Demo Data from VLDB 2026 / reviewers in the wild / expert
Clyde Fare
dblp:215/8022
· DBLP profile ↗
5ranked-venue papers
1as first author
4since 2021 · last 2024
0000-0001-5862-3014ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-author · 3 since 2021Systems, architecture and hardware · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 50% Bioinformatics and computational biology · 50% | |
| Artificial intelligence
1 paper |
Reinforcement learning · 62% Generative modeling · 38% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › hierarchical reinforcement learning
modular reinforcement learning |
0.8 | 1 | 2024 | MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery · AAAI 2024 |
Computational science and engineering › materials science
materials discovery |
0.8 | 1 | 2024 | MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery · AAAI 2024 |
Bioinformatics and computational biology › molecular informatics
molecular design |
0.8 | 1 | 2024 | MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery · AAAI 2024 |
Machine learning › Generative modeling › molecular generation
molecular design |
0.2 | 1 | 2024 | MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery · AAAI 2024 |
Machine learning › Generative modeling
molecular generation |
0.2 | 1 | 2024 | MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery · AAAI 2024 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | MANDREL: Modular Reinforcement Learning Pipelines for Material DiscoveryabstractAI-driven materials discovery is evolving rapidly with new approaches and pipelines for experimentation and design. However, the pipelines are often designed in isolation. We introduce a modular reinforcement learning framework for inter-operable experimentation and design of tailored, novel molecular species. The framework unifies reinforcement learning (RL) pipelines and allows the mixing and matching of choices for the underlying chemical action space, molecular representation, desired molecular properties, and RL algorithm. Our demo showcases the framework's capabilities applied to benchmark problems like quantitative estimate of drug-likeness and PLogP, as well as the design of novel small molecule solvents for carbon capture. Clyde Fare, George K. Holt, Lamogha Chiazor, Michail Smyrnakis, Robert Tracey, Lan Hoang |
AAAI | 1 |
| 2024 | An Adaptive Approach to Bayesian Optimization with Setup Switching Costs
Stefan Pricopie, Richard Allmendinger 0001, Manuel López-Ibáñez 0001, Clyde Fare, Matt Benatan, Joshua D. Knowles |
PPSN (2) | 4 |
| 2024 | Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jay Choi, Fred Chong, Charles Chung, Christopher Codella, Antonio D. Córcoles, James Cruise, Alberto Di Meglio, Ivan Duran, Thomas Eckl, Sophia E. Economou, Stephan J. Eidenbenz, Bruce Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente Gonzalez, Johannes Greiner, William Gropp, Michele Grossi, Emanuel Gull, Burns Healy, Matthew R. Hermes, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Bert de Jong, Petar Jurcevic, William M. Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Doga Murat Kürkçüoglu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matthew L. Leininger, Vicente Leyton-Ortega, Ang Li 0006, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel Moore, Sarah Mostame, Mario Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, Scott Pakin, Vincent R. Pascuzzi, Edwin Pednault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Georgy Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Kunal Sharma, Soyoung Shin, George Slessman, Mathias Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Joel Thompson, Synge Todo, Minh C. Tran, Dimitar Trenev, Christian Trott, Huan-Hsin Tseng, Norm M. Tubman, Esin Tureci, David García Valiñas, Sofia Vallecorsa, Christopher Wever, Konrad W. Wojciechowski, Xiaodi Wu 0001, Shinjae Yoo, Nobuyuki Yoshioka, Victor Wen-zhe Yu, Seiji Yunoki, Sergiy Zhuk, Dmitry Zubarev |
Future Gener. Comput. Syst. | 20 |
| 2022 | Expensive optimization with production-graph resource constraints: a first look at a new problem classabstractWe consider a new class of expensive, resource-constrained optimization problems (here arising from molecular discovery) where costs are associated with the experiments (or evaluations) to be carried out during the optimization process. In the molecular discovery problem, candidate compounds to be optimized must be synthesized in an iterative process that starts from a set of purchasable items and builds up to larger molecules. To produce target molecules, their required resources are either used from already-synthesized items in storage or produced themselves on-demand at an additional cost. Any remaining resources from the production process are stored for reuse for the next evaluations. We model these resource dependencies with a directed acyclic production graph describing the development process from granular purchasable items to evaluable target compounds. Moreover, we develop several resource-eficient algorithms to address this problem. In particular, we develop resource-aware variants of Random Search heuristics and of Bayesian Optimization and analyze their performance in terms of anytime behavior. The experimental results were obtained from a real-world molecular optimization problem. Our results suggest that algorithms that encourage exploitation by reusing existing resources achieve satisfactory results while using fewer resources overall. Stefan Pricopie, Richard Allmendinger 0001, Manuel López-Ibáñez 0001, Clyde Fare, Matt Benatan, Joshua D. Knowles |
GECCO | 4 |
| 2017 | PyLDM - An open source package for lifetime density analysis of time-resolved spectroscopic dataabstractUltrafast spectroscopy offers temporal resolution for probing processes in the femto- and picosecond regimes. This has allowed for investigation of energy and charge transfer in numerous photoactive compounds and complexes. However, analysis of the resultant data can be complicated, particularly in more complex biological systems, such as photosystems. Historically, the dual approach of global analysis and target modelling has been used to elucidate kinetic descriptions of the system, and the identity of transient species respectively. With regards to the former, the technique of lifetime density analysis (LDA) offers an appealing alternative. While global analysis approximates the data to the sum of a small number of exponential decays, typically on the order of 2-4, LDA uses a semi-continuous distribution of 100 lifetimes. This allows for the elucidation of lifetime distributions, which may be expected from investigation of complex systems with many chromophores, as opposed to averages. Furthermore, the inherent assumption of linear combinations of decays in global analysis means the technique is unable to describe dynamic motion, a process which is resolvable with LDA. The technique was introduced to the field of photosynthesis over a decade ago by the Holzwarth group. The analysis has been demonstrated to be an important tool to evaluate complex dynamics such as photosynthetic energy transfer, and complements traditional global and target analysis techniques. Although theory has been well described, no open source code has so far been available to perform lifetime density analysis. Therefore, we introduce a python (2.7) based package, PyLDM, to address this need. We furthermore provide a direct comparison of the capabilities of LDA with those of the more familiar global analysis, as well as providing a number of statistical techniques for dealing with the regularization of noisy data. Gabriel F. Dorlhiac, Clyde Fare, Jasper J. van Thor |
PLoS Comput. Biol. | 2 |