The New Faculty Highlights Program at AAAI‐21
Bibliographic record
Abstract
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 30min 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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.298 | 0.124 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".