Speakers

Rita Fernandes Neves is a Senior Solutions Architect at NVIDIA, working with a breadth of generative AI technologies. She currently focuses on retrieval systems and evaluation strategies for agentic pipelines, with a particular interest in sovereign AI and multilingual applications. Rita holds a PhD in Space Engineering, during which she developed mathematical models and optimization strategies to build interplanetary trajectories for asteroid retrieval.

Hugo Lopes is a Software Engineer at Google DeepMind in Zurich, where he focuses on Gemini Personalization, teaching the AI to remember users to foster a trustful relationship. His career began in the aerospace sector with European Space Agency projects before he led R&D in Data Science at James, a Fintech company acquired by Google. This path led him to Google's Payments team before his current role at DeepMind, in Zurich. He is driven by the challenge of transforming complex problems into concrete, solvable ones.

Diogo Sá is an AI/ML Engineer with over four years of experience in production-grade AI. At Airbus, he develops GenAI multi-agent applications, focusing critically on robust evaluation, LLM safety, and defining LLMOps standards for responsible, scalable deployment. Previously, he standardized ML development at Continental and led a data-centric transformation at Vestas that prevented over €20 million in repair costs. His expertise spans GenAI (LLMs, RAG, Agents), Deep Learning, and MLOps automation. Diogo is passionate about turning cutting-edge research into robust, scalable tools that deliver measurable business value.

João Gama is an Emeritus Professor at the University of Porto, Portugal. He received his Ph.D. in Computer Science from the University of Porto in 2000. He taught Informatics and data sciences at the School of Economics for more than 30 years. His main scientific contributions are in the area of learning from data streams, for which he has an extensive publication record.  He is the Editor-in-Chief of the International Journal of Data Science and Analytics, published by Springer.

Sam Debruyn is a Data Platform Architect passionate about modern data platforms. In his day-to-day, he designs and implements data platforms that support customers in harvesting the full potential of their data. Sam has over 10 years of experience and contributes to the data community through open-source projects, blog posts, and speaking engagements at data meetups and conferences. He received the Microsoft Data Platform MVP award as well as the dbt Community Award.

Bruno Veloso is an associate professor at Faculty of Economics - University of Porto, Portugal, and a Senior Researcher at LIAAD - INESCTEC. He received his degree in Electronics and Computer Engineer in 2010, and in 2012 he finished his MSc in Electronics and Computer Engineer in Telecommunications at ISEP, Portugal. He has an International PhD in Telematics Engineering from the University of Vigo. He develops scientific research in Artificial Intelligence, which involves Machine Learning and Data Streams. Bruno has authored over 80 publications in peer-reviewed international journals, book chapters and conference proceedings.

Born and raised in sunny Portugal and based in Porto, Clara enjoys working in the intersection of Machine Learning, Product, and Engineering, solving problems in a pragmatic and iterative way. She currently leads Applied AI at Sword Health, where her team is reinventing how patients access and receive care, by creating a more human, more clinically effective, and more scalable way to treat patients. She is focused on building and scaling machine learning systems that help achieve Sword's mission of freeing 2 million people from pain. She earned her master degree in Biomedical Engineering from FEUP.

Inês is a  dual PhD candidate at the University of Porto, Portugal, and Leiden  University, the Netherlands, specializing in artificial intelligence,  focusing on responsible AI and computer vision. Before returning to academia,  she worked as a data scientist across different areas, developing a strong  interest in the trustworthiness of AI models. Her research focuses on stress  testing, a model auditing approach that reveals weaknesses by examining  behavior near decision boundaries. Her work aims to enhance transparency and  trust in AI systems. Beyond her research, Inês is an invited assistant  professor and mentor at the University of Porto.

Jeff is a  Solutions Architect at DigitalOcean, where he designs and optimizes K8s-based  GPU infrastructure for gen-AI workloads, with a focus on LLM inference and  cost-efficient scaling. Before joining DigitalOcean, he helped build and run  mission-critical systems for several large German enterprises. Jeff now  distills complex cloud and AI concepts into clear, practical  guidance—publishing articles that turn deep technical challenges into  straightforward, actionable steps for engineering teams.

