Paulito Palmes

I am a research scientist at the IBM Research Europe (Dublin Research Lab) working in the areas of End to End AI (e2eai), analytics, datamining, machine learning, reinforcement learning, automated decisions, and automated AI. More information of my work in automated AI (AutoMLPipeline.jl and Lale.jl) can be found in IBM Research main page. I am also working as one of the PIs with 30 partners of a 3-year (2024-2026) project for Pan-European 6G Federated Infrastructure Flagship Project called SUstainable federation of Research Infrastructures for Scaling-up Experimentation in 6G (SUNRISE-6G). Our work tasks are related to the implementation of end-to-end AI that covers orchestration of infrastructure, model training and deployment as well as observability and drift detection to support federation of 6G testbeds.

Education and Training

I finished my Doctor of Engineering degree from the Toyohashi University of Technology in Japan (2005). I have a Master's degree in Computer Science majoring in Artificial Intelligence (Ateneo de Manila University, 1995) and a Bachelor's degree in Applied Mathematics (cum laude, valedictorian) at the University of the Philippines in the Visayas (1991).

I used to work as a technical staff for two years in the Neuroinformatics Lab of RIKEN Brain Science Institute, Japan. I was a Postdoctoral Fellow in the National University of Singapore and the National Neuroscience Institute working on diverse topics such as context-aware reasoning, datamining models for activity recognition in smarthome environment, detecting biomarkers for Parkinson's Disease by image processing of fMRI and DTI images, and automated diagnosis of movement disorder for intelligent healthcare. Moreover, I held an Asst. Professorship for a total of 6 years in the University of the Philippines and Ateneo de Manila University. In 2007, I was awarded the Outstanding Young Scientists in Information and Computer Science by the Department of Science and Technology (Philippines).

Other Research Interests

• Evolutionary Computation (EC)
• Activity Recognition and Context-aware Reasoning
• Biomedical Signal and Image Processing
• Mobile Computing Application Development
• Neuroinformatics
• Cluster-based Parallel Optimization for ML/AI

OpenSource Contributions as Creator and Maintainer

• Github Profile: https://github.com/ppalmes
• AutoMLPipeline.jl: Automated Machine Learning Pipeline
• TSML.jl: Time Series Machine Learning
• Lale.jl: Julia wrapper for Lale in Python
• JuliaConPaper 1: Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization
• JuliaConPaper 2 TSML: Time Series Machine Learning

Demos/Talks/Videos

• Youtube: JuliaCon 2023: Wrapping Up Offline RL as Part of AutoMLPipeline Workflow
• Youtube: JuliaCon 2022: Distributed AutoML Pipeline Search in PC/RasPi K8s Cluster
• Youtube: JuliaCon 2021: Finding an Effective Strategy for AutoML Pipeline Optimization
• Youtube: JuliaCon 2021: Lale in Julia: A package for semi-automated data science
• Youtube: JuliaCon 2020: AutoMLPipeline, A ToolBox for Building ML Pipelines
• Youtube: JuliaCon 2019: TSML (Time Series Machine Learning)
• Youtube: PyData Ireland Virtual Summer Meetup #4.1: Symbolic Pipeline Expression
• Youtube: Why R? Webinar 027: JuliaR, Combining Julia and R