Uppsala University Department of Information Technology


See all upcoming seminars in LäsIT and seminar web pages at the homepage for the PhD studentseminars, TDB, Vi2, Theory and Applications Seminars (TAS) @ UpMARC., Department of Mathematics and The Stockholm Logic Seminar.

Seminar (online)
Thursday 3 Dec
Prof. Isam Shahrour: Smart water: how the smart city concept could help in the optimal and safe management of urban water supply and sewage
, Time: 13:15-14:00

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Seminar by Isam Shahrour, distinguished professor in civil engineering and smart cities at Lille University (France). This seminar is part of the Smart City Arena seminar series.

The smart city concept is developing very quickly around the world with the objective to improve the quality of life in cities through the improvement of urban systems' efficiency, urban services and citizens' involvement. The scientific literature about smart cities is already very riche, but it still lacks feedbacks from the implementation of real projects. This conference aims at filling this gap through the presentation of feedback from large scale pilot project of the smart city "SunRise smart City". The conference will focus on the contribution of this concept to the management of urban water systems including both water supply and water sewage systems. For each system, it presents the major challenges, the design of the mart city solution, its implementation in a large-scale pilot (SunRise Smart City) and major lessons learned from the implementation.

Isam Shahrour is a distinguished professor at Lille University (France) and member of the French water academy. He is also professor at the college of Civil Engineering at Tongji University (China). He is strongly involved in research, higher education and partnership with industry and local government. He was vice president “Research” at Lille University (2007 - 2012) and director of the regional research laboratory “Civil and Geo-Environment Engineering” (2010 - 2019). He is the founder and coordinator of the smart City large-scale pilot “SunRise Smart City". His academic activity concerns smart and sustainable cities, sustainable management of natural resources and civil and geo-environmental Engineering. It resulted in about 150 refereed journal papers and the supervision of around 90 PhD dissertations and more than five million Euros of industrial and public contracts. He gave more than 30 lectures on topics related to cities: Sustainable cities, Smart cities, and Resilient Cities, including two TEDx talks.

Disputation | PhD defence
11 December
Yuan Gao: Machine Behaviour Development and Analysis using Reinforcement Learning
Location: Häggsalen, Ångströms, Time: 10:00

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Yuan Gao will present and defend his PhD thesis Machine Behaviour Development and Analysis using Reinforcement Learning.

Opponent: Elin Anna Topp (Lund University)

Supervisor: Ginevra Castellano

We are approaching a future where robots and humans will co-exist and co-adapt. To understand how can a robot co-adapt with humans, we need to understand and develop efficient algorithms suitable for our interactive purposes. Not only it can help us to advance the field of robotics but also it can help us to understand ourselves. A subject Machine Behavior, proposed by Iyad Rahwan in a recent Science article, studies algorithms and the social environments in which algorithms operate. What this paper's view tells us is that, when we would like to study any artificial robot we create, like natural science, a two-step method based on logical positivism should be applied. That is, we need to, on one hand, provide a complicated theory based on logical deduction, and on another hand, empirically setup experiments to conduct.

Reinforcement learning (RL) is a computational model that helps us to build a theory to explain the interactive process. Integrated with neural networks and statistics, the current RL is able to obtain a reliable learning representation and adapt over interactive processes. It might be one of the first times that we are able to use a theoretical framework to capture uncertainty and adapt automatically during interactions between humans and robots. Though some limitations are observed in different studies, many positive aspects have also been revealed. Additionally, considering the potentials of these methods people observed from related fields e.g. image recognition, physical human-robot interaction and manipulation, we hope this framework will bring more insights to the field of robotics. The main challenge in applying Deep RL to the field of social robotics is the volume of data. In traditional robotics problems such as body control, simultaneous localization and mapping and grasping, deep reinforcement learning often takes place only in a non-human environment. In such an environment, the robot can learn infinitely in the environment to optimize its strategies. However, applications in social robotics tend to be in a complex environment of human-robot interaction. Social robots require human involvement every time they learn in such an environment, which leads to very expensive data collection. In this thesis, we will discuss several ways to deal with this challenge, mainly in terms of two aspects, namely, evaluation of learning algorithms and the development of learning methods for human-robot co-adaptation.

Disputation | PhD defence
15 December
Xiuming Liu: Statistical Data Analysisfor Internet-of-Things: Scalability, Reliability and Robustness
Location: ITC1245, Time: 9:15

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Xiuming Liu will present and defend his PhD thesis "Statistical Data Analysisfor Internet-of-Things: Scalability, Reliability and Robustness".

Opponent: Tobias Oechtering (KTH)

Supervisor: Edith Ngai

Internet-of-Things is a set of sensing, communication, and computation technologies to connect physical objects, such as wearable devices, vehicles, and buildings. From those connected “Things”, a large amount of data is generated. Data analysis plays a central role in the automated and intelligent decision-making process to manage and optimize IoT systems. In this thesis, we focus on tackling the challenges of analyzing large, incomplete, and corrupt IoT data. This thesis consists of three topics. In the first topic, we study scalable GP regression for big IoT data. We propose a novel scalable GP model for urban air quality modeling and prediction. Comparing to the existing scalable GP models, the proposed scalable GP model enables tractable analysis of approximation errors. The second topic is to handle the missing data problem. In the case of missing labels in training data, we investigate different missing data mechanisms. We propose a reliable semi-supervised learning approach, which provides accurate predictive error probability. In the case of missing features in testing data, we design a robust predictor. The predictor significantly reduces the prediction error caused by rare values of missing features, while incurring only a small loss on the overall performance. The third topic is information fusion for IoT systems under false data injection attacks. We propose a robust and distributed information fusion method. This proposed information fusion method only requires exchanging the latest local posterior distributions, instead of synchronizing the full historical measurements. Furthermore, we design a false data detector based on the clustering of local posterior distributions. The distributed information fusion method and false data detector enable secure state estimation for mobile IoT networks with probabilistic communication links. Altogether, this thesis is a step to scalable, reliable, and robust IoT data analysis.

See also the list of all upcoming seminars.

Internal seminars. Lecturers may be either internal or external.

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