Thesis: 1.0) 

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B.Sc.:  Jadavpur University, Kolkata, India.

M.Sc.: Indian Institute of Technology (IIT), Kanpur, India.

Ph.D.: Emory University, Atlanta, U.S.A. (Supervisor: Prof. Joel M. Bowman)

Expertise:  Theoretical Chemistry, Reaction Dynamics, Machine-Learning, Electronic  Structure Theory, Molecular Dynamics, Nuclear Quantum Effects

Current  Interest: Development of reliable and efficient Machine-Learning models  for atomistic simulations of solar energy materials

Bachelor of Science in Physics, LADY BRABOURNE COLLEGE, CALCUTTA UNIVERSITY, India

Master of Science in Physics,VELLORE INSTITUTE OF TECHNOLOGY, India

PhD in Chemistry ,INSTITUTE OF PHYSICAL CHEMISTRY,POLISH ACADEMY OF SCIENCES, Poland

Expertise:  Non-linear dynamics, Machine-Learning, Chemical computing

Current research interests: Using quantum mechanical calculations with  DFTB to identify the most suitable descriptors for training machine  learning models. These models  are trained to predict important ADMET  properties of molecules, which are crucial in drug design. In other  words, I am exploring ways to use machine learning to make drug  discovery faster and more effective

Keywords: DFTB calculations, Computational Chemistry, Drug Design, Machine learning

History

2017 - 2022, Postdoctoral researcher,  Institute of Microbiology of the Academy of Sciences, Nové Hrady, Czechia.

2014 - 2016, Postdoctoral researcher, Institute of Organic Chemistry and Biochemistry, Prague, Czechia.

2010 - 2013, Phd student, University of Lorraine, Nancy, France.

Expertise

_ Molecular dynamics simulations of biomolecules

_ Free-energy calculations

_ Molecular modelling (Homology modelling, docking, …)

Research interests:

_ Phospholipidic membranes and transmembrane proteins

_ Biomolecules

_ Protein assembly

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Research interests:

- Development of multi-component molecular orbitals methods to include interparticle correlation. 

- Positronic and positronium chemistry. 

- Nuclear quantum effects.

- Software development of quantum chemistry packages. 

Education:

2015-2017: Research assistant. Universidad Nacional de Colombia. Bogotá, Colombia. Supervisor: Professor Andrés Reyes

2012-2015: M.Sc. in Chemistry. Universidad Nacional de Colombia. Bogotá, Colombia. Thesis title "Development and implementation of an explicitly correlated Gaussian function method under the Any Particle Molecular Orbital approach, APMO" (Spanish). Supervisor: Professor Andrés Reyes

2007-2012: B.Sc. in Chemistry. Universidad Nacional de Colombia. Bogotá, Colombia. Final project title "Effect of the inclusion of nuclear-electronic correlation on the nuclear delocalization". (Spanish). Supervisor: Professor Andrés Reyes

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Current interests:

             Theoretical study of intermolecular interactions and potentials
             Density functional modeling of van der Waals forces

Expertise:  

             Molecular simulations (quantum chemistry and molecular dynamics)
             Theoretical chemical reaction kinetics and dynamics
             Photophysics and photochemistry
             Computational chemistry

Education and research:

  2019-          Doctoral researcher, University of Luxembourg

  2018-2019  Researcher, Hungarian Academy of Sciences

  2016-2018  M.Sc in Chemistry, University of Pannonia, Hungary

  2013-2016  B.Sc. in Chemistry, University of Pannonia, Hungary

Interest:

The core of my current work is improving Potential Energy Surface (PES) or Force Field (FF) predictions for Machine Learning (ML) models in molecular simulations. Most recently achieved using clustering techniques to reveal molecular configurations in a data set that are typically underrepresented in usual ML training methods.

Education:

​

2017: B. Sc. in Physics at University of Luxembourg: "Simulation of cholesteric rod-like particles".

2019: M. Sc. in Physics at University of Luxembourg: "Improving Machine Learning force fields for out-of-equilibrium geometries"

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Current interest:
-Multiscale approaches to quantum correlations

Expertise:
-Many-body electronic structure theory and ab initio computations of molecular and extended systems
-Continuum variational and diffusion quantum Monte Carlo techniques
-Non-covalent interactions, fractional charge, excited states

Education and research:
2018-2019:  Research Assistant, University of Ostrava, Czech Republic
2016-2019:  M.Sc. in Solid State Physics, Comenius University in Bratislava, Slovakia
2013-2016:  B.Sc. in Physics, Comenius University in Bratislava, Slovakia

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Current project:

Machine learning force fields for accurate and efficient molecular simulations.

