Aboyut the project

Objective
This project aims to develop an open-source ROS-compatible real-time logic-based integrated planning, reasoning and control system for mobile robots. The key novelty in our project is including non-axiomatic reasoning in the robot software stack to complement common techniques such as deep learning in handling uncertainty. The system will be featured in a scavenger — a mobile robot used to inspect a city-like environment to carry out a collection of pieces of waste. With the final demonstrator, we aim to showcase the potential of our integrated planning, reasoning, and control system for mobile robots that need to carry out tasks in unknown environments.

Background
Today’s robotic control systems rely on big data, machine learning approaches and/or extensive (physical) modelling and behaviour pre-programming to achieve their required functionalities. While still utilizing such techniques, this demonstrator aims to introduce improvements towards low-energy, cost-efficient and effective mobile robots by integrating a reasoning-based system, the Non-Axiomatic Reasoning System (NARS). NARS is designed to build mission-relevant hypotheses from a stream of input events and to act upon the most successful predicting hypotheses. With its ability to learn and update hypotheses in real-time with little training or task pre-programming, NARS will be the key technology allowing our robot to improvise in challenging and uncertain situations, identify new types of objects and categorize them based on their perceived properties.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Electrical Engineering and Computer Science, Division of Robotics, Perception and Learning and KTH School of Industrial Engineering and Management, Department of Machine Design.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
The project objectives:

• Design and construction of a fixed hardware platform for simulating micromobility experiences in Virtual Reality (hardware development).
• Implementation of Virtual Reality simulations to replicate real world experiences (software development).
• Evaluation of experiences with users of different levels of riding proficiency in MicroVRide (evaluation).

Background
Micromobility refers to lightweight, typically electric, transportation modes like bicycles, e-scooters, e-bikes, and similar small vehicles. To simulate riding experience in micromobility, researchers typically design and construct indoor simulators to advance research, development, and understanding of the behaviour of micromobility riders.

While many bicycle simulators have been previously developed and investigated in terms of safety, realism, and motion sickness, other micromobility simulators still need to be explored. Particularly, four new types of micromobility are slowly growing within urban spaces: e-scooters, segways, electric unicycles, and one-wheeled skateboards. This opens new research opportunities focused on improving the safety of riders and understanding their behaviour in urban environments. This demonstrator project is focused on addressing how a VR micromobilty simulator can be designed to accommodate the current and future micromobility vehicle innovations and what the implications are for research and innovation in the domain of VR simulators.

To investigate this research question and understand riders’ behaviour on four types of micromobility vehicles safely, one indoor stationary Virtual Reality Micromobility Simulator (MicroVRide) will be designed and built. Different riding modes concerning four types of vehicles will be accommodated under safe and controlled conditions by this simulator. The design and construction of this simulator will involve the utilization of two main elements: (1) a fixed hardware platform (with a dismountable handlebar) with tracking of body weight distribution and (2) Virtual Reality (VR) simulation (presented in a wireless VR headset) to experience a virtual world. Following the construction of MicroVRide, experiences with users of different levels of riding proficiency will be investigated to understand their riding behaviour, performance, and experience.

The development of MicroVRide is motivated by a commitment to advancing VR simulator technology, establishing a secure and realistic environment for safe vehicle practice.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Electrical Engineering and Computer Science and RISE Research Institutes of Sweden, Digital Systems Division.

About the project

Objective
Based on empirical data from sidewalk robots’ trips, we will shed light on sidewalk mobility and improve real-world robot delivery operations. Through statistical analysis and Machine Learning (ML), we will assess the efficiency of robots’ paths and their relation to pedestrian infrastructure, interactions with different transport users (such as walkers, cyclists, e-scooters, and motorized vehicles), and other variables (e.g., weather).

A crucial task of the project will focus on integrating prediction models with routing algorithms to discover more effective routing solutions. Another task will involve identifying Walkability KPIs” to describe sidewalk mobility conditions based on the data collected.

