About the project
Objective
With over 1 billion people over 60 worldwide, creating technology that supports the aged to live independently for longer by assisting them in everyday tasks became essential. While companion robots are aimed toward this need, current technology falls short in maintaining engagement over long-term interactions. Among the reasons is the inability to learn from users and adapt, known as lifelong learning, especially in open-domain dialogue that is not limited to any topic.
This project aims to develop a long‐term memory model for open‐domain dialogue such that a robot can learn and recall a person’s attributes, preferences, and shared history to provide personalized assistance in a variety of tasks, such as performing preferred activities, adaptive collaboration in chores, and providing reminders based on their schedule and needs.
About the Digital Futures Postdoc Fellow
Bahar Irfan is a Postdoctoral researcher at KTH Digital Futures. Her research focuses on creating personal robots that can continually learn and adapt to assist everyday life. Previously, she was a Research and Development Associate at Evinoks Service Equipment Industry and Commerce Inc., developing customizable software for industrial robots and smart buffets. Before that, she worked as an R&D Lab Associate at Disney Research Los Angeles on emotional language adaptation in multiparty interactions.
She has a diverse background in robotics, from personalization in long-term human-robot interaction during her PhD at the University of Plymouth and SoftBank Robotics Europe as a Marie Skłodowska-Curie Actions fellow to user-centred task planning for household robotics during her MSc in computer engineering, and building robots for BSc in mechanical engineering at Boğaziçi University.
Main supervisor
Gabriel Skantze, Professor in Speech Communication and Technology, KTH.
Co-supervisor
Sanna Kouppamäki, Assistant Professor, Division of Technology in Health Care, KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
Arzu’s future research focus is co-designing and developing gamified robot-enhanced interventions for children and adolescents with neurodevelopmental disorders (NDDs). The research will be based on an iterative design approach to develop interventions for and with the target user groups tailored to the individual to enhance the functional recovery of sensorimotor, social, or cognitive functions in children with NDDs. She aims to investigate what the best roles for robots are in different inclusive practices, including neurodivergent and neurotypical groups where they play and learn together, how to involve children in the design process of robot-mediated activities, and how to design inclusive gamified practices to enhance social interaction between the neurotypical and neurodivergent children as well as their families.
Background
Neurodevelopmental disorders (NDDs) result in different degrees of emotional, physical, social, academic and economic consequences for individuals and in turn, families and society [1, 2]. Upon diagnosis, families report significant delays in treatment initiation and unsatisfactory levels of treatment monitoring. [2, 3].There is a need to establish effective easy-to-access strategies for assessing, treating and monitoring NDD.
Rapid progress in the area of robotics offers excellent chances for innovation in the treatment of children with NDDs, thanks to robots allowing the execution of specific and repetitive tasks which can be tailored according to the particular needs of the individuals. Robots thus offer the opportunity to deliver automated and independent interventions that enable therapy to be delivered over a distance in inclusive and collaborative education environments [4,5] and personalise treatment procedures [6,7]. Combined with gamification, which improves the learning rate and ensures effective improvement in the pedagogical, social and behavioural sense [8,9], robot-enabled therapy becomes a highly promising avenue for research.
About the Digital Futures Postdoc Fellow
Arzu Guneysu Ozgur is a Postdoctoral researcher at Digital Futures. Arzu got a PhD in Robotics on “Designing Gamified Activities with Haptic-Enabled Tangible Robots for Therapy and Assistance” from EPFL in 2021. Her research interests include various topics in Human-Robot Interaction, Adaptive Robot-Enhanced Therapy, Iterative Design, Participatory Design, Neurodevelopmental Disorders, Gamified Therapeutic Technologies, Healthy Aging, Intergenerational Practices for Elderly and Children, and Special Education.
Main supervisor
Iolanda Leite, Associate professor, Department of Robotics, Perception and Learning, KTH.
Co-supervisor
Ali Reza Majlesi, Associate Professor, Department of Education, Stockholm University.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
The project aims to fill urban population data gaps in developing countries by harnessing the power of Earth Observation (EO) data and AI. An innovative framework will fuse high-resolution satellite information with ancillary sources, such as Volunteer Geographic Information data and machine learning. The long-term goal of POPAI is to understand better the synergy and potential of AI and EO towards scalable population mapping, help address the United Nations Sustainable Development Goals, support evidence-based policymaking and foster a better future for the cities of tomorrow.
Background
Accurate urban population distribution information is necessary prerequisites for a wide range of applications related to urban sustainability. The quality and quantity of population data in numerous countries are often inadequate due to the absence of detailed censuses or large temporal gaps between them. The disparaging effects of this lack of information are most evident in Sub-Saharan Africa (SSA) and the Global South. As estimated by the UN, more than 60% of the African population will reside in cities by 2050, which further emphasizes the need for accurate population information.