Jeremy  began his career as an accountant before transitioning into data, recognising  the value of combining finance and data to solve complex problems. He has  hands-on expertise in building data pipelines, improving data quality, and  enabling data-driven business decisions.His background provides a unique perspective at the intersection of finance  and data, collaborating across teams - from finance to engineering in  fast-paced, global environments. Having worked in Berlin, Hong Kong, Paris,  and Singapore, Jeremy brings a broad understanding of diverse data challenges and teamwork dynamics.

Matthias Rochtus, experienced public speaker and Data Scientist, originally studied  aerospace engineering, since build an expertise in geospatial data research,  sattelite design, predictive maintenance, GenAI architectures, mainly  focussing on retrieval augmented generation. Matthias Rochtus, experienced public speaker and Data Scientist, originally studied aerospace engineering, since build an expertise in geospatial data research, sattelite design, predictive maintenance, GenAI architectures, mainly focussing on retrieval augmented generation.

Moiz did  his Doctorate in the field computer vision for medical images. He has been  working with images and textual data within medical domain. Currently, he has  been involved in several projects related to bioinformatics, ML and  multi-omics in veterinary science and evolutionary hologenomics. Apart from  the main projects, he is working with other computer vision projects related  to medical, infrastructure and other smaller projects. His future projects  will include multi-modal models within health science.

Pascal  holds a master’s degree in Computer Science from ETH Zürich, where he  specialized in Data Management Systems and Software Engineering. Since  joining Migros, Switzerland’s largest retail company and cooperative, he has  been dedicated to driving innovation in data enablement and governance.  Notably, he successfully scaled the group-wide Digital Governance Framework,  implementing optimizations that enabled the efficient management of over 400  initiatives within just one year.

Shikhar  has worked with Deep Learning for images and text for the duration of his  career. From trying to solve fake news with AI to automation of recycling of  electronic waste to now automating document processing for a bank where he  has been responsible for increment of >21% in STP rates over 5 million  documents a year.  He has also been a  contributor to transformers, torch vision and spacy. He loves to share  knowledge and keep up to date with the industry.

Dr. Poonam Chaudhary is currently working as is currently working as the Data Science Lead and Associate Professor with the Department of CSE, The Northcap University. She is a target-driven, dedicated professional with more than 15 years of experience in teaching, administration, industry, and research. She has guided around 35 B. Tech projects, and 15 M.Tech theses, and currently supervising 4 Ph.D. Scholars. She has published 20+ research papers, chapters, and books in national and international conferences and peer-reviewed journals.

Rita Costa is a Data Visualization professional based in Portugal. She is passionate about using data to tell stories that shed light on relevant topics. She trained and worked as a data journalist and now leads the Data Visualization team at Feedzai, working at the intersection of research and product development. Other works include "Flags of Inequality", an award-winning piece that uses visual metaphors to highlight inequalities in legal rights for queer people in Europe.

Sam Debruyn is a Microsoft Data Platform MVP and Cloud Data Solution Architect passionate about the modern data stack. In his day-to-day, he designs and implements data platforms that support customers in harvesting the full potential of their data. Sam has over 10 years of experience with Microsoft Azure and regularly blogs about his adventures in data. He contributes to the data community through blog posts, open-source projects, and speaking engagements at data meetups and conferences.

