Curriculum Vitae

Douglas H. Fisher
Department of Computer Science
Vanderbilt University
1400 18th Ave South
Office A4034 (Suite A4004 for US and Express mail)
Nashville, TN 37212

douglas.h.fisher@vanderbilt.edu
https://my.vanderbilt.edu/douglasfisher/

Citations = 10046, h-index = 31, i10-index = 55

Education

  • BS 1980 University of California at Irvine (1980) Information and Computer Science, with prior undergraduate study at University of California, Santa Cruz (75-76) in Psychology and the United States Naval Academy (76-78) in Political Science.
  • MS 1983 University of California at Irvine, Information and Computer Science.
  • PhD 1987 University of California at Irvine, Information and Computer Science.

Positions

  • Associate Professor of Computer Science, Vanderbilt University
  • Associate Professor of Computer Engineering, Vanderbilt University
  • Affiliated Faculty of Communication of Science & Technology, Vanderbilt University
  • Director of Outreach, Education, Diversity, and Synthesis of the Computational Sustainability Network (CompSustNet)
  • Founding Faculty Director of Warren (residential) College, Vanderbilt University (2013-2019)
  • Founding Director of the Vanderbilt Institute for Digital Learning (2013-2015)
  • Program Director, CISE/IIS National Science Foundation (2007-2010)

Publications organized chronologically by venue type

Archival Periodicals (peer reviewed)

  • Fisher, D. (1987) “Knowledge Acquisition Via Incremental Conceptual Clustering,” Machine Learning, 2, 139–172. Reprinted in Shavlik & T. Dietterich (eds.), Readings in Machine Learning, 267–283, Morgan Kaufmann, 1990.
  • Gennari, J., Langley, P., & Fisher, D. (1989). “Models of Incremental Concept Formation,” Artificial Intelligence, 40, 11–62.
  • Fisher, D., & Chan, P. (1990). “Statistical Guidance in Symbolic Learning,” Annals of Mathematics and Artificial Intelligence, 2, 135–148.
  • Fisher, D., & Langley, P. (1990). “The Structure and Formation of Natural Categories,” in G. Bower (ed.), The Psychology of Learning and Motivation, 26, San Diego, CA: Academic Press, 241–284.
  • Fisher, D., & Hapanyengwi, G. (1993). “Database Management and Analysis Tools of Machine Learning,” Journal of Intelligent Information Systems, 2, 5–38.
  • Fisher, D., & Yoo, J. (1993). “Problem solving, categorization, and concept learning: A unifying view,” in G. Nakamura, R. Taraban, & D. Medin (eds.), The Psychology of Learning and Motivation, 29, San Diego, CA: Academic Press, 219–255.
  • Fisher, D., Xu, L., Carnes, R., Reich, Y., Fenves, S., Chen, J., Shiavi, R., Biswas, G., & Weinberg, J. (1993). “Applying AI clustering to engineering tasks,” IEEE Expert, 8, 6, 51–60.
  • Evans, B., & Fisher, D. (1994). “Process delay analysis using decision tree induction,” IEEE Expert, 9, 1, 60–66.
  • Srinivasan, K., & Fisher, D. (1995). “Machine learning approaches to estimating software development effort,” IEEE Transactions on Software Engineering, 21, 2, 126–137. 
Reprinted in Zhang & J. Tsai, (eds.), Machine Learning Applications in Software Engineering, World Scientific Press, 2005.
An addendum to `Machine Learning Approaches to Estimating Software Development Effort’ (IEEE TSE, 1995)
  • Fisher, D. (1996). “Iterative Optimization and Simplification of Hierarchical Clusterings,” Journal of Artificial Intelligence Research, 4, 147–179.
  • Biswas, G., Weinberg, J., & Fisher, D. (1998). IEEE Transactions on Systems, Man, and Cybernetics, 28, 2, 219–230.
  • Frey, L., Li, C., Talbert, D., & Fisher, D. (1998). “A review of the Fourteenth International Conference on Machine Learning,” Intelligent Data Analysis, 2, 3, 245–255.
  • Fisher, D., Edgerton, M., Chen, Z., Tang, L., & Frey, L. (2006). “Backward Chaining Rule Induction,” Journal of Intelligent Data Analysis, 10, 5, 397–417.
  • Waitman, L. R., Fisher, D., & King, P. H. (2006). “Bootstrapping rule induction to achieve rule stability and reduction,” Journal of Intelligent Information Systems, 27, 49–77.
  • Whitley, K., Novick, L., & Fisher, D. (2006). “Evidence in favor of visual representation for the dataflow paradigm: An experiment testing LabVIEW’s comprehensibility,” International Journal of Human-Computer Studies, 64, 4, 281–303.
  • Edgerton, M., Fisher, D., Tang, L., Frey, L., & Chen, Z. (2007). “Data mining for gene networks relevant to poor prognosis in lung cancer via backward-chaining rule induction: An implementation in lung cancer research,” Cancer Informatics 3, 93–114.
  • Bruff, D., Fisher, D., McEwen, K., Smith, B., (2013). Wrapping a MOOC: Student Perceptions of an Experiment in Blended Learning, Journal of Online Learning and Teaching, V. 9, No. 2.
  • Grace, K., Maher, M., Fisher, D., & Brady, K. (2014). A data-intensive approach to predicting creative designs based on novelty, value and surprise. International Journal of Design Creativity and Innovation. 3(3–4):125–147.
  • Gomes, C., Dietterich, T., Barrett, C., Conrad, J., Dilkina, B., Ermon, S., Fang, F., Farnsworth, A., Fern, A., Fern, X., Fink, D., Fisher, D., Flecker, A., Freund, D., Fuller, A., Gregoire, J., Hopcroft, J., Kelling, S., Kolter, Z., Powell, W., Sintov, N., Selker, J., Selman, B., Sheldon, D., Shmoys, D., Tambe, M., Wong, W-K., Wood, C., Wu, X., Xue, Y., Yadav, A., Yakubu, A-A., Zeeman, M. L. (2019) “Computational Sustainability: Computing for a Better World and a Sustainable Future”, Communications of the ACM, September 2019, Vol. 62 No. 9, Pages 56-65
  • Hall, R., Shapiro, B., Hostetler, A., Lubbock, H., Owens, D., Daw, C., and Fisher, D. (2020). “Here-and-Then: Learning by Making Places with Digital Spatial Story Lines” Cognition and Instruction, Pages 348-373.
  • McDonald, Y., Anderson, K., Caballero, M., Ke, J. D., Fisher, D., Morkell, C., Hill, E. (2022). “A systematic review of geospatial representation of United States community water systems” AWWA Water Science, Volume 4, Issue 1

