Service

Memberships and Committees

• ELLIS Scholar (2021–)
  Theory, Algorithms and Computations of Modern Learning Systems
  European Laboratory for Learning and Intelligent Systems (ELLIS)

• Coordinating Professor (2020–)
  Next-generation Data-efficient Deep Learning Research Program
  Finnish Center for Artificial Intelligence (FCAI)

• Member (for the term 2021–2025)
  Young Academy Finland

• Aalto/FCAI Representative (2020–)
  NVAITC Technical Committee
  NVIDIA AI Technology Center Finland

• AML Track Co-Chair (2021–)
  HICT Steering Group
  The Helsinki Doctoral Education Network in Information and Communications Technology

In addition to internal committee work at Aalto.

Workshop/Conference Co-Organization

• Organizer (2024)
  ECCV Workshop on Uncertainty Quantification for Computer Vision (UNCV)
  Part of European Conference on Computer Vision (ECCV).
  Milan, Italy.
  Peer-reviewed papers published in the ECCV Workshop Proceedings.

• Organizer (2023)
  ICCV Workshop on Uncertainty Quantification for Computer Vision (UNCV)
  Part of International Conference on Computer Vision (ICCV).
  October 3, 2023; Paris, France.
  Peer-reviewed papers published in the ICCV Workshop Proceedings.

• Responsible Faculty (2023)
  ELLIS Doctoral Symposium (EDS 2023)
  The annual symposium is the highlight of the year in the ELLIS PhD Program.
  August 28 to September 1, 2023; Espoo, Finland.
  Hosted by Aalto University and ELLIS Unit Helsinki.

• Organizer (2022)
  ECCV Workshop on Uncertainty Quantification for Computer Vision (UNCV)
  Part of European Conference on Computer Vision (ECCV).
  October 23, 2022; Tel Aviv, Israel.
  Peer-reviewed papers published in the ECCV Workshop Proceedings.

• Publicity Chair (2020)
  The 30th IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
  Annual event organized by the IEEE Signal Processing Society MLSP Technical Committee. September 21–24, 2020; Espoo, Finland (in the end virtual due to pandemic).
  Peer-reviewed papers published by the IEEE.

Seminar Series

• Seminar on Advances in Probabilistic Machine Learning (2021–)
  Aalto University and ELLIS unit Helsinki Seminar on Advances in Probabilistic Machine Learning

Senior Area Chair

• 2025: AISTATS

Area Chair

• 2025: ICLR
• 2024: AISTATS, ICLR, ICML, NeurIPS
• 2023: AISTATS, ICML, NeurIPS
• 2022: AISTATS, NeurIPS
• 2021: NeurIPS

Program Committee Member

• 2023: AAAI
• 2021: AAAI, UAI
• 2020: AAAI
• 2019: AAAI

Reviewing

• 2024: WACV
• 2023: WACV, CVPR, ICLR
• 2022: ICML, WACV, JASA, JUQ
• 2021: AISTATS, ICLR, ICML, IFAC SYSID, NeurIPS
• 2020: AISTATS, ICML, ICML Workshop selection, Nature Communications, NeurIPS, NeurIPS Workshop selection, SPL, STCO
• 2019: AISTATS, ICML, ECC, IEEE Access, IEEE Sensors, NeurIPS,
• 2018: AISTATS, FUSION, IROS, JMLR, NeurIPS
• 2017: AISTATS, Automatica, JMLR, Sensors
• 2016: AISTATS, IEEE TKDE
• 2015: AISTATS, NIPS, IEEE SPL
• 2014: AISTATS, Automatica, NIPS
• 2013: IEEE SMC-C, IEEE TAC, IEEE Signal Processing
• 2012: IEEE ACC

Session Chair

• 2022: AISTATS

I also review grant proposals (e.g., for national funding agencies).

Mentoring

• 2022: ICLR Reviewer Mentorship Program

Opponent/Examiner/Dissertation Committee Member

• Martin Jørgensen (2020). “Stochastic Representations with Gaussian Processes and Geometry”. PhD thesis at Technical University of Denmark. Supervised by Prof. Søren Hauberg.
• Jarrad Courts (2021). “State and Parameter Estimation for Nonlinear State-Space Models using Variational Inference”. PhD thesis at University of Newcastle, Australia. Supervised by Prof. Adrian Wills and Dr. Christopher Renton.
• Anton Kullberg (2021). “On Joint State Estimation and Model Learning using Gaussian Process Approximations”. Licentiate Thesis at University of Linköping, Sweden. Supervised by Prof. Gustaf Hendeby and Prof. Isaac Skog.
• Théo Galy-Fajou (2022). “Latent Variable Augmentation for Approximate Bayesian Inference – Applications for Gaussian Processes”. PhD thesis at Technical University of Berlin, Germany. Supervised by Prof. Manfred Opper.
• James Thornton (2023). “Optimal Transport Based Simulation Methods for Deep Probabilistic Models”. PhD thesis at University of Oxford, UK. Supervised by Prof. George Deligiannidis and Prof. Arnaud Doucet.
• Pascal Sado (2023). “Machine Learning in Auroral Image Research Aurora Image Classification using Machine Learning Techniques and Substorm Forecasting”. PhD thesis at University of Oslo, Norway. Supervised by Prof. Lasse Clausen.
• Firas Laakom (2024). “Feature Diversity in Neural Networks: Theory and Algorithms”. Pre-examiner of PhD thesis at Tampere University, Finland. Supervised by Prof. Moncef Gabbouj.
• Jakob Lindqvist (2024). “On Uncertainty Estimation in Machine Learning”. Faculty opponent for PhD thesis at Chalmers University, Gothenburg, Sweden. Supervised by Prof. Lennart Svensson.