Research interests:

- Scientific software development

- Computational chemistry

- Kernel machines

Education:
2020-present: Doctoral researcher, University of Luxembourg

2017-2019: MSc in Chemoinformatics and Molecular Modelling, Kazan Federal University, Russia
2013-2017: BSc in Chemistry, Kazan Federal University, Russia

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Current interests:
- Development of universal method for the description of van der Waals  interaction based on the density functional (tight-binding) theory.

Previous experience:

- Computational materials science

- DFT modeling of solids

- Carbon nanosystems

Education:
2021 - present: Doctoral researcher, University of Luxembourg
2018 - 2020: M.Sc. [Applied Mathematics and Physics] School of  Electronics, Photonics, and Molecular Physics, Moscow Institute of  Physics and Technology (Russia)

2014 - 2018: B.Sc. [Applied Mathematics and Physics] Department of Molecular and Chemical Physics, Moscow Institute of Physics and Technology (Russia)

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Current Project:

Design and validation of novel  ML-based tools benchmarked on quantum mechanical reference data of  chemical reactions, to be used to accurately predict components of the vast chemical reaction  space emphasizing transition state features.

Education:

-  2014-2018: Bachelor in Physics at University of Florence, Italy. Thesis  “Optimal estimation of parameters of complex quantum dynamics”

-  2018-2021: Master in Physics at University of Milan, Italy. Thesis  ”Reinforcement learning for feedback control of continuous-variable  quantum systems”

- 2020-2021: Erasmus Traineeship (for master thesis) at University of Turku, Finland

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Current research:

Approaching Quantum Mechanical Accuracy for Drug-Protein  Binding with Machine Learning (AQMA), is a collaboration effort of the  TCP group with the Luxembourg Center for Systems Biomedicine. Our aim is to combine  Quantum Mechanics and Machine Learning approaches for the accurate characterization of the protein dynamics through the prediction of  protein-ligand binding affinity.

Education:

2021 – present: Doctoral researcher, University of Luxembourg

2019 – 2021: M.Sc. Physics, University of Luxembourg

2015 – 2018: B.A. Physical Sciences, University of Cambridge

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Homepage

Current Research:
- Molecular dynamics (both theoretical and computational)
- Neural network models for computing thermodynamic potential
- Computational simulation of molecular systems

Expertise:
- Monte-carlo simulations of a molecular systems
- Transformer and attention-based neural network models
- Natural language understanding and processing
- AI-based multilingual translation and speech models
- Video and image generation models
- Muscular signitures post-mortem on rodents

Professional Experience:
2019 - 2021 : Head of Research, SRuniverse, Seoul (an AI start-up)
2017 - 2018 : Researcher, Hankook Life Science Institute, Seoul
2010 - 2011 : Student Researcher, Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa

Education:
2016 - 2017 : Master of Science, University of Tours, France
- Program : Modèles non linéaires en Physique
- Master research : Simulation of vortex behaviour in the inner-crust of a neutron star
2008 - 2014 : Bachelor of Mathematics (Hon.) , University of Waterloo, ON, Canada
- Major 1 : Mathematical Physics
- Major 2 : Pure Mathematics
- Undergraduate research : Osmotic compaction of bacterial chromosomes

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Current research:
Thermodynamics of simple model systems using various approaches for
treating the dispersion interaction, comparing the phenomenology of
pairwise and many-body methods.

Academic history:
2021-present: Doctoral researcher, University of Luxembourg
2017-2020: M.Sc. in Physics, Albert-Ludwigs-Universität Freiburg, Germany
2013-2017: B.Sc. in Physics, Albert-Ludwigs-Universität Freiburg, Germany

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Current research:

Machine learning methods for navigating in chemical reaction space.

Development of accurate machine learning models for large and flexible molecules.

Education:

2015 - 2021 B.Sc. and M.Sc. in Chemistry (summa cum laude), Lomonosov Moscow State University

Current Interests:
Development of machine learning force fields to study layered halide perovskites in collaboration with the University of Mons.

