BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Sabre//Sabre VObject 4.5.8//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Zurich X-LIC-LOCATION:Europe/Zurich TZURL:http://tzurl.org/zoneinfo/Europe/Zurich BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19810329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19961027T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:news1552@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20230831T144147 DTSTART;TZID=Europe/Zurich:20230905T161500 SUMMARY:Kreativität und kritisches Denken im Informatikunterricht DESCRIPTION:Informatikunterricht hat nichts damit zu tun\, aus «Schülerin nen und Schülern Computer zu machen» — für die Förderung vieler zent raler Informatikkompetenzen werden Computer nicht einmal gebraucht. Anspre chender und sinnstiftender Informatikunterricht ist das Gegenteil davon: E r stellt kreatives Problemlösen ins Zentrum und ist gleichermassen spiele risch wie herausfordernd. In diesem Vortrag möchte ich Beispiele aufzeige n\, wie Grundkonzepte der Informatik im Unterricht thematisiert werden kö nnen\, die kritisches Denken sowie Kreativität fördern.\\r\\nDennis Komm studierte Informatik und Psychologie an der RWTH Aachen sowie Information stechnologie an der QUT Brisbane. Er promovierte 2012 an der ETH Zürich u nd arbeitete nach seiner Postdoc-Zeit an der ETH Zürich und anderen Hochs chulen als Dozent und Senior Scientist. Ab 2021 war er Professor für Fach didaktik Informatik an der PH Graubünden. Seit 2022 ist er ausserordentli cher Professor an der ETH Zürich für Algorithmen und Didaktik sowie Lehr gangsleiter des CAS Informatik und Informatikdidaktik für Lehrpersonen de r Primar- und Sekundarstufe. Seine Forschungsinteressen sind der Entwurf u nd die Analyse von Algorithmen sowie die Informatikausbildung\, speziell d ie Vermittlung von grundlegenden Ideen und Konzepten der Informatik. X-ALT-DESC:

Informatikunterricht hat nichts damit zu tun\, aus «Schüler innen und Schülern Computer zu machen» — für die Förderung vieler ze ntraler Informatikkompetenzen werden Computer nicht einmal gebraucht. Ansp rechender und sinnstiftender Informatikunterricht ist das Gegenteil davon: Er stellt kreatives Problemlösen ins Zentrum und ist gleichermassen spie lerisch wie herausfordernd. In diesem Vortrag möchte ich Beispiele aufzei gen\, wie Grundkonzepte der Informatik im Unterricht thematisiert werden k önnen\, die kritisches Denken sowie Kreativität fördern.

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Dennis Komm studierte Informatik und Psychologie an der RWTH Aachen sowie Inform ationstechnologie an der QUT Brisbane. Er promovierte 2012 an der ETH Zür ich und arbeitete nach seiner Postdoc-Zeit an der ETH Zürich und anderen Hochschulen als Dozent und Senior Scientist. Ab 2021 war er Professor für Fachdidaktik Informatik an der PH Graubünden. Seit 2022 ist er ausserord entlicher Professor an der ETH Zürich für Algorithmen und Didaktik sowie Lehrgangsleiter des CAS Informatik und Informatikdidaktik für Lehrperson en der Primar- und Sekundarstufe. Seine Forschungsinteressen sind der Entw urf und die Analyse von Algorithmen sowie die Informatikausbildung\, spezi ell die Vermittlung von grundlegenden Ideen und Konzepten der Informatik.< /p> END:VEVENT BEGIN:VEVENT UID:news1230@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20210929T113830 DTSTART;TZID=Europe/Zurich:20211007T183000 SUMMARY:Ein Bild sagt mehr als 1000 Lügen - Aktuelles zu visuellen Fake Ne ws DESCRIPTION:Deep Fakes\, Photoshop\, Fake News - Silvan Heller erzählt Akt uelles aus der Welt der visuellen Manipulation. Vortrag anlässlich des Na chwuchsförderpreises “Schwizerhüsli” der Ferdinand Neeracher-Pfrunde r Stiftung 2019.\\r\\nIm Anschluss an den Vortrag findet ein Apéro im Ver einslokal (Socinstrasse 8\, 4051 Basel) statt.\\r\\nVoraussetzung für die Teilnahme am Event ist ein gültiges Covid-Zertifikat. Bitte beachten Sie \, dass der Einlass aufgrund der Zertifikatskontrolle über die Hebelstras se 12 erfolgt. X-ALT-DESC:

Deep Fakes\, Photoshop\, Fake News - Silvan Heller erzählt A ktuelles aus der Welt der visuellen Manipulation. Vortrag anlässlich des Nachwuchsförderpreises “Schwizerhüsli” der Ferdinand Neeracher-Pfrun der Stiftung 2019.

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Im Anschluss an den Vortrag findet ein Apéro i m Vereinslokal (Socinstrasse 8\, 4051 Basel) statt.

