How could AI techniques be used to best improve the living standards of people around the world? This is the main question of interest for Laurens Arp, a Master student who joined the ADA research group in November 2019. He is currently working on his Master Thesis under the supervision of Mitra Baratchi and Holger Hoos. The project is about the data-driven evaluation and optimization methods of geographical regions.
The main focus of the project will be on (spatial) representation learning. Current methods would not sufficiently address the problem yet, as most approaches will either focus too much on spatial structure instead of how this structure affects the features of a neighborhood, or are aimed too much at encoding similarity rather than interaction. Once a suitable representation has been found, machine learning and deep learning could be used to automatically learn the relationship between geographical features and the measures one might like to use to evaluate a region. If the resulting model is sufficiently accurate, it could then be used to rate the quality of region configurations generated by an optimization algorithm, allowing for the optimization of the development of the region.
Zhou joined the ADA Research Group in September 2019 as a visiting PhD student for a period of one year. He has started his PhD in March 2017 under the supervision of dr. Gangquan Si at School of Electrical Engineering, Xi’an Jiaotong University.
In his research he focuses on time series data mining techniques, including pre-processing, representation, classification, and prediction. His work has been successfully applied to a project titled “Research of Data Mining Technique and Development of Intelligent Data Management System for Electrical Equipment”, supported by China Southern Power Grid. In this project, he tries to make full use of massive data collected from various tests and online monitoring systems, aimed at providing accurate evaluation and prediction of electrical equipment status.
Zhou is currently working on online time series segmentation with the purpose of dimension reduction, especially on how to apply Automated Machine Learning methods for this task. During his visit, he will be closely supervised by dr. Mitra Baratchi and prof. Holger Hoos.
Daniël Fokkinga joined the ADA research group as a Master’s student in September of 2018. His research is jointly supervised by Anna Louise Latour and Marie Anastacio and applies Automated Algorithm Configuration (AAC) on a pipeline for solving Stochastic Constraint Optimisation Problems (SCOPs).
An example of such a SCOP is the viral marketing problem. We are given a social network where an edge between two nodes represents a probabilistic communication relationship between two people in the network. We want to launch a new product by handing out a limited number of free samples to people in the network. We hope that they will like our product and spread the word to turn their acquaintances into buyers of our product. To whom in the network do we give the free samples, in order to maximise the expected number of people that will buy our product after they’ve heard about it from others? Who are the most influential people in our network?
Recently, a new pipeline for solving SCOPs was proposed. While it has shown its merit in a proof of principle, the different steps in the pipeline have not been optimised yet. While alternative design choices for elements in the pipeline have been proposed, their influence on the performance of the pipeline over a wide range of problems has not been extensively explored.
Daniël’s focus is on exposing these alternatives as parameters to optimise the pipeline for different applications, using Automated Algorithm Configuration.
Automated Algorithm Configuration allows a user to leave the tuning of parameters and thus the decision of the best approach to follow for parts of the full pipeline to a computer. Given a set of example instances, a configurator finds the combination of parameter values that is most likely to perform well on a new instance.
With this research Daniël hopes to show another successful application of Algorithm Configuration in a new field and improve the performance of previously developed algorithms.
Ada research group welcomes another guest researcher, Yi Chu. She will stay with Ada research group for a year.
Yi is a PhD candidate at the Institute of Computing Technology, Chinese Academy of Sciences. She received her master’s degree in computer technology from Beijing University on Posts and Telecommunications in 2014.
Yi’s research interests include heuristic algorithms for NP-hard problems and automatic algorithm design using optimization and learning techniques. Currently, she is working on using programming by optimization to improve the performance of heuristic algorithms for solving the maximum clique problem.
ADA research group welcomes Yanyan Xu, a new visiting scholar!
Yanyan Xu is a visiting scholar at LIACS. She joined ADA Research Group in September 2018. Yanyan holds M.Sc. and B.Sc. degrees from Sun Yat-sen University. She also received her Ph.D. degree from the Institute of Software, Chinese Academy of Sciences. Since then, she has been working at the School of Information Science and Technology of Beijing Forestry University. Currently, she holds an associate professor position there.
Yanyan Xu has a broad interest in the areas of artificial intelligence and algorithm design. She is particularly interested in pattern recognition and deep learning, as well as, heuristic algorithms in robotics and formal methods. Currently, she is working on combining formal methods with deep learning.