Robert R. Snapp

Robert R. Snapp
Department of Computer Science,
University of Vermont
Burlington, Vermont 05405
[email protected]
1–802–656–0735
Employment
• Associate Professor of Computer Science, with a secondary appointment in Mathematics and Statistics,
University of Vermont, Burlington, VT 05405. August 1996 to present.
• Assistant Professor of Computer Science and Electrical Engineering, University of Vermont, Burlington, VT
05405. August 1990 to August 1996.
• Senior Research Fellow, Electrical Engineering Department, California Institute of Technology, Pasadena,
California, 91125. March 1990 to June 1990.
• Research Fellow, Electrical Engineering Department, California Institute of Technology, Pasadena, California,
91125. March 1987 to March 1990.
• Research Assistant, Physics Department, University of Texas, Austin, Texas, 78712. Jan. 1985 – Dec. 1986
• Mathematics Instructor Austin Community College, 1212 Rio Grande, Austin, Texas, 78701. Sept. 1980 –
May 1984.
• Teaching Assistant, Physics Department, University of Texas, Austin, Texas, 78712. Sept. 1978 – Dec. 1980.
• Numerical Analyst, Horizons Technology, Inc., 7830 Clairemont Mesa Blvd., San Diego, CA 92111 Sept. 1977–
June 1978.
Visiting Positions
• Visiting Faculty, Center for Applied Scientific Computing, Lawrence Livermore National Laboratory,
Livermore, CA, July to August 2000.
• Visiting Associate Professor of Electrical Engineering, The Technion, Haifa Israel, September 1998 to June
1999.
• Visiting Scientist Rome Laboratories, Rome, NY, June to August 1992.
• Visiting Scientist Rome Laboratories, Rome, NY, June to August 1991.
• Visiting Scientist IBM Burlington, Essex Junction, VT, July to August, 1990.
Academic Degrees
• Ph.D. in Physics, The University of Texas at Austin. Dissertation Title: A Stability Analysis of a Nonlinear
Optical Ring Cavity. December 1986.
• A.B. in Physics, Revelle College, University of California at San Diego. June 1978.
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Publications
Manuscripts under review
• Robert R. Snapp, Elyse Goveia, Lindsay Peet, Nichole A. Bouffard, Gary J. Badger, and Helene M. Langevin,
“Spatial organization of fibroblast nuclear chromocenters: component tree analysis,” (submitted, 2013).
• Lingbo Yu, Robert R. Snapp, Theresa Ruiz, Michael Radermacher, “Projection-based volume alignment,” (submitted, 2012).
Journal Articles
1. Gary W. Johnson, Kenneth J. Bagstad, Robert R. Snapp, and Ferdinando Villa, “Service path attribute networks
(SPANs): a network flow approach to ecosystem service assessment,” International Journal of Agricultural and
Environmental Information Systems, 3(2), July 2012, pp. 54–71,
2. Lingbo Yu, Robert R. Snapp, Theresa Ruiz, Michael Radermacher, “Probabilistic principal component analysis
with expectation maximization (PPCA - EM) facilitates volume classification and estimates the missing data,”
Journal of Structural Biology, 171 2010, pp. 18–30.
3. Helene M. Langevin, Kirsten N. Storch, Robert R. Snapp, Nicole A. Bouffard, Gary J. Badger, Douglas J.
Taatjes, “Tissue stretch induces nuclear remodeling in connective tissue fibroblasts,” Histochemistry and Cell
Biology, vol. 133(4), 2010, pp. 405–415.
4. R. Costanza, B. Fisher, S. Ali, C. Beer, L. Bond, R. Boumans, N. L. Danigelis, J. Dickinson, C. Elliott, J. Farley,
D. E. Gayer, L. MacDonald Glenn, T. Hudspeth, D. Mahoney, L. McCahill, B. McIntosh, B. Reed, S. A. T.
Rizvi, D. M. Rizzo, T. Simpatico, and R. Snapp. 2007. “An integrative approach to quality of life measurement,
research, and policy,” Surveys and Perspectives Integrating Environment and Society, 1(1), 2008, pp. 11–15.