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Voraus setzung für die Teilnahme am Event ist ein gültiges Covid-Zertifikat. Bi tte beachten Sie\, dass der Einlass aufgrund der Zertifikatskontrolle übe r die Hebelstrasse 12 erfolgt.

END:VEVENT BEGIN:VEVENT UID:news1131@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20201208T113917 DTSTART;TZID=Europe/Zurich:20201210T173000 SUMMARY:Fragerunde mit dem Turing-Preisträger Leslie Lamport DESCRIPTION:Im Rahmen des Bachelor-Seminars "Turing-Preisträger und ihre B eiträge" organisiert der Fachbereich Informatik eine einstündige Frageru nde mit dem Turing-Preisträger Leslie Lamport. Die Veranstaltung ist nich t auf die Seminarteilnehmer beschränkt. Alle Mitglieder des Departements\ , insbesondere Studierende aller Fachsemester\, sind herzlich eingeladen!\ \r\\nLeslie Lamport erhielt 2013 den Turing Award "for fundamental contrib utions to the theory and practice of distributed and concurrent systems\, notably the invention of concepts such as causality and logical clocks\, s afety and liveness\, replicated state machines\, and sequential consistenc y". Neben diesen wissenschaftlichen Kernbeiträgen ist er auch für die Er findung des LaTeX-Dokumentationsvorbereitungssystems bekannt.\\r\\nIn der Fragerunde liegt der Fokus auf Lamports technischen Beiträgen und deren A uswirkungen\, seinen Ansichten zur Vergangenheit\, Gegenwart und Zukunft d er Informatik sowie seinen Gedanken zum wissenschaftlichen Schreiben und z um Leben als Forscher fragen. Die Sitzung wird moderiert und basiert haupt sächlich auf Fragen der Seminarteilnehmenden. Am Ende besteht die Möglic hkeit Fragen zu stellen. X-ALT-DESC:

Im Rahmen des Bachelor-Seminars "\;Turing-Preisträger un d ihre Beiträge"\; organisiert der Fachbereich Informatik eine einst ündige Fragerunde mit dem Turing-Preisträger Leslie Lamport. Die Veranst altung ist nicht auf die Seminarteilnehmer beschränkt. Alle Mitglieder de s Departements\, insbesondere Studierende aller Fachsemester\, sind herzli ch eingeladen!

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Leslie Lamport erhielt 2013 den Turing Award "\ ;for fundamental contributions to the theory and practice of distributed a nd concurrent systems\, notably the invention of concepts such as causalit y and logical clocks\, safety and liveness\, replicated state machines\, a nd sequential consistency"\;. Neben diesen wissenschaftlichen Kernbeit rägen ist er auch für die Erfindung des LaTeX-Dokumentationsvorbereitung ssystems bekannt.

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In der Fragerunde liegt der Fokus auf Lamports t echnischen Beiträgen und deren Auswirkungen\, seinen Ansichten zur Vergan genheit\, Gegenwart und Zukunft der Informatik sowie seinen Gedanken zum w issenschaftlichen Schreiben und zum Leben als Forscher fragen. Die Sitzung wird moderiert und basiert hauptsächlich auf Fragen der Seminarteilnehme nden. Am Ende besteht die Möglichkeit Fragen zu stellen.