Archival Periodicals (NOT peer reviewed)

  • Fisher, D. (2001). Unsupervised Learning (Editorial), Machine Learning, 45, 1, 5–7. (Special issue editor, 30 submissions, 2 installments).
  • Fisher, D. (2002). Unsupervised Learning (Editorial). Machine Learning, 47, 1, 5–6.
  • Fisher, D. (2011). “Computing and AI for a Sustainable Future” IEEE Intelligent Systems, Vol, 26, No. 6.
  • Fisher, D. (2012) “Recent Advances in AI for Computational Sustainability” IEEE Intelligent Systems, 27, 4.
  • Fisher, D. (2014). Leveraging AI Teaching in the Cloud for AI Teaching on Campus. AI Magazine, 35,3, pp. 98-100 (DOI: http://dx.doi.org/10.1609/aimag.v35i3.2546)
  • Fisher, D. H. (2016). “Recent Advances in AI for Computational Sustainability”, In IEEE Intelligent Systems, Vol. 31, No. 4, pp. 56-61.
  • Fisher, D., H, Bian, Z., Chen, S. (2016). Incorporating Sustainability into Computer Science Education”, In IEEE Intelligent Systems, Vol. 31, No. 5, pp. 93-96.
  • Fisher, D., Isbell. C., Littman, M. (2017) “Ask Me Anything About Moocs” AI Magazine, V. 38, No. 2, Summer 2017, pp. 7-12.

Books (authored)

Anderson, C. & Fisher, D. (in preparation, under contract). Artificial Intelligence for Academic Libraries, Routledge.

Books and Proceedings (edited)

Chapters in Self-Edited Books

Other Book Chapters

  • Fisher, D., & Langley, P. (1986). “Methods of conceptual clustering and their relation to numerical taxonomy,” in W. Gale (ed.), Artificial Intelligence and Statistics. Addison-Wesley, 77–116.
  • Biswas, G., Goldman, S., Fisher, D., Bhuva, B., & Glewwe, (1995). “Assessing Design Activity in Complex CMOS Circuit Design,” in P. Nichols, S. Chipman, & B. Brennan (eds.), Cognitively Diagnostic Assessment, Lawrence Erlbaum, 167–188.
  • Fisher, D. (2002). “Conceptual Clustering,” in W. Klosgen and J. Zytkow (eds.), Handbook of Data Mining and Knowledge Discovery, Oxford University Press, 388–396, Chapter 16.5.2. preprint
  • Evans, R., & Fisher, D. (2002). “Using decision tree induction to minimize process delays in the printing industry,” in W. Klosgen and J. Zytkow (Eds.), Handbook of Data Mining and Knowledge Discovery. Oxford University Press, 874–881. preprint
  • Fisher, D. (2011). “Sustainability” in Leadership in Science and Technology: A Reference Handbook (preprint). William Sims Bainbridge, Ed: SAGE Publications, pp. 201–209.
  • Fisher, Douglas H. (2015). “Online Courses”. In Handbook of Science and Technology Convergence, Editors: William S. Bainbridge and Mihail C. Roco: Springer, pp. 1105–1118

Conferences (peer reviewed unless otherwise noted)

AAAI and IJCAI Conferences

  • Fisher, D., & Langley, P. (1985). “Approaches to Conceptual Clustering,” Proceedings of the International Joint Conference on Artificial Intelligence, Los Angeles, CA: Morgan Kaufmann, 691–697.
  • Schlimmer, J., & Fisher, D. (1986). “A Case Study of Incremental Concept Formation,” Proceedings of the Fifth National Conference on Artificial Intelligence, Philadelphia, PA: Morgan Kaufmann, 496–501.
  • Fisher, D. (1987). “Improving Inference Through Conceptual Clustering,” Proceedings of the Sixth National Conference on Artificial Intelligence, Seattle, WA: Morgan Kaufmann, 461–465.
  • Fisher, D. (1988). “A Computational Account of Basic Level and Typicality Effects”, Proceedings of the Seventh National Conference on Artificial Intelligence. Minneapolis, MN: Morgan Kaufmann, 233–238.
  • Fisher, D. (1989). “Noise-Tolerant Conceptual Clustering” Proceedings of the International Joint Conference on Artificial Intelligence, Detroit, MI: Morgan Kaufmann, 825–830.
  • Fisher, D., & McKusick, K. (1989). “An Empirical Comparison of ID3 and Back-propagation” Proceedings of the International Joint Conference on Artificial Intelligence, Detroit, MI: Morgan Kaufmann, 788–793.
  • Yoo, J., & Fisher, D. (1991). “Concept formation over explanations and problem-solving experiences” Proceedings of the International Joint Conference on Artificial Intelligence, Sydney, Australia: Morgan Kaufmann, 630–636.
  • Ortega, J., & Fisher, D. (1995). “Flexibly exploiting prior knowledge in empirical learning,” Proceedings of the International Joint Conference on Artificial Intelligence, (pp. 1041–1047). Montreal, Canada: AAAI Press.
  • Fisher, D. H. (2017). “A Selected Summary of Research in Computational Sustainability”, The 31st International Conference on the Advancement of Artificial Intelligence, 4852–4857
  • Roberts, J, Moore, K., Wilenzick, D., Fisher, D. (2024). Using Artificial Populations to Study Psychological Phenomena in Neural Models (preprint). The 38th International Conference on the Advancement of Artificial Intelligence. (earlier draft on arxiv).

