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X-WR-CALDESC:Events for TECoSA
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20240307T150000
DTEND;TZID=UTC:20240307T160000
DTSTAMP:20240208T082736Z
CREATED:20240208T082243Z
LAST-MODIFIED:20240208T082736Z
UID:7062-1709823600-1709827200@www.tecosa.center.kth.se
SUMMARY:TECoSA Seminar – Software-Enabled Solutions for Human-on-the-Loop Autonomous Systems
DESCRIPTION:We aim to bring you a TECoSA Seminar on the first Thursday of each month during term-time.  All are welcome to attend and we look forward to some lively discussions. Members can accept the Outlook invitations\, non-members can email tecosa-admin@kth.se to register.\nOur guest speaker for March is Rebekka Wohlrab\, Assistant Professor in Software Engineering at Chalmers University of Technology\, and adjunct faculty member at Carnegie Mellon University.  The session will be given via Zoom (https://kth-se.zoom.us/j/66857695267). \nABSTRACT: Software is a key driver of innovation in complex autonomous systems\, such as IoT systems\, smart cloud systems\, or robots. To ensure that such systems are useful and trusted by members of our society\, they need to act in an understandable way and meet their users’ requirements. Requirements in this context are often related to qualities\, such as performance\, security\, reliability\, or safety. Humans tend to have preferences regarding what requirements are important in different contexts. To help systems understand these requirements and how they change\, software-based approaches are needed that enable humans to give input\, monitor systems\, and intervene when necessary. In this talk\, Rebekka will present recent work on how such solutions can be designed to develop better human-on-the-loop autonomous systems. \nBIO: Rebekka Wohlrab is an assistant professor at Chalmers University of Technology\, and an adjunct faculty member at Carnegie Mellon University. Her research interests include requirements engineering and software architecture\, especially in connection with human-on-the-loop autonomous systems. She received her Ph.D. in computer science from Chalmers University of Technology. You can find more information about her work on her website: https://rebekkaa.github.io/
URL:https://www.tecosa.center.kth.se/event/tecosa-seminar-software-enabled-solutions-for-human-on-the-loop-autonomous-systems/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20240223T120000
DTEND;TZID=UTC:20240223T130000
DTSTAMP:20240126T114516Z
CREATED:20240126T113851Z
LAST-MODIFIED:20240126T114516Z
UID:7054-1708689600-1708693200@www.tecosa.center.kth.se
SUMMARY:TECoSA Research Seminar: Toward Explainable Robots: Integrating Modeling Humans and Adaptive Learning
DESCRIPTION:[For TECoSA members only.] \nSpeaker: Elmira Yadollahi\, TECoSA postdoc\n(Venue\, Zoom link and sign-up link circulated to members)\nPlease email vickid@kth.se if you have any questions. \nABSTRACT: Explainability in human‑robot collaboration refers to the robot’s ability to provide clear\, transparent\, and interpretable explanations for its actions and decisions. It bridges the communication gap between complex machine functionalities and humans. An active area of investigation in robotics and AI is understanding and generating explanations that can enhance collaboration and mutual understanding between humans and machines. A key to achieving such seamless collaborations is understanding end-users\, whether naive or expert\, and tailoring explanation features that are intuitive\, user-centered\, and contextually relevant. Advancing on the topic includes modelling humans’ expectations for generating explanations and developing metrics to evaluate them and assess how effectively autonomous systems communicate their intentions\, actions\, and decision-making rationale. This seminar will present multiple user studies\, focusing on understanding how humans perceive robots’ or autonomous vehicles’ explanations in different scenarios\, e.g.\, failures or accidents. Subsequently\, we transition to a phase of basic modelling of human expectations and machine understanding\, leading us to develop adaptive learning approaches that leverage reinforcement learning strategies to refine and enhance these machines’ explanatory capabilities. \n  \n 
URL:https://www.tecosa.center.kth.se/event/tecosa-research-seminar-toward-explainable-robots-integrating-modeling-humans-and-adaptive-learning/
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240201T150000
DTEND;TZID=UTC:20240201T160000