DTEND;TZID=Europe/Zurich:20201210T183000 END:VEVENT BEGIN:VEVENT UID:news875@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20190504T143219 DTSTART;TZID=Europe/Zurich:20190514T121500 SUMMARY:Computer Science Kolloquium: Robert Holte\, University of Alberta\, Canada DESCRIPTION:Abstract:\\r\\nI begin this talk with a review of long-establis hed results in heuristic search and the early history of bidirectional heu ristic search. I then describe a recent (2016) breakthrough in bidirection al heuristic search (the MM algorithm)\, which challenges long-held assump tions and exposes exciting new research directions. Although the technical details in this talk are focused on heuristic search\, the general  less ons with which I conclude are relevant to researchers in all branches of A .I.\\r\\nSpeaker Bio:\\r\\nProfessor Emeritus Robert Holte of the Computin g Science Department at the University of Alberta is a former editor-in-ch ief of the journal "Machine Learning" and co-founder and former director o f the Alberta Innovates Center for Machine Learning (AICML\, now known as Amii). In addition to machine learning\, Professor Holte has made seminal contributions to the subfield of A.I. known as single-agent heuristic sear ch\, most notably his recent work on bidirectional heuristic search.\\r\\n Professor Holte was elected a Fellow of the Association for the Advancemen t of Artificial Intelligence (AAAI) in 2011 and received a Lifetime Achiev ement Award for his work on heuristic search from the Symposium on Combina torial Search in 2018. Another Lifetime Achievement Award will be announce d at the end of May X-ALT-DESC:Abstract:\nI begin this talk with a review of long-established r esults in heuristic search and the early history of bidirectional heuristi c search. I then describe a recent (2016) breakthrough in bidirectional he uristic search (the MM algorithm)\, which challenges long-held assumptions and exposes exciting new research directions. Although the technical deta ils in this talk are focused on heuristic search\, the general \; less ons with which I conclude are relevant to researchers in all branches of A .I.\nSpeaker Bio:\nProfessor Emeritus Robert Holte of the Computing Scienc e Department at the University of Alberta is a former editor-in-chief of t he journal "\;Machine Learning"\; and co-founder and former direct or of the Alberta Innovates Center for Machine Learning (AICML\, now known as Amii). In addition to machine learning\, Professor Holte has made semi nal contributions to the subfield of A.I. known as single-agent heuristic search\, most notably his recent work on bidirectional heuristic search.\n Professor Holte was elected a Fellow of the Association for the Advancemen t of Artificial Intelligence (AAAI) in 2011 and received a Lifetime Achiev ement Award for his work on heuristic search from the Symposium on Combina torial Search in 2018. Another Lifetime Achievement Award will be announce d at the end of May\n\n DTEND;TZID=Europe/Zurich:20190514T131500 END:VEVENT BEGIN:VEVENT UID:news826@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20190220T080023 DTSTART;TZID=Europe/Zurich:20190226T121500 SUMMARY:Computer Science Kolloquium: Dr. Etzard Stolte\, Global Head Knowle dge Management Roche\, Schweiz DESCRIPTION:Abstract:\\r\\nThe time to develop new drugs can be dramaticall y reduced through intelligent data-integration and re-use. To reduce this time by a factor of 10\, Roche Technical Development\, an organisation of 2500 colleagues\, has embraced a central information integration approach . In this talk I will present our vision and technology platform\, give an update on where we are on our journey\, and mention some of the barriers we encountered. \\r\\nOur approach to balance real world needs with accura cy and speed has gained some good success over the course of the last four years for a complex information landscape with hundreds of information en tities.\\r\\nSpeaker Bio:\\r\\nEtzard leads the global Information / Knowl edge Management effort in Pharma Technical Development for F. Hoffmann-La Roche in Basel\, with a focus on processes and tools for effective knowled ge utilisation. Etzard has worked at the interface of the Life- and Comput er-Sciences for more than 20 years\, in technical\, managerial as well as strategic roles. \\r\\nBefore joining Roche\, Etzard worked as CIO at the Jackson Lab (a US based genomics research institutes with 1800 employees)\ , and was CTO for the Life Sciences at Hewlett Packard. Etzard has earned academic degrees in Biology\, Bio-Informatics and Informatics\, with a PhD in Computer Science from ETH Zurich on “A Scalable Architecture for Sci entific Databases”. X-ALT-DESC:Abstract:\nThe time to develop new drugs can be dramatically red uced through intelligent data-integration and re-use. \;To reduce this time by a factor of 10\, Roche Technical Development\, an organisation of 2500 colleagues\, has embraced a central information integration approach . In this talk I will present our vision and technology platform\, give an update on where we are on our journey\, and mention some of the barriers we encountered. \nOur approach to balance real world needs with accuracy a nd speed has gained some good success over the course of the last four yea rs for a complex information landscape with hundreds of information entiti es.\nSpeaker Bio:\nEtzard leads the global Information / Knowledge Managem ent effort in Pharma Technical Development for F. Hoffmann-La Roche in Bas el\, with a focus on processes and tools for effective knowledge utilisati on. Etzard has worked at the interface of the Life- and Computer-Sciences for more than 20 years\, in technical\, managerial as well as strategic ro les. \nBefore joining Roche\, Etzard worked as CIO at the Jackson Lab (a U S based genomics research institutes with 1800 employees)\, and was CTO fo r the Life Sciences at Hewlett Packard. Etzard has earned academic degrees in Biology\, Bio-Informatics and Informatics\, with a PhD in Computer Sci ence from ETH Zurich on “A Scalable Architecture for Scientific Database s”.\n\n DTEND;TZID=Europe/Zurich:20190226T140000 END:VEVENT BEGIN:VEVENT UID:news346@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20181031T163430 DTSTART;TZID=Europe/Zurich:20181108T161500 SUMMARY:Computer Science Kolloquium: Dr. Evangelos Pournaras\, ETH Zürich DESCRIPTION:Abstract:\\r\\nThe Internet of Things equips citizens with phen omenal new means for online participation in sharing economies. When agent s self-determine options from which they choose\, for instance their resou rce consumption and production\, while these choices have a collective sys tem-wide impact\, optimal decision-making turns into a combinatorial optim ization problem known as NP-hard. In such challenging computational proble ms\, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This paper envisio ns an alternative unsupervised and decentralized collective learning appro ach that preserves privacy\, autonomy and participation of multi-agent sys tems self-organized into a hierarchical tree structure. \\r\\nRemote inter actions orchestrate a highly efficient process for decentralized collectiv e learning. This disruptive concept is realized by I-EPOS\, the Iterative Economic Planning and Optimized Selections\, accompanied by a paradigmatic software artifact. Strikingly\, I-EPOS outperforms related algorithms tha t involve non-local brute-force operations or exchange full information. T his paper contributes new experimental  findings about the influence of n etwork topology and planning on learning efficiency as well as  findings on techno-socio-economic trade-offs and global optimality. Experimental ev aluation with real-world data from energy and bike sharing pilots demonstr ates the grand potential of collective learning to design ethically and so cially responsible participatory sharing economies. X-ALT-DESC:\nAbstract:\nThe Internet of Things equips citizens with phenome nal new means for online participation in sharing economies. When agents s elf-determine options from which they choose\, for instance their resource consumption and production\, while these choices have a collective system -wide impact\, optimal decision-making turns into a combinatorial optimiza tion problem known as NP-hard. In such challenging computational problems\ , centrally managed (deep) learning systems often require personal data wi th implications on privacy and citizens’ autonomy. This paper envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy\, autonomy and participation of multi-agent system s self-organized into a hierarchical tree structure. \nRemote interactions orchestrate a highly efficient process for decentralized collective learn ing. This disruptive concept is realized by I-EPOS\, the Iterative Economi c Planning and Optimized Selections\, accompanied by a paradigmatic softwa re artifact. Strikingly\, I-EPOS outperforms related algorithms that invol ve non-local brute-force operations or exchange full information. This pap er contributes new experimental  \;findings about the influence of net work topology and planning on learning efficiency as well as  \;findin gs on techno-socio-economic trade-offs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demon strates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.