Bibliographic record
Abstract
This interview was conducted by CBI for CS&E in conjunction with the 50th Anniversary of the University of Minnesota Computer Science Department (now Computer Science and Engineering, CS&E). The first part of the interview Professor Maly discusses his education in Vienna before his doctoral work at the Courant Institute at New York University working under Jack Schwartz, and the dissertation he wrote on the programming language SETL. He joined the newly formed Computer Science Department at the University of Minnesota in the early 1970s and in 1974 became the Director of Undergraduate Education for the department. After promotion to Associate Professor, he became the Department Chair. In the oral history, he discusses the early faculty member of the department such as Marvin Stein, Bill Munro, Jay Leavitt, Bill Franta, Ben Rosen, and others. Among other topics he explores are the Computer Center and its equipment, collaborating with industry to enhance resources and facilities, the early curriculum, early lessons and continuing leadership as Department Chair, serving on the board of the Microelectronics Institute (MEIC). He also highlights the early and continuing impact of the Cray Lectureship for some world-renowned computer scientists to come to the department for a short stretch to give a number of talks and interact with faculty and students. Early lecturers included Barry Boehm, Nikolas Wirth, and other computer top scientists. At Minnesota, he was learning the ropes of being Chair as rank junior to full professor in the department. Having successfully led the Department of CS to a very strong if not elite level, he decided to take on the challenge of building a program up in both research and education at Old Dominion (when he arrived it had no significant research profile). He chaired the department for many years and formed strong partnerships in the region with William Wulf and Anita Jones at Virginia, and schools in the DC area as well as with NSF.
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.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".