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X-WR-CALDESC:Events for WASP
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DTSTART;TZID=+01:00:20241121T000000
DTEND;TZID=+01:00:20241122T235900
DTSTAMP:20260601T134937
CREATED:20240923T162244Z
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SUMMARY:Women in Machine Learning Workshop
DESCRIPTION:Join the community and take part in shaping the future!\nMachine Learning (ML) is undeniably at the cutting edge of technological advancement today. ML tools are already influencing contemporary society\, and they are believed to have the potential to drive revolutionary changes in both research and everyday life by transforming a wide range of fields\, including healthcare\, cybersecurity\, and transportation. \nGiven its capacity to create such profound impact\, it is essential for all segments of society to participate in its development. However\, multiple groups are currently underrepresented within the field\, including women and non-binary groups. The primary goal of the “Women in Machine Learning Workshop – Join the community and take part in shaping the future!” is to help reduce this gender imbalance. \nThe workshop aims to bring together both beginners and experts to connect\, gain inspiration\, and deepen their understanding of machine learning and the ongoing research in the field. It is a lunch-to-lunch event that includes talks by leading female researchers and industry representatives\, as well as a tutorial and discussion session. \nWhen and where?\nDates: November 21st-22nd\, 2024\nLocation: Linköping University (LiU)\, Linköping. \nThe main venue is lecture hall Ada Lovelace at Linköping University (Linköping University\, Campus Valla\, House B\, Floor 2. Link to map. \nWho can participate?\nThe workshop is targeted towards those working within or interested in the field of machine learning\, aiming to make it more gender balanced. We  encourage participation from  underrepresented gender groups. \nThe workshop  is focussed on PhD students\, early-career industry professionals interested in research\, and master’s students in technical disciplines. Participants are advised to have at least an introductory understanding of machine learning\, such as having completed a foundational course on the subject. \nRegistration\nWelcome to register to the workshop via this sign-up form. \nThe registration is closed. \nInvited speakers\nThe workshop will feature two keynote talks\, a tutorial\, and an industry session. Below is a list of invited speakers: \n\nJudith Bütepage (ML Team Lead\, Electronic Arts)\nShizhen Chang (Assistant Professor\, Linköping University)\nKelsey Cotton (PhD student\, Chalmers University of Technology)\nAmanda Berg (Research Developer\, Maxar intelligence)\nKarin Stacke (Research Scientist\, Sectra)\nSaga Bergdahl (Software Engineer\, Husqvarna)\nElsa Björling (Software Engineer\, Husqvarna)\nFrida Blomstedt (Software Engineer Computer Vision\, Combitech)\nErika Anderskär (Data Scientist\, Combitech)\nJenny Kunz (Postdoctoral researcher\, Linköping University)\n\nDetailed information\nPlease find detailed information about the workshop\, including the schedule\, in this information packet.. \nOrganization and contact\nThis workshop is organized by representatives from Linköping University (LiU) and Chalmers University of Technology\, in collaboration with the Wallenberg AI\, Autonomous Systems and Software Program (WASP) Diversity and Inclusion Group. \nFor any questions or issues related to the workshop\, please send an email to amanda.olmin@liu.se or yushan.zhang@liu.se.