International Conferences and Workshops on Machine Learning

  • Fisher, D. (1985). “A Proposed Method of Conceptual Clustering for Structured and Decomposable Objects,” in the Proceedings of the Third International Machine Learning Workshop, Skytop, PA, 38–40. (not peer reviewd)
  • Fisher, D. (1987). “Conceptual Clustering, Learning from Examples, and Inference,” Proceedings of the Fourth International Workshop on Machine Learning. Irvine, CA: Morgan Kaufmann.
  • Fisher, D., & Schlimmer, J. (1988). “Concept Simplification and Prediction Accuracy,” Proceedings of the Fifth International Machine Learning Conference. Ann Arbor, MI: Morgan Kaufmann.
  • Fisher, D., McKusick, K., Mooney, R., Shavlik, J., & Towell, G. (1989). “Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems” Sixth International Machine Learning Workshop, Ithaca, NY: Morgan Kaufmann.
  • Yang, H., & Fisher, D. (1989). “Conceptual Clustering of Means-Ends Plans,” Sixth International Machine Learning Workshop, Ithaca, NY: Morgan Kaufmann.
  • Yoo, J., & Fisher, D. (1989). “Conceptual Clustering of Explanations,” Sixth International Machine Learning Workshop, Ithaca, NY: Morgan Kaufmann.
  • Carlson, B., Weinberg, J., & Fisher, D. (1990). “Search Control, Utility, and Induction” Seventh International Conference on Machine Learning. Austin, TX: Morgan Kaufmann, pp. 85-92.
  • Fisher, D., & Yoo, J. (1991). “Combining evidence from deep and surface features” Proceedings of the International Workshop on Machine Learning, Chicago, IL: Morgan Kaufmann.
  • Yoo, J., & Fisher, D. (1991). “Identifying cost-effective boundaries of operationality” Proceedings of the International Workshop on Machine Learning, Chicago, IL: Morgan Kaufmann.
  • Fisher, D., Xu, L., & Zard, N. (1992). “Ordering Effects in Clustering,” Proceedings of the Eighth International Machine Learning Conference, Aberdeen, UK: Morgan Kaufmann.
  • Talbert, D., & Fisher, D. (1999). “OPT-KD: An Algorithm for Optimizing KD-Trees” Proceedings of the Sixteenth International Conference on Machine Learning, Bled, Slovenia (398–405). San Francisco, CA: Morgan Kaufmann.

Conferences of the Cognitive Science Society

  • Silber, J., & Fisher, D. (1989). “A Model of Natural Category Structure and its Behavioral Implications,” Proceedings of the Eleventh Annual Conference of the Cognitive Science Society, Ann Arbor, MI: Lawrence Erlbaum, 884–891.
  • Billman, D., Fisher, D., Gluck, M., Langley, P., & Pazzani, M. (1990). “Computational Models of Category Learning” (symposia), Proceedings of the Twelfth Annual Conference of the Cognitive Science Society, Boston, MA: Lawrence Erlbaum, 989–996.
  • Whitley, K., Novick, L., & Fisher, D. (2001). “Advantages of a visual representation for computer programming” (abstract), Proceedings of the Twenty-Third Annual Meeting of the Cognitive Science Society, Edinburgh, Scotland.
  • Frey, L., & Fisher, D. (2003). “Augmented Naive Bayesian Model of Classification Learning,” Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society Boston, MA (on CD).

Data Mining and Data Analysis Conferences and Workshops

  • Fisher, D. (1995). “Optimization and Simplification of Hierarchical Clusterings,” First International Conference on Knowledge Discovery in Databases, Montreal, Canada: AAAI Press, 118–123.
  • Frey, L., & Fisher, D. (1999). “Modeling Decision Tree Performance with the Power Law” Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, Lauderdale, FL: Morgan Kaufmann, 59–65.
  • Talbert, D., & Fisher, D. (1999). “Exploiting Sample-Data Distributions to Reduce the Cost of Nearest-Neighbor Searches with KD-Trees,” Advances in Intelligent Data Analysis, Lecture Notes on Computer Science No. 1642 (Third International Symposium on Intelligent Data Analysis), Amsterdam, Netherlands: Springer, 407–414.
  • Talbert, D. & Fisher, D. (2000). “An Empirical Analysis of Techniques for Constructing and Searching K-Dimensional Trees,” Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining, AAAI Press, 26–33.
  • Frey, L., Fisher, D., Tsamardinos, I., Aliferis, C., & Statnikov, A. (2003). “Identifying Markov Blankets with Decision Tree Induction,” Third IEEE International Conference on Data Mining, Melbourne, FL, 59-66.
  • Waitman, L. R., Fisher, D., & King, P. H. (2003). “Bootstrapping rule induction,” Third IEEE International Conference on Data Mining, Melbourne, FL, 677-680.
  • Fisher, D., Edgerton, M., Tang, L., Frey, L., & Chen, Z. (2005). “Searching for Meaningful Feature Interactions with Backward-Chaining Rule Induction,” Advances in Intelligent Data Analysis VI, Lecture Notes on Computer Science No. 3646 (Sixth Biennial Conference on Intelligent Data Analysis), Madrid, Spain: Springer, 86–96.