5. Robert Costanza, Brendan Fisher, Saleem Ali, Caroline Beer, Lynne Bond, Roelof Boumans, Nicholas L.
Danigelis, Jennifer Dickinson, Carolyn Elliott, Joshua Farley, Diane Elliott Gayer, Linda MacDonald Glenn,
Thomas Hudspeth, Dennis Mahoney, Laurence McCahill, Barbara McIntosh, Brian Reed, S. Abu Turab Rizvi,
Donna M. Rizzo, Thomas Simpatico, and Robert Snapp, “Quality of life: an approach integrating opportunities,
human needs, and subjective well-being,” Ecological Economics, 61, 2007, pp. 267–276.
6. Zhen He, Byung S. Lee, and Robert R. Snapp, “Self-tuning cost modeling of user-defined functions in an objectrelational DBMS,” ACM Transactions on Database Systems, vol. 30, issue 3, 2005, pp. 812–853.
7. B. Lee, T. Critchlow, G. Abdulla, C. Baldwin, R. Kamimura, R. Musick, R. Snapp, and N. Tang, “The framework
for approximate queries on simulation data,” International Journal of Information Sciences, vol. 157, 2003,
pp. 3–20.
8. Byung S. Lee, Robert R. Snapp, Ron Musick, and Terence Critchlow, “Metadata models for ad hoc queries
on terabyte-scale scientific simulations,” Journal of the Brazilian Computer Society, vol. 8, no. 1, July 2002,
pp. 9–22.
9. R. R. Snapp and S. S. Venkatesh, “Asymptotic series representations of the finite-sample risk of k nearest
neighbor classifiers,” Annals of Statistics, vol. 26, no. 3, 1998, pp. 850–878.
10. Kenneth I. Golden, G. Kalman, Limin Miao, and Robert R. Snapp, “Retardation effects on collective excitations
in correlated superlattices,” Physical Review B, 57 1998, pp. 9883–9893.
11. Kenneth I. Golden, G. Kalman, Limin Miao, and Robert R. Snapp, “Plasmon and shear modes in correlated
superlattices,” Physical Review B, 55, 1997, pp. 16,349–16,358.
12. D. Psaltis, R. R. Snapp, and S. S. Venkatesh, “On the finite sample performance of the nearest neighbor classifier,” IEEE Transactions on Information Theory 40, 1994, pp. 820–837.
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13. C. Ji, R. R. Snapp, D. Psaltis, “Generalizing smoothness constraints from discrete samples,” Neural Computation
2 (1990), pp. 188–197.
14. R. R. Snapp and W. C. Schieve, “Singular perturbation analysis of the mean-field limit of semiclassical optics,”
Physical Review A 41 (1990), pp. 421–425.
15. H. J. Carmichael, R. R. Snapp, and W. C. Schieve, “Oscillatory instabilities leading to ‘optical turbulence’ in a
bistable ring cavity,” Physical Review A 26 (1982), pp. 3408–3422.
16. R. R. Snapp, H. J. Carmichael, and W. C. Schieve, “The path to ‘turbulence:’ optical bistability and universality
in the ring cavity.” Optics Communications 40 (1981), pp. 68–72.
Invited Articles and Book Chapters
17. J. C. Englund, R. R. Snapp, and W. C. Schieve, “Fluctuations, instabilities, and chaos in the laser-driven nonlinear ring cavity,” in Progress in Optics, ed. E. Wolf, Vol XXI. Amsterdam: North Holland Physics Publishing,
1984, pp. 355–428.
Conference Articles
18. Gary W. Johnson, Jr. and Robert R. Snapp, “Modelling ecosystem under uncertainty with stochastic SPAN,”
Proceedings of the International Congress on Environmental Modelling and Software (iEMSs), Leipzig, Germany, July 1–5, 2012. (6 pages).