\n\n DTEND;TZID=Europe/Zurich:20181108T180000 END:VEVENT BEGIN:VEVENT UID:news358@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20181031T163526 DTSTART;TZID=Europe/Zurich:20181106T121500 SUMMARY:Computer Science Kolloquium: Dr. Tanja Christina Kaeser\, Stanford University DESCRIPTION:Abstract:\\r\\nLearning technologies are becoming increasingly important in today's education. This includes game-based learning and simu lations\, which produce high volume output\, and MOOCs (massive open onlin e courses)\, which reach a broad and diverse audience at scale. The users of such systems often are of very different backgrounds\, for example in t erms of age\, prior knowledge\, and learning speed. Adaptation to the spec ific needs of the individual user is therefore essential. In this talk\, I will present two of my contributions on modeling and predicting student l earning in computer-based environments with the goal to enable individuali zation. The first contribution introduces a new model and algorithm for re presenting and predicting student knowledge. The new approach is efficient and has been demonstrated to outperform previous work regarding predictio n accuracy. The second contribution introduces models\, which are able to not only take into account the accuracy of the user\, but also the inquiry strategies of the user\, improving prediction of future learning. Further more\, students can be clustered into groups with different strategies and targeted interventions can be designed based on these strategies. Finally \, I will also describe lines of future research. X-ALT-DESC:\nAbstract:\nLearning technologies are becoming increasingly imp ortant in today's education. This includes game-based learning and simulat ions\, which produce high volume output\, and MOOCs (massive open online c ourses)\, which reach a broad and diverse audience at scale. The users of such systems often are of very different backgrounds\, for example in term s of age\, prior knowledge\, and learning speed. Adaptation to the specifi c needs of the individual user is therefore essential. In this talk\, I wi ll present two of my contributions on modeling and predicting student lear ning in computer-based environments with the goal to enable individualizat ion. The first contribution introduces a new model and algorithm for repre senting and predicting student knowledge. The new approach is efficient an d has been demonstrated to outperform previous work regarding prediction a ccuracy. The second contribution introduces models\, which are able to not only take into account the accuracy of the user\, but also the inquiry st rategies of the user\, improving prediction of future learning. Furthermor e\, students can be clustered into groups with different strategies and ta rgeted interventions can be designed based on these strategies. Finally\, I will also describe lines of future research. DTEND;TZID=Europe/Zurich:20181106T133000 END:VEVENT BEGIN:VEVENT UID:news305@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20181113T175101 DTSTART;TZID=Europe/Zurich:20180928T121500 SUMMARY:Computer Science Kolloquium: Prof. Schöffmann DESCRIPTION:Abstract:In hospitals all around the world\, medical multimedia information systems have gained high importance over the last few years. One of the reasons is that an increasing number of interventions is perfor med in a minimally invasive way. These endoscopic inspections and surgerie s are conducted with a tiny camera – the endoscope – which enables cli nicians to control the intervention via an external display. Apart from th e viewing purpose\, the video signal can also be recorded and used for pos t-procedural scenarios\, such as communicating operation techniques (i.e.\ , training and teaching)\, planning future interventions\, and medical for ensics and analytics. The problem\, however\, is the sheer amount of unstr uctured video data that is added to the multimedia archive on a daily basi s. Without proper management and content analytics the videos cannot be us ed efficiently by clinicians. In this talk I will summarize our recent inv estigations in this challenging research field and conclude on the achieve d results. \\r\\nBio:Dr. Klaus Schöffmann is an Associate Professor in th e Distributed Multimedia Systems research group at the Institute of Inform ation Technology (ITEC) at Klagenfurt University\, Austria. He received hi s PhD in 2009 and his habilitation (venia docendi) in 2015\, both in compu ter science and from Klagenfurt University. His research focuses on video analytics and interactive multimedia systems\, particularly in the medical domain. He has co-authored more than 100 publications on various topics i n multimedia\, inclusive of more than 25 on different aspects of medical m ultimedia systems. He has co-organized several international conferences\, workshops\, and special sessions in the field of multimedia. Furthermore\ , he is co-founder of the Video Browser Showdown (VBS) – an internationa l live evaluation competition of interactive video search. He is a member of the IEEE and the ACM\, and a regular reviewer for international confere nces and journals in the field of multimedia. Klaus Schöffmann teaches va rious courses in computer science\, including mobile app development\, vid eo retrieval\, distributed multimedia systems\, and operating systems. X-ALT-DESC:\nAbstract:
In hospitals all around the world\, medi cal multimedia information systems have gained high importance over the la st few years. One of the reasons is that an increasing number of intervent ions is performed in a minimally invasive way. These endoscopic inspection s and surgeries are conducted with a tiny camera – the endoscope – whi ch enables clinicians to control the intervention via an external display. Apart from the viewing purpose\, the video signal can also be recorded an d used for post-procedural scenarios\, such as communicating operation tec hniques (i.e.\, training and teaching)\, planning future interventions\, a nd medical forensics and analytics. The problem\, however\, is the sheer a mount of unstructured video data that is added to the multimedia archive o n a daily basis. Without proper management and content analytics the video s cannot be used efficiently by clinicians. In this talk I will summarize our recent investigations in this challenging research field and conclude on the achieved results. \nBio:
Dr. Klaus Schöffmann is an As sociate Professor in the Distributed Multimedia Systems research group at the Institute of Information Technology (ITEC) at Klagenfurt University\, Austria. He received his PhD in 2009 and his habilitation (venia docendi) in 2015\, both in computer science and from Klagenfurt University. His res earch focuses on video analytics and interactive multimedia systems\, part icularly in the medical domain. He has co-authored more than 100 publicati ons on various topics in multimedia\, inclusive of more than 25 on differe nt aspects of medical multimedia systems. He has co-organized several inte rnational conferences\, workshops\, and special sessions in the field of m ultimedia. Furthermore\, he is co-founder of the Video Browser Showdown (V BS) – an international live evaluation competition of interactive video search. He is a member of the IEEE and the ACM\, and a regular reviewer fo r international conferences and journals in the field of multimedia. Klaus Schöffmann teaches various courses in computer science\, including mobil e app development\, video retrieval\, distributed multimedia systems\, and operating systems. DTEND;TZID=Europe/Zurich:20180928T140000 END:VEVENT BEGIN:VEVENT UID:news204@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20181113T175129 DTSTART;VALUE=DATE:20180703 SUMMARY:Public lecture on “Massive-Scale Analytics Applied to Real-World Problems” by Prof. David A. Bader (Georgia Institute of Technology\, US) DESCRIPTION:This public lecture is an outreach opportunity on behalf of the PASC18 conference and the University of Basel to inform the general publi c about the societal challenges we solve through research and innovation i n and around advanced scientific computing. Details of the lecture open to the general public (free of charge) Emerging real-world graph problems in clude: detecting and preventing disease in human populations\; revealing c ommunity structure in large social networks\; and improving the resilience of the electric power grid. Unlike traditional applications in computatio nal science and engineering\, solving these social problems at scale often raises new challenges because of the sparsity and lack of locality in the data\, the need for research on scalable algorithms and development of fr ameworks for solving these real-world problems on high performance compute rs\, and for improved models that capture the noise and bias inherent in t he torrential data streams. In this talk\, Bader will discuss the opportun ities and challenges in massive data-intensive computing for applications in social sciences\, physical sciences\, and engineering.Biography of the speakerDavid Bader is Professor and Chair of the School of Computational S cience and Engineering at Georgia Institute of Technology\, and is regarde d as one of the world’s leading experts in data sciences. His interests are at the intersection of high performance computing (HPC) and real-world applications\, including cybersecurity\, massive-scale analytics\, and co mputational genomics. Bader has co-authored over 200 articles in peer-revi ewed journals and conferences\, and is an associate editor for high-impact publications including IEEE Transactions on Computers\, ACM Transactions on Parallel Computing\, and ACM Journal of Experimental Algorithmics. He i s a Fellow of the IEEE and AAAS\, and has served on a number of advisory c ommittees in scientific computing and cyber-infrastructure\, including the White House's National Strategic Computing Initiative. Bader has served a s a lead scientist in several DARPA programs and is a co-founder of the Gr aph500 list\, a rating of "Big Data" computing platforms. He was recognize d as a “Rock Star of HPC” by InsideHPC and as HPCwire's “People to W atch” in 2012 and 2014.More details: pasc18.pasc-conference.org/program/ public-lecture/ [https://pasc18.pasc-conference.org/program/public-lecture /] X-ALT-DESC:This public lecture is an outreach opportunity on behalf of the PASC18 conference and the University of Basel to inform the general public about the societal challenges we solve through research and innovation in and around advanced scientific computing.

