The research area focuses on studying methodological issues in AI, with the goal is to produce computationally efficient, theoretically justified and reliable methods. The research area unites machine learning, artificial intelligence, and data science to build systems that learn from data, handle uncertainty, and act responsibly in the real world. The research is tightly connected to national and international AI initiatives such as the ELLIS network.
The goal of our group is to develop efficient and easily manageable data science systems and use them for novel applications. Towards this end, we conduct research at all stages of data science, from data management and processing to model inference and applied data analysis.
The Autotelic Interaction Research (AIR) group seeks to understand, model and support self-directed behaviour in AI, in people, and in their interaction.
In the Bidirectional Research in AI and Neuroscience Lab (BRAIN), we seek to understand how both humans and artificial systems treat visual scenes, with a particular attention to cases where humans are more robust than AI
The Discovery Research Group works on artificial intelligence and data science, especially on computational creativity and data mining.
We are the Computational Social Science research group in the Computer Science Department at Aalto University. We combine ideas and methods both from computer sciences and the social sciences to solve societally relevant problems. Our primary research interest lies in utilizing digital behavioral data to study our internet mediated lives. Through a blend of diverse data sources and research methods, we examine human behavior on the web and its influence on individual’s opinions, attitudes and behaviors both online and offline.
The data science and evolution group employs data science to build an understanding about the evolutionary processes in nature and society and about their causal mechanisms. The group’s present research focuses on methods for better interpreting and modelling fossil records and past conditions.
The Exploratory Data Analysis group, led by Associate Professor Kai Puolamäki, is located at the Department of Computer Science and Institute for Atmospheric and Earth System Research (INAR) at the University of Helsinki. We work at the intersection of computer science and natural sciences. We study and develop machine learning models of measured and simulated natural world phenomena. Our objective is to find ways for scientists and others to understand data and the underlying processes - and to build better models of nature.
The game research group works on gameplay innovation, understanding player experience, and developing novel game creation tools.
My group works on inductive logic programming (ILP), a form of relational machine learning that learns logical rules from examples and background knowledge. We develop the ILP system Popper.
We are a machine learning research group at Aalto University lead by Prof. Arno Solin.We focus on both theory and practice. Our goal is to develop new methods and algorithms that are able to adapt/learn from data but also obey structural or principal constraints. The research has a probabilistic (some say Bayesian) twist with a focus on uncertainty quantification and emphasis on scalable and even real-time inference.
The Multi-source Probabilistic Inference (MUPI) research group studies statistical machine learning and artificial intelligence. We develop new methods and algorithms for coping with uncertainty in artificial intelligence, focusing in particular on approximate Bayesian inference of probabilistic programs. We also solve interesting practical problems across multiple application fields, developing machine learning techniques in particular for setups with limited amount of training examples.
The group's current mission is to implement the vision by studying: algorithm theory of computing sums of products, sums of products in computational statistics, applications in science and technology.
The Trustworthy Machine Learning group studies machine learning and artificial intelligence (AI) that we could trust with sensitive data and critical applications.
Our main foci are privacy-preserving machine learning and handling uncertainty, although we consider other aspects of trustworthy AI as well.
The complex systems computation group (CoSCo) investigates computational problems related to complex systems, focusing on prediction and modelling. Working at the intersection of computer science, information theory and mathematical statistics, the group carries out both basic research and applied research, solving problems in the fields of social sciences, ecology and medicine.
The KEPACO group develops machine learning methods, models and tools for data science, in particular computational metabolomics. The methodological backbone of the group is formed by kernel methods and regularized learning. The group particularly focusses in learning with multiple and structured targets, multiple views and ensembles. Applications of interest include metabolomics, biomedicine, pharmacology and synthetic biology.
We work on the intersection of information retrieval, artificial intelligence and human-computer interaction. In particular, we focus on all aspects of recommendation, user modelling and personalisation. We are part of the Department of Computer Science at the University of Helsinki.