Serg Masís is a Data Scientist in agriculture with a lengthy background in entrepreneurship and web and mobile development, and the author of the bestselling book "Interpretable Machine Learning with Python", and the upcoming book "DIY AI" for Addison-Wesley for a broader audience of curious developers, makers, and hobbyists. He's passionate about data-driven decision-making, Explainable AI, Responsible AI, behavioral economics, and making AI more accessible

Shawn is an innovative technologist and data strategist with over 15 years of experience. He is passionate about harnessing data strategy, engineering, and analytics to help businesses uncover new opportunities. His holistic view of technology and emphasis on developing strong engineering talent, focusing on delivering outcomes while minimizing outputs, sets him apart. Shawn's deep technical knowledge includes distributed computing, cloud architecture, data science, machine learning, and engineering analytics platforms. He worked as a consultant for prestigious clients, from secret-level government organizations to Fortune 500 companies.

Shikhar Chauhan has worked with Deep Learning for images and text throughout his entire career, from solving fake news with AI to automating electronic waste recycling to automating document processing for a bank and optimizing internal processes using GenAI. Shikhar has also been a contributor to Transformers, Torchvision, and spaCy. He loves to share knowledge and keep up to date with the industry and the latest in the field. Shikhar is fascinated by NLP, Images, and Multi-Modal AI.

Sofia Bourhim is a Research Scientist. She is working on Graph Machine Learning and its interdisciplinary applications to AI for Good problems. She recently obtained her Ph.D. in the field of AI. She previously interned at Microsft research lab (MARI) as a Research intern and is a recipient of the Microsoft PhD Fellowship. Sofia's contributions extend to receiving recognition for the best research papers at various conferences and notable events like NeurIPS'23 and Gitex'23.

Vivek Kumar is a Senior Researcher/Scientist and the Chair of Open Source Intelligence at the University of Federal Armed Forces under the Federal Ministry of Defence, Germany. He coordinates STELAR, an EU's Horizon 2020 program targeting "Artificial Intelligence, Data, and Robotics" in agri-food. Dr. Kumar won the Marie Sklodowska-Curie fellowship in 2019 for the EU's Horizon 2020 ITN network program PhilHumans & worked with Philips Research, Netherlands, and the University of Cagliari to receive a doctorate in 2023. His current research focuses on NLP, Knowledge Graphs, LLMs, AI Fairness & Bias.

Vinitra Swamy is a 4th-year machine learning PhD at EPFL in Switzerland. Her research on explainability and human-centric machine learning is co-advised by ML4ED and MLO labs. Prior to entering Switzerland, Vinitra graduated from UC Berkeley with both a Bachelor's and Master's in Computer Science at age 20, holding the record for the youngest graduate in the EECS department. She spent two years at Microsoft AI working on open-source AI frameworks as a lead engineer for the Open Neural Network eXchange (ONNX). She also lectured data science courses at the UC Berkeley Division of Data Sciences and the University of Washington CSE Department.

Wojtek Kuberski is an AI professional and entrepreneur with a master's in AI from KU Leuven. He founded Prophecy Labs, a consultancy specializing in machine learning, before getting his current role as a co-founder and CTO of NannyML. NannyML is an open-source library for ML monitoring. At NannyML, he leads the research and product teams, contributing to novel algorithms in the model monitoring space. Wojtek was featured in the Breakout Session at Web Summit for "some of the world's most exciting early-stage startups".

José Varela is passionate about Machine Learning, he created his first speech-to-text solution at INESC Macau back in 1998. Since then he's never stopped diving deeper and deeper into programming, data processing, and deep learning. For the last years 6 José has been at NTT Data Portugal, focusing on Deep Learning, and helping deliver value to Advanced Analytics customers in fields such as Utilities, Banking and Industry. José regularly speaks at different universities and corporate events and teaches Data Analytics at a Post-Grad course at IPS.

Rutuja Pawar combines a deep foundation in Computer Science enriched with a specialization in Data Science through her Masters. Her experience in technology, strategy, and consulting is broad, spanning years of collaboration with diverse teams across IT and Business. In her current role as a VIS Consultant at Accenture Germany, her focus lies on Data & Design, empowering clients to unlock the potential of their critical business data through intuitive, user-centric dashboards. Beyond her primary role, Rutuja wears many hats - a mentor, a Design Thinking Coach, a Tech Trainer, a #IAmRemarkable Facilitator, and a skilled Public Speaker.

