Alexander Svozil – Curriculum Vitæhomepage.univie.ac.at/Alexander.Svozil/2016-cv.eng.pdf ·...

Post on 13-Jun-2020

12 views 0 download

Transcript of Alexander Svozil – Curriculum Vitæhomepage.univie.ac.at/Alexander.Svozil/2016-cv.eng.pdf ·...

AlexanderSvozilCurriculum Vitæ H +43-677-61298956

B alexander.svozil@gmail.com

Personal data4.6.1992 born in Vienna, Austria

Education2002–2010 Matura (Austrian secondary school certificate, passed with honours),

Vienna, Austria2010 (WS) Business Informatics, Vienna University of Technology2012–2014 BSc, Software and Information Engineering, Vienna University of Technology2014–2016 MSc, Computational Intelligence, Vienna University of Technology, The-

sis: Complexity of Well-Designed SPARQL http://repositum.tuwien.ac.at/obvutwhs/content/titleinfo/1421161GPA: 1.3 (on a scale between “1 = excellent” and “5 = unsatisfactory”)

2017– Ph. D., Supervisor: Prof. Dr. Monika Henzinger, Research Group Theory andApplications of Algorithms, University of Vienna

ExperienceDecember

2016Software Engineer Siemens, Corporate Technology

WS 2015 Tutor, Vienna University of Technology, Introduction to Artificial Intelligence,Knowledge Based Systems

SS 2015 Tutor, Vienna University of Technology, Algorithms and Datastructures, In-troduction to Artificial Intelligence

Summer 2014 Volunteer, Vienna Summer of Logic, http://vsl2014.at/WS 2014 Tutor, Vienna University of Technology, Introduction to Logic Programming2011-2012 Civil Service, Johanniter-Unfall-Hilfe e.V (IT), Programming their online

duty-roster system

1/2

Publications1. Krishnendu Chatterjee, Monika Henzinger, Wolfgang Dvořák, and Alexander Svozil. “Al-

gorithms and Conditional Lower Bounds for Planning Problems”. In: to appear in ICAPS.2018

2. Krishnendu Chatterjee, Monika Henzinger, and Alexander Svozil. “Faster Algorithms forMean-Payoff Parity Games”. In: MFCS. 2017. doi: 10.4230/LIPIcs.MFCS.2017.39. url:https://doi.org/10.4230/LIPIcs.MFCS.2017.39

3. Alexander Svozil and Karl Svozil. “Induction and physical theory formation by MachineLearning”. In: ArXiv e-prints (Sept. 2016). arXiv: 1609.03862 [physics.gen-ph]

2/2