Details of the l ecture open to the general public (free of charge)
Emerging real -world graph problems include: detecting and preventing disease in human p opulations\; revealing community structure in large social networks\; and improving the resilience of the electric power grid. Unlike traditional ap plications in computational science and engineering\, solving these social problems at scale often raises new challenges because of the sparsity and lack of locality in the data\, the need for research on scalable algorith ms and development of frameworks for solving these real-world problems on high performance computers\, and for improved models that capture the nois e and bias inherent in the torrential data streams. In this talk\, Bader w ill discuss the opportunities and challenges in massive data-intensive com puting for applications in social sciences\, physical sciences\, and engin eering.

Biography of the speaker
David Bader is Pro fessor and Chair of the School of Computational Science and Engineering at Georgia Institute of Technology\, and is regarded as one of the world’s leading experts in data sciences. His interests are at the intersection o f high performance computing (HPC) and real-world applications\, including cybersecurity\, massive-scale analytics\, and computational genomics. Bad er has co-authored over 200 articles in peer-reviewed journals and confere nces\, and is an associate editor for high-impact publications including I EEE Transactions on Computers\, ACM Transactions on Parallel Computing\, a nd ACM Journal of Experimental Algorithmics. He is a Fellow of the IEEE an d AAAS\, and has served on a number of advisory committees in scientific c omputing and cyber-infrastructure\, including the White House's National S trategic Computing Initiative. Bader has served as a lead scientist in sev eral DARPA programs and is a co-founder of the Graph500 list\, a rating of "\;Big Data"\; computing platforms. He was recognized as a “Roc k Star of HPC” by InsideHPC and as HPCwire's “People to Watch” in 20 12 and 2014.

