Research
The MUPI group carries research on statistical machine learning and artificial intelligence.

Our main goals are to

  1. Develop computational techniques for learning under uncertainty, based on approximate Bayesian inference and probabilistic programming
  2. Solve interesting data-driven applications, focusing in problems short on data that hence need advanced modeling techniques based on data integration, semi-supervised learning etc.
Probabilistic inference

Statistical machine learning provides tools for understanding complex data collections, using Bayesian inference to cope with uncertainty on model parameters originating from learning from finite data. We develop computationally efficient and maximally automatic approximate algorithms for Bayesian inference, in context of probabilistic programming and machine learning. Our goal is to allow the user to focus on model specification, not needing to worry about the specifics of inference.

Highlights:

  1. Efficient Riemannian Monte Carlo methods based on a metric with computationally efficient inverse (Hartmann et al. Lagrangian manifold Monte Carlo on Monge patches, AISTATS'22).
  2. Integration of approximate posterior inference and decision-making, by calibrating variational approximations to bettere account for the eventual decision task (Kuśmierczyk et al. Variational Bayesian Decision-making for Continuous Utilities, NeurIPS'19) and by post-hoc calibtration with flexible decision-making modules (Kuśmierczyk et al. Correcting Predictions for Approximate Bayesian Inference, AAAI 2020).
  3. Using prior predictive distribution for expert knowledge elicitation (Hartmann et al. Tackling uncertainty in prior predictive elicitation, UAI 2020) and for fast hyperparameter optimization of hierarchical Bayesian models (da Silva et al. Prior specification via prior predictive matching: Poisson matrix factorization and beyond, arXiv 2019).
  4. Flexible modeling of skewed data in probabilistic programming. (Klami et al. Lambert matrix factorization, ECML'18)
  5. Efficient model-independent variational approximation by re-using gradient computations during optimization. (Sakaya and Klami. Importance sampled stochastic optimization for variational inference, UAI 2017)
  6. Bayesian inference under combinatorial constraints. (Klami and Jitta. Probabilistic size-constrained microclustering, UAI 2016)

Projects:

  1. Scalable probabilistic analytics (SPA, 2016-2018). Development of computationally efficient variational inference algorihms for probabilistic programs. Funded by Tekes, Reaktor, Ekahau and M-Brain. Collaboration with Petri Myllymäki and Teemu Roos.
  2. Reliable Automatic Bayesian Machine Learning (RAB-ML, 2018-2019). Development of reliable and efficient solutions for Bayesian machine learning and probabilistic programming. Funded by Academy of Finland ICT-2023 program. Consortium with Aki Vehtari and Antti Honkela.
  3. Efficient Riemannian Inference (ERI, 2022-2024). Development of efficient geometric MCMC algorithms for Bayesian inference. Funded by Academy of Finland ICT-2023 program.
  4. Finnish Center for Artificial Intelligence (FCAI), Agile probabilistic AI research theme.
AI for ultrasonics

We build machine learning and artificial intelligence tools for modeling ultrasound propagation in complex environments. We develop methods e.g. for inverse problems (detecting fouling or deformations), focusing ultrasound for cleaning, and acoustic levitation. The work is done is done together with the group of Ari Salmi and Edward Haeggström working on ultrasound physics, and we also collabotare with Altum Technologies that provides practical ultrasonic cleaning solutions. 

Highlights:

  1. First example of AI model for detection and monitoring of fouling during ultrasonic sensing, based on monotonicity. (Rajani et al. Detecting industrial fouling by monotonicity during ultrasonic cleaning, MLSP'18).
  2. Detection and fouling quantification with Gaussian processes using line integral observations. (Sillanpää et al. Ultrasonic Fouling Detector Powered by Machine Learning, IEEE International Ultrasonics Symposium (IUS), 2019; Longi et al. Sensor Placement for Spatial Gaussian Processes with Integral Observations, Uncertainty in Artificial Intelligence (UAI) 2020).
  3. Neural networks for localization of internal structure with chaotic cavity. (Sillanpää et al. Localizing a target inside an enclosed cylinder with a single chaotic cavity transducer augmented with supervised machine learning, AIP Advances, 2021).

Projects:

  1. Machine learning for ultrasonic cleaning (ML-UC, 2019-2023). Development of machine learning and artificial intelligence solutions for modeling ultrasound propagation in fouled structures. Funded by Academy of Finland.
  2. Ultrasonic AI-powered Industrial Sensing Platform (UA-ISP, 2021-2023). Development of AI-driven distributed ultrasonic sensing platform for low-cost holistic monitoring of complex environments. Funded by Academy of Finland ICT 2023 program. Consortium with Ari Salmi.
Human behavior

Today it is easy to collect information about individuals by monitoring their activities, either based on explicit sensors or by log data collected by computers they interact with. We develop models required for inferring interesting and useful information based on such data, to describe, understand and enchance our daily life.

