{"id":353801,"date":"2017-01-19T05:07:23","date_gmt":"2017-01-19T13:07:23","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=353801"},"modified":"2021-03-30T13:36:08","modified_gmt":"2021-03-30T20:36:08","slug":"swiss-jrc-workshop","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/swiss-jrc-workshop\/","title":{"rendered":"Swiss Joint Research Center Workshop 2017"},"content":{"rendered":"

Venue:<\/strong> Microsoft Research Cambridge Lab<\/a><\/p>\n

This event is by invitation only.<\/strong><\/p>\n

\"4621.SwissJRC_blog\"<\/a>
\nRead more about the Swiss JRC Workshop 2014 on this blog<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

With this workshop, we started a new phase of our engagement. Ten project collaborations kicked off, four between ETH Zurich and Microsoft and six between EPFL and Microsoft respectively. Project PIs presented their collaboration plans to an audience from all three organizations and got the opportunity to discuss their research plans in project teams.<\/p>\n","protected":false},"featured_media":366281,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2017-02-02","msr_enddate":"2017-02-03","msr_location":"Cambridge, UK","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":false,"msr_private_event":false,"footnotes":""},"research-area":[13556,13562,13563],"msr-region":[239178],"msr-event-type":[197944],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-353801","msr-event","type-msr-event","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-computer-vision","msr-research-area-data-platform-analytics","msr-region-europe","msr-event-type-hosted-by-microsoft","msr-locale-en_us"],"msr_about":"Venue:<\/strong> Microsoft Research Cambridge Lab<\/a>\r\n\r\nThis event is by invitation only.<\/strong>\r\n\r\n\"4621.SwissJRC_blog\"<\/a>\r\nRead more about the Swiss JRC Workshop 2014 on this blog<\/a>","tab-content":[{"id":0,"name":"About","content":"\"2017\r\n\r\nThe Swiss Joint Research Center<\/a> (Swiss JRC) is a collaborative research engagement between Microsoft Research and the two universities that make up the Swiss Federal Institutes of Technology: ETH Zurich<\/a> (Eidgen\u00f6ssische Technische Hochschule Z\u00fcrich<\/em>, which serves German-speaking students) and EPFL<\/a> (\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne<\/em>, which serves French-speaking students). The Swiss JRC is a continuation of a collaborative engagement that began back in 2009, when the same three partners embarked on ICES (Innovation Cluster for Embedded Software) and was renewed for another five years in 2014.\r\n\r\nWith this workshop, we started a new phase of our engagement. Ten project collaborations kicked off, four between ETH Zurich and Microsoft and six between EPFL and Microsoft respectively. Project PIs presented their collaboration plans to an audience from all three organizations and got the opportunity to discuss their research plans in project teams. More details can be found on the project overviews tab. The full agenda is included on the agenda tab.\r\n\r\nSwiss JRC Steering Committee<\/strong>\r\n\r\n[caption id=\"attachment_355253\" align=\"alignleft\" width=\"133\"]\"James James Larus<\/strong>, Dean of School of Computer and Communications Science, EPFL, Switzerland[\/caption]\r\n\r\n[caption id=\"attachment_354470\" align=\"alignleft\" width=\"133\"]\"Markus Markus P\u00fcschel<\/strong>,\u00a0 Professor of Computer Science, ETH Zurich, Switzerland[\/caption]\r\n\r\n[caption id=\"attachment_354464\" align=\"alignleft\" width=\"133\"]\"Ant<\/a> Ant Rowstron<\/strong>, Microsoft Research Cambridge, UK[\/caption]\r\n\r\n[caption id=\"attachment_354473\" align=\"alignleft\" width=\"133\"]\"Scarlet<\/a> Scarlet Schwiderski-Grosche<\/strong>,\u00a0 Microsoft Research Cambridge, UK (Workshop Chair)[\/caption]\r\n\r\n[caption id=\"attachment_354467\" align=\"alignleft\" width=\"133\"]\"Marc Marc Weder<\/strong>, Microsoft Switzerland, Switzerland[\/caption]"},{"id":1,"name":"Project overviews","content":"[accordion]\r\n\r\n[panel header=\"Data Science with FPGAs in the Data Center\"]\r\n\r\nPI: Gustavo Alonso, ETH Zurich;\u00a0<\/strong>Co-PI: Ken Eguro, Microsoft Research<\/strong>\r\n\r\nWhile in the first phase of the project we explored the efficient implementation of data processing operators in FPGAs as well as the architectural issues involved in the integration of FPGAs as co-processors in commodity servers, in this new proposal we intend to focus on architectural aspects of in-network data processing. The choice is motivated by the growing gap between the bandwidth and very low latencies that modern networks support and the overhead of ingress and egress from VMs and applications running on conventional CPUs. A first goal is to explore the type of problems and algorithms that can be best run as the data flows through the network so as to be able to exploit the bare wire speed and allow off-loading of expensive computations to the FPGA. A second, but not less important goal, is to explore how to best operate FPGA based accelerators when directly connected to the network and operating independently from the software part of the application. In terms of applications, the focus will remain on data processing (relational, No-SQL, data warehouses, etc.) with the intention of starting to move towards machine learning algorithms at the end of the two-year project. On the network side, the project will work on developing networking protocols suitable to this new configuration and how to combine the network stack with the data processing stack.