More details:
pasc18.pasc-conference.org/pro gram/public-lecture/

DTEND;VALUE=DATE:20180703 END:VEVENT BEGIN:VEVENT UID:news202@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20181113T175225 DTSTART;TZID=Europe/Zurich:20180621T100000 SUMMARY:Computer Science Colloquium: Prof. Allen D. Malony DESCRIPTION:Abstract: Since the beginning of ``high-performance'' parallel computing\, observingand analyzing performance for purposes of finding bot tlenecks andidentifying opportunities for improvement has been at the hear t ofdelivering the performance potential of next-generation scalable syste ms.Interestingly\, it is the ever-changing parallel computing landscape th at isthe main driver of requirements for parallel performance technology a nd theimprovements necessary beyond the current state-of-the-art.  Indeed \, thedevelopment and application of our TAU Performance System over many yearslargely follows an evolutionary path of addressing measurement and an alysisproblems in new parallel machines and programming environments.Howev er\, the outlook to future parallel systems with high degrees ofconcurrenc y\, heterogeneous components\, dynamic runtime environments\,asynchronous execution\, and power constraints suggests a new perspectivewill be needed on the role of performance observation and analysis inrespect to tool tec hnology integration and performance optimizationmethods.  The reliance on post-mortem analysis of application-level("1st person") performance measu rements is prohibitive forexascale-class machines because of the performan ce data volume\, theprimitive basis for performance data attribution\, and the fundamentalproblem of performance variation that will exist.  Instea d\, it will beimportant to provide introspection support across the exasca le softwarestack to understand how system ("3rd person") resources are use d duringexecution.  Furthermore\, the opportunity to couple a global perf ormanceintrospection capability (a "performance backplane") with onlineper formance decision analytics inspires the concept of an autonomicperformanc e system that can feed back policy-based decisions to guidethe computation to better states of execution.The talk will explore these issues by givin g a brief retrospective onperformance tool evolution\, setting the stage f or current researchprojects where a new performance perspective is being p ursued.  It willalso speculate on what might be included in next-generati on parallelsystems hardware\, specifically to make the exascale machines m oreperformance-aware and dynamically-adaptive. Speaker Bio: Allen D. Malon y is a Professor in the Department of Computer andInformation Science at t he University of Oregon (UO) where he directsparallel computing research p rojects\, notably the TAU parallelperformance system project.  He has ext ensive experience in performancebenchmarking and characterization of high- performance computing systems\,and has developed performance evaluation to ols for a range of parallelmachines during the last 30 years.  Malony is also interested incomputational and data science.  He is the Director of the new OregonAdvanced Computing Institute for Science and Society (OACISS ) at UO.Malony was awarded the NSF National Young Investigator award\, was aFulbright Research Scholar to The Netherlands and Austria\, and received the Alexander von Humboldt Research Award for Senior U.S. Scientists bythe Alexander von Humboldt Foundation.  Last year he was theFulbright-Tocque ville Distinguished Chair to France.  Recently\, he wasaward the Fulbrigh t for the Future by the Franco-Americaine FulbrightCommission.  Malony is the CEO of ParaTools\, Inc.\, which he founded withDr. Sameer Shende in 2 004.  ParaTools SAS is a French company theystarted in 2014\, and is whol ly-owned company by ParaTools\, Inc.ParaTools specializes in performance a nalysis and engineering\, HPCapplications and optimization\, and parallel software\, hardware\, andtools. X-ALT-DESC:\nAbstract: Since the beginning of ``high-performance'' p arallel computing\, observing
and analyzing performance for purposes of finding bottlenecks and
identifying opportunities for improvement has been at the heart of
delivering the performance potential of next -generation scalable systems.
Interestingly\, it is the ever-changing parallel computing landscape that is
the main driver of requirements for parallel performance technology and the
improvements necessary b eyond the current state-of-the-art. \; Indeed\, the
development a nd application of our TAU Performance System over many years
largely follows an evolutionary path of addressing measurement and analysis
p roblems in new parallel machines and programming environments.

