Prof. Dr. Rainer Gemulla

I am heading the Chair of Practical Computer Science I: Data Analytics at the University of Mannheim. The chair is part of the Data and Web Science Group.

If you consider applying to our lab, please read:

• Applications should include CV, transcripts, and a short (!) cover letter or will be ignored.
• We generally do not offer short interships (3 months or less). Applications for such internships will be ignored.
• If you are a BSc or MSc student here, have very good transcripts, and are interested in a student job with us, contact me directly.

• Data analysis and data mining
• Text mining and information extraction
• Optimization
• Approximation techniques
• Algorithms for modern hardware

• Named Distinguished PC Member for SIGMOD, 2017
• Junior-Fellow of the Gesellschaft für Informatik (GI), 2013
• AWS in Education Research Grant Award, 2013
• Busy Beaver teaching award (winter term 2012/2013)
  _{for Non-Traditional Data Management (NoSQL and more)}
• IBM's 2011 Pat Goldberg Memorial best paper award in CS, EE and Math
  _{(for „Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent“ with P. J. Haas, E. Nijkamp, and Y. Sismanis; KDD 2011)}
• Best paper of NIPS 2011 Biglearn workshop
  _{(for „Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent“ with P. J. Haas, Y. Sismanis, C. Teflioudi, and F. Makari)}
• Google Focused Research Award 2011: Robust and Scalable Fact Discovery from Web Sources
  _{(with G. Weikum and M. Theobald)}
• Research Highlight in Communications of the ACM
  _{(for „Distinct-Value Synopses for Multiset Operations“ with K. Beyer, P.J. Haas, B. Reinwald, Y. Sismanis)}
• The VLDB Journal, Special Issue: Best Papers of VLDB 2006
  _{(for „A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets“ with W. Lehner and P.J. Haas)}

• Samuel Broscheit
• Kiril Gashteovski
• Daniel Ruffinelli
• Yanjie Wang

Former students

• Dr. Kaustubh Beedkar
• Dr. Luciano del Corro
• Dr. Faraz Makari Manshadi
• Stefan Kain
• Dr. Christina Teflioudi

If you are interested in writing a seminar, Bachelor or Master thesis with us, please read the following guidelines.

Current semester (HWS 2019)

• CS 560: Large-Scale Data Management (Master Course)
• IE 675: Machine Learning (Master course)
• CS 707: Data and Web Science Seminar (Bachelor / Master seminar)
• Colloquim (for PhD candidates)

Previous semester (FSS 2019)

• Praktische Informatik II (Bachelor course)
• Data Mining and Matrices (Master course)
• Data and Web Science Seminar (Bachelor / Master seminar)
• Team projects

Organizer

• LWDA 2018 conference
• GI Dagstuhl-Seminar: Informatik@Schule 2016 - Das Verhältnis von informatischer Bildung und „Digitaler Bildung“ (2016)
• GI Dagstuhl-Seminar: Informatik@Schule - Agenda für informatische Bildung in der Schule (2014)

Associate editor / area chair

• 2015-2018: TKDE

• 2019: VLDB
• 2018: DASP
• 2017: EDBT
• 2016: JISA
• 2015: VLDB
• 2014: CIKM

PC member / reviewer (since 2011)

• 2019: AKBC, BTW, DEEM, IJCAI, INFORMATIK, LWDA, SUM
• 2018: VLDB, EDBT, DEEM
• 2017: SIGMOD, BTW, DEEM, TALG, VLDB, ECML-PKDD
• 2016: SIGMOD, ECML-PKDD
• 2015: BTW, DAMI, GvDB, IS, JODS, JWS, PODS
• 2014: BDDC, BigData, Buda, DMC, JMLR, SIGMOD, VLDB
• 2013: AKBC, BigData, BTW, CIKM, DMC, ICALP, TKDE, TML, VLDB
• 2012: KDD, JMLR, TODS
• 2011: BTW, IS, VLDB, VLDBJ

• MinIE: Open information extractor (spiritual successor to ClausIE)
• DSGDpp: Various parallel algorithms for matrix factorization (including DSGD++)
• DESQ: Frequent sequence mining with subsequence constraints
• Rounding rank: algorithms for computing rounding-rank decompositions
• CORE: Context-aware open relation extraction with factorization machines
• FINET: Context-aware fine-grained named entity typing
• Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning
• ClausIE: Clause-Based Open Information Extraction
• LEMP: Fast Retrieval of Large Entries in a Matrix Product
• LASH: Large-Scale Sequence Mining with Hierarchies
• MG-FSM: Large-Scale Frequent Sequence Mining