DTSTAMP:20240125T153451Z
CREATED:20240125T150217Z
LAST-MODIFIED:20240125T153451Z
UID:7042-1706799600-1706803200@www.tecosa.center.kth.se
SUMMARY:TECoSA Seminar - Evolving Wireless Technology Towards 6G: The vision behind the SweWIN Center
DESCRIPTION:We aim to bring you a TECoSA Seminar on the first Thursday of each month during term-time.  All are welcome to attend and we look forward to some lively discussions. Members can accept the Outlook invitations\, non-members can email tecosa-admin@kth.se to register.\nOur first speaker of 2024 is Emil Björnsson\, Professor of Wireless Communication at KTH.  The seminar will take place in Gladan (Brinellvägen 85\, KTH Campus) and via Zoom (https://kth-se.zoom.us/j/66857695267). \nABSTRACT:  The world is becoming increasingly digitalized and connected\, and mobile broadband connectivity is the backbone of this development. The demand for capacity and service quality expectations are constantly increasing\, which calls for continuous technological evolution. The wireless technology is developed in cycles; a new generation is developed during each decade and then deployed during the next decade. The first phase of the 5G roll-out has now finished\, and the research community has shifted focus towards 6G\, the sixth generation of mobile network technology. \nWhy do we need to further evolve the wireless technology towards 6G? In this seminar\, I will describe the wireless evolution with a focus on mobile broadband applications\, where the service quality is characterized by the data speed and traffic capacity. I will discuss how much faster wireless technology must become and what the real bottlenecks and technological challenges are. This leads us to the vision behind the new Vinnova Competence Center\, Swedish Wireless Innovation Network (SweWIN)\, which focuses on making future technology more sustainable\, resilient\, and fair. \nBIO:  Emil Björnson is a Professor of Wireless Communication at the KTH Royal Institute of Technology\, Stockholm\, Sweden. He is an IEEE Fellow\, Digital Futures Fellow\, and Wallenberg Academy Fellow. He has a podcast and YouTube channel called Wireless Future. His research focuses on multi-antenna communications and radio resource management\, using methods from communication theory\, signal processing\, and machine learning. He has authored four textbooks and has published a large amount of simulation code. \nHe has received the 2018 and 2022 IEEE Marconi Prize Paper Awards in Wireless Communications\, the 2019 EURASIP Early Career Award\, the 2019 IEEE Communications Society Fred W. Ellersick Prize\, the 2019 IEEE Signal Processing Magazine Best Column Award\, the 2020 Pierre-Simon Laplace Early Career Technical Achievement Award\, the 2020 CTTC Early Achievement Award\, the 2021 IEEE ComSoc RCC Early Achievement Award\, and the 2023 IEEE Communications Society Outstanding Paper Award. His work has also received six Best Paper Awards at conferences.
URL:https://www.tecosa.center.kth.se/event/tecosa-seminar-evolving-wireless-technology-towards-6g-the-vision-behind-the-swewin-center/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20231124T120000
DTEND;TZID=UTC:20231124T130000
DTSTAMP:20231109T131229Z
CREATED:20231109T131229Z
LAST-MODIFIED:20231109T131229Z
UID:6965-1700827200-1700830800@www.tecosa.center.kth.se
SUMMARY:TECoSA Research Seminar: Learn and Align RL Policies from Human Feedback
DESCRIPTION:Speaker: Daniel Simões Marta\, TECoSA PhD student\n(Venue\, Zoom link and sign-up link circulated to members)\nPlease email vickid@kth.se if you have any questions. \nABSTRACT: Reinforcement learning from informed by human feedback (RLHF) has emerged as a novel domain in machine learning\, where human insights are crucial in shaping the behavior of an AI agent. Within this domain\, a significant strategy is preference-based reinforcement learning\, in which a human-informed reward system is developed through the evaluation and selection among different sets of action sequences. In this talk\, I will present several works conducted by our group on aligning RL policies with human feedback. I will primarily focus on aligning relevant features such as safety and perceived safety—even though the work can be extended to any desired feature—and will discuss the application of these principles in the context of RL policies. Additionally\, I will provide concrete examples of leveraging human intrinsic knowledge through methods such as ranking\, stating preferences\, and analyzing text.
