BRAIN Lab
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
Algorithms & Theory
Overview
The research area of algorithms and theory pursues fundamental research at the synthesis of algorithmic and mathematical foundations of computing, integrating a broad range of expertise including multiple areas of algorithmics, computational complexity, cryptography, distributed computing, logic, and quantum computing.
Interested in joining us?
To participate in our activities, attend our open events described below. To get invited to email lists, chat channels, and so forth., please contact Petteri Kaski (Aalto University) or Mikko Koivisto (University of Helsinki). Our community is growing. See Helsinki Algorithms and Theory for a non-exhaustive list of researchers involved.
Activities
Helsinki CS Theory Seminar. A weekly series of talks on a broad scope of CS theory hosted by the Aalto University CS Theory Group. Link to seminar page.
Helsinki Logic Seminar. A weekly series of talks in mathematical logic hosted by the Helsinki Logic Group. Link to seminar page.
Foundations Friday. A monthly get-together event for the community typically consisting of a tutorial and lunch as well as follow-up activity and discussions. Link to seminar page.
Research Area Leaders
Petteri Kaski
Research Area Leader
Mikko Koivisto
Research Area Deputy Leader
People
Andrew Cropper
inductive logic programming; relational learning, program synthesis
Stéphane Deny
visual perception, symmetries, artificial neural networks, theory of machine learning
Patrik Floréen
Antti Honkela
machine learning, trustworthy machine learning, privacy, differential privacy
Sándor Kisfaludi-Bak
computational geometry, exact and approximation algorithms
Mikko Koivisto
algorithms, Bayesian learning, causal graphs, sampling
Russell W. F. Lai
cryptography
Kjell Lemström
Computational musicology, algorithms, learning analytics
Pekka Orponen
DNA and RNA nanotechnology, algorithmics of self-organisation, computational complexity
Simon Puglisi
Algorithms; Data Structures; Genomics; Data Compression; Pattern Matching
Kai Puolamäki
machine learning, explainable AI, statistical methods, natural sciences
Teemu Roos
machine learning
Jukka Suomela
Distributed algorithms
Alexandru Tomescu
bioinformatics, pangenomics, graph algorithms, graph decompositions, parameterized algorithms
Research Groups
Content-based retrieval and analysis of harmony and other music structures (C-BRAHMS)
The C-BRAHMS research group aims at designing and developing efficient methods for computational musicology problems. In particular, it focuses on three computational musicology areas: 1) music pattern matching and detection, 2) automated music generation with the help of human computer interaction, and 3) score following with the help of the first area of this project, optical music recognition and audio transcription. The project develops cutting edge technology and algorithms to these areas and aim to make significant strides in these intertwined fields. Our initiative stands at the intersection of advanced computer science and the legacy of musicology, music composition and performance. It aims to push the boundaries on how music is created, engineered, and interacted with in the future.
Cryptography
In the Aalto crypto group, we work on a wide range of cryptography research areas, including foundations, protocol analysis, side-channel analysis, lattice-based cryptography, zero-knowledge and succinct argument systems, and privacy enhancing technologies. The effort is led by Chris Brzuska and Russell W. F. Lai.
Distributed Algorithms
Our current research focuses on the foundations of distributed and parallel computing.
One of our key research questions is locality in the context large computer networks. There are many computational tasks that are of a global nature: to solve such a task, it is necessary to transfer information over a long distance, from one side of the network to another. However, there are also tasks that are of a local nature: it is sufficient for each network device to gather information from its own local neighborhood. We aim at understanding which computational tasks are local and which are global.
Exploratory Data Analysis
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.
Logic and learning group
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.
The Sums of Products
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.
Trustworthy machine learning
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.
Complex Systems Computation Group
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.
Natural Computation
The group seeks to understand, model, and program naturally occurring or nature-inspired self-organising processes. The current main focus is on DNA and RNA self-assembly, but related areas of interest are e.g. algorithms for swarm robotics and programmable matter, control of distributed sensor networks, and stochastic optimisation methods for complex energy landscapes.
Graph Algorithms and Bioinformatics
Our research is focused on graph algorithms, from both a theoretical perspective, and a practical perspective motivated by real-world problems in Bioinformatics, such as genome sequencing technologies.
We also study related algorithmic topics, such as combinatorial optimization, enumeration algorithms, string algorithms. Our main application area is Bioinformatics, where we work on various assembly problems of high-throughput sequencing data, pan-genomics, protein evolution.