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Minh Phan

dblp:48/3091 · DBLP profile ↗
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4ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 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.

Software engineering, system software, and programming languages
1 paper
Operating systems · 67% Program verification · 33%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Operating systems › extensible operating systems › kernel extensibility
kernel extensions
0.912025
Rex: Closing the language-verifier gap with safe and usable kernel extensions · USENIX ATC 2025
Operating systems › extensible operating systems › kernel extensibility › kernel extensions
safe kernel extensions
0.912025
Rex: Closing the language-verifier gap with safe and usable kernel extensions · USENIX ATC 2025

Methods — techniques the papers use, named apart from their topics

verification · 0.9
YearPublicationVenuePosition
2025 Rex: Closing the language-verifier gap with safe and usable kernel extensions
Jinghao Jia, Ruowen Qin, Milo Craun, Egor Lukiyanov, Ayush Bansal, Minh Phan, Michael V. Le, Hubertus Franke, Hani Jamjoom, Tianyin Xu, Dan Williams 0001
USENIX ATC6
2023 A decision support framework to incorporate textual data for early student dropout prediction in higher education
Minh Phan, Arno De Caigny, Kristof Coussement
Decis. Support Syst.1
2020 Predicting student dropout in subscription-based online learning environments: The beneficial impact of the logit leaf model
Kristof Coussement, Minh Phan, Arno De Caigny, Dries F. Benoit, Annelies Raes
Decis. Support Syst.2
2018 Rapid Access and Visualization of Spaceborne Altimetry Data from ICESat and ICESat-2
abstract
The goal of OpenAltimetry is to provide altimetry-specific data discovery and access with a focus on ease-of-use and quick response times. OpenAltimetry currently supports NASA's laser altimeter missions: ICESat (2003~2009) and ICESat-2 (scheduled for launch in 2018) with a powerful, web-based interactive interface that provides data discovery, processing and visualization capabilities targeting both novice and expert users across different science specializations. OpenAltimetry is modelled after NASA's highly popular EOSDIS Worldview application and uses a unique solution to allow users to efficiently interact with altimetry data. For data management, we are using a hybrid solution with highly optimized tiered storage databases and object storage. the OpenAltimetry application was built cloud-ready, with its highly scalable design and service-oriented architecture. We have developed an efficient data ingestion pipeline that streamlines processing and ingestion of altimetry datasets into the OpenAltimetry system. The waveform energy (for ICESat) and photon (for ICESat-2) data are stored in their original HDF5 format for efficiency. This will be critical for getting large volumes of altimetry data (e.g. ICESat-2) into the system rapidly for access by the wider community.
Viswanath Nandigam, Minh Phan, Adrian A. Borsa, Siri Jodha S. Khalsa, Christopher J. Crosby
IGARSS3