Background
Sidewalk robots appear to be a promising solution for City Logistics. Hubs, retail locations, and even retrofitted vehicles might dispatch them for short-range trips and partially replace standard, less sustainable delivery methods. The ISMIR project aims to develop a more comprehensive understanding of sidewalk robot delivery in realistic scenarios. The investigation of sidewalk navigation challenges will also provide the opportunity to explore pedestrian infrastructure and sidewalk mobility from a novel perspective.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Architecture and the Built Environment, Department of Urban Planning & Environment, and KTH School of Electrical Engineering and Computer Science, Department of Intelligent Systems.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
In recent years, Sweden has emerged as one of the most welcoming European countries for migrants from diverse backgrounds and cultures. However, the challenge lies in effectively integrating these individuals into Swedish society, given the nation’s relatively small population and the rapid influx of migrants. Language learning is critical to this integration process, posing significant challenges for learners and educators. We aim to develop a language learning system focusing specifically on conversational skills utilizing social robots and conversational AI. This will enable learners to practice language through conversation with diverse personas in real-life situations.

Background
Recent advancements in Large Language Models (LLMs) have transformed our approach to complex challenges such as language learning, opening up for new opportunities that were not available a few years ago. Alongside LLMs, advancements in speech technology — specifically in the fields of speech recognition and synthesis — enable the creation of much more natural, lifelike conversational experiences with AI. This progress empowers us to develop a system that not only comprehensively understands Swedish but can also understand the user’s native language. This multilingual competence facilitates a more effective, personalized, and user-centric language learning platform.

Crossdisciplinary collaboration
Our project benefits from the expertise of a diverse team drawn from the Division of Speech, Music, and Hearing at KTH and the Department of Education at Stockholm University. This interdisciplinary collaboration ensures that we have the necessary theoretical and practical background to develop an innovative solution that addresses the challenges of language development and cultural integration in the context of digital transformation.

About the project

Objective
We propose energy-efficient, high-speed, real-time monitoring of complex traffic scenarios in urban areas. Reliable real-time information about urban traffic supports emergency responders and city planners and reduces carbon emissions, thereby increasing the urban citizens’ quality of life. Using decentralized neuromorphic sensing and processing, we obtain four benefits compared to state-of-the-art: (1) low latency, (2) minimal transmission bandwidth, (3) low power, and (4) enhanced privacy.

As a result of the project, we expect to provide a Live Demonstrator which, in real-time, identifies objects in traffic (e.g., cars, bicycles, and scooters) while continuously running on a negligible energy budget (e.g., a solar cell).

Background
In the upcoming decades, urban areas will become “much larger, more complex, and interconnected, ” requiring optimized infrastructure with increasing automatization at increasingly larger scales. More broadly, real-time information about traffic and infrastructure utilization will become an increasingly valuable commodity for companies and officials to adapt and update the infrastructure as needed. Such data is generated by video cameras in cities today.

Embedding low-power event cameras with low-power neuromorphic local computation provides a uniquely scalable solution for the growing need to monitor urban traffic and resource flows in real time. Our project estimates a 100x reduction in power and a 20x reduction in installation cost.

Crossdisciplinary collaboration
The teams contribute expertise in neuromorphic hardware and algorithms for system development and expertise in traffic observation and urban traffic management. Only both teams and their complementary expertise can develop real-time low-power computing systems for the societal relevant task of urban traffic monitoring.

About the project

Objective
The research team’s ambition is to develop a new research area in urban development (studies). Well-being in smart cities is the defined research area, focusing on interactions of human-machine-computers or “cyber-physical-human systems” based on human decision-making on an institutional, individual and neurological abstraction level. The smart city of the future is our main application area, as these are complex cyber-physical-human systems. The project will develop a framework for capturing interactions and dynamics in these systems and demonstrate the applications in user case studies.

Background
The health condition of a human being is the basis of individual and social well-being. The driving force for human-social behaviour and many choices individuals make is the desire for well-being, which will manifest in the future of smart cities. Networks, human agents, cyber agents, and physical infrastructure perform feedback and interactions in smart cities. Smart cities can efficiently and sustainably increase human well-being.

Cross-disciplinary collaboration
The research team represents the School of Electrical Engineering and Computer Science (EECS, KTH), the School of Industrial Engineering and Management (ITM, KTH) and the School of Architecture and the Built Environment (ABE, KTH).

