My research covers a broad range of statistical learning and advanced machine learning methods that are needed in modern data analysis problems. The aim is to take into account for a) complexity of the data including latent low rank structures and subspaces, sparsity and missing values, or the sheer variety of the data, b) large scale settings which refers to high-dimensionality but also settings where the sample size is smaller or not much larger than the data dimension which make traditional asymptotically optimal methods perform poorly and c) dynamic nature of the data, or data velocity, where data accumulates or streams at fast pace and thus batch processing is not a viable solution.

A particular focus area is the analysis of *high-dimensional (HD)* sparse data,
i.e., data for which the sample size *n* is
smaller or not much larger than the dimension *p* of the data set, where *p* is
potentially very large. In such cases, the number of parameters to estimate can greatly
exceed the number of observations. For example, in genomic studies, *n* is often
the number of patients (only few tens!) and *p* is the number of genes (tens of thousands).
Yet a classifier needs to learn a high-dimensional parameter with a limited data.
This demands new approaches such as regularized optimization or imposing some structure on
the unknown parameter to reduce the number of unknowns.
Sparse HD data sets are becoming more common place in practice, particularly with the
development of areas such as genomics, multimedia imaging, or financial economics.

# Doctoral students

**Xinjue Wang**(6/2022 - )

**Lei Wang**(3/2024 - )

# Research assistants

**Juho Kuikka**

# Visitors

**Leatile Marata**(8/2023-2/2024)

# Alumni

**Elias Raninen**, engineer at Nokia Bell Labs

Doctoral student 6/2017- 6/2022. *Thesis*: Contributions to the Theory and Estimation of High-dimensional Covariance Matrices

*M.Sc. student* (6/2016-5/2017). Thesis: Scaled sparse linear regression with the elastic net

**Muhammad Naveed Tabassum,** R&D and Specification Engineer at Nokia

Doctoral student 1/2016 - 3/2021.

*Thesis*: Sparsity Driven Statistical Learning for High-Dimensional Regression and Classification

**Nora Ouzir**, Assistant professor at CentraleSupelec, France.

post-doc 11/2018 - 12/2020 (jointly supervised with Prof. S. Vorobyov)

*Topic*: ultrasound imaging, dictionary learning

**Jari Miettinen**

post-doc 8/2017 - 7/2020 (jointly supervised with Prof. S. Vorobyov)

*Topic*: Graph signal processing

**Ammar Mian**, Associate professor at University Savoie Mont Blanc, France

post-doc 10/2019 - 9/2020

*Topic*: Machine learning methods on manifolds, pedestrian detection.

**Shahab Basiri**,
Reserach Scientist at Varian Medical Systems

Doctoral student 3/2014 - 6/2018. *Thesis*:
Robust large-scale statistical inference and ICA using bootstrapping

M.Sc. student 7/2012 - 2/2014. *Thesis*: Hypothesis Testing in Independent Component Analysis

**Aqib Ejaz**,
Senior Engineer at Emberion Oy

M.Sc. student, 12/2014 - 5/2015.

*Thesis*:
Algorithms for Sparse Signal Recovery in Compressed Sensing

**Alireza Razavi**, Senior AI Development Engineer at Scania Group

Post-doctoral fellow, 9/2011-8/2012.
*Topic*: Compressed sensing.