URL:https://wasp-sweden.org/event/women-in-machine-learning-workshop-2/
LOCATION:Linköping University\, Linköping University\, Linköping\, Sweden
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DTSTART;TZID=+01:00:20210618T130000
DTEND;TZID=+01:00:20210618T235900
DTSTAMP:20260601T134937
CREATED:20210510T130022Z
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SUMMARY:PhD Defense: Uncertainty-Aware Convolutional Neural Networks for Vision Tasks on Sparse Data
DESCRIPTION:Welcome to Doctoral Defense of Abdelrahman Eldesokey\n\nDate and time:  June 18\, 13:00\n \nLocation: Online \nDoctoral student: Abdelrahman Eldesokey\, Department of Electrical Engineerin\, Linköping University \nTitel: Uncertainty-Aware Convolutional Neural Networks for Vision Tasks on Sparse Data \nSupervisor:  Professor Michael Felsberg\, Department of Electrical Engineering\, Linköping University \nOpponent: Professor Richard Bowden\, University of Surrey\, United Kingdom \n\n 
URL:https://wasp-sweden.org/event/phd-defense-uncertainty-aware-convolutional-neural-networks-for-vision-tasks-on-sparse-data/
LOCATION:Linköping University\, Linköping University\, Linköping\, Sweden
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BEGIN:VEVENT
DTSTART;TZID=+01:00:20210604T091500
DTEND;TZID=+01:00:20210604T235900
DTSTAMP:20260601T134937
CREATED:20210504T132058Z
LAST-MODIFIED:20260601T083906Z
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SUMMARY:PhD Defense: Learning Visual Perception for Autonomous Systems
DESCRIPTION:Welcome to Doctoral Defense of Gustav Häger\n\nDate and time: June 4\, 09:15 \nLocation: Online \nTitel: Learning Visual Perception for Autonomous Systems \nDoctoral student: Gustav Häger\, Department of Electrical Engineering\, Linköping University \nSupervisor: Michael Felsberg\, Department of Electrical Engineering\, Linköping University \nOpponent: Roman Pflugfelder\, Technische Universität Wien och Austrian Institute of Technology
URL:https://wasp-sweden.org/event/phd-defense-learning-visual-perception-for-autonomous-systems/
LOCATION:Linköping University\, Linköping University\, Linköping\, Sweden
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DTSTART;TZID=+01:00:20200429T131500
DTEND;TZID=+01:00:20200429T235900
DTSTAMP:20260601T134937
CREATED:20200406T131036Z
LAST-MODIFIED:20260601T083912Z
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SUMMARY:Olov Andersson – Learning to Make Safe Real-Time Decisions Under Uncertainty for Autonomous Robots
DESCRIPTION:Abstract\nRobots are increasingly expected to go beyond controlled environments in laboratories and factories\, to act autonomously in real-world workplaces and public spaces. Autonomous robots navigating the realworld have to contend with a great deal of uncertainty\, which poses additional challenges. Uncertainty in the real world accrues from several sources. Some of it may originate from imperfect internal models of reality. Other uncertainty is inherent\, a direct side effect of partial observability induced by sensor limitations and occlusions. \nRegardless of the source\, the resulting decision problem is unfortunately computationally intractable under uncertainty. This poses a great challenge as the real world is also dynamic. It will not pause while the robot computes a solution. Autonomous robots navigating among people\, for example in traffic\, need to be able to make split-second decisions. Uncertainty is therefore often neglected in practice\, with potentially catastrophic consequences when something unexpected happens. \nThe aim of this thesis is to leverage recent advances in machine learning to compute safe real-time approximations to decision-making under uncertainty for realworld robots. We explore a range of methods\, from probabilistic to deep learning\, as well as different combinations with optimization-based methods from robotics\, planning and control. Driven by applications in robot navigation\, and grounded in experiments with real autonomous quadcopters\, we address several parts of this problem. From reducing uncertainty by learning better models\, to directly approximating the decision problem itself\, all the while attempting to satisfy both the safety and real-time requirements of real-world autonomy. \n————————————————– \nThis work has been supported by the Wallenberg AI\, Autonomous Systems and Software Program\, the Swedish Foundation for Strategic Research (SSF) project Symbicloud and the ELLIIT Excellence Center at Linköping-Lund for Information Technology\, in addition to those sources already acknowledged in the individual papers. \nOriginal location: Linköping University\, Online Disputation
URL:https://wasp-sweden.org/event/olov-andersson-learning-to-make-safe-real-time-decisions-under-uncertainty-for-autonomous-robots/
LOCATION:Linköping University\, Linköping University\, Linköping\, Sweden
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