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Find out more on HiSS webpage

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

• HiSS Workshop 2021: From smart to intelligent cities: A human-social choice? Smart cities of a digitalized society are envisioned as cyber-physical-human systems of sustainable economic growth that enhance human well-being. The grand challenge for designing and developing smart cities is to achieve mutually beneficial interactions between cyber and human systems where machines learn from humans, and humans learn from machines. Theories of human behaviour are traditionally context-dependent and specialized in economics, education, games, social interactions, management, etc. Smart cities, as cyber-physical-human systems, set a new context for modelling and understanding human-social behaviour at different levels – from neuro-cognition of individual choices to collective decisions and emergence in social networks at multiple time scales.
• A Workshop was held in September 2021 with three online sessions:
  • Session 1: 6/9 Interactional Intelligence with speakers Assoc. Prof. Sarah Williams, MIT, Univ.-Prof. Dr. Marcel Schweiker, University Hospital RWTH Aachen, Dr. Umberto
  • Fugiglando, MIT.
  • Session 2: 13/9 Reflective Intelligence with speakers Prof. Katina Michael, Arizona State University, Assoc. Prof. Esteban Moro, Universidad Carlos III de Madrid and MIT, Prof. Angelia Nedich, Arizona State University,
  • Session 3: 20/9 Neuro-cognition Prof. Jerome Busemeyer, Univ of Indiana, Prof. Alan Safney, Radboud University, Prof. Scott Huettel, Duke University

Results

A general objective of the project is to link dominant mechanisms of decision-making and choice between the micro, meso and macro scales that are most relevant for advancing the sustainability agenda in smart cities. The specific objectives are related to theoretical and experimental studies of different aspects of decision-making at micro, meso and macro scales that help answer the following questions:

• Which human choice/decision models are suitable for understanding decision processes, policy making, etc., in the sustainable smart city context
• What aspects of human choices and decisions in the present context can be related to and explained by neuro-cognitive processes?
• How do social network interactions (e.g. collaboration and competition) affect human choices from smart homes to smart cities?

Publications

We like to inspire and share interesting knowledge!

1. M. Lenninger, M. Skoglund, P. Herman & A. Kumar. Are single-peaked tuning curves tuned for speed rather than accuracy? Nature Communications (in review).
2. M. Lundqvist, S.L. Brincat, M.R. Warden, T.J. Buschman, E.K. Miller & P. Herman. Working memory control dynamics follow principles of spatial computing. Nature Communications (in review).
3. M. Molinari, J. Anund Vogel, D. Rolando. Using Living Labs to tackle innovation bottlenecks: the KTH Live-In Lab case study,Applied energy(Extension under review).
4. N. Chrysanthidis, F. Fiebig, A. Lansner & P. Herman. “Traces of semantization-from episodic to semantic memory in a spiking cortical network model”, eNeuro, July 2022, 9 (4). https://doi.org/10.1523/ENEURO.0062-22.2022.
5. Fontan, V. Cvetkovic, K. H. Johansson. On behavioral changes towards sustainability for connected individuals: a dynamic decision-making approach, in 4th IFAC Workshop on Cyber-Physical Human Systems, Houston, Texas, December 1-2, 2022.
6. Taras Kucherenko, Rajmund Nagy, Michael Neff, Hedvig Kjellström, and Gustav Eje Henter. Multimodal analysis of the predictability of hand-gesture properties. In
7. International Conference on Autonomous Agents and Multi-Agent Systems, 2022.
8. M. Lundqvist, J. Rose, S.L. Brincat, M.R. Warden, T.J. Buschman, P. Herman, & E.K. Miller. “Reduced variability of bursting activity during working memory.” Scientific Reports 12, no. 1 (2022): 1-10.
9. N.B. Ravichandran, A. Lansner & P. Herman. “Brain-like combination of feedforward and recurrent network components achieves prototype extraction and robust pattern recognition”. In: Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, Springer, Cham
10. D. Rolando, W. Mazzotti, M. Molinari. Long-Term Evaluation of Comfort, Indoor Air Quality and Energy Performance in Buildings: The Case of the KTH Live-In Lab Testbeds, Energies, vol. 15, no. 14, pp. 4955, 2022.
11. N. Chrysanthidis, F. Fiebig, A. Lansner & P. Herman. “Semantization of episodic memory in a spiking cortical attractor network model”, Journal of Computational Neuroscience, vol. 49, no. SUPPL 1, pp. S86–S87, 2021.
12. A. Karvonen, V. Cvetkovic, P. Herman, K.H. Johansson, H. Kjellström, M. Molinari & M. Skoglund. “The ‘New Urban Science’: towards the interdisciplinary and transdisciplinary pursuit of sustainable transformations.” Urban Transformations 3, no. 1 (2021): 1-13.
13. M. Molinari, J. Anund Vogel, D. Rolando. Using Living Labs to tackle innovation bottlenecks: the KTH Live-In Lab case study, in Energy Proceedings – Applied Energy Symposium: MIT A+B, 2021.
14. N.B. Ravichandran, A. Lansner & P. Herman. “Semi-supervised learning with Bayesian Confidence Propagation Neural Network”, in Proc. European Symposium on Artificial Neural Networks (ESANN) 2021. doi.org/10.14428/esann/2021.es2021-156.
15. Ruibo Tu, Kun Zhang, Hedvig Kjellström, and Cheng Zhang. Optimal transport for causal discovery. In International Conference on Learning Representations, 2022.
16. Carles Balsells Rodas, Ruibo Tu, and Hedvig Kjellström. Causal discovery from conditionally stationary time-series, arXiv:2110.06257, 2021.
17. M. Lenninger, M. Skoglund, P. Herman and A. Kumar. Bandwidth expansion in the brain: Optimal encoding manifolds for population coding. In Cosyne, 2021.
18. S. Molavipour, G. Bassi, and M. Skoglund. On neural estimators for conditional mutual information using nearest neighbors sampling. IEEE Transactions on Signal Processing 69:766-780, 2021.
19. M. Sorkhei, G. Eje Henter, and H. Kjellström. Full-Glow: Fully conditional Glow for more realistic image generation. In DAGM German Conference on Pattern Recognition, 2021.
20. Chenda Zhang and Hedvig Kjellström. A subjective model of human decision making
21. based on Quantum Decision Theory, arXiv:2101.05851, 2021.
22. M. Molinari and D. Rolando. Digital twin of the Live-In Lab Testbed KTH: Development and calibration. In Buildsim Nordic, 2020.
23. D. Rolando and M. Molinari. Development of a comfort platform for user feedback: The experience of the KTH Live-In Lab. In International Conference on Applied Energy, 2020.
24. E. Stefansson, F. J. Jiang, E. Nekouei, H. Nilsson, and K. H. Johansson. Modeling the decision-making in human driver overtaking. In IFAC World Congress, 2020.
25. Y. Yi, L. Shan, P. E. Paré, and K. H. Johansson. Edge deletion algorithms for minimizing spread in SIR epidemic models. arXiv preprint arXiv:2011.11087, 2020.

