Current PhD projects in Cognitive Systems

• Making sense from senses - where do brains integrate audio and visual cues?

  Agata Wlaszczyk: Model-based functional Magnetic Resonance Imaging of neural processes underlying audio-visual integration of speech
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  Discovering Polytopes in High Dimensional, Heterogeneous Datasets using Bayesian...

   
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  Geometric Deep Learning on Protein Sequences

   
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  The geometry of protein representations

   
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  Why are males and females moving differently?

   
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  Towards trustworthy and accurate deep learning models for medical time series...

   
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  A self-supervised model of the brain to help us understand mental disorders

   
• Finslerian aspects of random geometry

  Alison Marie Sandrine Pouplin: Finslerian aspects of random geometry
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  Improving Deep Learning Explainability with Interpretable Outputs and Representations

  Anders Christensen: Opening the black box: Making machine learning interpretable
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  Uncovering Brain Dynamics using Directional Statistics and Functional Neuroimaging...

  Anders Stevnhoved Olsen: Uncovering Brain Dynamics using Directional Statistics
• Uncertainty and robustness in deep learning

  Anshuk Uppal: Reliable artificial intelligence achievable through Bayesian Deep Learning Automated systems powered by machine learning algorithms have become...
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  Federated Machine Learning for Spectroscopy

  Bo Li: Rapid spectroscopy-based detection and identification of chemical hazards in low concentration with federated machine learning
• Statistics under stochastic metrics

  Cilie Werner Feldager Hansen: Learning to trust artifical intelligence Imagine a patient comes into a doctor’s office. The patient says ”I am not feeling well...
• Personalized Micro-Intervention Technology for Diabetes Self-Management

  Dan Roland Persson: Helping persons with type 2 diabetes manage their lifestyle
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  Towards Real Time Raman Molecular Imaging of Living Organisms

  David Frich Hansen: Machine learning-based signal processing for Raman spectroscopy
• The Geometry of Deep Generative Models

  Dimitrios Kalatzis: The Geometry of Deep Generative Models
• Deep Generative Models for Collaborative Filtering

  Federico Bergamin: Generative Models with Sparse and Missing Data
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  Machine learning for electronic scale inverse design of enzymatic catalysts

  François Cornet: Inverse-designing materials with desired properties using Machine Learning
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  Uncertainty-Aware Deep Learning for Autonomous Driving

  Frederik Warburg: Computers must learn to make mistakes
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  Life2Vec: Numerical Representations of Social Behaviour

  Germans Savcisens: What if we can summarize one’s life based on the sequence of the past events? In that case, will predicting one’s future still be off limits...
• Few-shot Generative Models

  Giorgio Giannone: Few-shot Generalization in Deep Generative Models
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  Machine Learning for Molecular Science

  Jacob Mathias Schreiner: Machine Learning for Molecular Science
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  Uncertainty estimation for machine learning system self-assessment on medical...

  Jakob Drachmann Havtorn: Uncertainty aware models and algorithms can accelerate widespread adoption of AI
• RRS-DK: Responsible Recommender Systems for Danish News Publishing

  Johannes Kruse: Responsible Recommender Systems for Danish News Publishing
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  Deep Generative Models for Dental Scans and Dental Treatments

  Johan Ziruo Ye: Deep Generative Models for Dental Scans and Dental Treatments
• Machine Learning on a Quantum Computer: Applications in Neuroscience and Hearing...

  Jonathan Foldager: Machine Learning & Quantum Computing: Leveraging Quantum Physics to Solve Computationally Hard Problems in Machine Learning
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  Large-scale social networks

  Lasse Mohr Mikkelsen: Large-scale social networks; how to make heads or tails of the Gordian knot.
• Explainability of uncertainty for neural networks

  Laura Rieger: Neural networks, a wildly successful machine learning algorithm, are used more and more often for critical applications where getting the correct...
• Deep Learning for Image-Based Quality Assessment

  Lenka Hýlová: Giving Machine Learning Algorithms Additional Knowledge About The Real World Can Improve Their Performance
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  Characterizing Temporal Social Networks using Dynamic Embeddings

  Louis Boucherie: How does information about an individual’s social network contribute to understanding the variability in key life outcomes such as wealth, education...
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  Data-driven classification of disease symptoms using neuroimaging

  Lærke Gebser Krohne: Data-driven prediction of symptoms and diagnosis in psychiatric disorders using neuroimag-ing data
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  Deep Generative Modelling in the Spatial Domain

  Maxim Khomiakov: Learning to synthesize depth from sparse input
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  Counterfactual Techniques in Healthcare

  Mikkel Jordahn: Causal Machine Learning and Counterfactual Techniques in Healthcare
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  Learning the Structure and Dynamics of Complex Networks

  Nikolaos Nakis: Learning the Structure and Dynamics of Complex Networks
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  COVID‐19 Through the Lens of Behavioural Data

  Peter Edsberg Møllgaard: How behavioural data such as social media and mobility might help the fight against the pandemic.
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  Learning Data Augmentations for Bias-Correction

  Pola Elisabeth Schwöbel: Bias in, Fairness out: Learning Data Augmentation for Bias-Correction.
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  Explainable Neural Networks for Skin Lesion Diagnosis

  Raluca Alexandra Jalaboi: Explaining diagnostic reasoning from a human and AI perspective
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  Probabilistic deep learning for hearing aid speech separation

  Rasmus Malik Thaarup Høegh: Uncertain, learning, and listening machines
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  Analysis of resting state EEG in patients with post-traumatic stress disorder

  Qianliang Li:  It is natural to feel afraid and other emotional reactions after a trauma. Most people recover and learn to cope with the trauma naturally. Unfortunately...
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  Personalizing audiology by learning behavioral graphs based on user centred AI

  Tiberiu-Ioan Szatmari: Contextual User Personalization – How to Use Deep Learning Without Giving Away Your Data
• Deep Generative Modelling for Natural Language

  Valentin Victor David Julien Lievin: Deep Generative Modelling for Natural Language
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  Discovering new medicine with Artificial Intelligence

  Yevgen Zainchkovskyy: Probabilistic Generative Models for Automatic Guided Drug Discovery