However\, the outlook to future parallel systems with high degrees of
concurrency\, heterogeneous components\, dynamic runtime environments\,asynchronous execution\, and power constraints suggests a new perspect ive
will be needed on the role of performance observation and analysi s in
respect to tool technology integration and performance optimizat ion
methods. \; The reliance on post-mortem analysis of applicati on-level
("\;1st person"\;) performance measurements is prohi bitive for
exascale-class machines because of the performance data vo lume\, the
primitive basis for performance data attribution\, and the fundamental
problem of performance variation that will exist. \; Instead\, it will be
important to provide introspection support acro ss the exascale software
stack to understand how system ("\;3rd p erson"\;) resources are used during
execution. \; Furthermore \, the opportunity to couple a global performance
introspection capab ility (a "\;performance backplane"\;) with online
performance decision analytics inspires the concept of an autonomic
performance system that can feed back policy-based decisions to guide
the computa tion to better states of execution.

The talk will explore these issues by giving a brief retrospective on
performance tool evolution \, setting the stage for current research
projects where a new perfor mance perspective is being pursued. \; It will
also speculate on what might be included in next-generation parallel
systems hardware\, specifically to make the exascale machines more
performance-aware an d dynamically-adaptive.

Speaker Bio: Allen D. Malony is a Professor in the Department of Computer and
Information Science at the University of Oregon (UO) where he directs
parallel computing re search projects\, notably the TAU parallel
performance system project . \; He has extensive experience in performance
benchmarking and characterization of high-performance computing systems\,
and has deve loped performance evaluation tools for a range of parallel
machines d uring the last 30 years. \; Malony is also interested in
computat ional and data science. \; He is the Director of the new Oregon
A dvanced Computing Institute for Science and Society (OACISS) at UO.
M alony was awarded the NSF National Young Investigator award\, was a
F ulbright Research Scholar to The Netherlands and Austria\, and received
the Alexander von Humboldt Research Award for Senior U.S. Scientists by
the Alexander von Humboldt Foundation. \; Last year he was theFulbright-Tocqueville Distinguished Chair to France. \; Recently\, he was
award the Fulbright for the Future by the Franco-Americaine F ulbright
Commission. \; Malony is the CEO of ParaTools\, Inc.\, w hich he founded with
Dr. Sameer Shende in 2004. \; ParaTools SAS is a French company they
started in 2014\, and is wholly-owned compan y by ParaTools\, Inc.
ParaTools specializes in performance analysis a nd engineering\, HPC
applications and optimization\, and parallel sof tware\, hardware\, and
tools.