\r\n\r\n[\/panel]\r\n\r\n[panel header=\"Human-Centric-Flight II: End-user Design of High-level Robotic Behavior \"]\r\n\r\nPI: Otmar Hilliges, ETH Zurich;\u00a0<\/strong>Co-PI: Marc Pollefeys, Microsoft and ETH Zurich<\/strong>\r\n\r\nMicro-aerial vehicles (MAVs) have been made accessible to end-users via the emergence of simple to use hardware and programmable software platforms and have seen a surge in consumer and research interest as a consequence. Clearly there is a desire to use such platforms in a variety of application scenarios but manually flying quadcopters remains a surprisingly hard task even for expert users. More importantly, state-of-the-art technologies offer only very limited support for users who want to employ MAVs to reach a certain high-level goal. This is maybe best illustrated by the currently most successful application area \u2013 that of aerial videography. While manual flight is hard, piloting and controlling a camera simultaneously is practically impossible. An alternative to manual control is offered via waypoint based control of MAVs, shielding novices from the underlying complexities. However, this simplicity comes at the cost of flexibility and existing flight planning tools are not designed with high-level user goals in mind.\r\n\r\nBuilding on our own (MSR JRC funded) prior work, we propose an alternative approach to robotic motion planning. The key idea is to let the user work in solution-space \u2013 instead of defining trajectories the user would define what the resulting output should be (e.g., shot composition, transitions, area to reconstruct). We propose an optimization-based approach that takes such high-level goals as input and generates the trajectories and control inputs for a gimbal mounted camera automatically. We call this solution-space driven, inverse kinematic motion planning. Defining the problem directly in the solution space removes several layers of indirection and allows users to operate in a more natural way, focusing only on the application specific goals and the quality of the final result, whereas the control aspects are entirely hidden.\r\n\r\n[\/panel]\r\n\r\n[panel header=\"Tractable by Design\"]\r\n\r\nPIs: Thomas Hofmann and Aur\u00e9lien Lucchi, ETH Zurich;<\/strong> Co-PI: Sebastian Nowozin, Microsoft Research<\/strong>\r\n\r\nThe past decade has seen a growth in application of big data and machine learning systems. Probabilistic models of data are theoretically well understood and in principle provide an optimal approach to inference and learning from data. However, for richly structured data domains such as natural language and images, probabilistic models are often computationally intractable and\/or have to make strong conditional independence assumptions to retain computational as well as statistical efficiency. As a consequence, they are often inferior in predictive performance, when compared to current state-of-the-art deep learning approaches. It is a natural question to ask, whether one can combine the benefits of deep learning with those of probabilistic models. The major conceptual challenge is to define deep models that are generative, i.e. that can be thought of as models of the underlying data generating mechanism.\r\n\r\nWe thus propose to leverage and extend recent advances in generative neural networks to build rich probabilistic models for structured domains such as text and images. The extension of efficient probabilistic neural models will allow us to represent complex and multimodal uncertainty efficiently. To demonstrate the usefulness of the developed probabilistic neural models we plan to apply them to challenging multimodal applications such as creating textual descriptions for images or database records.\r\n\r\n[\/panel]\r\n\r\n[panel header=\"Enabling Practical, Efficient and Large-Scale Computation Near Data to Improve the Performance and Efficiency of Data Center and Consumer Systems\"]\r\n\r\nPIs: Onur Mutlu and Luca Benini, ETH Zurich;\u00a0<\/strong>Co-PI: Derek Chiou, Microsoft<\/strong>\r\n\r\nToday's systems are overwhelmingly designed to move data to computation. This design choice goes directly against key trends in systems and technology that cause performance, scalability and energy bottlenecks:\r\n