Demo Data from VLDB 2026 / reviewers in the wild / expert
Ashley DeMange
dblp:245/3633
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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 |
Computing education · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computing education › STEM education
engineering education |
0.4 | 1 | 2019 | From Lab to Internship and Back Again: Learning Autonomous Systems through Creating a Research and Development Ecosystem · AAAI 2019 |
Methods — techniques the papers use, named apart from their topics
cognitive models · 0.4bayesian methods · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | From Lab to Internship and Back Again: Learning Autonomous Systems through Creating a Research and Development EcosystemabstractAs research and development (R&D) in autonomous systems progresses further, more interdisciplinary knowledge is needed from domains as diverse as artificial intelligence (AI), bi-ology, psychology, modeling and simulation (M&S), and robotics. Such R&D efforts are necessarily interdisciplinary in nature and require technical as well as further soft skills of teamwork, communication and integration. In this paper, we introduce a 14 week, summer long internship for developing these skills in undergraduate science and engineering interns through R&D. The internship was designed to be modular and divided into three parts: training, innovation, and application/integration. The end result of the internship was 1) the development of an M&S ecosystem for autonomy concepts, 2) development and robotics testing of reasoning methods through both Bayesian methods and cognitive models of the basal ganglia, and 3) a process for future internships within the modular construct. Through collaboration with full-time professional staff, who actively learned with the interns, this internship incorporates a feedback loop to educate and per-form fundamental R&D. Future iterations of this internship can leverage the M&S ecosystem and adapt the modular internship framework to focus on different innovations, learning paradigms, and/or applications. Trevor J. Bihl, Todd Jenkins, Chadwick Cox, Ashley DeMange, Kerry Hill, Edmund Zelnio |
AAAI | 4 |