Technion Program for Excellence

Daniel Vainsencher / Computer Science, Mathematics


First degree in Mathematics and Computer Science at the Technion.

MSc. in Computer Science at the Technion.


Participated in the Technion Excellence Program: October 2003 – July 2007.


Daniel took an advanced course (Introduction to AI) in his first semester, and also engaged in two lines of research in the Technion – one in the analysis of how Virtual Reality is perceived (helped with the analysis of EEG data from people playing a game). The other, which led to his MSc topic, was in discrete geometry – an exploration of the shapes in the integer grid that are like disks, in enclosing the most area, while having a minimal perimeter. “Being in the program helped me get at least the first of these opportunities, probably both”.


Daniel also did an internship in Portland State University, Oregon. There he engaged in some more research, in the area of software engineering. They eventually wrote a patterns paper, which documents some existing and new good practices in writing advanced software development environment. 

During his BA, he presented one poster, coauthored one conference paper and one pattern paper, in completely different areas. He also did an interesting project in face recognition, though it has not resulted in a research paper.


Recommendation to Program participants: “The Technion is full of people doing interesting things, and doing various kinds of research during your BA is a wonderful way of learning what and who you like, without having to agree in advance to “marry” for years like in an advanced degree. So do challenging projects, not just HW and tests.

Good work or research summer internships abroad are a great way to see different ways of working and living, while also earning a little money. So: get your head out of your books occasionally – these things require time and effort to find and organize”.


Today (2012): Daniel is a PhD student at the Technion, at the EE department, working on the topic of Machine Learning.  

Daniel Vainsencher