Jose Luis Silva, Ph.D.
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PhD. in Physics
UU 🇸🇪 -
Postdoc in AI:
LiU 🇸🇪 -
Founder
Aicavity Academy 🇸🇪 -
Co-Founder:
Oxaala LTDA. 🇸🇪 🇧🇷 -
Life:
Brazilian-Swedish 🇧🇷 🇸🇪
Research Interests:
- Artificial Intelligence & Machine Learning
- Graphs, Computer Vision & NLP
- Data Science, Analytics & Decision Making
- Deep Learning & Reinforcement Learning
Wavelet Gated Multiformer for Groundwater Time Series Forecasting
I’m Jose Luis Silva, Postdoc in Artificial Intelligence at Linköping University (Sweden). I hold a Ph.D. in Physics from Uppsala University. I am all in to solving real-world problems at scale. I have Co-Founded Oxaala Technologies (AI Company) which recently raised 1.6M+ SEK through the CNPq/MCTI Startup 2022 Brazilian Call to develop an AI-based software prototype for Exploration Geophysics. I was also granted the VFN Grant 2022 at LiU for further development of initiatives in collaboration with the Industry. I was a Chair at NeurIPS 2021 for the Workshop LatinX in AI, current member at LatinX in AI, and peer-reviewer for the NeurIPS, CVPR, Elsevier, and Gulf Arab Journal of Scientific Research covering AI & Machine Learning. I am also writing a book chapter regarding Advances in Artificial Intelligence and Data Mining.
If you have any questions, do not hesitate in contacting me! (NO SPAM)
My Scientific Publications / Reviewer
Wavelet Gated Multiformer for Groundwater Time Series Forecasting
Authors: Vitor Hugo Serravalle, Paulo Barros, Dr. Euler Bentes, Dr. J. Luis Silva
Journal: Scientific Reports
Publisher: Nature Publishing Group
Status: Under Review
Multi-Fractal Detrended Cross-Correlation Heatmaps for Time Series Analysis
Authors: Paulo Roberto, Kianny Lopes, Vitor Hugo Serravalle, Michell Thompson, Euler Bentes, J. Luis Silva
Journal: Scientific Reports
Publisher: Nature Publishing Group
Publication Date: 15 Dec 2022
g-C3N4/WTe2 Hybrid Electrocatalyst for Efficient Hydrogen Evolution Reaction
Authors: J. Luis Silva et. al.
Journal: The Journal of Physical Chemistry C
Publisher: American Chemical Society
Publication Date: 30 March 2020
Cite this: J. Phys. Chem. C 2020, 124, 16, 8726–8735
X‑ray Photoelectron Fingerprints of High-Valence Ruthenium−Oxo Complexes along the Oxidation Reaction Pathway in an Aqueous Environment
Authors: J. Luis Silva et al.
Journal: The Journal of Physical Chemistry Letters
Publisher: American Chemical Society
Publication Date: 20 November 2019
DOI:10.1021/acs.jpclett.9b02756
Cite this: J. Phys. Chem. Lett. 2019, 10, 7636−7643
Unveiling the Interfacial Effects for Enhanced Hydrogen Evolution Reaction on MoS2/WTe2 Hybrid Structures
Authors: Yu Zhou, Joshua V. Pondick, J. Luis Silva et al.
Journal: Small
Publication Date: 08 April 2019
Cite this: Small 2019, 15, 1900078
Spectroscopic Fingerprints of Intermolecular H-Bonding Interactions in Carbon Nitride Model Compounds
Authors: Dr. Valeria Lanzilotto, J. Luis Silva et al.
Journal: Chemistry - A European Journal
Publication Date: 15 July 2018
Cite this: Chem. Eur. J. 2018, 24, 14198
Hydrated Electron Generation by Excitation of Copper Localized Surface Plasmon Resonance
Authors: Mariia V. Pavliuk, Sol Gutiérrez Álvarez, Yocefu Hattori, Maria E. Messing, Joanna Czapla-Masztafiak, Jakub Szlachetko, J. Luis Silva et al.
Journal: The Journal of Physical Chemistry Letters
Publication Date: 28 March 2019
DOI:10.1021/acs.jpclett.9b00792
Cite this: J. Phys. Chem. Lett. 2019, 10, 8, 1743–1749
Revealing the Contribution of Individual Factors to Hydrogen Evolution Reaction Catalytic Activity
Authors: Yu Zhou, J. Luis Silva et. al.