About the project

Objective
The main goal of the dBrain collaborative project is to develop an interdisciplinary approach to improve early diagnosis and prognosis for brain diseases and to facilitate the development of therapies by combining computational modelling of the brain and advanced artificial intelligence (AI) based data analyses.
The computational approaches will initially focus on Parkinson’s (PD) and Alzheimer’s diseases (AD), the two most common neurodegenerative diseases. These disease cases are strategically selected in collaboration with the Karolinska Hospital (KS) and Karolinska Institute (KI).

This research is important because the incidence of brain diseases will only increase with increasing human longevity. It is estimated that by 2030, mental disorders will be the largest contributor to the total disease burden.

Background
With increased longevity, the number of patients with brain-related diseases will grow. In 2030, mental illness is estimated to be the main imposition on health care. New advances in data analysis and computational modelling of the brain can use the potential of patient data to prevent and treat brain diseases.

Cross-disciplinary collaboration
The research team represents the School of Electrical Engineering and Computer Science (EECS), the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH) and the School of Engineering Sciences (SCI).

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

• Organized with our collaborations at KI/KS a session during the SPIE conference 2021, presented the dBrain work there and participated in a panel discussion on AI in neurosciences (see https://doi.org/10.1117/12.2606005)
• Organized the BrainNet workshop 2022 and presented our progress in the dBrain consortium (https://www.digitalfutures.kth.se/event/brainnet-workshop/)
• Master thesis student Georgios Moschovis participated in the Image CLEF 2022 challenge and obtained 4th place in the concept detection challenge and 5th place in the caption prediction task.
• SPIE 2021 presentation: Synaptic heterogeneity in the brain at single synapse resolution.
• Presentation at KTH ML-day workshop 220117, “Machine learning-based unsupervised feature extraction from magnetoencephalography and eye-tracking data”.
• Participation in the KTH ML-day and chairing one of the sessions.
• In particular one of the software used and further developed in dBrain in WP1 is called Snudda, a software for setting up detailed microcircuit models. Snudda has been developed further to also be able to support the building of diseased neuronal networks. Snudda has been adopted by EBRAINS, and the use of Snudda to model the healthy and disease brain have been disseminated at various events listed below:
  • BCBT 2021 summer school: “How can one build and simulate basal ganglia microcircuits in a data-driven, bottom-up manner”
  • SPIE 2021: “Creating a microcircuit of the striatum using connectivity, morphological and electrophysiological data”
  • INCF workshop 2021: “Snudda: Open source tool for creating micro-circuits in silico” and “Dealing with neuron morphologies”
  • Baltic Neuroscience Summer School – September 2021
  • Codejam – November 2021: “Build NEURON microcircuits using touch detection with Snudda”
  • SWEBAGS conference – December 2021: “From Morphology to Microcircuity using Snudda”
  • BrainNet workshop KTH, May 2022: “Exploring striatum in health and disease using data-driven models”
  • FENS – July 2022 – Two posters using Snudda

Results

dBrain addresses two major challenges in our understanding of the brain in health and disease: a) what is the relationship between the structural connectivity and functional interactions between different brain regions and within local neuronal networks?; and b) how do disease-related slow changes in the chemical balance, gene expression and neuronal degeneration shape the brain activity dynamics? We address these challenges via four work packages (WPs), using digital approaches comprising AI-based data analysis, math, computational modelling and simulations.

With the help of Digital Futures, we created a vibrant environment involving three KTH schools and collaborators at KS and KI. In addition to regular weekly meetings at the Digital Futures for the whole consortium (i.e. PIs, the recruited postdocs, and other students), several subgroups also meet more frequently in one of the labs or via zoom. Also, several Master’s Student projects have been completed in addition to the work of PhD students and postdocs. In the weekly meetings, postdocs have particular roles – regularly presenting updates on their work and journal articles selected for general interest. These activities provide the postdocs with training in presenting their work and leading discussions. The postdocs also have arranged team-building activities.

Publications

We like to inspire and share interesting knowledge!

