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I have/had the privilege of supervising researchers/students at KTH.
Postdoc Reseacher:
PhD students:
- Miquel Martí i Rabadán (main-supervisor)
- Zhu Mandy Xiaomeng (main-supervisor)
- Louise Rixon Fuchs (main-supervisor)
- Daniel Olof Sabel (main-supervisor)
- Vladimir Li (main-supervisor)
- Shuangshuang Cheng (co-supervisor)
- Ali Razavian (main-supervisor, 2017)
- Ali Ghardizadeh (co-supervisor, 2018)
- Miroslav Valan (co-supervisor, Swedish Museum of Natural History, 2021)
- Marcus Nordström (co-supervisor, 2023)
Research engineer:
- Rickard Maus (2024)
- Ennio Rampello (2022)
- Frédéric Huy Tran (2021)
Master's students:
- Oscar Danielsson, current
- Jacob Hernberg, current
- Olivia Herber, current
- Jianting Guo, current
- Teo Jansson Minne, current
- Simon Ekman von Huth, Multi-Scale Task Dynamics in Transfer and Multi-Task Learning (2023)
- Giovanni Castellano, Graph Neural Networks For Events Detection in Footbal (2023)
- Sandra Tor, Football Trajectory Modeling Using Masked Autoencoders (2023)
- Alessandro Sanvito, NeRF for Dynamic Scenes Modeling from In-the-Wild Monocular Videos with Humans (2023)
- Love Marcus, Predicting user churn using temporal information (2022)
- Oliver Petri, Using Deep Learning for Urban Change Detection Onboard Satellites (2021)
- Daniel Wass, Transformer learning for traffic prediction in mobile networks (2021)
- Fredrik Segerhammar, Learning to Price Apartments in Swedish Cities (2021)
- Oscar Eriksson, Scenario dose prediction for robust automated treatment planning in radiation therapy (2021)
- Najib Yabari, Few-shot Learning with Deep Neural Networks for Visual Quality Control: Evaluations on a Production Line (2020)
- Sergio Liberman, Dose Prediction from Partial Dose Calculations using Conditional Generative Adversarial Networks (2020)
- Luca Marson, Enforcing low confidence class predictions for out of distribution data in deep convolutional networks (2020)
- Panteleimon Myriokefalitakis, Real-time conversion of monodepth visual odometry enhanced network (2020)
- Alessandro Soci, A study of feature contraction and logit squeezing on adversarial attacks (Universita degli Studi di Firenze, 2020)
- Manon Deprette, Estimation of player trajectories from context in foolball games using AutoEncooders (2020)
- Gustaf Jacobzon, Multi-site Organ Detection in CT Images using Deep Learning (2020)
- Lucas Chizzali, Deep Learning based Turn Light Detection at Road Intersections for Autonomous Driving (2019)
- Wenjie Yin, Machine Learning for Adaptive Cruise Control Target Selection (2019)
- Xiao Wei, Deep Active Learning for 3D Object Detection for Autonomous Driving (2019)
- Linus Härenstam-Nielsen, Deep Convolutional Networks with Recurrence for Eye-Tracking (2018)
- Mathilde Caron, Unsupervised Representation Learning with Clustering in Deep Convolutional Networks (2018)
- Mateusz Buda, A systematic study of the class imbalance problem in convolutional neural networks (2017)
- Sudhanshu Mittal, Semi-supervised Learning for Real-world Object Recognition using Adversarial Autoencoders (2017)
- Ondrej Holesovský, Compact ConvNets with Ternary Weights and Binary Activations (2017)
- Oscar Friberg, Recognizing Semantics in Human Actions with Object Detection (2017)
- Miquel Marti I Rabadan, Multitask Deep Learning models for real-time deployment in embedded systems (2017)
- Vladimir Li, Evaluation of the CNN Based Architectures on the Problem of Wide Baseline Stereo Matching (2016)
- Yanbei Chen, Language Semantic Embeddings in Deep Visual Representation (2016)
- Ludvig Jansson, Automatic Matching of Multimodular Images in Live Golf Environments (2016)
- Remi Blateyron, Automatic detection of manipulated packages by image comparison (2016)
- Petter Djupfeldt, Dr. Polopoly - Intelligent System Monitoring (2016)
- Georgios Dikaros, Energy Efficient Motion Capturing System Using Low-Power Image Sensor (2015)
- Ioannis Papakostas, Efficient Motion Capturing and Variable Refresh Rate Display System Utilizing CAAC-IGZO Semiconductor FETs (2015)
- Dennis Sångberg, Automated Glioma Segmentation in MRI using Deep Convolutional Networks (2015)
- Johan Johansson, Interest Point Detectors and Descriptors for IR Images: An Evaluation of Common Detectors and Descriptors on IR images (2015)
- Johan Wikström, Evaluating supervised machine learning algorithms to predict recreational fishing success: A multiple species, multiple algorithms approach (2015)
- Vladan Nikolic, Creating a Graph Database from a Set of Documents (2015)
- Rickard Norlander, Finding wooden knots in images using ConvNets (2015)
- Luis Fernando, Online DBSCAN for semi-supervised classification of sound spectra (2014)
- Mikael Brudfors, Towards real-time, tracker-less 3D ultrasound guidance for spine anaesthesia (2014)
Bachelor's students:
- Erik Dackander
- Emil Raksanyi
- Victor Eriksson
- Thujeepan Varatharajah
- Jamila Yusuf Isse
- Chaimae El Ghouch
- Apphiah Kabata
- Felix Engström
- Eli Pleaner
- Safir Najafi
- Ziad Salam
- Christopher Lilthors
- Samuel Philipson
Visiting researchers/students:
- Romeo Lanzino (2023)
- Hiroki Azuma (2023)
- Shiu Mochiyama (2018)
- Atsushi Kawasaki (2018)
- Ryo Nakashima (2017)
- Takayuki Sugiura (2017)
- Yuto Yamaji (2017)
- Yuta Shirakawa (2016)
- Shinya Nawata (2016)
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