Journal: Advanced Materials
Publication Date: 24 March 2018
Cite this: Adv. Mater. 2018, 30, 1706076
Modelling and Simulation of Electro-catalysts for Green Energy: From Solvated Complexes to Solid-Liquid Interfaces
Author: J. Luis Silva
Publisher: Acta Universitatis Upsaliensis
Publication: 2020
ISBN: 978-91-513-0987-3
Github & More
Optimize ML Models and Deploy Human-in-the-Loop Pipelines
Optimize ML Models and Deploy Human-in-the-Loop Pipelines 3 Projects – Description: When training ML models, hyperparameter tuning is a step…
Build, Train & Deploy Machine Learning Pipelines using BERT, RoBERTa and Amazon Sagemaker
Build and Deploy Machine Learning Pipelines using BERT and RoBERTa and Amazon Sagemaker 3 Projects Description: Automate a natural language…
Analyze Datasets and Train ML Models using AutoML
4 Projects Description: Perform a multiclass classification for sentiment analysis by first ingesting product reviews data into a central repository,…
Backtesting
Backtesting Project Description: Build a fairly realistic backtester that uses the Barra data. The backtester will perform portfolio optimization that…
Combining Signals for Enhanced Alpha
Combining Signals for Enhanced Alpha Project Description: Combine signals on a random forest for enhanced alpha, and solve the problem…
Sentiment Analysis with Neural Networks
Sentiment Analysis with Neural Networks Project Description: Here you will build your own deep learning model to classify the sentiment…
My Timeline & Experience:
Education & Academia
PostDoc in Artificial Intelligence
Development of Artificial Intelligence models.
Steering Committee Advisory Board Member
Licenciate and Ph.D. in Physics
My Thesis: Modelling and Simulation of Electro-catalysts for Green Energy: From Solvated Complexes to Solid-Liquid Interfaces
• Teaching Experience: Mekanik Baskurs (2 years)
• Administrative Experience: Responsible for the Materials Theory Division Seminars.
• Resources: My Ph.D. was financed by Uppsala University, and the projects/travels were mixed - Supervisor Dr. Moyses Araujo, and Co-Supervisors Dr. Olle Björnholm, and Dr. Barbara Brena (i.e. STINT-CAPES Brazil-Sweden, Knut and Alice Wallenberg Foundation, and Vetenskapsrådet).
• Computational Resources: KTH Beskow HPC, Tetralith SNIC, and Kebnekaise HPC. These projects involved approximately 300,000 cores*hours/month. I have written the project that was approved by the HPC Facility SNIC (JPCL 2019 Published).
• Experience: I have led the computational simulations of six projects using high-performance computing facilities to design materials for green energy.
•Programming Languages used: C/C++, Python, Matlab, and Shell-script.
• Metrics: 6 high-profile publications in international Journals.
• My Presentations:
STINT-Capes Conference, Rio de Janeiro, Búzios, and Natal, 2017 & 2018, Brazil (Seminar) - Photo1 Photo2
Workshop: Trieste - Uppsala Meeting on Photoelectron Spectroscopy, Uppsala University, Sweden 2018. (Seminar)
CoTXS mini-workshop, Uppsala University, Sweden 2018. (Seminar)
SUNCAT Summer Institute, Fundamentals and Applications of Heterogeneous Catalysis, Stanford University, California, USA 2017 - Poster
European Congress and Exhibition on Advanced Materials and Processes EUROMAT2019, Stockholm, Sweden 2019 (Seminar) - Photo
Baltic Sea Region Hydrogen Network, Opera Hotel, Riga, Latvia 2017 - Photo
College on Multiscale Computational Modeling of Materials for Energy Applications - ICTP, Trieste, Italy 2016 - Photo
BSc and MSc in Physics
Teaching Experience:
• Lecturer in Theoretical and Experimental Physics I and II (UFBA/Devry University) Download
• Lecturer in Mechanics of Materials I and II (Devry University)
Achievements:
• MSc in Physics (UFBA) - Ranked 1st - Group: LaPO (Laboratory of Optical Properties) - Field: Solid State Physics and Computational Simulations. Group Leader: Prof. Antonio Ferreira. Supervisor: Prof. Denis Gilbert Francis David
• BSc in Mining and Petroleum Engineering (UFBA) (Completed 16 Courses in 1 year before moving to Physics) - Ranked 1st
• Marketing Director at Crystal Mining and Petroleum - Junior STARTUP (UFBA)
• BSc in Geophysics (UFBA) (Completed 2 years before moving to Sweden) - Ranked 1st
• Ph.D. in Geophysics (UFBA) (Completed 1 year before moving to Sweden) - Ranked 1st
• Junior and Master Scientific Research Scholarship (2,5 years BSc and 2 years of MSc - National Council for Scientific and Technological Development, CNPq, Brazil)
1 - Project: Simulation of CO2 and water injection in front reservoir using Apollonian Networks as a porous-media to improve oil recovery mechanisms. (Ansys Fluent) - Group: Statistical Physics and Complex Systems, Supervisor: Prof. Roberto Andrade (UFBA)
2 -Project: Study of Magnetic Properties in Paramagnetic Aggregates via First Principles Calculations - Supervisor: Prof. Roberto Rivelino (UFBA)
3 - CV Lattes
Certificates before 2015:
Conferences and Events
- School on Electronic Structure and Quantum Transport Methods, UNESP, ICTP, SP, Brazil, 2014.
