Suggestions
Eliezer de Souza da Silva
ML Researcher
Eliezer da Silva is a highly educated professional in the fields of machine learning, probabilistic models, and statistical modeling. He obtained his Doctor of Philosophy degree from the Norwegian University of Science and Technology (NTNU), where he specialized in recommender systems. Additionally, da Silva holds a Master of Science in Computer Engineering from the University of Campinas and a Bachelor's degree in Computer Science from the Federal University of Espírito Santo in Brazil.
He has a diverse range of professional experience, having worked as a lead data scientist at Searis and as a machine learning researcher at Curious AI. Da Silva has also held positions as a visiting researcher at the University of Helsinki and a visiting research student at RMIT University. Prior to his career in data science, he worked as a software engineer and Java programmer at several organizations.
Da Silva has also contributed to the academic community as an organizer at the Nordic Probabilistic AI School (ProbAI) and as a PhD research fellow at NTNU. He has demonstrated keen interest in algorithms for probabilistic machine learning and bayesian statistical modeling.
Overall, Eliezer da Silva's breadth of experience and knowledge make him a valuable asset to any organization looking for a skilled data scientist.
Highlights
I want to contact my network for an essential call for sponsorship for the Tropical Probabilistic AI School! The event will occur from Jan 29 to Feb 2, 2024, in Rio de Janeiro, Brazil (hence summer in the Global South).
After four successful editions of the summer school in the Nordics, we are bringing this event to Latin America, one of the reasons being the lack of high-quality events being organized in the region and the high costs for anyone, both in academia or industry, to be able to participate in such events when they are all located in the global north.
Successfully organizing this school in Brazil will increase diversity and opportunities for underrepresented groups and regions. If you also think about the progress of AI / Data Science for many and want your institutions to contribute to our initiative, reach out and let us talk! We are open to many forms of collaboration, from scholarships to organizing social events or directly wiring funds. Also, if you can facilitate for employees or colleagues in your organization to attend as paying participants, this will significantly help us!
Check the link for more details!
I am happy to share that the Tropical Probabilistic AI School applications are open! The event will occur from Jan 29 to Feb 2 in Rio de Janeiro at the Botafogo Campus of FGV.
https://lnkd.in/dhSjw6f4
The application deadline is Nov 24; I look forward to meeting you in person for this great week! Let us enjoy a whole week of deep technical learning, discussions, and ideation, combined with the great weather and environment of Rio de Janeiro's summer.
The program will cover topics such as introduction to probabilistic modeling, variational inference and optimization, deep generative models, representation learning, and causality. We will also have a poster session, an excellent opportunity to discuss your ideas with a motivated and knowledgeable community and learn what others are doing in a laid-back setting.
The list of speakers includes: - Adèle H. Ribeiro (University of Marburg)
- Denis Deratani Mauá (University of São Paulo)
- Hugo Neves de Oliveira (UFV) - Diane Bouchacourt (Meta, FAIR) - Helge Langseth (NTNU)
- Sam Witty (Basis Research Institute)
- Timothy J. O'Donnell (McGill University)
- Vikash Mansinghka (MIT)
- Zenna Tavares (Columbia University, Basis Research Institute)
Following our Nordic Probabilistic AI School (ProbAI) tradition, the goal, approach, and strive for quality of program, lectures, and diversity in the community continue to be central for the organizing team. As such, it is crucial to bring such events to Latin America (and generally places outside of the North America/EU circuit), inviting the international community to less represented regions and benefitting the local community.
