General Information

Peter Wonka Photo
Name: Peter Wonka
Professor in Computer Science
Email: peter.wonka@kaust.edu.sa
Phone: +966 12 808 0235
Work:

KAUST - King Abdullah University of Science and Technology
CEMSE
Computer Science Program
Visual Computing Center

Address KAUST

Peter Wonka
CEMSE
Building 1, office 2108
4700 King Abdullah University of Science and Technology
Thuwal 23955-6900
Kingdom of Saudi Arabia

Note for Students and Post-docs interested in joining my group

What background is a good match for my research? I look at multiple criteria when evaluating a student, mainly: reputation of the undergraduate (graduate) school, GPA, the quantitative GRE score, mathematics skills (grades in mathematics courses), and programming skills. The students that I recruit are typically either very good in mathematics, programming (system building), or both. A nice bonus is the  knowledge of visual computing, research experience, and publications. I am looking for students who are interested in learning optimization, machine learning (a lot of deep learning these days), or geometry related techniques and innovating existing techniques to improve applications in the areas of graphics, vision, visualization, or remote sensing.

Publications: for my research I typically target publications in the following venues. The publication venues are not an exclusive list.

  • computer graphics (ACM Siggraph, ACM Siggraph Asia, ACM TOG, Eurographics, IEEE TVCG)
  • computer vision (ECCV, ICCV, CVPR, IEEE PAMI)
  • visualization (IEEE Visualization, IEEE TVCG)
  • remote sensing (IEEE TGRS).

How to contact me? If you are interested to join as a student please send me an email including a CV. The CV should include your GPA.

Note for post-docs: If you are interested to join as a post-doc, please send a CV including your GPA and publication list. Almost all post-docs I previously hired had publications in some of the venues highlighted above or very related publication venues.

What are some good courses to prepare for research? Here are some courses that are beneficial to prepare for my current research. The online course offering changes so fast, it is hard to keep track.

  • basic machine learning, e.g. coursera (Machine Learning, Andrew Ng)
  • deep learning, e.g. coursera (Deep Learning Specialization), udactity nanodegree, fastai, or Stanford
  • optimization, e.g. Boyd's convex optimization courses
  • linear algebra, e.g. Gilbert Strang on youtube, Boyd's linear dynamical systems

Research

Research Topics:

  • Computer Graphics
  • Computer Vision
  • Visualization
  • Remote Sensing
  • Image Processing
  • Machine Learning
  • Data Mining

Keywords: 3D reconstruction, laser scanning, urban modeling, urban reconstruction, urban planning, computational design, procedural modeling, layout synthesis, shape modeling, interactive editing, geometry processing, architectural geometry, geo-spatial visualization, surface sampling, surface remeshing, image editing, image analysis, image segmentation, texture synthesis, texture analysis, lighting design, machine learning for graphics applications, optimization, deep learning.

Keywords - example tools used in recent research: machine learning, deep learning, generative adverserial networks, graphical models, gaussian process regression, optimization, integer programming, mixed integer programming, ADMM, quadratic programing, heuristic optimization, quadratic assignment, functional maps, graph-cuts

Publications

Current Students, Post-docs, and Research Scientists

  • Jing Ren (previous school: Oxford, UK)
  • Anna Fruehstueck (previous school: Technical University of Vienna, Austria)
  • Lama Affara (previous school: American University of Beirut, Lebanon)
  • Tian Yu (previous school: Harbin Institute of Technology, China)
  • Peihao Zhu (previous school: Chinese Academy of Science, China)
  • Yazeed AlHarbi (previous school: Purdue, USA)
  • Wamiq Reyaz (previous school: National Institute of Technology, Srinagar, India)
  • Ibraheem AlHashim, Post-doc (previous school: Simon Frasier University, Canada)
  • Chi-han Peng, Research Scientist (previous school: UCL London, UK)
  • Liangliang Nan, Research Scientist (previous school: Chinese Academy of Sciences, China)

The university (country) listed in brackets is where the group member came from before joining KAUST. This list is just to give you an idea on the current group decomposition.

Other Information