19. John T. Evans, Robert R. Snapp, Gagan Mirchandani, Richard M. Foote, “Using wavelets for fast Monte Carlo
simulation of Ising systems with distribution matching,” Proceedings of the 2011 IEEE/SP 17th Workshop on
Statistical Signal Processing, 2011, pp. 313–316.
20. Gary W. Johnson, Kenneth J. Bagstad, Robert R. Snapp, Ferdinando Villa, “Service path attribution networks
(SPANs): spatially quantifying the flow of ecosystem services from landscapes to people,” Computational Science and Its Applications (ICCSA 2010), Lecture Notes in Computer Science, 6016, 2010, pp. 238–253.
21. Gagan Mirchandani, John T. Evans, Robert R. Snapp, Richard Foote, “Looking through wavelets to the Ising
problem,” Procedings of the 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 2009, pp. 777–780.
22. Duane C. Compton and Robert R. Snapp, “Detecting trace components in liquid chromatography/mass spectrometry data sets with two-dimensional wavelets,” Proceedings of SPIE: Wavelet Applications in Industrial
Processing V , Boston, August 2007.
23. Robert R. Snapp, “A PuzzlesFirst approach to computer science,” Proceedings of the 11th annual SIGSCE conference on Innovation and Technology in Computer Science Education (ITiCSE06), 2006, p. 310; also appears
in ACM SIGCSE Bulletin, Vol. 38, Issue 3, 2006, p. 310.
24. Robert R. Snapp, “Teaching graph algorithms in a corn maze,” Proceedings of the 11th annual SIGSCE conference on Innovation and Technology in Computer Science Education (ITiCSE06), 2006, p. 347; also appears in
ACM SIGCSE Bulletin, Vol. 38, Issue 3, 2006, p. 347.
25. Xianhua Jiang, Robert R. Snapp, Yuichi Motai, and Xingquan Zhu, “Accelerated kernel feature analysis,” Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR
2006), vol. 1, pp. 109–116.
26. Zhen He, Byung S. Lee, Robert R. Snapp, “Self-tuning UDF cost modeling using the memory limited quadtree,”
Proceedings of the 9th International Conference on Extending Database Technology (EDBT), Heraklion, Crete,
Greece, March 14-18, 2004. This paper is also known as UVM, CS Technical Report CS–03–18.
27. Robert R. Snapp, “Local polynomial metrics for k nearest neighbor classifiers,” in Joab Winkler and Mahesan
Niranjan, ed., Uncertainty in Geometric Computations, Kluwer, Boston, 2002, pp. 155–164.
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28. Byung S. Lee, Robert R. Snapp, and Ron Musick, “Ad hoc query support for very large scientific data: the
metadata approach,” Proceedings of the 16th Brazilian Symposium on Databases (SBBD), 2001.
29. G. Abdulla, C. Baldwin, T. Critchlow, R. Kammimura, I. Lozares, R. Musick, N. Tang, B. Lee, and R. Snapp,
“Approximate ad hoc query engine for simulation data,” Proceedings of the First ACM and IEEE Joint Conference on Digital Libraries (JCDL), 2001, pp. 255–256.
30. Byung S. Lee, Robert R. Snapp, and Ron Musick, “Towards a query language on simulation mesh data: an
object-oriented approach,” Proceedings of the International Conference on Database Systems and Advanced
Applications (DASFAA), 2001.
31. Omri Guttman, Ron Meir, and Robert R. Snapp, “Nonlinear Fisher discriminant using Mercer kernels,” Proceedings of Neural Computation in Science and Technology, (NCST–99), Oct. 10–13, 1999.
32. Alessandro Palau and Robert R. Snapp, “The labeled cell classifier,” in Anil K. Jain, Svetha Venkatesh, and
Brian C. Lovell, etc., Proceedings of the 14th International Conference on Pattern Recognition, IEEE Computer
Society Press, 1998, pp. 823–827.
