The New Faculty Highlights Program at AAAI‐21
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.
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 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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it