Publications

1. Sanna Persson, Rodrigo Moreno, Bounding tractogram redundancy, Frontiers in Neuroscience. 18:1403804, 2024, https://doi.org/10.3389/fnins.2024.1403804
2. Malin Siegbahn, Daniel Jörgens, Filip Asp, Malou Hultcrantz, Rodrigo Moreno, Cecilia Engmér Berglin. Asymmetry in cortical thickness of the Heschl’s gyrus in unilateral ear canal atresia. Otology & Neurotology. 45(4),e342-e350, 2024. https://doi.org/10.1097/MAO.0000000000004137
3. Caroline Dartora, Anna Marseglia, Gustav Mårtensson, Gull Rukh, Junhua Dang, J- Sebastian Muehlboeck, Lars-Olof Wahlund, Rodrigo Moreno, José Barroso, Daniel Ferreira, Helgi Schiöth, Eric Westman. A deep learning model for brain age prediction using minimally preprocessed T1w-images as input. Frontiers in Aging Neuroscience 15, 1303036, 2024. https://doi.org/10.3389/fnagi.2023.1303036
4. Antonia Hain, Daniel Jörgens, Rodrigo Moreno. Randomized iterative spherical-deconvolution informed tractogram filtering. Neuroimage 278, 120248, 2023. https://doi.org/10.1016/j.neuroimage.2023.120248
5. Jingru Fu, Antonios Tzortzakakis, José Barroso, Eric Westman, Daniel Ferreira, Rodrigo Moreno. Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration. Human Brain Mapping 44 (4), 1289-1308, 2023. https://doi.org/10.1002/hbm.26165
6. Kajsa Ahlgren, Christoffer Olsson, Inna Ermilova, Jan Swenson. New insights into the protein stabilizing effects of trehalose by comparing with sucrose. Physical Chemistry Chemical Physics 25 (32), 21215-21226, 2023. https://doi.org/10.1039/D3CP02639F
7. Zhou Zhou, Chirstoffer Olsson, T Christian Gasser, Xiaogai Li, Svein Kleiven. The White Matter Fiber Tract Deforms Most in the Perpendicular Direction During In Vivo Volunteer Impacts. J Neurotrauma. 2024 41(23-24):2554-2570, 2024. https://doi.org/10.1089/neu.2024.0183
8. Jingru Fu, Daniel Ferreira, Örjan Smedby, Rodrigo Moreno. A deformation-based morphometry framework for disentangling Alzheimer’s disease from normal aging using learned normal aging templates. arXiv.2311.08176, 2023. https://doi.org/10.48550/arXiv.2311.08176
9. Daniel Jörgens, Pierre Marc Jodoin, Maxime Descoteaux, Rodrigo Moreno. Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering. arXiv:2307.05786, 2023. https://doi.org/10.48550/arXiv.2307.05786
10. Jingru Fu, Simone Bendazzoli, Örjan Smedby, Rodrigo Moreno. Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation. MICCAI 2024 Workshop on Advancing Data Solutions in Medical Imaging AI (ADSMI’24) (in press). https://doi.org/10.48550/arXiv.2406.16848
11. Sanna Persson, Xinyi Wan, and Rodrigo Moreno. Randomly COMMITting: Iterative Convex Optimization for Microstructure-Informed Tractography. MICCAI 2024 Workshop on Computational Diffusion MRI (CDMRI’24) (in press)
12. Yu Zhou, Jingru Fu, Örjan Smedby, Rodrigo Moreno. Synthesis of Pediatric Brain Tumor Images with Mass Effect. SPIE – Medical Imaging: Image Processing 12464, 699-707, 2023. San Diego, USA, February 2023. https://doi.org/10.1117/12.2654366