- V Winter School of Physics - UFBA, BA, Brazil, 2012.
- Certificate in Thin Films Applied to Solar Energy.
- Certificate in Study of Semiconductor Materials via DFT.
- Certificate in Electron and ion accelerators.
- Certificate in Molecular Orbital theory.
- IV Winter School of Physics - UFBA, BA, Brazil, 2011.
- Certificate in Topics in Field Theory.
- Certificate in Solar energy.
- Physics Summer School at Aeronautics Institute of Technology (ITA). Seminar: Study of Magnetic Properties in Paramagnetic Aggregates via Calculation of First Principles, ITA, SP, Brazil, 2011.
- Winter School in Physics Applied to Medicine and Biology - USP, SP. 2011.
- AMS/SBF First Joint Meeting - IMPA, RJ. 2008.
- International Symposium of Dynamical Systems -. IMPA, RJ. 2006.
- V Workshop on Molecular Physics and Spectroscopy - UFBA, BA. 2006.
- XIV School of Differential Geometry - Honors to Shiing-Shen Chern (IMPA) - UFBA, BA. 2006.
Industry & Non-Profit
Co-Founder
Data-Driven Software Consulting
Head Manager
Non-profit Artificial Intelligence community in Stockholm.
Research Engineer (Summer Intern)
Deep Neural Network Architectures and Markov Decision Processes for tracking multiple objects.
Recent Research Projects
Physics-based animation using Graph Neural Networks
- Applications of Graph Nets and Graph Neural Networks to simulate physical behavior of real particles and systems.
- Examiner: Prof. Rickard Armiento
Opponent: Linda Le
Plats: Laplace, FysikHuset - Defended 29th of August
Projects Approved for Research Development
- Development of DL and Graph Neural Network Architectures for high-throughput design and discovery of new molecular and 2D-based Materials with applications in Energy devices & Catalysis. (2021-2022)
- AI for Physics and Engineering: Modeling and Simulation (2022-2023)
AI & Decision-Making
- Data-driven Analytics for Decision-Making in Geophysics. (Collaborations: CPRM, Petrobras, and GAIA UFBA - Brazil)
Computational Resources:
Tetralith, SNIC, Sweden
- Tetralith consists of 1908 compute nodes each with two Intel Xeon Gold 6130 cpus with 16 CPU cores each, giving a total of 61056 CPU cores.
- The performance of the complete system is around 3 Pflop/s (LINPACK Rmax).
Kebnekaise Cluster, Sweden
- 432 nodes with Intel Broadwell E5-2690v4, 2x14 cores, 128 GB/node
- 20 nodes, 3TB/node, Intel Broadwell E7-8860v4, 4x18 cores
- 32 nodes with 2x NVIDIA K80; 4 nodes with 4x NVIDIA K80; 128 GB/node
- 36 nodes, Intel Knights Landing; 7250 SKU (68 cores, 1.4 Ghz/1.2 Ghz/AVX), 192 GB/node.
- 52 nodes with Intel Skylake Gold 6132 2x14 cores, 196 GB/node
- 10 GPU Volta nodes with Intel Skylake Gold 6132 2x14 cores, 2xNvidia V100 (2x5120 CUDA, 2x640 Tensor), 192 GB/node
- InfiniBand EDR/FDR interconnect
Rackham Cluster, Uppsala University
- Rackham comprises 9720 cores in the form of 486 nodes with two 10-core Intel Xeon V4 CPU's each. There are "fat" nodes,4 with 1TB memory and 32 with 256 GB memory, with the rest having 128 GB.
- Rackham's storage system (named Crex) uses the Lustre file system and provides 6.6PB of storage.
- The interconnect is Infiniband FDR which supports a theoretical bandwidth of 56Gb/s and a latency of 0.7 microseconds.
C3SE Alvis, Chalmers University
Alvis cluster is a national SNIC resource dedicated for Artificial Intelligence and Machine Learning research.
Recent Certificates:
Introduction to Cyber Attacks
Optimize ML Models and Deploy Human-in-the-Loop Pipelines
Analyze Datasets and Train ML Models using AutoML
Build, Train and Deploy ML Pipelines using BERT
AI for Trading Nanodegree
Deep Learning Specialization
Natural Language Processing Specialization
Natural Language Processing with Attention Models
Natural Language Processing with Classification and Vector Spaces
Natural Language Processing with Probabilistic Models
Neural Networks and Deep Learning
Structuring Machine Learning Projects
Sequence Models
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Convolutional Neural Networks
Practical Data Science on the AWS Cloud
Build Basic Generative Adversarial Networks (GANs)
AI for Medical Diagnosis
Natural Language Processing with Sequence Models
Introduction to Software Product Management
Software Processes and Agile Practices
Fundamentals of Reinforcement Learning
Sample-based Learning Methods
Introduction to Data Science
Fundamentals and Applications of Heterogeneous Catalysis
Poster: Nanodevices as micro-reactors for studying electrocatalytic hydrogen evolution reaction