About the Digital Futures Postdoc Fellow
Stefanos Georganos is a research fellow at the Division of Geoinformatics, Royal Institute of Technology. He does research in quantitative human geography, remote sensing, spatial epidemiology and machine learning. He is particularly interested in the use of geo-information to help address the UN Sustainable Development Goals, with a geographical interest in sub-Saharan African cities. His latest research unravels the potential of Artificial Intelligence and Earth Observation to detect, measure and characterize socio-economic inequalities in deprived urban areas in support of the most vulnerable populations.
Main supervisor
Yifang Ban, Professor and Head of Division Geoinformatics at KTH.
Co-supervisor
Anders Wästfeldt, Professor at Stockholm University.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
This project aims to improve a Non-Axiomatic Reasoning System design and combine it with state-of-the-art Deep Learning models for perception. This allows the system to be applied in real-world environments, intending to enhance the autonomy of robots where human intervention is to be kept at a minimum. Application-wise, the system is expected to autonomously perform inspection and maintenance operations of city infrastructure such as power plants. This will ultimately lead to new digitisation technology, which can help solve environmental and societal problems.
Background
The human’s ability to reason has evolved to adapt to difficult situations and changes in the environment faster than current AI models allow. Animals that reason effectively outsmart other species and gain key survival advantages. Non-Axiomatic Reasoning can explain most of these cognitive abilities and provides a roadmap for cognitive enhancements based on psychological and neuroscientific insights. Also, Learning can be explained as inductive reasoning using Non-Axiomatic Logic, an aspect most reasoning systems lack while being a key aspect of intelligence.
About the Digital Futures Postdoc Fellow
Patrick Hammer is a postdoc researcher at Stockholm University, Department of Psychology, working with Robert Johansson and Pawel Herman. Before joining Stockholm University, he got his PhD in Computer Science (AI track) at Temple University, Pennsylvania, United States, where he was a full-time research assistant of Pei Wang. His research interests include Artificial Intelligence, Reasoning Systems, Autonomous Robots, Machine Learning, Deep Learning and Cognitive Science.
Main supervisor
Robert Johansson, Associate Professor at Stockholm University.
Co-supervisor
Pawel Herman, Associate Professor, Computer Science, Division of Computational Science and Technology at KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
In this project, we shall develop, implement, and evaluate a mobile health (mHealth) platform for educating patients about cancer and mental illnesses in Uganda, linkage of patients to peer support workers (“expert patients” and survivors), and collecting patient-reported outcomes (e.g. self symptom assessment and quality of life surveys). We shall follow a design science research approach and principles of user-centred design. We shall use familiar and feasible technologies such as SMS, USSD and IVR. The health information content and communication flows shall also be developed and iteratively evaluated with the target users of the system to ensure it is contextually appropriate and correctly translated. Evaluation of the project will be qualitative and quantitative, including assessment of usability, fidelity, and the clinical impact, such as the impact of the intervention on patient self-efficacy, loss to follow-up, quality of life, and satisfaction with care.
Background
The health and economic development challenges of infectious diseases in Africa and other LMICs are well recognized. Controlling these infectious diseases (especially HIV/AIDS, Malaria and Tuberculosis) in Africa has thus been the priority for many national and global players, such as the US CDC and PEPFAR, the Bill and Melinda Gates Foundation, and the Global Fund. Consequently, significant progress has been made in the past decades in controlling infectious diseases in Africa. In contrast, non-communicable diseases (NCDs) such as cancer and mental illnesses have remained under-prioritized.
Today, cancer kills approximately 10 million people per year globally. This is more than deaths from HIV/AIDS, Malaria, Tuberculosis, and COVID-19 combined. In Africa, the ongoing socio-economic transitions (urbanization, ageing population, and westernization of lifestyles) are escalating the cancer burden (incidence predicted to rise 38% over the next decade) faster than in any other part of the world. Similarly, approximately one out of every four persons globally has a mental disorder, leading to over 8 million deaths and about 2.5 trillion US dollars lost in the loss of productivity. In Africa, 85% of people with mental illnesses do not have access to the necessary healthcare. Disruptions in healthcare, e.g. due to COVID-19, as well as social inequalities and marginalization, further exacerbate the problem.
