Grab a coffee with The WASP PHD students

 

The Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) is a major national initiative for strategically motivated basic research, education and faculty recruitment in autonomous systems and software development. The ambition is to advance Sweden into an internationally recognized and leading position in these areas.

The starting point for WASP is the combined existing world-leading competence in Electrical Engineering, Computer Engineering, and Computer Science at Sweden’s four major ICT universities: Chalmers University of Technology, KTH Royal Institute of Technology, Linköping University, and Lund University. WASP will strengthen, expand, and renew the national competence through new strategic recruitments, a challenging research program, a national graduate school, and collaboration with industry.

 
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Victor Millnert

PhD Student, Lund University

In order to enable smart manufacturing and smart collaborations in the coming industrial internet-of-things, or Industry 4.0, a key element is to be able to utilize smart services residing in the cloud. The challenge however, is that the applications, such as collaborating industrial robots, autonomous cars, etc. require a very low and predictable end-to-end latency. With the coming 5G technology standard part of this puzzle will be solved since it will allow for a low-latency wireless connection to/from the cloud. Our research targets the problem of ensuring that we have a sufficiently low and predictable end-to-end latency within the cloud. For instance, if your robot wants to use a network of smart services such as machine learning inference, control loops, analytics, etc. we develop theory that allows these services to automatically scale in order to guarantee you the necessary end-to-end latency.

 
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Christian Nelson

PhD, Lund University

Christian Nelson received his M.Sc. degree in Engineering Physics from Lund University 2016. From 2013 through 2015 he was employed by the Swedish Defense Research Agency (FOI) where he worked with electronic warfare simulations and evaluation. Since 2016 he holds a position at Lund University as Ph.D. student at the department for Electronic and Information Technology (EIT), Lund University. Currently, he is at UC Berkeley as visiting scholar at the Berkeley Wireless Research Center (BWRC). His research interests are wireless, vehicular communications and control over wireless. More specifically his research concerns link modelling for cooperative transport solutions, where we put emphasis on safety critical aspects such as latency, relative positioning, reliability and their interaction with the control system for collaborative transport solutions.

 

Lars Svensson

PhD, KTH

Lars Svensson received an MSc degree in Engineering Physics from Uppsala University, Sweden in 2015, with two semesters at Colorado University, Boulder, and a MSc thesis project in automated driving at Volvo Cars. In 2015-16, Lars was part of the KTH team that participated in the Grand Cooperative Driving Challenge 2016, a European competition in collaborative automated driving. Since 2016 he is pursuing a PhD degree at the Mechatronics and Embedded Control Systems Group at the Department of Machine Design, KTH. His research topic is trajectory planning and control for automated vehicles in general, with emphasis on critical and highly dynamic maneuvers under varying road and vehicle conditions. During the fall of 2018, he is a visiting researcher at the model predictive control lab at University of California, Berkeley.

 
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Arian Ranjbar

Industrial PhD Student på Zenuity

Arian Ranjbar received his M.Sc degree in Engineering Physics from Chalmers University of Technology in 2014. From 2015-2016 he was employed by Autoliv Research as a research engineer before pursuing a PhD as an industrial PhD student at Chalmers. Since 2017 he is part of the joint venture between Volvo Cars and Autoliv - Zenuity. His research topic is verification of automated road vehicles with emphasis on developing verification methods and confidence models for machine learning. Currently he is a visiting researcher at DeepDrive at University of California, Berkeley.