Prof. Dr. Tilmann Rabl
Prof. Dr. Tilmann Rabl
Fellow
Fellow | BIFOLD
Professor | Hasso Plattner Institute, University of Potsdam
Tilmann Rabl holds the chair for Data Enineering Systems at the Hasso Plattner Institute and is Professor at the Digital Engineering Faculty of the University of Potsdam. He is also cofounder and scientific director of the startup bankmark. Tilmann Rabl received his PhD at the University of Passau in 2011. He spent 4 years at the University of Toronto as a postdoc in the Middleware Systems Research Group (MSRG). From 2015 to 2019, he was senior researcher and visiting professor at the Database Systems and Information Management (DIMA) group at Technische Universität Berlin and Vice Director of the Intelligent Analytics for Massive Data (IAM) Group at the German Research Center for Artificial Intelligence (DFKI).
| 2019 | BTW Winner Data Science Challenge |
| 2019 | EDBT Best Paper Award |
| 2017 | EDBT Best Demo Award |
| 2015 | Weconomy Award |
| 2015 | IBM CAS Research 2015 Fellowship (Canada) |
| 2014 | IBM CAS Research 2014 Fellowship (Canada) |
| 2014 | IKT Innovativ Gründerpreis |
| 2013 | MITACS Elevate Postdoctoral Fellowship Award (Canada) |
| 2013 | IBM CAS Research 2013 Fellowship (Canada) |
| 2013 | EXIST Start-Up Award |
| 2013 | Runner-Up for Best Industry Paper Award, ICPE ’13 |
| 2012 | SPEC Distinguished Dissertation Award – Honorable Mention |
| 2012 | Best DEBS Challenge Award – Public Voting, DEBS ’12 |
| 2011 | Technical Contribution Award, TPC |
- Data Engineering
- Big Data
- Stream Processing
- Databases
- Benchmarking
- ACM
- ACM SIGMOD
- GI
Bonaventura Del Monte, Steffen Zeuch, Tilmann Rabl, Volker Markl
Rethinking Stateful Stream Processing with RDMA
Clemens Lutz, Sebastian Breß, Steffen Zeuch, Tilmann Rabl, Volker Markl
Triton Join: Efficiently Scaling the Operator State on GPUs with Fast Interconnects
Behrouz Derakhshan, Alireza Rezaei Mahdiraji, Zoi Kaoudi, Tilmann Rabl, Volker Markl
Materialization and Reuse Optimizations for Production Data Science Pipelines
Gábor E. Gévay, Tilmann Rabl, Sebastian Breß, Lorand Madai-Tahy, Jorge-Arnulfo Quiané-Ruiz, Volker Markl
Imperative or Functional Control Flow Handling: Why not the Best of Both Worlds?
Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios Goumas, Dirk Habich, Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaž Kosar, Alexander Krause, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies, Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pınar Tözün, Wojciech Ulatowski, Yuanyuan Wang, Izajasz Wrosz, Aleš Zamuda, Ce Zhang, Xiao Xiang Zhu
DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines
An overview of the current state of research in BIFOLD
Since the official announcement of the Berlin Institute for the Foundations of Learning and Data in January 2020, BIFOLD researchers achieved a wide array of advancements in the domains of Machine Learning and Big Data Management as well as in a variety of application areas by developing new Systems and creating impactfull publications. The following summary provides an overview of recent research activities and successes.
TU Berlin master’s student wins 2nd place at the SIGMOD 2020 Student Research Competition
TU Berlin’s student Hendrik Makait reached the 2nd place at the ACM SIGMOD 2020 Student Research Competition at the 2020 ACM SIGMOD/PODS International Conference on the Management of Data with his paper “Rethinking Message Brokers on RDMA and NVM”. This is a joint work with the Data Engineering Systems Group at the Hasso Plattner Institute in Potsdam led by Prof. Rabl.
BIFOLD researchers receive SIGMOD 2020 best paper award
Database systems researchers at TU Berlin, HPI and DFKI were highly successful this year. Four of their papers were accepted at the 2020 ACM SIGMOD/PODS International Conference on the Management of Data. And, in particular, one of the paper’s received the 2020 ACM SIGMOD Best Paper Award. The paper entitled “Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects,” by Clemens Lutz, Sebastian Breß, Steffen Zeuch, Tilmann Rabl (now at HPI), and Volker Markl explores the use of GPUs to accelerate database query processing.
Three papers presented at EDBT 2020
Researchers in TU Berlin’s Database Systems and Information Management (DIMA) Group and DFKI’s Intelligent Analytics for Massive Data (IAM) Group presented three systems papers at EDBT 2020, the 23rd International Conference on Extending Database Technology, held from March 30 to April 2. Originally planned to take place in Copenhagen, Denmark, this year’s EDBT conference was held online instead.
Four papers authored by TU Berlin and DFKI researchers have been accepted at SIGMOD 2020
Data management systems researchers in the Database Systems and Information Management (DIMA) Group at TU Berlin and the Intelligent Analytics for Massive Data (IAM) Group at DFKI (the German Research Institute for Artificial Intelligence) were informed that their papers have been accepted at the 2020 ACM SIGMOD/PODS International Conference on the Management of Data.