Mobile phones are ubiquitous in Africa and have allowed leapfrogging of technological limitations. Mobile solutions are accelerating finance (mobile money), the energy sector (pay-as-you-go solar mobile solutions), and agriculture (access to market prices, micro-insurance), among others. Mobile technologies in healthcare (mHealth) are also gaining traction in Africa with a demonstrated positive impact on patient treatment adherence, provision of health education and awareness to the general public, data collection and reporting, drug supply chain and stock management, and disease surveillance. However, most implementations have been isolated pilots, focused on infectious diseases, and lacked robust evaluation methods. Most evaluations have focused on feasibility, usability and acceptability, with limited focus and evidence on clinical outcomes.
About the Digital Futures Postdoc Fellow
Johnblack K. Kabukye is a medical doctor and health informatics specialist at the Uganda Cancer Institute in Kampala, Uganda. He did a Bachelor of Medicine and a Bachelor of Surgery from Makerere University, a Master of Science in Health informatics from Karolinska Institute and Stockholm University, and a PhD in medical informatics from the University of Amsterdam.
His research interests are designing, implementing and evaluating digital health solutions for healthcare providers and patients in developing countries, including electronic medical records, patient advice telephone lines, and telehealth and artificial intelligence-enabled apps to support cervical cancer screening.
Main supervisor
John Owuor, PhD, Director, SPIDER Department of Computer and Systems Sciences (DSV) Stockholm University.
Co-supervisor
Susanne Nilsson, Researcher, Integrated product development and design, Machine design, School of Industrial Engineering and Management, KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
This project aims to propose innovative distributed learning methods based on adaptive gradient coding techniques. Within this framework, workers’ participation is fluidly adjusted in real-time during training to enhance learning performance under practical constraints. We will offer rigorous theoretical proofs to ensure the convergence of the proposed methods, solidifying their reliability. We will also test the performance of the proposed methods on both simulated and actual datasets in real-world scenarios. This evaluation will benchmark the effectiveness of our techniques and underscore their superiority over current practices.
Background
In the framework of distributed learning, a central server aggregates computational results from various workers to update the trained model. However, in practical scenarios, “stragglers”—workers who are slow or unresponsive—can significantly impede overall training time. Addressing these slowdowns is crucial for real-time processing requirements in the healthcare and smart transportation sectors.
While current distributed learning methods employ gradient coding to mitigate the effects of stragglers, they rely on a fixed number of the fastest workers throughout the entire training process, which have limited flexibility in balancing training time and loss. Based on that, our research question is how to transcend the limitations inherent in existing distributed learning methods and to reduce the training time required to achieve a specified training loss.
About the Digital Futures Postdoc Fellow
Chengxi Li received a PhD in 2022 from the Department of Electronic Engineering at Tsinghua University and a bachelor’s degree in 2018 from the University of Electronic Science and Technology of China. Her research interests lie in distributed learning, federated learning, signal processing and information theory.
Main supervisor
Mikael Skoglund, Professor, Head of Department, Division of Information Science and Engineering, EECS, KTH.
Co-supervisor
Ming Xiao, Professor, Division of Information Science and Engineering, EECS, KTH.
About the project
Objective
The goal of the project is to design reactive, intelligent planning and control algorithms for underwater vehicles which quantify and reason about risk as well as incorporate machine learning. This will enable the use of AUVs for more autonomous environmental data collection with reduced human involvement and, therefore, reduced human risk.
Background
Autonomous underwater vehicles have great potential for environmental monitoring and exploration, but there are important technical challenges that prevent their widespread use. Some of the major challenges are that GPS location information is not available underwater, communication underwater is limited, and there may be significant vehicle drift due to local hydrodynamic disturbances. As a result, it is difficult to ensure high levels of reliability for these vehicles. To bridge the reliability gap, this project aims to design and test planning and control algorithms that explicitly reason about uncertainty and produce intelligent policies to minimize that uncertainty while gathering information about the vehicle’s environment.
About the Digital Futures Postdoc Fellow
Chelsea Sidrane began her studies with a Bachelor’s degree in mechanical engineering at Cornell University, where she developed an interest in dynamical systems and control theory. She went on to study machine learning and robot planning in her Master’s studies at Stanford University before beginning a PhD in the Stanford Intelligent Systems Laboratory focused on verifying neural networks. She defended her thesis, “Neural Network Verification for Nonlinear Systems”, in the summer of 2022. She is now a Digital Futures Postdoctoral Research Fellow at KTH based in the Planiacs group at the Division of Robotics, Perception and Learning (RPL).
Main supervisor
Jana Tumova, Associate Professor in the Division of Robotics, Perception and Learning, KTH
Co-supervisor
Ivan Stenius, Associate Professor in Vessel Engineering & Solid Mechanics, KTH
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.