URL:https://www.tecosa.center.kth.se/event/tecosa-research-seminar-learn-and-align-rl-policies-from-human-feedback/
CATEGORIES:Seminar,Talks,webinar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20231102T150000
DTEND;TZID=UTC:20231102T160000
DTSTAMP:20231023T084022Z
CREATED:20230822T092154Z
LAST-MODIFIED:20231023T084022Z
UID:6811-1698937200-1698940800@www.tecosa.center.kth.se
SUMMARY:TECoSA Seminar - Learning Optimal Edge Processing with Offloading and Energy Harvesting
DESCRIPTION:We aim to bring you a TECoSA Seminar on the first Thursday of each month during term-time.  All are welcome to attend and we look forward to some lively discussions. Members can accept the Outlook invitations\, non-members can email tecosa-admin@kth.se to register.\nOur guest speaker for November is Francesco De Pellegrini\, Professor of networking and AI at the University of Avignon\, France.  The seminar will take place via Zoom (https://kth-se.zoom.us/j/66857695267). \nABSTRACT:  Modern portable devices can execute increasingly sophisticated AI models on sensed data. The complexity of such processing tasks is data-dependent and has relevant energy cost. This work develops an Age of Information markovian model for a system where multiple battery-operated devices perform data processing and energy harvesting in parallel. Part of their computational burden is offloaded to an edge server which polls devices at given rate. The structural properties of an optimal policy for a single device-server system are derived. They permit to define a new model-free reinforcement learning method specialized for monotone policies\, namely Ordered Q-Learning\, providing a fast procedure to learn the optimal policy. The method is oblivious to the devices’ battery capacities\, the cost and the value of data batch processing and to the dynamics of the energy harvesting process. Finally\, the polling strategy of the server is optimized by combining this policy improvement technique with stochastic approximation methods. Extensive numerical results provide insight into the system properties and demonstrate that the proposed learning algorithms outperform existing baselines. \nBIO: Francesco De Pellegrini received the MSc 2000\, and the Ph.D. 2004\, in Information Engineering at the University of Padova\, Italy. He is currently full professor at the University of Avignon\, where he teaches networking and artificial intelligence  He has published 100+ papers in major conferences and journals of computer science\, networking and control theory. He applies algorithms on graphs\, stochastic and control\, and game theory for the design and perfomance evaluation of wireless and wired networked systems. He has co-authored two best papers\, published in WiOPT 2014 and at NetGCoop 2016. His current H-index (Google) is 30 with 7000+ citations. He is anassociated editor for TNSE. He has been general co-chair of IEEE NETGCOOP 2012 and IEEE WIOPT2022\, and TPC Co-Chair of IEEE NETGCOOP 2014\, IEEE WIOPT 2018 and ITC 2021.
URL:https://www.tecosa.center.kth.se/event/tecosa-seminar-learning-optimal-edge-processing-with-offloading-and-energy-harvesting/
CATEGORIES:Seminar,Talks,webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20231027T120000
DTEND;TZID=UTC:20231027T130000
DTSTAMP:20231109T131300Z
CREATED:20231017T070710Z
LAST-MODIFIED:20231109T131300Z
UID:6892-1698408000-1698411600@www.tecosa.center.kth.se
SUMMARY:TECoSA Research Seminar: Getting the Best Out of Both Worlds: Algorithms for Hierarchical Inference at the Edge
DESCRIPTION:Speaker: Vishnu Narayanan Moothedath\, TECoSA PhD student\nVenue\, Zoom link and sign-up link circulated to members\nPlease email vickid@kth.se if you have any questions. \nABSTRACT: We consider a resource-constrained Edge Device (ED)\, such as an IoT sensor or a microcontroller unit\, embedded with a small-size ML model (S-ML) for a generic classification application\, and an Edge Server (ES) that hosts a large-size ML model (L-ML). Since the inference accuracy of S-ML is lower than that of the L-ML\, offloading all the data samples to the ES results in high inference accuracy\, but it defeats the purpose of embedding S-ML on the ED and deprives the benefits of reduced latency\, bandwidth savings\, and energy efficiency of doing local inference. In order to get the best out of both worlds\, i.e.\, the benefits of doing inference on the ED and the benefits of doing inference on ES\, we explore the idea of Hierarchical Inference (HI)\, wherein S-ML inference is only accepted when it is correct\, otherwise the data sample is offloaded for L-ML inference. However\, the ideal implementation of HI is infeasible as the correctness of the S-ML inference is not known to the ED. We thus propose an online meta-learning framework that the ED can use to predict the correctness of the S-ML inference. In particular\, we propose to use the probability corresponding to the maximum probability class output by S-ML for a data sample and decide whether to offload it or not. The resulting online learning problem turns out to be a Prediction with Expert Advice (PEA) problem with continuous expert space. We consider two scenarios\, a full feedback scenario\, where the ED receives feedback on the correctness of the S-ML once it accepts the inference\, and a no-local feedback scenario. We propose the HIL-F and HIL-N algorithms and prove that both of them has sublinear regret bounds without any assumption on the smoothness of the loss function. We evaluate and benchmark the performance of the proposed algorithms for image classification application using different datasets.
URL:https://www.tecosa.center.kth.se/event/tecosa-research-seminar-getting-the-best-out-of-both-worlds-algorithms-for-hierarchical-inference-at-the-edge/
CATEGORIES:Seminar
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