Demo Data from VLDB 2026 / reviewers in the wild / expert
Michael Eugene Stillman
dblp:82/2374
· DBLP profile ↗
11ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0003-0078-3116ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 9 · 1 first-authorArtificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% | |
| Artificial intelligence
1 paper |
Reinforcement learning · 100% | |
| Theoretical computer science
1 paper |
Algorithms and data structures · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
systems biology |
0.7 | 1 | 2023 | Cyclone: open-source package for simulation and analysis of finite dynamical systems · Bioinform. 2023 |
Machine learning › Reinforcement learning
policy learning |
0.4 | 1 | 2020 | Learning Selection Strategies in Buchberger's Algorithm · ICML 2020 |
Algorithms and data structures
symbolic computation |
0.4 | 1 | 2020 | Learning Selection Strategies in Buchberger's Algorithm · ICML 2020 |
Bioinformatics and computational biology › network bioinformatics › biological network analysis › network analysis
boolean network analysis |
0.2 | 1 | 2023 | Cyclone: open-source package for simulation and analysis of finite dynamical systems · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 0.9proximal policy optimization · 0.9state space simulation · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Cyclone: open-source package for simulation and analysis of finite dynamical systemsabstractMOTIVATION: While there are software packages that analyze Boolean, ternary, or other multi-state models, none compute the complete state space of function-based models over any finite set. Results: We propose Cyclone, a simple light-weight software package which simulates the complete state space for a finite dynamical system over any finite set. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/discretedynamics/cyclone under the Apache-2.0 license. Elena S. Dimitrova, Adam C. Knapp, Brandilyn Stigler, Michael Eugene Stillman |
Bioinform. | 4 |
| 2020 | Learning Selection Strategies in Buchberger's AlgorithmabstractStudying the set of exact solutions of a system of polynomial equations largely depends on a single iterative algorithm, known as Buchberger’s algorithm. Optimized versions of this algorithm are crucial for many computer algebra systems (e.g., Mathematica, Maple, Sage). We introduce a new approach to Buchberger’s algorithm that uses reinforcement learning agents to perform S-pair selection, a key step in the algorithm. We then study how the difficulty of the problem depends on the choices of domain and distribution of polynomials, about which little is known. Finally, we train a policy model using proximal policy optimization (PPO) to learn S-pair selection strategies for random systems of binomial equations. In certain domains, the trained model outperforms state-of-the-art selection heuristics in total number of polynomial additions performed, which provides a proof-of-concept that recent developments in machine learning have the potential to improve performance of algorithms in symbolic computation. Dylan Peifer, Michael Eugene Stillman, Daniel Halpern-Leistner |
ICML | 2 |
| 2012 | Practical Gröbner basis computationabstractWe report on our experiences exploring state of the art Gröbner basis computation. We investigate signature based algorithms in detail. We also introduce new practical data structures and computational techniques for use in both signature based Gröbner basis algorithms and more traditional variations of the classic Buchberger algorithm. Our conclusions are based on experiments using our new freely available open source standalone C++ library. Bjarke Hammersholt Roune, Michael Eugene Stillman |
ISSAC | 2 |
| 2011 | Parameter estimation for Boolean models of biological networks
Elena S. Dimitrova, Luis David García-Puente, Franziska Hinkelmann, Abdul Salam Jarrah, Reinhard C. Laubenbacher, Brandilyn Stigler, Michael Eugene Stillman, Paola Vera-Licona |
Theor. Comput. Sci. | 7 |
| 2007 | On the geometry of parametrized bicubic surfaces
André Galligo, Michael Eugene Stillman |
J. Symb. Comput. | 2 |
| 2005 | Algebraic geometry of Bayesian networks
Luis David García-Puente, Michael Eugene Stillman, Bernd Sturmfels |
J. Symb. Comput. | 2 |
| 2003 | Computing in algebraic geometry and commutative algebra using Macaulay 2
Michael Eugene Stillman |
J. Symb. Comput. | 1 |
| 2000 | Cohomology on Toric Varieties and Local Cohomology with Monomial Supports
David Eisenbud, Mircea Mustala, Michael Eugene Stillman |
J. Symb. Comput. | 3 |
| 1998 | Strategies for Computing Minimal Free Resolutions
Roberto La Scala, Michael Eugene Stillman |
J. Symb. Comput. | 2 |
| 1992 | Computation of Hilbert Functions
David Bayer, Michael Eugene Stillman |
J. Symb. Comput. | 2 |
| 1988 | On the Complexity of Computing SyzygiesabstractWe give a self-contained exposition of Mayr & Meyer's example of a polynomial ideal exhibiting double exponential degrees for the ideal membership problem, and generalise this example to exhibit minimal syzygies of double exponential degree. This demonstrates the existence of subschemes of projective space of double exponential regularity. David Bayer, Michael Eugene Stillman |
J. Symb. Comput. | 2 |