Other Conferences, Workshops, Symposia

  • Ortega, J., Lee, G., & Fisher, D. (1989). “Representation Issues in Learning from Examples” Second International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Tullahoma, TN: ACM Press.
  • Rodriguez-Moscoso, J., & Fisher, D. (1989). “A Connectionist Model of Intelligent, Real-Time Traffic Control” Second International Workshop on Neural Networks and Their Applications (Neuro-Nimes).
  • Rodriguez-Moscoso, J., & Fisher, D. (1989). “Intelligent, Real-Time Traffic Control: A Connectionist Model” Second International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Tullahoma, TN: ACM Press.
  • Fisher D., Yang, H., & Yoo, J. (1990). “Case-Based and Abstraction-Based Reasoning” AAAI Symposium on Case-Based Reasoning, Palo Alto, CA: AAAI Press.
  • Yang, H., Franke, H., & Fisher D. (1990). “Planning, Replanning, and Learning with an Abstraction Hierarchy” AAAI Symposium on Planning in Uncertain Environments, Palo Alto, CA: AAAI Press.
  • Yang, H., Fisher D., & Franke, H. (1990). “Improving Planning Efficiency by Conceptual Clustering” Third International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, ACM Press.
  • Fisher, D., Subramanian, D., & Tadepalli, P. (1992). “An overview of current research on knowledge compilation and speedup learning.” Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, UK.
  • Fisher, D., Manganaris, S., & Yoo, J. (1992). “Clustering Approaches to Speedup Learning,” Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, UK.
  • Fisher, D., Carnes, R., Yang, H., & Yoo, J. (1992). “Basic Levels of Problem Solving and Related Phenomena,” AAAI Workshop on Approximations and Abstractions, San Jose, CA.
  • Carnes, R., & Fisher, D. (1992). “Inductive Learning Approaches to Sensor Placement and Diagnosis,” Second International Workshop on Principles of Diagnosis, Rosario, WA.
  • Fisher, D. (1993). “Ordering Effects in Incremental Learning,” AAAI Spring Symposium on Training Issues in Incremental Learning, Palo Alto, CA.
  • Fisher, D., Ortega, J., & Gallaher, M. (1993). “Induction over All: A Hybrid Approach to Speedup Learning,” Third International Workshop on Knowledge Compilation and Speedup Learning, Amherst, MA.
  • Manganaris, S., Fisher, D., & Kulkarni, D. (1993). “Towards a Machine Learning Framework for Acquiring and Exploiting Monitoring and Diagnostic Knowledge,” The Eleventh International Conference on Applications of AI, Orlando, FL: International Society for Optical Engineering.
  • Manganaris, S., Fisher, D., & Kulkarni, D. (1993). “Induction of Operating Modes for Monitoring,” The Seventh Annual Space Operations, Applications, and Research Symposium, Houston, TX.
  • Ortega, J., & Fisher, D. (1993). “Inductive Speedup Learning Revisited with FOIL,” Third International Workshop on Knowledge Compilation and Speedup Learning, Amherst, MA.
  • Yang, H., & Fisher, D. (1993). “Planning Speedup by Learning, Reusing, and Patching Macro Operators,” Third International Workshop on Knowledge Compilation and Speedup Learning, Amherst, MA.
  • Manganaris, S., & Fisher, D. (1994). “Learning Time Series for Intelligent Monitoring,” Third International Symposium on Artificial Intellegence, Robotics, and Automation for Space (i-SAIRAS), pp. 71–74, Pasadena, CA.
  • Fisher, D., & Talbert, D. (1997). “Inference using Probablistic Concept Trees,” Sixth International Workshop on Artificial Intelligence and Statistics, Ft. Lauderdale, FL.
  • Balac, N., Gaines, D. & Fisher, D. (2000). Using Regression Trees to Learn Action Models. Proceedings of the IEEE Systems, Man, and Cybernetics Conference, Nashville.
  • Balac, N., Gaines, D., & Fisher, D. (2000). Learning Action Models for Navigation in Noisy Environments. International Conference on Machine Learning of Spatial Knowledge, Palo Alto.
  • Balac, N., Gaines, D., Fisher, D., & Thongchai, S. (2001). Learning Action Models to Support Efficient Navigation Planning for Unmanned Ground Vehicles. In Proceedings of SPIE Unmanned Ground Vehicle Technology Conference, Orlando.
  • Frey, L. J., Edgerton, M. E., Fisher, D. H., Tang, L., & Chen, Z. (2005) Using Prior Knowledge and Rule Induction Methods to Discover Molecular Markers of Prognosis in Lung Cancer. American Medical Informatics Association Symposium (Washington, DC).
  • Fisher, D. (2008). AI and Developing Socially-engaged Computational Thinkers (and talk slides), in AAAI Spring Symposium on Using AI to Motivate Greater Participation in Computer Science.
  • Fisher, D. & Maher, M. (2011). Towards Grammars for Cradle-to-Cradle Design In Proceedings of the AAAI Spring Symposium on Artificial Intelligence and Sustainable Design Technical Report SS-11-02. Published by The AAAI Press, Menlo Park, California. (not peer reviewed)
  • Fisher, Doug & Maher, Mary. (2011). Free Play in Contemplative Ambient Intelligence. In Ambient Intelligence: Second International Joint Conference, AmI 2011243-247. 10.1007/978-3-642-25167-2_32.
  • Maher, M.L. and Fisher, D.H. (2012). Using AI to Evaluate Creative Designs, In Proceedings of International Conference on Design Creativity, pp 45-54. (Glasgow, Scotland, UK)
  • Maher, M.L. and Fisher, D.H. (2012). The Role of AI in Wisdom of the Crowds for the Social Construction of Knowledge on Sustainability, AAAI Spring Symposium 2012 Wisdom of the Crowd. (Palo Alto, CA)
  • Fisher, D., Dilkina, B., Eaton, E., Gomes, C. (2012). Incorporating Computational Sustainability into AI Education through a Freely-Available, Collectively-Composed Supplementary Lab Text in Educational Advances in Artificial Intelligence. http://aaai.org/ocs/index.php/EAAI/EAAI12/paper/view/4888 (and associated Wikibook at http://en.wikibooks.org/wiki/Artificial_Intelligence_for_Computational_Sustainability:_A_Lab_Companion)
  • Dasarathy, B., Sullivan, K., Schmidt, D., Fisher, D., Porter, A. (2014). The Past, Present, and Future of MOOCs, In Proceedings of the 36th International Conference on Software Engineering, May 31 – June 7, 2014, Hyderabad, India.
  • Grace, K.; Maher, M. L.; Fisher, D.; and Brady, K. (2015). Modeling expectation for evaluating surprise in design creativity. In Gero, J., ed., Design Computing and Cognition’14. Springer. 189–206 (best paper award)
  • Ye, C., Kinnebrew, J., G., Biswas, G., Evans, B., Fisher, D., Narasimham, G., & Brady, K., (2015). Behavior Prediction in MOOCs Using Higher Granularity Temporal Information. Proceedings of the Second (2015) ACM Conference on Learning @ Scale. Vancouver, pp 335-338.
  • Fisher, D., Cameron, J., Clegg, T., and August, S. (2018). Integrating Social Good into CS Education. In SIGCSE ’18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education, February 2018 Pages 130–131. https://doi.org/10.1145/3159450.3159622
  • Brady, K., Sun, J. C., Narasimham, G., Fisher, D., Goodwin, A.  Is Scrolling Disrupting While Reading? The 13th International Conference on the Learning Sciences, London, June 23-27, 2018
  • Fisher, D. & Shin, H. (2019). Critique as Creativity: Towards Developing Commentators on Creative Works. In Proceedings of the International Conference on Computational Creativity, pp. 172-179. http://computationalcreativity.net/iccc2019/assets/iccc_proceedings_2019.pdf
  • Fisher, D., Markert, E., Roberts, A., & Varma, K. (2019). Region Radio: An AI that Finds and Tells Stories about Places In Proceedings of the International Conference on Computational Creativity, pp. 336-340 http://computationalcreativity.net/iccc2019/assets/iccc_proceedings_2019.pdf
  • Roberts, J. & Fisher,  D. (2020). pReview: The Artificially Intelligent Conference Re-
    viewer, IEEE International Conference on Machine Learning Applications.
  • Roberts, J. & Fisher, D. (2020). ”Extending the Philosophy of Computational Criticism,” International Conference on Computational Creativity.

Wikibooks (creator and solo author thus far)

Blogs

Technical Reports (selected)

Students Supervised

Doctoral Students

First employment is listed and is updated in some cases. Also, some publications of students (while they were under my supervision) that are not coauthored by me are listed, reflecting my philosophy and practice of endorsing their independent work.