Highlights:

  1. Modelling risk behavior of individuals from observed data in conctext of computer games. (Tanskanen et al. Modeling Risky Choices in Unknown Environments, ACML'21)
  2. Models for learning personalised effect of interactions (personalised treatment effect of uplift) from highly unbalanced data collections. (Nyberg et al. Uplift Modeling with High Class Imbalance, ACML'21)
  3. Modeling keyboard usage in programming education and other educational contexts. (Leinonen et al. Automatic inference of programming performance and experience from typing patterns, ACM TSCSE'16)
  4. Human computer interaction in touch-based information retrieval. (Andolina et al. Intentstreams: smart parallel search streams for branching exploratory search, IUI'15)
  5. Understanding intentions based on brain signal analysis, in particular MEG recordings during natural tasks. (Kauppi et al. Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals, NeuroImage 2015)

Projects:

  1. Traces of Information: Intelligence from Fragmented Sources (ToI, 2013-2019). Academy Research Fellow project of Arto Klami.
  2. Machine Insight for Behavioral Analytics (MINERAL, 2019-2022). Business Finland Co-Innovation project. Consortium with Antti Oulasvirta (Aalto University) and companies.
Data-efficient modeling

Many modern machine learning models are complex and require large training data sets, which makes learning difficult in applications where labeling examples is costly or difficult. We study data-efficient techniques for learning complex models from limited supervision, by utilizing related learning tasks (multi-task learning, transfer learning) and unlabeled observations or external constraints (semi-supervised learning). We also develop solutions for changing enrivonments based on domain adaptation.

Highlights:

  1. Structured pseudo-labels for semi-supervised learning based on output-space constraints or smoothness assumptions. (Longi, Pulkkinen and Klami. Semi-supervised convolutional neural networks for identifying Wi-Fi interference sources, ACML'17).
  2. Transfer learning in computer science education. (Lagus et al. Transfer learning methods in programming course outcome prediction. ACM Transactions on Computer Education, 2018:18(4)).
  3. Segmentation of large multispectral satellite images based on coarse annotations in limited-memory computational architecture. (Luotamo et al. Multi-scale Cloud Detection in Remote Sensing Images using a Dual Convolutional Neural Network, IEEE Transactions in Geoscience and Remote Sensing, 2020).

Projects:

  1. Scalable probabilistic analytics (SPA, 2016-2018). Development of computationally efficient variational inference algorihms for probabilistic programs. Funded by Tekes, Reaktor, Ekahau and M-Brain. Collaboration with Petri Myllymäki and Teemu Roos.
Data integration

Machine learning research is often carried out in the context of elegant but simplified setups: It is assumed that all relevant data is provided in form a simple matrix or tensor. In most practical applications this is not the case, but instead we need to combine information scattered in multiple data sources of heterogeneous nature. We provide fundamental modeling solutions for combining such data sources.

Highlights:

  1. Bayesian canonical correlation analysis and inter-battery factor analysis (IBFA) and their extension Group factor analysis (GFA) for discovering relationships between more than two parallel data sources. (Klami, Virtanen and Kaski. Bayesian canonical correlation analysis, JMLR, 2013; Klami et al. Group factor analysis, IEEE Transactions on Neural Networks and Learning Systems, 2015).
  2. Matrix factorization tools for discovering relationships between more complex setups and heterogeneous data. (Klami, Bouchard and Tripathi. Group-sparse embeddings in collective matrix factorization, ICLR 2014; Klami. Polya-gamma augmentations for factor models, ACML 2015)
  3. Cross-domain object matching for discovering relationships between data sources with no known pairing between objects. (Jitta and Klami. Few-to-few cross-domain object matching, Advanced methodologies for Bayesian networks, 2017; Klami. Bayesian object matching, Machine Learning, 2013).

Projects:

  1. Traces of Information: Intelligence from Fragmented Sources (ToI, 2013-2019). Academy Research Fellow project of Arto Klami.
  2. Improved Learning by Combinin Information Sources (ILCIS, 2013-2015). Funded by Xerox Research Foundation, collaboration with Abhishek Tripathi (Xerox Research Center India) and Petri Myllymäki.
Hyperspectral imaging

Hyperspectral cameras capture the full spectrum of light, instead of jus the three channels of red, green and blue that mimic the limited vision of humans. Having access to this richer information makes most computer vision problems easier. The existing HS cameras are, however, expensive and large. We develop a low-cost alternative that uses AI to process images captured with a passive add-on device that can be attached to any camera, brining HS imaging to smartphones and DSLRs. We also work on hyperspectral image analysis.

Highlights:

  1. Deep learning algorithms for hyperspectral image acquisition (Toivonen et al. Snapshot hyperspectral imaging using wide dilation networks, Machine Vision and Applications, 2020).
  2. Methods and applications for hyperspectral image interpretation: Luotamo et al. Multi-scale Cloud Detection in Remote Sensing Images using a Dual Convolutional Neural Network, IEEE Transactions in Geoscience and Remote Sensing, 2020 and Toivonen et al. Epiphyte colonisation of fog nets in montane forests of the Taita Hills, Kenya, Annales Botanici Fennici, 2020.

Projects:

  1. Mobile hyperspectral imaging and computer vision platform (2019-2021). Development of methods and algorithms for acquisition of hyperspectral images with mobile devices, their use for computer vision, and preparation for commercialization of the result. Funded by Business Finland under the New Business from Research Ideas (TUTLI) instrument.