Alle News von der Fakultät für Technische Wissenschaften
Das Österreichische Weltraum Forum gilt als wichtige Drehscheibe für internationale High-Tech-Forschung. Seit Jahren kooperiert das ÖWF mit der Klagenfurter Forschungsgruppe „Control of Networked Systems“ rund um Stephan Weiss. Die Forscher präsentierten kürzlich ihre Ergebnisse der Mars-Simulation AMADEE-20, die im Oktober 2021 in der Negev Wüste in Israel stattfand.
Seit Jahren sind wir es nun gewöhnt, via Zoom, FaceTime & Co. online miteinander zu kommunizieren. Auch Operationen oder die Fertigung in Industrieunternehmen sind mittlerweile aus der Ferne möglich. Ein Forschungsprojekt, kürzlich bewilligt durch EU Horizon Europe, möchte nun zu einer neuen Generation immersiver Telepräsenztechnologien beitragen: Die Grenzen zwischen virtueller und physischer Welt sollen dabei noch mehr verschwimmen und die Technologie soll es uns noch besser ermöglichen, an einem anderen Ort zu „sein“, ohne dorthin fahren zu müssen.
Harald Gietler has just finished his PhD in Technical Sciences, specializing in Information and Communications Engineering. His research work focuses on localization technology. Instead of radar or sonar, Harald uses electromagnetic fields. We talked to him about his field of research and the influence of artificial intelligence. Moreover, he also told us about the reasons why he decided to study at the University of Klagenfurt and why he would advise others to study in Klagenfurt too. Weiterlesen →
Anuj Shah, Ph. D. | Senior Machine Learning Research Practitioner at Netflix |
Click here to register for the meeting:
https://zoom.us/meeting/register/tJYvdO-gqzMiEtKOfNIgcZAZOQ8jA3i_b3Pi
Abstract:
Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.
Bio:
Anuj Shah is a Senior Machine Learning Research Practitioner at Netflix. For the past 10+ years, he’s been working on an applied research team focused on developing the next generation of algorithms used to generate the Netflix homepage through machine learning, ranking, recommendation, and large-scale software engineering. He is extremely passionate about algorithms and technologies that help improve the Netflix customer experience with highly personalized consumer-facing products like the Continue Watching row, the Top 10 rows amongst many others. Prior to Netflix, he worked on machine learning in the Computational Sciences Division at the Pacific Northwest National Laboratory focusing on technologies at the intersection of proteomics, bioinformatics and Computer Science for 8 years. He has a Ph.D. from the Computer Science department at Washington State University and a Masters in C.S. from Virginia Tech