Stephen is a Sr. Solutions Architect with AWS and concentrates on Data Analytics and Data Platforms. With over a decade of experience in software engineering and DevOps, he is committed to enhancing data engineering productivity and architecting resilient data platforms. Building on engineering best practices like automation or iterative software development, Stephen is passionate to create efficient solutions that empower organizations to deliver analytical workloads of high quality.

Having built vast know-how on forecasting and inventory management, Diogo is able to bolster companies on how to set up analytical models to anticipate demand uncertainty and, further on the chain, use these forecasts on state-of-the-art inventory optimization algorithms and operations planning. Before LTPlabs, Diogo had completed a Master’s degree in Industrial Engineering and Management at FEUP. Outside of work, Diogo is a sports enthusiast, especially for everything played with a racket.

Luís Espírito Santo is a joint PhD student working on a theoretical computational framework that includes both learning and creativity. He is also one of the main organizers of Deep Learning Sessions Portugal. His general interests lie in Artificial Intelligence, Maths, Cognition Sciences and the Arts and he is currently researching on Generative Machine Learning Models, Computational Creativity and Formal Learning Theory. He is also the CTO at two startups bandwaggon.ai and MagicSync.

Aldan studied CS in Spain, France and Switzerland, graduating as valedictorian. He completed 4 internships and was selected by Django as a Google Summer of Code contributor. He received a grant from the Spanish government to undertake research on Natural Language Processing. He garnered recognition for leadership and academic excellence through several awards. He's also the founder of 3 associations and contributes to open-source. Currently, he works as Technology Research Specialist in AI at The Dock, Accenture’s flagship R&D and global innovation center.

Alexey Yudin started his journey in the software development industry back in 2016. At first, he worked with back-end systems using languages like Java, Scala, and Python. By 2019, he transitioned into the Data field and mastered tools like Spark, Airflow, and Hive. In 2021, he started working with the Google Cloud Platform for building data pipelines. Currently, Alexey Yudin is a Senior Data Engineer at inDrive – the ride-hailing app with 200+ million app installs across 46 countries. He is responsible for streaming delivery of the most important data inside the company; his pipelines process 500+ million records daily.

During his BSc in Biomedical Engineering in Portugal, Bernardo Pereira had his first contact with AI, sparking his curiosity and leading him to continue studying AI during his MSc in Medical Imaging in the Netherlands. Through a series of internships in hospitals and research labs, Bernardo found out how to best combine these two worlds. Working at Philips Healthcare, he gained a deeper understanding of medical imaging as both a technology and a business, realizing that these are intrinsically linked. Now at Quibim, Bernardo applies all these learnings, pouring them into algorithms that turn images into actionable insights.

Filipa Marques, a data scientist with 16 years of experience in insurance, specializes in actuarial science and data analysis. With a strong math background, particularly in statistics, she's made significant contributions to the field. Her passion for advancing analytical methods led to research in machine learning, guiding 4 master's theses, one focused on causal ML. Demonstrating a commitment to data-driven decision-making, she continues to explore innovative approaches, extracting actionable intelligence from complex datasets to drive industry value.

Nuno is an engineer who loves to build things, especially if it involves data and machine learning. After helping build different startups in the Artificial Intelligence (AI) space, Nuno is now working on the OutSystems AI strategy to accelerate the way software powers innovation around the world.Nuno has a Masters in Industrial Engineering and Management from the Faculty of Engineering of the University of Porto (FEUP), and has published work in journals such as Decision Support Systems.

Filipa is currently leading the AI Tech Lab at Euronext, driving the development of AI solutions within the financial market. Formerly an AI Program Manager at Continental, she played a pivotal role in establishing and steering innovative AI solutions across the value chain of the tire industry. With a strong foundation in computer vision, deep learning, and generative AI, Filipa brings a deep technical perspective to her cross-functional work with business and engineering. She holds an Integrated Masters in Bioengineering from the University of Porto, complemented by international study experiences in the UK and the Netherlands. Filipa also belongs to the Lead Team of Data Science for Social Good Portugal, a community that uses data for social impact.