  • Jungsoon Yoo (graduated Dec. 1991). Assistant Professor (now Professor), Department of Computer Science, Middle Tennessee State University. Dissertation: Concept Formation Over Explanations and Problem-Solving Experiences.
  • Hua Yang (graduated Dec. 1992). Project Manager/Research Associate, Department of Computer Science, UCLA (now Director of Search Science, Traffic and Invenory Insights at China Center of Excellence, EBAY, Shanghai) Dissertation: Learning Abstract and Macro Operators in AI Planning.
  • Rajeev Saraf (graduated August 1994). Indian Institute of Technology, New Dehli Dissertation: Adaptive Traffic Control Using Neural Networks. Co-advised with Mark Abkowitz (Civil Engineering).
  • Julio Ortega (graduated Dec. 1995). IBM Almaden Labs Dissertation: Making the Most of What You’ve Got: Using Models and Data to Improve Prediction Accuracy.
  • Stefanos Manganaris (graduated Dec. 1997). IBM Almaden Labs Dissertation: Supervised Classification with Temporal Data.
  • Kirsten Whitley (graduated May 2000). Department of Defense Dissertation: Empirical Research of Visual Programming Languages: An Experiment Testing the Comprehensibility of LabView
    • Whitley, K. (1997). “Visual Programming Languages and the Empirical Evidence For and Against, Journal of Visual Languages and Computing,” 8(1), pp. 109–142
    • Whitley, K. & Blackwell, A. F. (2001). “Visual Programming in the Wild: A Survey of LabView Programmers,” Journal of Visual Languages and Computing.
  • Doug Talbert (graduated May 2001). Assistant Professor, Biomedical Informatics Program, Vanderbilt University (now Interim Chair, Department of Computer Science, Tennessee Tech University). Dissertation: Empirical Analysis of KD-Tree Variants in Support of Nearest-Neighbor Search
  • Lewis Frey (graduated May 2003). Research Fellow in Department of Biomedical Informatics, Vanderbilt University Medical Center (now faculty at University of Utah). Dissertation: Augmented Naive Bayesian Model of Classification Learning.
  • Katherine Brady (graduated May 2019). Google Research. Dissertation: TESS: A tool for optically measuring digital reading interactions from screen recordings.
  • Jesse Roberts (expected graduation Summer 2024).
    • Roberts, J. (2021). ”Finding an Equilibrium in the Traveler’s Dilemma with Fuzzy Weak Domination,” IEEE International Conference on Games 2021. Nominated for
      best paper.
    • Roberts, J. (2023). On the Computational Power of Decoder-Only
      Transformer Language Models. arXiv preprint arXiv:2305.17026.
  • Kyle Moore (expected graduation Spring 2025).

Outside Doctoral Committees

  • Membership on many doctoral committees at Vanderbilt in CS, EE, CE, BME, Psychology, Education;
  • Two Georgia Tech (computer science) Ph.D. committees (Hong Shinn, 1989; Joel Martin, 1992);
  • One CMU (computer science) Ph.D. committee (Xuemei Wang, 1996);
  • the `opponent’ at a Ph.D. dissertation defense at the University of Stockholm (Sweden: Fredrik Kilander, 1994); and a member of Ph.D. committees at the
  • Universitat Politecnica de Catalunya (Spain: Javier Bejar, 1995),
  • Louis Pasteur University (France: Alain Ketterlin, 1995), and
  • Katholieke University (Belgium; Hendrik Blockeel, 1998)

Masters Students

A reader on other Masters student committees in CS and EE.

Invited Presentations, Panels, Tutorials, Interviews (Selected)

Most talks are based on research that is published and can be found under publications. A few presentations are not based on published research per se, and have an online presence:

  • Theological implications of perceived machine intelligence Invited presentation and panelist at the Symposium on Theological implications of machine intelligence. Carson-Newman College, Jefferson City, TN, September, 1998. (My thinking has changed somewhat on these matters, but the main concerns with degradation of perceptions of personhood are largely the same).
  • Artificial intelligence and machine learning: Now and the future Doug Fisher, associate professor of computer science and computer engineering at Vanderbilt University, talks about the state of the art in artificial intelligence and robotics in this interview by Adelyn Jones of WRLT FM radio in Nashville. The interview was aired Sunday, March 19, 2006 and was produced by Dan Buckley and Adelyn Jones. Music by John Scofield. (Used with permission from Tuned In Broadcasting and John Scofield.)
  • Intelligence in Context, National Science Foundation, March 2007. The talk discussed the desirability of various forms of synthesis — synthesis across the fragmented areas of AI and synthesis of ethics and contemporary issues with technical curricula generally. The importance of balanced research communities was discussed, by including those with strengths at synthesis, those that are strong depth-oriented (traditional) researchers, and those that can translate the findings of our field to inform policy on the important issues of our day such as rapid global climate change (modeling, predicting, mitigating, adapting). Open in a Web Browser and optionally take hyperlinks from main pages to auxiliary pages; return to the main pages’ storyline by way of the first small “house” encountered in the bottom left corner of a subsequent page.
  • Online Learning ITHAKA S+R Sustainable Scholarship Symposium, New York, NY, October 2012, my presentation begins about 26min)
  • Vanderbilt Campus Protest Speech (on Youtube) against Hate, January 17, 2015, invited by the protest leader as Faculty Director of Warren College.
  • Panelist on topic of Disinformation, Symposium on AI, Free Speech and Human Rights (Oct 13 Livestream, approximately 1:06:30 remaining), 2023.

All presentations that are not a paper presentation are listed below:

