I am a PhD student at WSAudiology and the Technical University of Denmark (DTU).
I am interested in probabilistic approaches to deep learning - especially deep generative models of
sequential data and machine learning applied in health technology.
Currently, I primarily research how we can make audio models that generalize to real-world use
Specifically, I've been working on variational autoencoders and how probabilistic modelling enables us
to, e.g., learn robust latent representations, incorporate prior knowledge, and utilize uncertainty
Below is a collection of some recent work and news.
Recent Work and News
UC Berkeley/ICSI stay: Continuously deep hierarchical VAE
I'm visiting Berkeley working with M. Mahoney. We are developing continuously deep hierarchical
variational autoencoders by combining neural stochstic differential equations with hierarchical
variational autoencoder models.
Directional archetypal analysis
Multi-subject, multi-modal modelling of functional neuroimaging data using directional
statistics. Published in Frontiers in Neuroscience (paper, Twitter
The Variational Inference Time-domain Audio Separation Network (VI-TasNet) is probabilistic
extension of TasNets.
The work shows how we can incorporate domain-knowledge priors in modelling and quantify
separation performance uncertainty unintrusively.
We also investigate how speaker separation generalization can be understood through the lense of
Research Pitch Battle winner
I won Danish Sound Days research pitch battle competition, see their post on the competition above.
Client adaptation in federated learning
We present a federated learning approach for learning a client adaptable, robust model when data
is non-identically and non-independently distributed (non-IID) across clients, as well as a way
to simulate non-IID clients.
(arXiv, presented at FL-ICML 2020 )
Deep unsupervised learning course
I organized and helped teach a course on deep unsupervised learning at DTU, modelled on the
IFD Industrial PhD scholarship for probabilistic deep learning for hearing aid speech separation
Together with WSAudiology and DTU CogSys
, I received 60k EUR industrial PhD scholarship from the Innovation Fund Denmark.
Teaching high schools students about sound and machine learning using Google Colab (see export on
). Photo by Forskningens Døgn.
Interactive machine learning demos
I developed a series of
demos for an introductory machine learning course at DTU .
Stanford stay: Interpretable deep learning for stroke treatment
The work was done for my Master's thesis, as a visiting student
research at Stanford University (School of Medicine) in collaboration between DTU, Stanford
Center for Sleep Sciences and Medicine, and Rigshospitalet Denmark
Danish Foreign Ministry's World Image Grant//Visiting biomedical engineer in Nepal
We received a grant of 7k EUR for the production of a documentary for an alternative view on a
developing country. Through Engineering World Health at DTU
I was deployed at Okhaldhunga Community Hospitals. My work at the hospital entailed
hospital equipment repair, healthcare staff training (proper use and maintenance), and
needfinding (initial phase design research and planning). Photo by S. Sundgaaard.