Gonçalo Almeida holds a Masters' degree in Data Science and Advanced Analytics from Nova IMS and is currently pursuing a PhD in Data Science in the same institution. He started working at JTA, three years ago as a Data Scientists, and has been involved in the foundation of the Machine Learning & AI Pillar, which he now leads. Over the years, Gonçalo has worked in multiple projects, ranging from applying Machine Learning to more business oriented problems to projects involving the developments of Computer Vision and Natural Language Process solutions.

Gustavo Pereira is a data scientist with expertise in machine learning and statistics. He holds a master’s degree in Data Analytics from the University of Porto, where he conducted his thesis on modeling wildfires through extreme values distributions. He has three years of professional experience as a data scientist, working firstly for a consultancy firm and currently for NOS. At NOS, he has applied regression, classification, and time-series models for predicting customer dissatisfaction and new sales. He is proficient in exploring new data sources and techniques, as well as in improving machine learning automation and reliability.

João has been working with Data for a while now, building systems for it, using it, presenting it and teaching it. He has strong opinions, albeit weakly held, on managing data as infrastructure, configuration driven development and SQL still being king in the data world. With a background in Information Systems, João worked with Big Data systems and visualization in his masters before moving on to the industry where he works as a Data Engineer developing cloud-based data systems and analytics use cases for the BMW Group.

Luís Ferreira is a Data Scientist and Machine Learning Engineer who loves to turn data into powerful insights and smart solutions. During his PhD at the University of Minho, Luís spent several years as a Machine Learning researcher and sharing what he knows in courses and bootcamps at various educational institutions. Currently, he's part of the team at Critical TechWorks. There, he focuses on developing, deploying, and managing ML models that help BMW unlock the full value of its data.

Miguel Freire, born in 1997 in Covilhã, Portugal, discovered his passion for technology early in life. Pursuing his interests, he moved to Lisbon in 2015 to study Electrical and Computer Engineering at IST-Lisbon. From 2018 to 2020, Freire worked as a Full-stack web developer at Aptoide, honing his skills. In 2020-2021, he conducted early-stage research at INESC-ID and completed his Master's Thesis in Deep Reinforcement Learning under the guidance of Arlindo Oliveira in 2022. Driven by his expertise, Freire co-founded NeuralShift in 2021 alongside Alexandre Borges and Arlindo Oliveira.

Philipp Bach, a postdoctoral researcher at the Chair of Statistics with Applications in Business Administration at the University of Hamburg and Head of Trainings and Executive Teaching at Economic AI, focuses on statistical methods for causal machine learning and their software implementation. Philipp applies these methods mainly in the context of economic research questions and collaborations with industry. Along with colleagues from Economic AI, Philipp offers industry training in the context of Causality, and Causal AI/ML targeted at data scientists and/or managers.

Telmo holds a Master’s in Electrical and Computer Engineering and has been working in the machine learning industry for the past six years. He's currently ML Team Lead at Loka, an AWS Partner for Life sciences and GenAI, leading projects on the application of Biological Large Language Models (BioLLMs) for AI-driven Drug Discovery. An expert in deep learning, probabilistic modelling and time series, Telmo also passionately contributes to education as an instructor at the Lisbon Data Science Academy and organizer of PyData Lisbon.

Dr. Tolga Kaya is a Professor and Director of Electrical and Computer Engineering programs at the School of Computer Science and Engineering. Dr. Kaya’s research primarily focuses on sports data analytics on wearable devices to monitor athletes’ physiology. He currently incorporates machine learning techniques to predict athlete performance and injury. He is well-known in the field of hydration research and sports data analytics. Besides his Ph.D. in Electronics Engineering, Dr. Kaya also has an M.A. in Education Technology and an MBA.