  • Approaches to Inductive Concept Learning. Oakridge National Laboratory. Oakridge, TN, April 1988.
  • Conceptual Clustering in Problem Solving. University of California, Irvine, CA, April 1988.
  • Conceptual Clustering: An Overview and Future Directions. Naval Research Laboratory, Washington, D.C., March 1988.
  • Models of Concept Formation in Problem-Solving. Georgia Institute of Technology, Atlanta, Georgia, April, 1989.
  • A Model of Natural Category Structure and it Paradigmatic Implications. ONR-sponsored Workshop on Complex Human Reasoning. Cornell University, Ithaca, NY, June, 1989.
  • Panel Discussant, Best Bets in Machine Learning (Ryszard Michalski, organizer). IEEE Workshop on Tools for Artificial Intelligence. Washington, D.C. October, 1989.
  • Concept Formation and Problem Solving. University of North Carolina Charlotte, NC, November, 1989 (Simulcast at Duke University and University of North Carolina, Chapel Hill).
  • Concept Formation and Problem Solving. Carnegie Mellon University, Pittsburgh, PA, November, 1989.
  • Basic Levels of Problem Solving. NASA Ames, Moffett Field, CA, July, 1990.
  • Machine Learning Tutorial. International Conference on Industrial and Engineering Applications of Artificial Intelligence, Charleston, SC, July, 1990.
  • Panel Discussant. Computational Approaches to Category Learning, Annual Conference of the Cognitive Science Conference, Boston, MA, July, 1990.
  • Panel Discussant. Challenges and Prospects for Intelligent Systems, International Conference on Industrial and Engineering Applications of Artificial Intelligence, Charleston, SC, July, 1990.
  • Concept Formation and Problem Solving. The Turing Institute, Glasgow, Scotland, August, 1990.
  • Concept Formation and Problem Solving. University of Aberdeen, Scotland, August, 1990.
  • Keynote Speaker, Florida AI Research Symposium, Cocoa Beach, Florida, April, 1991.
  • Problem Solving and Categorization, Texas Tech University, Lubbock, Texas, October, 1991.
  • Problem Solving and Categorization, Georgia Institute of Technology, Atlanta, Georgia, November, 1991.
  • Industrial Applications of Machine Learning, Middle Tennessee State University, Murfreesboro, TN, October, 1992.
  • Tutorial on Machine Learning, Fourth International Workshop on AI and Statistics, Ft. Lauderdale, FL, Jan. 1993
  • Panel Discussant, Industrial Applications of Machine Learning, AAAI Workshop on AI in Business, Washington, DC, July, 1993.
  • Opponent, Dissertation defense of Fredrik Kilander, Stockholm University, Sweden, May 1994.
  • Mitigating Process Delays in Printing using Machine Induction, Universitat Politecnica de Catalunya, Barcelona, Spain, January, 1995.
  • Keynote Speaker, Florida AI Research Symposium, Cocoa Beach, Florida, April, 1995.
  • Mitigating Process Delays in Printing using Machine Induction, Louis Pasteur University, Strasbourg, France, November, 1995.
  • Problem Solving, Categorization, and Concept Learning: A Unifying View, Louis Pasteur University, Strasbourg, France, November, 1995.
  • Invited Speaker, Lewis Society, Vanderbilt University, Natural Intelligence Meets Artificial Intelligence: Mitigating Process Delays in Printing using Machine Induction, November, 1995.
  • Mitigating Process Delays in Printing using Machine Induction, Carnegie Mellon University, June, 1996.
  • Mitigating Process Delays in Printing using Machine Induction, University of the South, October, 1997.
  • Panel member, Doctoral consortium, National Conference on Artificial Intelligence, July, 1998.
  • Organizing memory to optimize inference. Conference of the Society of Mathematical Psychology. Vanderbilt University, August, 1998.
  • Theological implications of perceived machine intelligence Invited presentation and panelist at the Symposium on Theological implications of machine intelligence. Carson-Newman College, Jefferson City, TN, September, 1998.
  • Mitigating Process Delays in Printing using Machine Induction, Katholieke University, Leuven, Belgium, December, 1998.
  • Evaluating Cluster Quality, Jet Propulsion Laboratory, Pasadena, CA, April, 1999.
  • Personal-data mining. Keynote Talk, Workshop on Data Mining, 9th Portuguese Conference on Artificial Intelligence, Evora, Portugal, September, 1999
  • Learning Multidimensional Trees from Data, University of the South, April, 2000.
  • Data Mining Applications, Integrated Data Systems 2000 Symposium, Vanderbilt University, November 2000.
  • Artificial intelligence and machine learning: Now and the future Doug Fisher, associate professor of computer science and computer engineering at Vanderbilt University, talks about the state of the art in artificial intelligence and robotics in this interview by Adelyn Jones of WRLT FM radio in Nashville. The interview was aired Sunday, March 19, and was produced by Dan Buckley and Adelyn Jones. Music by John Scofield. (Used with permission from Tuned In Broadcasting and John Scofield.)
  • Intelligence in Context, National Science Foundation, March 2007.
  • Numerous talks as an NSF Program Director overseeing research on AI and machine learning (2007 – 2010)
  • Sole member of the US delegation to OECD’s Workshop on ICTs and Environmental Challenges, Copenhagen, Denmark May 22-23, 2008 (with Webinar)
  • Member of the US delegation (incl NSF CISE Asst Director, State Department, Commerce Department) to High-Level OECD Conference on ICTs, the Environment, and Climate Change, Helsingør, Denmark May 27-28, 2009.
  • Predicting sustainability consequences as a source of constraints for creative design, International Conference on Computational Creativity, Mexico City, April 27-29, 2011
  • Panel Presenter, on Online Learning ITHAKA S+R Sustainable Scholarship Symposium, New York, NY, October 2012, (http://vimeo.com/53361649, my presentation begins about 26min)
  • “Regional Sections of Massively Open Online Courses” Presentation to the 18th Annual Conference of the Coalition of Urban and Metropolitan Universities, UT Chattanooga, October, 2012
  • Panel Presentation “Nurturing the Computational Sustainability Community” Third International Conference on Computational Sustainability, Copenhagen, July 2012.
  • Fisher, D., Dilkina, B., Eaton, E., Gomes, C. (2012). “Incorporating Computational Sustainability into AI Education through a Freely-Available, Collectively-Composed Supplementary Lab Text” (Fisher, Dilkina, Eaton, Gomes) Third International Conference on Computational Sustainability, Copenhagen, July 2012.
  • Presentation on Blended Learning with MOOCs at ABET Symposium, Portland OR, April 11-14, 2013
  • Presentation on A Software Engineering MOOC on behalf of Doug Schmidt at ABET Symposium, Portland OR, April 11-14, 2013
  • Panel Presentation on Incorporating MOOCs and other Online Resources into On-Campus Courses, Panel on Open Access Textbooks and MOOCs, Association of American University Presses (AAUP), Boston, June 21, 2013
  • Presentation with David Cordes on MOOCs at SEC Engineering Dean’s Meeting, Atlanta, GA August 1, 2013
  • Presentation on The Vanderbilt Institute for Digital Learning (VIDL) to Medical Grand Rounds, Vanderbilt University, School of Medicine, Sept 10, 2013
  • Presentation on A SWOT analysis of MOOCs to The Andrew W. Mellon Foundation, NYC, October 28, 2013
  • Presentation on A SWOT analysis of MOOCs to the Association of Research Libraries Leaders Fellows Institute, November 12, 2013
  • Speech Vanderbilt Campus Protest against Hate, January 17, 2015 (Youtube)
  • Speaker on Panel on Education in Cognitive Systems (with Patrick Winston and Ashok Goel) at the Third International Conference on Advances in Cognitive Systems, Georgia Tech, Atlanta, GA, May 28 – May 31, 2015
  • Presentation on Creating Community Education Meeting Grounds through Self-Paced, Open, Online Courses, STEM Think Tank and Conference, Harpeth Hall School, Nashville, July 13-15. 2016
  • Presentation on Region Radio: an Artificially Intelligent Story Teller for Learning on the Move at the Learning on the Move Symposium, July 27, 2017
  • Speaker at Residential College Symposium 2017 at Vanderbilt University, Nashville with Jim Lovensheimer and Matthew Sinclair October 27-29, 2016
  • Speaker at Residential College Symposium 2017 at Washington University, St Louis with Chalene Hemuth, “Variation among residential faculty roles: Understanding the connections”
  • Speaker at Residential College Symposium 2017 at Washington University, St Louis with Rishi Sriram, “Live & Learn: What Faculty need to know about residential living and learning”
  • Speaker and chair of panel on “Integrating Social Good into CS Education” at SIGCSE 2018, February 21-24.
  • Presentation at Emerge 2018 on AIs as Positive Role Models (Vanderbilt University, Oct 27, 2018)
  • Speaker at Residential College Symposium in Norman OK on The Role of Residential Faculty in Disasters, November 1-3, 2018
  • Commencement Talk, with Professor Helen Shin, The Ethics of Artificial Intelligence: from Computational to Humanistic. Pragmatic to Transcendental, May 9, 2019, Vanderbilt University
  • Keynote speaker, “Mechanisms for Advancing Broader Impacts of Computational Sustainability” at 2019 Computational Sustainability Doctoral Consortium, Carnegie Mellon University, October 18-20, 2019.
  • Invited Talk: Region Radio: A Convergence of Computational Creativity and Computational Sustainability at Tennessee Tech University, November 14, 2019
  • Panelist on WPLN “This is Nashville” How AI is Impacting Local Artists, live show January 5, 2023
  • Panelist, Panel on AI and Disinformation, Symposium on AI, Free Speech and Human Rights, Oct. 12-13, 2023, Nashville. Panel is discussed on Oct 13 stream at about 2:21:00 on the symposium site.
  • Invited Talk on Artificial Intelligence to the Annual Convention of the Southern Seed Association, January 15, 2024, Nashville, TM
  • Invited Talk: Understanding the Technology of AI, and followup discussion (Considering the Ethics of AI) and panel participation (Thinking in the Age of AI). Center for Theological Inquiry, Board of Trustees, Conference on AI, Princeton, February 3, 2024.
  • Tutorial Presenter, AAAI-24 tutorial on Cobweb, with Pat Langley and Chris MacLellan. My presentation on the Basics of Cobweb. February 21, 2024, Vancouver, CA.
  • Presenter (proxy), “Do LLMs Human-like Learn?“, AAAI Spring Symposium on Human-Like Learning, March 25-27, Stanford, CA.