DTEND;TZID=Europe/Zurich:20180621T120000 END:VEVENT BEGIN:VEVENT UID:news56@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20181113T175313 DTSTART;TZID=Europe/Zurich:20180524T150000 SUMMARY:Computer Science Colloquium: Osman Abul DESCRIPTION:Abstract: Location privacy is getting increased attention due t o mass use of Location Based Services (LBSs). Location privacy aims at pro tecting individuals' exact location from LBS providers. For this purpose\, sharing coarse locations respecting users' location privacy profiles are commonly applied. The problem gets more complicated especially in urban ar eas where user movements are restricted on roads and the visited places ar e semantically annotated like bars\, night clubs etc. This way location ca n act as an intrusive inference on people's personal preferences and habit s. So\, location privacy is not confined to privacy of location itself. Mo reover\, any sensitive pattern in users' trajectories can be resurfaced by data mining.In this talk\, especially for urban area mobility\, I will gi ve how location privacy profiles can be defined and maintained w.r.t. poss ible attack models. Then\, I will introduce temporally annotated sequences as a means to define sensitive location patterns. Finally\, a sensitive l ocation pattern suppression mechanism running in online stream fashion of LBS access will be detailed.  Speaker Bio: Osman Abul is currently an ass ociate professor of computer science at TOBB University of Economics and T echnology\, Ankara\, Turkey. He received his PhD degree in computer engine ering from Middle East Technical University\, Ankara\, Turkey. He held vis iting posts in University of Calgary\, Norwegian University of Science and Technology\, and Italian Institute of Information Science and Technology. His research interests include data mining\, data privacy and bioinformat ics. X-ALT-DESC:\nAbstract: Location privacy is getting increased attenti on due to mass use of Location Based Services (LBSs). Location privacy aim s at protecting individuals' exact location from LBS providers. For this p urpose\, sharing coarse locations respecting users' location privacy profi les are commonly applied. The problem gets more complicated especially in urban areas where user movements are restricted on roads and the visited p laces are semantically annotated like bars\, night clubs etc. This way loc ation can act as an intrusive inference on people's personal preferences a nd habits. So\, location privacy is not confined to privacy of location it self. Moreover\, any sensitive pattern in users' trajectories can be resur faced by data mining.
In this talk\, especially for urban area mobili ty\, I will give how location privacy profiles can be defined and maintain ed w.r.t. possible attack models. Then\, I will introduce temporally annot ated sequences as a means to define sensitive location patterns. Finally\, a sensitive location pattern suppression mechanism running in online stre am fashion of LBS access will be detailed. \;

Speaker B io: Osman Abul is currently an associate professor of computer science at TOBB University of Economics and Technology\, Ankara\, Turkey. He rece ived his PhD degree in computer engineering from Middle East Technical Uni versity\, Ankara\, Turkey. He held visiting posts in University of Calgary \, Norwegian University of Science and Technology\, and Italian Institute of Information Science and Technology. His research interests include data mining\, data privacy and bioinformatics.

DTEND;TZID=Europe/Zurich:20180524T160000 END:VEVENT BEGIN:VEVENT UID:news199@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20181113T175346 DTSTART;TZID=Europe/Zurich:20180227T121500 SUMMARY:Computer Science Colloquium: Nathan Sturtevant DESCRIPTION:Abstract: What does it take to build a high-quality pathfinding engine? One might think that pathfinding in games is a simple case of usi ng A*\, which finds shortest paths. But\, in practice\, there are many oth er considerations for finding high-quality paths quickly in dynamic enviro nments. This talk will give an overview of the techniques required to buil d the pathfinding engine of Dragon Age: Origins. Five iterations of improv ements will be presented\, starting from basic pathfinding approaches\, an d moving to a final system that creates high-quality\, smooth paths. To co nclude\, we will discuss open challenges in pathfinding and what it would take to create a pathfinding system that finds human-quality paths.\\r\\nS peaker Bio: Nathan Sturtevant is an Associate Professor in the Computer Sc ience Department at the University of Denver. His scientific research focu ses on search in Artificial Intelligence. This includes work on heuristic and combinatorial search for single and multiple agents\, including bidire ctional search\, automated abstraction\, heuristics\, refinement search\, search for game design\, heuristic learning\, inconsistent heuristics\, co operative search\, large-scale and parallel search.Particular applications include pathfinding and planning in memory-constrained real-time environm ents (e.g. commercial video games) as well as algorithms for building and using memory-based heuristics via large-scale search. Other work considers theoretical and practical issues in games with more than two players\, in cluding opponent modeling\, learning\, and imperfect information. X-ALT-DESC:\nAbstract: What does it take to build a high-quality pat hfinding engine? One might think that pathfinding in games is a simple cas e of using A*\, which finds shortest paths. But\, in practice\, there are many other considerations for finding high-quality paths quickly in dynami c environments. This talk will give an overview of the techniques required to build the pathfinding engine of Dragon Age: Origins. Five iterations o f improvements will be presented\, starting from basic pathfinding approac hes\, and moving to a final system that creates high-quality\, smooth path s. To conclude\, we will discuss open challenges in pathfinding and what i t would take to create a pathfinding system that finds human-quality paths .\nSpeaker Bio: Nathan Sturtevant is an Associate Professor in the Computer Science Department at the University of Denver. His scientific re search focuses on search in Artificial Intelligence. This includes work on heuristic and combinatorial search for single and multiple agents\, inclu ding bidirectional search\, automated abstraction\, heuristics\, refinemen t search\, search for game design\, heuristic learning\, inconsistent heur istics\, cooperative search\, large-scale and parallel search.
Partic ular applications include pathfinding and planning in memory-constrained r eal-time environments (e.g. commercial video games) as well as algorithms for building and using memory-based heuristics via large-scale search. Oth er work considers theoretical and practical issues in games with more than two players\, including opponent modeling\, learning\, and imperfect info rmation. DTEND;TZID=Europe/Zurich:20180227T140000 END:VEVENT END:VCALENDAR