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Michael Krusmark

dblp:11/8525 · DBLP profile ↗
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11ranked-venue papers
0as first author
4since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 10 · 4 since 2021Artificial intelligence and machine learning · 9 · 3 since 2021Systems, 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

TopicWeightPapersLastEvidence papers
Distributed systems › grid computing
volunteer computing
0.112010
Simultaneous performance exploration and optimized search with volunteer computing · HPDC 2010
Performance modeling and evaluation
parameter space exploration
0.012010
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
YearPublicationVenuePosition
2022 Extending the Predictive Performance Equation to Account for Multivariate Performance
Michael G. Collins, Florian Sense, Michael Krusmark, Tiffany S. Jastrzembski
CogSci3
2022 Fuzzy Performance Profiles: Towards Personalized CPR Refresher Training
Florian Sense, Lauren Sanderson, Joshua Onia, Michael Krusmark, Joshua Fiechter, Michael G. Collins, Tiffany S. Jastrzembski
CogSci4
2021 Additional acquisition sessions monotonically benefit retention and relearning
Joshua Fiechter, Florian Sense, Michael G. Collins, Michael Krusmark, Tiffany S. Jastrzembski
CogSci4
2021 Combining Cognitive and Machine Learning Models to Mine CPR Training Histories for Personalized Predictions
Florian Sense, Michael Krusmark, Joshua Fiechter, Michael G. Collins, Lauren Sanderson, Joshua Onia, Tiffany S. Jastrzembski
EDM2
2020 Improving Predictive Accuracy of Models of Learning and Retention Through Bayesian Hierarchical Modeling: An Exploration with the Predictive Performance Equation
Michael G. Collins, Florian Sense, Michael Krusmark, Tiffany S. Jastrzembski
CogSci3
2020 Using K-means Clustering for Out-of-Sample Predictions of Memory Retention
Florian Sense, Michael G. Collins, Tiffany S. Jastrzembski, Michael Krusmark
CogSci4
2019 Deconvolving a Complex, Real-Life Task: Do standard lab tasks predict CPR learning and retention?
Sarah C. Maaß, Florian Sense, Michael Krusmark, Kevin A. Gluck, Hedderik van Rijn
CogSci3
2019 An Integrated Trial-Level Performance Measure: Combining Accuracy and RT to Express Performance During Learning
Florian Sense, Tiffany S. Jastrzembski, Michael Krusmark, Siera Martinez, Hedderik van Rijn
CogSci3
2017 Using Prior Data to Inform Initial Performance Predictions of Individual Students
Michael G. Collins, Kevin A. Gluck, Matthew M. Walsh, Michael Krusmark
CogSci4
2016 Using Prior Data to Inform Model Parameters in the Predictive Performance Equation
Michael G. Collins, Kevin A. Gluck, Matthew M. Walsh, Michael Krusmark, Glenn Gunzelmann
CogSci4
2010 Simultaneous performance exploration and optimized search with volunteer computing
abstract
Volunteer 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
HPDC4