I am a third year PhD student at the Robotics, Perception and Learning Lab, CSC, at KTH in Stockholm, Sweden. My research is centered around the representation and interpretation of human activity based on visual data.
In more detail, I am interested in latent variable models of human behavior. My theoretical focus lies on deep generative models and amortized inference, see our recent survey on Advances in variational inference.
As my background merges cognitive science, mathematics and computer science, my goal is to apply advanced machine learning algorithms and statistical inference in order to gain a better understanding of human behavior based on raw visual data.
I am also an active member of Stockholm AI and organize their biweekly Machine Learning Reading Group.
A (not up-to-date CV) can be found here.
I am also reachable on Linkedin.
Sep. 2018: Will participate in the Forskar Grand Prix 2018.
Aug. 2018: Visited the team at Centre for Applied Autonomous Sensor Systems, Örebro university, and presented my work.
Mar. 2018: Gave a talk for a fantastic audience at Women in Data Science Sweden.
Dec. 2017: Gave a 1 hour presentation for the algorithms team at Tobii.
Nov. 2017: Gave a 30 min talk about Human-Robot Interaction at Internetdagarna, Internet Days.
Apr. 2017: Second round of the "Hur tänker en robot" - workshop at the Tekla festival.
Feb. 2017: Invited to a panel talk about the life as a female PhD student at the Equality week.
Apr. - Jul. 2016: Internship at the Max Planck Institute for Intelligent Systems.
Apr. 2016: Coorganized the "Hur tänker en robot" - workshop (How does a robot think - workshop) at the Tekla festival.
Apr. 2016: I defended my master thesis.
Dec. 2015: Supervised to students from the International English school in Täby for a week. They learned basic programming skills.
Zhang, C. Bütepage, J., Kjellström, H. & Mandt, S. (2018), Advances in variational inference, arXiv preprint arXiv:1711.05597.
Bütepage, J., Kjellström, H. & Kragic, D. (2018), Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration, ICRA 2018.
Bütepage, J. & Kragic, D. (2017), Human-Robot Collaboration: From Psychology to Social Robotics.
Bütepage, J., Black, M., Kragic, D., & Kjellström, H. (2017), Deep representation learning for human motion prediction and classification., CVPR 2017.
Vesper, C., Abramova E., Bütepage, J., et al. (2016), Joint Action: Mental Representations, Shared Information and General Mechanisms for Coordinating with Others., Frontiers in Psychology (7) 2016.
Ghadirzadeh, A., Bütepage, J., Maki. A., Kragic, D., & Björkman, M. (2016), A sensorimotor reinforcement learning framework for physical Human-Robot Interaction., IROS 2016.
Ghadirzadeh, A., Bütepage, J., Kragic, D., & Björkman, M. (2016), Self-learning and adaptation in a sensorimotor framework., ICRA 2016.
Bütepage, J., Kjellström, H. & Kragic, D.(2016), Social Affordance Tracking over Time - A Sensorimotor Account of False-Belief Tasks., CogSci 2016.
Ongoing since Autumn 2015 Machine Learning Reading Group (PhD)
Autumn 2018: TA in Project Course in Data Science (Msc)
Autumn 2018: TA in Machine Learning Advanced (Msc)
Autumn 2017: TA in Machine Learning Advanced (Msc)
Autumn 2016: TA in Computer Vision and Image Analysis (Msc)
Autumn 2016: TA in Artificial Intelligence (Msc)
Autumn 2016: TA in Machine Learning Advanced (Msc)
Autumn 2015: TA in Artificial Intelligence (Msc)
Autumn 2015: TA in Machine Learning Advanced (Msc)
Spring 2016: TA in Search Engines and Information Retrieval Systems (Msc)