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NUS School of Computing Summer Workshop. (1) 4 July to 26 July 2019 (Year 2 & above). (2) 14 July to 5 August 2019 (Year 1). Organizer : NUS School of Computing (Graduate Division) Program Director : Prof. Tan Tiow Seng Publicity & Execution : Liv Dai.
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NUSSchool of ComputingSummer Workshop (1) 4 Julyto26 July 2019 (Year 2 & above) (2) 14 July to5 August 2019 (Year 1) Organizer : NUS School of Computing (Graduate Division) Program Director : Prof.Tan Tiow Seng Publicity & Execution : Liv Dai
National University ofSingapore (NUS) • Founded in 1905, NUS has since established itself as one of the top universities worldwide Education Resource Center University Town
School of Computing • Department • Computer Science • Information Systems and Analytics • Staff • 109 (teaching) • 118 (research) • Student • 2300 undergraduate students • 600 PhD/Master students
For Students • (1) 2nd Year & above: arrive on 3rd or 4th July 2019 to check-in(2) 1st Year: arrive on 14 July 2019 to check-in • While in School of Computing (SOC) • Each student will attend 3 topics in the assigned clustereach for 1 days (total 3 lecture days) • Each student ranks the topics in order of preference in the cluster to do a group project (4 persons in a team) • Each group has about 4 hours of consultation time with the professor (mentor), and each mentor may provide a few more hours of lecture/guidance to all teams • (1) 2nd Year & above: Final showcase on 25 July 2019 (Thursday)(2) 1st Year: Final showcase on 2 August 2019 (Friday) • Check-out of student dormitory early morning of 26 July 2019 for all (and move to outside campus accommodation if needed)
Program Itinerary (Year 1) • 26th July • Move to off-campus accommodation
Selection of Topics (Year 2 & Above) Year 2 Students will select 1 out of 3 clusters. They will then attend 3 lectures covering the topics in the chosen cluster (two half-day of lecture per topic). For clusters with 4 topics, students will only be allowed to attend lectures for 3 topics as there are parallel lectures that are conducted concurrently. Each student will rank his/her preferences of the topics in the cluster to do a project. Each topic can take up to 11 project teams (maximum 44 students). The organizer, in consultation with the professors (mentors), has the final say on which topic a student will do a project.
Selection of Topics (Year 1) Students will attend all topics, each for two half-days of lecture. Each student will then rank his/her preferences of the topics to do a project. Each topic can take up to 44 students. The organizer, in consultation with the professors (mentors), has the final say on which topic a student will do a project.
Available Topics Year 2 and above Year 1
Cluster 1 Multimedia & VR/AR Building a Video Streaming System with DASH 2D Videogame Development Real-Time and Realistic Graphics Rendering Immersive VR Experience Development
Topic 1 • Real-Time and Realistic Graphics Rendering
Topic 2 2D Videogame Development
Topic 3 Immersive VR Experience Development
Topic 4 • Building a Video Streaming System with Dynamic Adaptive Streaming over HTTP (DASH)
Cluster 2 Big Data and Cloud Computing Mining Communities in Big-Data with Algorithms and Computational Thinking Simulation – Allowing “What if?” Scenarios • Data Analytics for Winning Data Competitions Cloud Computing with Big Data
Topic 1 Mining Communities in Big-Data with Algorithms and Computational Thinking
Topic 2 Cloud Computing with Big Data
Topic 3 Simulation – Allowing ‘What if?’ scenarios
Topic 4 • Data Analytics for Winning Data Competitions
Cluster 3 AI and Security Tele-Robotic Deep Learning DOTA Defense of the Ancients Securing service in untrusted environment
Topic 1 • Tele-Robotic Deep Learning
Topic 1 • Tele-Robotic Deep Learning
Topic 2 • DOTA Defense of the Ancients
Topic 3 • Securing service in untrusted environment
Year 1 Topics Computational Pearls with Functional Programming Building Your Dream Smart Home Descriptive Analytics with R
Topic 1 • Computational Pearls with Functional Programming -- You think you know programming? Think again!
Topic 2 • Descriptive Analytics with R
Topic 3 Building Your Dream Smart Home