Ziv Levy works at Wix.com as a software engineer in the Data Science division. He is passionate about open-source projects and has a particular interest in web performance optimization. Ziv appreciates technology most when it incorporates visual elements and spends his free time mixing techno music.

Miguel Vicente is a recent Master’s graduate in Electrical and Computer Engineering from Instituto Superior Técnico, where he specialized in cyber-physical systems. Currently, he is working on his thesis at Fidelidade's Centre for Artificial Intelligence and Analytics, exploring the application of causal machine learning and A/B testing to optimize marketing strategies and enhance business outcomes. Miguel is looking to apply his technical skills and drive for innovation to make an impactful contribution to data science and machine learning.

Nuno B. Brás is a Senior Data Engineer and Data Scientist with extensive experience in consulting, teaching, and research.
He is currently a Partner and co-founder at DareData, an international company operating in the field of data and AI.
With over ten years of experience in developing startups and international businesses, Nuno has a background in Physics Engineering and holds a PhD in Electrical and Computer Engineering in the area of Signal Processing. He also has been a university professor for several years in the fields of programming, data, and machine learning.

Pedro Santos is a passionate physicist and a data scientist who loves uncovering hidden patterns. His transition from physics to data science was fueled by his curiosity about the inner workings of things. When he's not unraveling data puzzles, he enjoys exploring new destinations as a travel enthusiast or honing his skills in his favorite PS5 games, proudly embracing his inner nerd. Pedro brings vigor and passion to everything he does, whether it's applying statistical principles to data or using machine learning to solve problems. If you're interested in science intertwined with geek culture, Pedro is the go-to person.

Ricardo Araújo holds an MSc in Bioengineering from the Faculty of Engineering and a PhD in Computer Science from the Faculty of Sciences, both obtained at the University of Porto. Currently, he works as a Senior Computer Vision & AI Engineer at JTA: The Data Scientists. He is driven by the pursuit of algorithms that can imbue computers with human-like capabilities and is dedicated to innovating in the field of Artificial Intelligence to develop technologies that address societal needs.

Yoav Nordmann is a Backend & Data Architect and Tech Lead with over 25 years of experience. His experience ranges from being part of infrastructure teams at big corporate firms and other times being the single developer at startups coding late into the night. At Tikal he holds the position of a Group Leader mentoring fellow workers. He is passionate about new and emerging technologies, knowledge sharing and a fierce advocate for open source. Being in the industry for so long gives him a sense of perspective on different languages, architectures, and hypes.

Bongani is a data engineer working in personalization and recommendations for Adevinta's central team for the online classified markets across Europe and North America. Recently, he switched to exploring the possibilities that LLM can bring. He has a background as a full-stack software engineer but switched to Data Engineering as it allowed him to follow his passion for large-scale distributed systems and related topics. Bongani obtained his MSc in Computer Science from the University of the Witwatersrand in 2014, focusing on peer-to-peer infrastructures for online gaming.

Peter Flach has been Professor of Artificial Intelligence at the University of Bristol since 2003. His research interests include evaluation and improvement of machine learning models, mining highly structured data, knowledge-driven and explainable AI, and the methodology of data science. His books include Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994; interactive online edition, 2022) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012). From 2010 until 2020, Prof Flach was Editor-in-Chief of the Machine Learning journal.

Rui Dias Morais (PhD) is the Head of Analytics Consulting Data Lab at BNP Paribas Portugal, in which he focuses on the development of AI/ML systems enabling operations process automation. Rui has more than 10 years of experience on designing automated solutions for the tech and financial industries. He joined BNP Paribas in 2021, previously he authored and co-authored more than 50 scientific papers in major journals and conferences and 2 patents.

José Gama is a Data Engineer for Quantitative Data Intelligence, currently working on LLM development and data pipelines. José has around 5 years of experience on data engineering in different industries and has worked on a few ML solutions. He joined BNPP in 2021.