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Cognitive Systems
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PhD
Current PhD projects
Cognitive Systems
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Algorithms, Logic and Graphs
Cognitive Systems
Agata Wlaszczyk
Anna Emilie Jennow Wedenborg
Hrittik Roy
Marco Miani
Silvia De Sojo Caso
Thea Brüsch
Fabian Mager
Alison Marie Sandrine Pouplin
Anders Christensen
Anders Stevnhoved Olsen
Anshuk Uppal
Bo Li
Cilie Werner Feldager Hansen
Dan Roland Persson
David Frich Hansen
Dimitrios Kalatzis
Federico Bergamin
François Cornet
Frederik Warburg
Germans Savcisens
Giorgio Giannone
Jacob Mathias Schreiner
Jakob Drachmann Havtorn
Johannes Kruse
Johan Ziruo Ye
Jonathan Foldager
Lasse Mohr Mikkelsen
Laura Rieger
Lenka Hýlová
Louis Boucherie
Lærke Gebser Krohne
Maxim Khomiakov
Mikkel Jordahn
Nikolaos Nakis
Peter Edsberg Møllgaard
Pola Elisabeth Schwöbel
Raluca Alexandra Jalaboi
Rasmus Malik Thaarup Høegh
Qianliang Li
Tiberiu-Ioan Szatmari
Valentin Victor David Julien Lievin
Yevgen Zainchkovskyy
Cybersecurity Engineering
Dynamical Systems
Embedded Systems Engineering
Mathematics
Scientific Computing
Software Systems Engineering
Statistics and Data Analysis
Visual Computing
PhD projects affiliated with DTU Compute
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
Discovering Polytopes in High Dimensional, Heterogeneous Datasets using Bayesian...
Geometric Deep Learning on Protein Sequences
The geometry of protein representations
Why are males and females moving differently?
Towards trustworthy and accurate deep learning models for medical time series...
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
Improving Deep Learning Explainability with Interpretable Outputs and Representations
Anders Christensen: Opening the black box: Making machine learning interpretable
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...
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
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
Machine learning for electronic scale inverse design of enzymatic catalysts
François Cornet: Inverse-designing materials with desired properties using Machine Learning
Uncertainty-Aware Deep Learning for Autonomous Driving
Frederik Warburg: Computers must learn to make mistakes
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
Machine Learning for Molecular Science
Jacob Mathias Schreiner: Machine Learning for Molecular Science
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
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
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
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...
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
Deep Generative Modelling in the Spatial Domain
Maxim Khomiakov: Learning to synthesize depth from sparse input
Counterfactual Techniques in Healthcare
Mikkel Jordahn: Causal Machine Learning and Counterfactual Techniques in Healthcare
Learning the Structure and Dynamics of Complex Networks
Nikolaos Nakis: Learning the Structure and Dynamics of Complex Networks
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.
Learning Data Augmentations for Bias-Correction
Pola Elisabeth Schwöbel: Bias in, Fairness out: Learning Data Augmentation for Bias-Correction.
Explainable Neural Networks for Skin Lesion Diagnosis
Raluca Alexandra Jalaboi: Explaining diagnostic reasoning from a human and AI perspective
Probabilistic deep learning for hearing aid speech separation
Rasmus Malik Thaarup Høegh: Uncertain, learning, and listening machines
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...
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
Discovering new medicine with Artificial Intelligence
Yevgen Zainchkovskyy: Probabilistic Generative Models for Automatic Guided Drug Discovery
Updated on 24 May 2022
Updated on 24 May 2022