Thursday, 13.30 - 14.15, HFB|A
Meta-algorithmics is a subject on the intersection of learning and optimization whose objective is the development of effective automatic tools that tune algorithm parameters and, at runtime, choose the approach that is best suited for the given input. In this talk I summarize the core lessons learned when devising such meta-algorithmic tools.
Meinolf Sellmann received his doctorate degree in 2002 from Paderborn University (Germany) and then went on to Cornell University as Postdoctoral Associate. From 2004 to 2010 he held a position as Assistant Professor at Brown University and was Program Manager for Cognitive Computing at IBM Watson Research. Now he is Technical Operations Lead for Machine Lerning and Knowledge Discovery at General Electric.
Meinolf has published over 70 articles in international conferences and journals, filed nine US patents, served as PC Chair of LION 2016 and CPAIOR 2013, Conference Chair of CP 2007, and Associate Editor of the Informs Journal on Computing. He received an NSF Early Career Award in 2007, IBM Outstanding Technical Innovation Awards in 2013 and 2014, and an IBM A-level Business Accomplishment Award 2015. For six years in a row, Meinolf and his team won at international SAT and MaxSAT Solver Competitions, among others two gold medals for the most CPU-time efficient SAT solver for random and crafted SAT instances in 2011, the best multi-engine approach for industrial SAT instances in 2012, the overall most efficient parallel SAT Solver in 2013 (at which point portfolios were permanently banned from the SAT competition), and 17 gold medals s at the 2013 to 2016 MaxSAT Evaluations.