Solutions to Grand Challenges Demand Innovation
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
In his talk, Panch considers the ever important question: How do we foster a culture of innovation?\n\nSethuraman Panchanathan is the chief research and innovation officer at Arizona State University. He is also the executive vice president of the ASU Office of Knowledge Enterprise Development, which advances research, innovation, strategic partnerships, entrepreneurship, global and economic development at ASU.\n\nPanchanathan was the founding director of the School of Computing and Informatics and was instrumental in founding the Biomedical Informatics Department at ASU. He also served as the chair of the Computer Science and Engineering Department. He founded the Center for Cognitive Ubiquitous Computing (CUbiC) at ASU, to develop person-centered tools and ubiquitous computing technologies for enhancing the quality of life for individuals with disabilities.\n\nPanchanathan was appointed by President Barack Obama to the U.S. National Science Board (NSB) and is Chair of the Committee on Strategy. He has also been appointed by U.S. Secretary of Commerce Penny Pritzker to the National Advisory Council on Innovation and Entrepreneurship (NACIE). Panchanathan is a Fellow of the National Academy of Inventors (NAI), the Canadian Academy of Engineering, and the Institute of Electrical and Electronics Engineers (IEEE) among other prestigious organizations. He currently serves as the Chair of the Council on Research (CoR) within the Association of Public and Land-grant Universities (APLU).\n\nHis research interests are in the areas of human-centered multimedia computing; haptic user interfaces; ubiquitous computing technologies; and machine learning for multimedia applications, medical image processing, and media processor designs.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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