Demo Data from EDBT 2026 / reviewers in the wild / expert
Thomas Mielke
dblp:02/8525
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Applied, interdisciplinary, general and emerging computing · 2Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 77% Performance modeling and evaluation · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › grid computing
volunteer computing |
0.1 | 1 | 2010 | Simultaneous performance exploration and optimized search with volunteer computing · HPDC 2010 |
Performance modeling and evaluation
parameter space exploration |
0.0 | 1 | 2010 | Simultaneous performance exploration and optimized search with volunteer computing · HPDC 2010 |
Methods — techniques the papers use, named apart from their topics
volunteer computing · 0.1stochastic optimization · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Controlling State-Space Explosion in Chunk Learning
Vladislav Daniel Veksler, Kevin A. Gluck, Christopher W. Myers, Jack Harris, Thomas Mielke |
CogSci | 5 |
| 2013 | Explorations in Human and Machine Learning of Decision Trees
Kevin A. Gluck, Jack Harris, Thomas Mielke, Christopher W. Myers, Vladislav Daniel Veksler |
CogSci | 3 |
| 2010 | Simultaneous performance exploration and optimized search with volunteer computingabstractVolunteer computing is a powerful platform for solving complex scientific problems. [email protected] is a volunteer computing project available to the cognitive modeling community for conducting research to better understand the human mind. We are interested in optimizing search processes on volunteer resources, yet we are also interested in exploring and understanding changes in model performance across interacting, non-linear mechanisms and parameter spaces. To support both of these goals, we have developed a stochastic optimization approach and integrated it with [email protected] We tested this approach with a cognitive model on a sample parameter space, demonstrating significant decreases in computational resource utilization and search runtime, while also providing useful visual representations of performance surfaces. Future work will focus on scaling the technique to more volunteers and larger parameter spaces, as well as optimizing the performance of the search algorithm in regards to the challenges inherent with volunteer computing. L. Richard Moore Jr., Matthew Kopala, Thomas Mielke, Michael Krusmark, Kevin A. Gluck |
HPDC | 3 |