Service (Selected)

Professional

Received NSF’s Director’s Award of Excellence in 2010 “in recognition of his intellectual leadership, his contributions to innovation in merit review, his leadership in the development of CISE’s scientific stance on the role of computing research in energy and the environment, and his deep commitment to the advancement of science and engineering.”

My Independent Research and Development (IRD) while serving at (and supported by) the NSF focused on applications of artificial intelligence to problems of sustainability and climate change, quantifying the energy footprints of computing infrastructure and its use (including its potential to reduce energy footprints in other sectors such as travel), and infusing computing education with treatments of contemporary and ethical issues so as to encourage the development of socially engaged computational thinkers.

  • Advised NSF/CISE Advisory Council representative (at his request) on promoting government service at NSF and other Federal Agencies (my blog post on “Life as a NSF Program Director” was featured at a CISE Advisory Council meeting (5/11/12) with the NSF Director, Subra Suresh, and Deputy Director, Cora Marrett: http://www.cccblog.org/2011/08/24/first-person-life-as-a-nsf-program-director/)

US Representative

Editor

  • Associate Editor, Machine Learning (1993 — 2002).
  • Associate Editor, IEEE Expert/Intelligent Systems (1994 — 1999).
  • Area Editor, AI and Sustainability, IEEE Intelligent Systems (beginning September 2011)

Boards

  • Member, Editorial Board of Machine Learning (1989 — 1993, 2002 — 2005).
  • Member, Governing Board of the Society for Artificial Intelligence and Statistics (1993–1999).
  • Member, Editorial Board of the Journal of Artificial Intelligence Research (1994–1997).
  • Member, Editorial Board of Intelligent Data Analysis — An International Journal (1996–2005).
  • Member, Editorial Board of the Journal of Machine Learning Research (2000–2005).
  • Member, Advisory Council of the Society for Intelligent Data Analysis (2001—2019).

Conference and Workshop Chairs

Conference and Workshop Committees

(Inter)National Conference on Artificial Intelligence (AAAI and IJCAI):

  • Member, Program Committee, Eighth National Conference on Artificial Intelligence, Boston, MA (1990).
  • Area Cochair (machine learning with Steve Minton), Program Committee, Ninth National Conference on Artificial Intelligence, Anaheim, CA (1991).
  • Member, Program Committee, Eleventh National Conference on Artificial Intelligence, Washington, DC (1993).
  • Member, Program Committee, Twelfth National Conference on Artificial Intelligence, Seattle, Washington (1994).
  • Member, Program Committee, Fourteenth National Conference on Artificial Intelligence, Portland, Oregon (1996).
  • Member, Program Committee, Computational Sustainability track, Twenty-Fifth National Conference on Artificial Intelligence, San Francisco, CA (2011).
  • Member, Senior Program Committee, Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Spain (2011).
  • Computational Sustainability, Special Track, Program Committee, 26th Conference of the Association for the Advancement of Artificial Intelligence, Toronto, CANADA, July 22-26, 2012: http://www.aaai.org/Conferences/AAAI/aaai12.php
  • Cognitive Systems, Special Track, Program Committee, 26th Conference of the Association for the Advancement of Artificial Intelligence, Toronto, CANADA, July 22-26, 2012: http://www.aaai.org/Conferences/AAAI/aaai12.php
  • Computational Sustainability, Special Track, co-Chair, 27th Conference of the Association for the Advancement of  Artificial Intelligence, Bellevue, WA, USA, July 14-18, 2013: http://www.aaai.org/Conferences/AAAI/aaai13.php

International Conferences and Workshops on Machine Learning:

  • Member, Program Committee, Fifth International Machine Learning Conference, Ann Arbor, MI (1988).
  • Member, Program Committee, Sixth International Machine Learning Workshop, Ithaca, NY (1989). Also Chair, “Incremental Learning” session.
  • Member, Program Committee, Seventh International Machine Learning Conference, Austin TX (1990).
  • Member, Organizing Committee, Eighth International Machine Learning Workshop, Evanston, IL (1991).
  • Member, Program Committee, Ninth International Machine Learning Conference, Aberdeen, UK (1992).
  • Member, Program Committee, Tenth International Machine Learning Conference, Amherst, MA (1993).
  • Member, Program Committee, Eleventh International Machine Learning Conference, Princeton, NJ (1994).
  • Conference (General and Program) Chair, Fourteenth International Conference on Machine Learning (1997).
  • Program committee, Fifteenth International Conference on Machine Learning. (1998)
  • Program committee, Sixteenth International Conference on Machine Learning. (1999)
  • Area Chair (Unsupervised Learning), Program Committee, Seventeenth International Conference on Machine Learning. (2000).
  • Program committee, Eighteenth International Conference on Machine Learning (2001).
  • Area Chair, Program Committee, Nineteenth International Conference on Machine Learning. (2002).
  • Program committee, Twentieth International Conference on Machine Learning (2003).