13. I Carannante, Y Johansson, G Silberberg, J Hellgren Kotaleski. Data-Driven Model of Postsynaptic Currents Mediated by NMDA or AMPA Receptors in Striatal Neurons. Front Comput Neurosci. 2022; 16: 806086.
14. J Fu, A Tzortzakakis, J Barroso, E Westman, D Ferreira, R Moreno. Generative Aging of Brain Images with Diffeomorphic Registration. arXiv preprint. 2022; arXiv:2205.15607. https://doi.org/10.48550/arXiv.2205.15607
15. A Hain, D Jörgens, R Moreno. Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering. arXiv preprint 2022; arXiv:2205.04843. https://doi.org/10.48550/arXiv.2205.04843
16. M Siegbahn, C Engmér Berglin, R Moreno. Automatic segmentation of the core of the acoustic radiation in humans. Front Neurol. 2022 : 934650. https://doi.org/10.3389/fneur.2022.934650
17. C Dartora, A Marseglia, G Mårtensson, G Rukh, J Dang, JS Muehlboeck, LO Wahlund, R Moreno, J Barroso, D Ferreira, HB Schiöth, E Westman. Predicting the Age of the Brain with Minimally Processed T1-weighted MRI Data. medRxiv preprint. 2022; medRxiv:2022.09.06.22279594. https://doi.org/10.1101/2022.09.06.22279594
18. Chakravarty K, Roy S, Sinha A, Nambu A, Chiken S, Hellgren Kotaleski J, Kumar A. Transient Response of Basal Ganglia Network in Healthy and Low-Dopamine State. eNeuro. 2022 Mar 18;9(2):ENEURO.0376-21.2022. doi: 10.1523/ENEURO.0376-21.2022.
19. Hjorth, J.J.J., Hellgren Kotaleski, J. & Kozlov, A. Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda. Neuroinform 19, 685–701 (2021). https://doi.org/10.1007/s12021-021-09531-w
20. G Colombo, R Cuber, L Kanari, A Venturino, R Schulz, M Scolamiero, J Agerberg, H Mathys, L Tsai, W Chachólski,, K Hess, S Siegert. Microglial morphOMICs, a tool for mapping microglial morphology, reveals brain-region-and sex-dependent phenotypes. Nature neuroscience, 2022.
21. W Chachólski, A Guidolin, I Ren, M Scolamiero, F Tombari. Effective computation of relative homological invariants for functors over posets. arXiv preprint 2022; arXiv:2209.05923
22. W Chachólski, A Jin, F Tombari. Realisations of posets and tameness. arXiv preprint 2022; arXiv:2112.12209
23. W Chachólski, René Corbet, Anna-Laura Sattelberger. The Shift-Dimension of Multipersistence Modules. arXiv preprint 2022; arXiv:2112.06509.
24. W Chachólski, B Gunti, C Landi, Decomposing filtered chain complexes: Geometry behind barcoding algorithms. Computational Geometry, Volume 109, 2023.
25. Georgios Moschovis. NeuralDynamicsLab at ImageCLEF Medical 2022. ImageCLEF, 2022.
26. Helson Pascal, Lundqvist Daniel, Svenningsson Per, Vinding Mikkel, Kumar Arvind (2023). Cortex-wide topography of 1/f-exponent in Parkinson’s disease. Nature Parkinson’s Disease, 9, Article number: 109.
27. Zang, Jie, Liu Shenquan, Helson Pascal, Kumar Arvind (2024). Structural constraints on the emergence of oscillations in multi-population neural networks. eLIFE, 12(Feb 5).
28. Wärnberg Emil, Kumar Arvind (2023). Feasibility of dopamine as a vector-valued feedback signal in the basal ganglia. PNAS 32(120):e2221994120