Previous experience:
 - Particle physics
 - Particle detector design
 - Computer vision with convolutional neural network and graph neural network

 Education:
 2018 – 2021 M.Sc. in Weizmann Institute of Science:
 "Application of deep learning for improvement of particle flow algorithm for dijet events"
 2014 – 2018 BSc in applied physics. V. N. Karazin Kharkiv National  University: "Exact solution of the problem of tunneling spin-polarized  electrons in a magnetic field through a quantum dot"

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Current Research
Build computationally efficient multi-scale models that incorporate a  quantum many-body treatment of van der Waals, and define continuum  mechanics properties arising from quantum mechanics.

Experience
- Continuum modeling of large ensembles of coupled non-linear systems.
- High efficiency computing and GPU acceleration trough CUDA.
- End-to-end voice conversion/speech generation using attention-based NN models.
- Data augmentation for speech recognition and image classification models.

Education
2022-present: Doctoral researcher, University of Luxembourg
2019-2021: M.Sc. in Physics, Instituto Balseiro, Argentina
2017-2019: B.Sc. in Physics, Instituto Balseiro (combined degree with Universidad de Buenos Aires), Argentina

2015-2017: B.Sc. in Physics, Universidad de Buenos Aires (combined degree with Instituto Balseiro), Argentina

Research topics:  Predict with machine learning algorithm forces & energies for  complex biomolecular systems learning from DFT calculations. Positive results  would be matching a relatively good accuracy and a competitive  computational time regarding the calculation methods.

Last positions:

-Master In Silico Drug Design: double degree master:

-Strasbourg University (6months)

-Degli Studi Di Milano (6months)

-Paris Diderot (6months)

-Internship in UHA Mulhouse (6months)

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Research  Interests: PT Symmetry, Non-Hermitian Quantum Mechanics, Path-Integral  Methods, General Relativity, Gravitational Waves, Black hole scattering

Jun 2022 - November 2022 Honorarium (Remote research consultant) AEI, Hannover, Germany

Oct-Dec 2021 Visiting Scholar at Institut de Mathématiques de Bourgogne, University of Burgundy Franche-Comté, Dijon

2020-2021 Research Intern, AEI, Hannover

2015–2017 Master of Science (Physics), National Institute of Technology, Rourkela, India.

2011–2015 Bachelor of Science (Physics), National Institute of Technology, Rourkela, India

Research Topics

Machine-learned quantum Force Fields for molecule-protein interactions.

Education

2023-present: Doctoral researcher, University of Luxembourg

2020-2022:  M.Sc. in Physics of Complex Systems, University of Torino, Italy: “A  Hopfield-like algorithm for cell type classification”

2017-2020: B.Sc. in Physics, University of Torino, Italy: “Study of the critical behavior of the 2-D Ising model”

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Current Research:

​

​My current research focuses on the understanding of nuclear quantum effects in inorganic and organic molecules and clusters using machine learned force fields. We use two machine learning methodologies for the force fiend reconstruction process: 1) symmetric gradient domain machine learning (sGDML), and 2) continuous-filter convolutional neural network (SchNet). Specifically, the ongoing projects are:

- Development of new machine learning models to describe large molecules. 

- Understanding the dynamical implications of local and non-local electronic interactions (e.g. H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion and n→π∗transitions).

- Nuclear quantum effects in inorganic clusters.

Expertise:

​

- Vibrational and thermodynamical properties of molecules, clusters and nanoparticles

- Vector Valued Kernel Ridge Regression 

- Contributor to the sGDML package (http://quantum-machine.org/gdml/doc/index.html)

Education:

​

2012 - 2016:  PhD in Materials Science (Hons.)  form Universidad Nacional Autónoma de México, Mexico. 

2009 - 2011: Master in Science (Physics) (Hons.) from Universidad Nacional Autónoma de México, Mexico. 

2003 - 2008: Bachelor in Physics (Hons.)  from Universidad Autónoma de Sinaloa, Mexico. 

Enrolled in a PhD in Economics, my current research focuses on applying Machine Learning algorithms to Economic data to predict and interpret the determinants of multiple Economic phenomena, including individual well-being and self-assessed life satisfaction.

Education:

2011 - 2014: Bachelor of Science (BSc), Economics and Management, Bocconi University

2014 - 2017: Master of Science (MSc), Economic and Social Sciences, Bocconi University Final year thesis: "P - value and its frequentist misinterpretation: a case study in a Bayesian framework".

Professional Experience:

Sep. 2015 - Jan. 2016: Research Assistant intern, lavoce.info

Jun 2017 - Nov. 2017: EU Inbound Analytics intern, Amazon.com