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Record W4312117451 · doi:10.1002/aaai.12074

The New Faculty Highlights Program at AAAI‐21

2022· article· en· W4312117451 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAI Magazine · 2022
Typearticle
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWork (physics)NarrativeComputer sciencePsychologyEngineering

Abstract

fetched live from OpenAlex

At AAAI 2021, we introduced a “New Faculty Highlights” program. The aim was to showcase top young researchers who had taken up their first faculty or research scientist position at a research-intensive university or lab in the preceding year. Each selected participant presented a 30-min talk at the conference, summarizing their work to the broad AAAI audience. We see many ways in which this program benefits the AAAI community. First, it deepens the conference experience for attendees. Participants are encouraged to draw on their highly polished job talks. Because job talks are designed for accessibility to broad audiences, they are ideal for helping researchers from diverse AI subfields to understand important emerging trends and simultaneously to become familiar with AI researchers leading the new generation. The longer talk format also enables speakers to describe a body of work rather than a single paper and to situate different elements within a coherent narrative. Second, the program benefits the selected faculty members. It is hard to get known in a community as big as AAAI. These talks offer participants a high-profile opportunity to make their work more broadly known. We expect the program to act as an important source of recognition for such young researchers. Finally, the program benefits students. AAAI's plenary talks tend to focus on senior researchers; New Faculty Highlights expose students to examples of exceptional work by researchers who were recently students themselves. We hope that this experience is both inspiring and helpful to students about to embark upon their own job searches. In the first iteration of the program at AAAI 2021, 119 young researchers applied; a distinguished committee of seven AAAI fellows selected 18 of these to participate in the program. The selected speakers were a demographically diverse group (11 male and seven female; seven different countries represented). They were just as diverse in terms of their research perspectives, which spanned multi-agent systems, robotics, theoretical machine learning, information retrieval, planning, green AI, social context of language, justice for machine learning, online privacy, and more. We are delighted that AI Magazine has invited these young stars to contribute articles. In this edition, four of the 18 are included. Lili Mou explores neural models for the generation of text using unsupervised techniques; Jundong Li studies causal effects in network data; Pascal Bercher develops theory, algorithms, and applications for two prominent planning techniques; and Hang Ma advances decision-making in multi-agent systems. We hope that the New Faculty Highlights program becomes a permanent feature of AAAI conferences, and does its bit in celebrating early career successes and encouraging more students to pursue a career in research. The authors declare that there is no conflict.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.109
GPT teacher head0.425
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it