Master School Theses

1. Yuqi Zheng. Predictive MR Image Generation for Alzheimer’s Disease and Normal Aging Using Diffeomorphic Registration. MS thesis KTH 2023. Supervisors Rodrigo Moreno, Jingru Fu. Diva diva2:1833589
2. Xinyi Wan. Assessing the Streamline Plausibility Through Convex Optimization for Microstructure Informed Tractography(COMMIT) with Deep Learning. MS thesis KTH 2023. Supervisor Rodrigo Moreno. Diva diva2:1754277
3. Teodor Pstrusiński. Impact of the autoencoder-based FINTA tractogram filtering method on brain networks in subjects with Mild Cognitive Impairment. MS thesis KTH 2023. Supervisors Rodrigo Moreno, Fabian Sinzinger. Diva diva2:1799683
4. Erika Bengtsdotter. Towards Anatomically Plausible Streamline Tractography with Deep Reinforcement Learning. MS thesis KTH 2022. Supervisors Rodrigo Moreno, Fabian Sinzinger. Diva diva2:1695872
5. Filippo Maschio. Influence of tractogram filtering in the analysis of tractography data in rat brains. MS thesis University of Padua, Italy 2022. Supervisor Rodrigo Moreno.
6. Yu Zhou. Synthesis of Pediatric Brain Tumor Image With Mass Effect. MS thesis KTH 2022. Supervisors Rodrigo Moreno, Jingru Fu. Diva diva2:1686799
7. Antonia Hain. Assessing individual streamline plausibility through randomized spherical deconvolution-informed tractogram filtering. MS thesis University of Saarland, Germany 2021. Supervisor Rodrigo Moreno.
8. N Hulst, Exploring persistent homology as a method for capturing functional connectivity differences in Parkinson’s Disease. Master Thesis. DiVA, id: diva2:1687257.
9. Marvin Köpff. Impact of tractogram filtering and graph creation for structural connectomics in subjects with mild cognitive impairment. MS thesis KTH 2020. Supervisors Rodrigo Moreno, Joana Pereira. Diva: diva2:1463915
10. Johannes Wennberg. Longitudinal assessment of functional connectivity impairment in rat brains. MS thesis KTH 2019. Supervisor Rodrigo Moreno, diva2:1327919
11. Cristina Zanin. Graph Convolutional Networks for static and dynamic functional connectivity: an application to the Autism Spectrum Disorder. MS thesis University of Padua, Italy 2020. Supervisor Rodrigo Moreno.
12. Lucas Höglund. Analysis of Eye Tracking Data from Parkinson’s Patients using Machine Learning. MS thesis, KTH.
13. Leo Bergman. Feature extraction with self-supervised learning on eye-tracking data from Parkinson’s patients and healthy individuals. MS thesis, KTH.
14. Emma Lind. Analysis of Brain Signals from Patients with Parkinson’s Disease using Self-Supervised Learning. MS thesis, KTH.
15. Wilhelm Ågren. Feature extraction from MEG data using self-supervised learning. MS thesis, KTH.
16. Giulia Tuccio. Parameter estimation in a cardiovascular computational model using numerical optimization. MS thesis, KTH.
17. Paolo Calderaro. Patient simulation. Generation of a machine learning “inverse” digital twin. MS thesis, KTH.