💻About Me
I am a final-year PhD candidate in machine learning at Chalmers University of Technology, specializing in interpretable models for clinical decision-making. My researach interests lie at the intersection of machine learning and healthcare, such as predictions with missing values, and time series predictions, with the goal of developing interpretable and accurate models to assist with clinical decision-making. As I approach graduation in summer 2025, I am eager to apply my expertise in machine learning and data analysis to industry, contributing to the design of human-centered AI systems that create meaningful real-world impact.
- Machine learning for health care
- Interpretable machine learning
- Predictions under missing values
PhD Candidate in Machine Learning
Chalmers University of Technology
MSc Information Engineering and Management
Karlsruhe Institute of Technology (KIT)
Visiting Research Student
MIT - Massachusetts Institute of Technology, Cambridge/USA
Visiting Research Student
PreMeDICaL Inria-Inserm team Montpellier, Montpellier/France
At the Healthy AI Lab, we take inspiration from real-world healthcare challenges to develop machine learning models and theory that improve clinical decision-making. Collaborating closely with clinician networks, hospitals, and healthcare providers, we aim to enhance decision-making, improve patient outcomes, and deepen our understanding of complex medical conditions.
I am a Ph.D. student in the Data Science and AI division at Chalmers University of Technology, working at the intersection of machine learning and healthcare. My research focuses on predictive modeling with missing values, time series forecasting, and building interpretable, reliable models to support clinical decision-making.
Let’s collaborate! 🚀
- [Jan 2025] I am giving a talk at the Traumabase Annual Meeting on a user study with the clinician network, exploring interpretable ML for missing values.
- [Nov 2024] My work on Expert Study on Interpretable Machine Learning Models with Missing Values has been accepted to ML4H as a workshop paper. I co-authored the paper Representing Patient History for Interpretable Policy Modeling, also accepted to ML4H 2024.
- [Apr 2024] My paper on MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values has been accepted to AISTATS.
- [Jun 2023] My paper on Learning Replacement Variables in Interpretable Rule-based Models has been accepted to the 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH).
- [Nov 2022] My paper on Sharing Pattern Submodels for Prediction with Missing Values has been published in AAAI.
- [Feb 2021] I co-authored the paper on Predicting Progression & Cognitive Decline in Amyloid-Positive Patients with Alzheimer’s Disease, published in Alzheimer’s Research & Therapy.