Other:

  • Member, Program Committee, Third Midwest Artificial Intelligence and Cognitive Science Society Conference, Carbondale, Illinois (1991).
  • Member, Program Committee, Fourth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Kauai, Hawaii (1991).
  • Member, Program Committee, Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, UK (1992).
  • Member, Program Committee, Workshop on Knowledge Compilation and Speedup Learning, Amherst, MA (1993).
  • Member, Program Committee, International Symposium on Integrating Knowledge and Neural Heuristics, Pensacola Beach, FL (1994).
  • Member, Program Committee, Sixth International Workshop on Artificial Intelligence and Statistics (1997).
  • Program committee, Seventh International Workshop on Artificial Intelligence and Statistics (1999).
  • Program Co-chair, Third International Symposium on Intelligent Data Analysis (1999).
  • Program committee, Eleventh European Conference on Machine Learning (2000).
  • General Chair, Fourth International Symposium on Intelligent Data Analysis (2001).
  • Program committee, 12th European Conference on Machine Learning (2001).
  • Vice Chair, Program Committee, Third IEEE International Conference on Data Mining, Melbourne, FL, Nov 19-22 (2003).
  • Member, Program Committee, Ninth ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 24-27 (2003).
  • Co-organizer, Artificial Intelligence and Sustainable Design, AAAI Spring Symposium (March 2011)
  • Co-Organizer of CRA/CCC Workshop on the Role of Information Sciences and Engineering in Sustainability (2011).
  • Member, Program Committee, Eighth International Conference on Intelligent Environments (IE’12), June 27-28, 2012, Guanajuato, Mexico, http://www.intenv.org/?q=conferences/ie12 (2012).
  • Participant in Dagstuhl Workshop on Massive Open Online Courses and Contributor to Dagstuhl Report and Manifesto (see link above) March 10-14 2014
  • Third International Conference on Computational Sustainability, Program Committee, July 4-6, 2012, Copenhagen, http://www.computational-sustainability.org/compsust12
  • Member, Program Committee, Fourth International Conference on Computational Sustainability, July 6-8, 2014, Ithaca, NY, http://www.compsust.net/compsust-2016/
  • Participant and contributor to the Computing Community Consortium (of CRA) 20 year roadmap on AI research (Integrated Intelligence working group, Chicago, 2018)
  • Participant and facilitator, NSF sponsored workshop on Expanding Capacity and Diversity in Lifelong AI Education, https://sites.google.com/uncc.edu/ai-education-workshop/.
  • Participant and collaborator in Policy Talks 2024, When Human Narratives meet Artificial Intelligence: Responsible Design and User Protection. Center for Practical Ethics, University of Mississippi, February 9, 2024
  • Facilitator for AI and Broadening Participation Working. Group at the CRA Level-Up Broadening Participation Workshop in Portland, Oregon (March 2024).
  • Co-organizer, Human-Like Learning, AAAI Spring Symposium (March, 2024)

Review Panels

  • Service on numerous NSF proposal review panels

University (Selected)

  • Member of Faculty Senate (mid-1990s)
  • Faculty-in-residence (McGill Hall) I hosted events for students at the faculty apartment and regularly participated in other activities on campus (e.g., I regularly attended and participated in student group meetings across campus, usually after regular business hours, to include weekly McGill Hours in the dorm itself)
  • Member, Committee on Social Media and Digital Technology (prepared and delivered the presentation to the committee on Engineering School use of Social and digital media and other tools; frequent blogger on Committee blog; among other activities) 2012
  • Member, College Halls Role Definition Committee
  • Member, Search Committee for Director of Office of Student Conduct and Academic Honesty
  • Member, Search Committee for Director of Religious Life
  • Member, Committee on Community Service (ACE)
  • Member, Selection Committee for Creative Campus Innovation Grants
  • Responsible Conduct of Research classes for graduate students (Mentor/Trainee relationships and Conflicts of Interest; Software Truth in Advertising), once each semester since 2011 to present.
  • Founding Faculty Director of Warren (residential) College, Vanderbilt University (2013-2019)
  • Founding Director of the Vanderbilt Institute for Digital Learning (2013-2015)
  • Chair, Technology Review Committee (2015-2018). Under my leadership, the committee significantly revised Vanderbilt IP policies
  • AI Grand Challenge advisory committee for A&S, chaired by Michael Bess and Ole Molvig (2022 – present).
  • Chair, Faculty Manual Committee of the Faculty Senate (2022-present, expected). Under my leadership, 2-3 motions a year on changes to faculty manual created and passed by Faculty Senate.

Department and School (Selected)

  • Advisor for CS majors Class of 1993
  • Director of Graduate Studies for Computer Science (early 1990s)
  • Director of Undergraduate Studies for Computer Science (2006-2007)
  • Advisor for CS majors who are not in School of Engineering (8/2020 – present)
  • Advisor for all CS minors (8/2020 – present)
  • Represented the CS Department at CRA Level-Up Broadening Participation Workshop (Atlanta, September 2023). My report to the Department.

Community (selected)

  • Director of the Middle Tennessee Science and Engineering Fair (1993-2003), overseeing Fair operations, participating in outreach, and taking representatives to the International Science and Engineering Fair.
  • Instructor for OSHER Lifelong Learning Class at Vanderbilt (periodically)

Honors and Awards

  • Chancellor’s Cup, Vanderbilt, “for extraordinary contributions outside of the classroom to foster relationships between undergraduate students and faculty.” (2006)
  • National Science Foundation (NSF) Director’s Award of Excellence “in recognition of his intellectual leadership, his contributions to innovation in merit review, his leadership in the development of CISE’s scientific stance on the role of computing research in energy and the environment, and his deep commitment to the advancement of science and engineering.” (2010)
  • KC Potter Award , presented annually by the Dean of Students Office to a faculty member and staff  member in recognition of “outstanding service to students” in honor of the former Dean of Residential and Judicial Affairs, KC Potter. (2012)
  • Outstanding Service Award for service to students as Faculty Director of Warren College (2019)

Teaching Innovations

 

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