33. Robert R. Snapp and Tong Xu, “Estimating the Bayes risk from sample data,” in D. S. Touretzky, M. C. Mozer,
and M. E. Hasselmo, ed., Advances in Neural Information Processing Systems 8, MIT Press, 1996, pp. 232–238.
34. Robert R. Snapp, “Predicting the accuracy of Bayes classifiers,” in K. M. Hanson and R. N. Silver, ed., Maximum
Entropy and Bayesian Methods: Sante Fe, New Mexico, U.S.A., 1995 Kluwer Academic Publishers, Dordrecht,
Netherlands, 1996, pp. 295–302.
35. Robert R. Snapp and Santosh S. Venkatesh, “k nearest neighbors in search of a metric,” Proceedings of the 1995
IEEE International Symposium on Information Theory, Whistler, BC, Canada, 1995, p. 256.
36. Robert R. Snapp and Santosh S. Venkatesh, “Asymptotic predictions of the finite-sample risk of the k-nearestneighbor classifier,” in Proceedings of the 12th International Conference on Pattern Recognition, (Jerusalem,
Israel), vol. 2, IEEE Computer Society Press: Los Alamitos, CA, 1994, pp. 1–7.
37. Robert R. Snapp and Santosh S. Venkatesh, “The finite-sample risk of the k-nearest-neighbor classifier under
the Lp Metric,” Proceedings of the 1994 IEEE-IMS Workshop on Information Theory and Statistics, Alexandria,
VA, 1994, p. 98.
38. Demetri Psaltis, Robert R. Snapp and Santosh S. Venkatesh, “On the finite-sample performance of the nearestneighbor classifier,” Proceedings of the 1993 IEEE International Symposium on Information Theory, San Antonio Texas, 1993, p. 354.
39. Santosh S. Venkatesh, Robert R. Snapp, and Demetri Psaltis, “Bellman Strikes Again: The rate of growth of
sample complexity with dimension for the nearest neighbor classifier,” Proceedings of the Fifth Annual ACM
Workshop on Computational Learning Theory (COLT) 1992, pp. 93–102.
40. Robert R. Snapp, Demetri Psaltis, and Santosh S. Venkatesh, “Asymptotic slowing down of the nearest-neighbor
classifier,” Richard M. Lippmann, John E. Moody, and David S. Touretzky, ed., Advances in Neural Information
Processing Systems, 3, Morgan Kaufmann, San Mateo, CA, 1991, pp. 932–938.
41. John C. Englund, Robert F. Gragg, William C. Schieve, and Robert R. Snapp, “Fluctuations and instabilities in
laser-like systems,” Peralta Fabi, ed., Proc. 1st Escuela Mexicana de Fisica Estadstica, Soc. Mex. Fisica, 1983
Technical Reports
1. R. R. Snapp and S. S. Venkatesh, “Asymptotic derivation of the finite-sample risk of the k nearest neighbor
classifier,” Technical Report UVM-CS-1998-0101, Department of Computer Science, University of Vermont,
1998.
2. R. R. Snapp, “IPToolkit: an image processing environment for the X Window system,” Final Report for the
Summer Faculty Research Program, Rome Laboratory, 1993.
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Conference Abstracts
1. Robert R. Snapp, Lindsay S. Peet, Gary J. Badger and Helene M. Langevin. “Spatial redistribution of fibroblast
nuclear chromocenters in response to tissue stretch: component tree analysis,” Canadian Connective Tissue
Conference, University of Toronto, Toronto, Canada, June 8–12, 2012.
2. Gary W. Johnson, Jr. and Robert R. Snapp, “Tackling the spatial scale problem in ecosystem service modelling: hierarchies, information, and performance,” the 4th International EcoSummit: Ecological Sustainability,
Restoring the Planet’s Ecosystem Services, Ohio State University, Columbus, OH, October 2, 2012.
3. Lingbo Yu, Robert R. Snapp, Teresa Ruiz, Michael Radermacher, “Projection-based volume alignment,” 68th
Annual Meeting of the Microscopy Society of America, July 29–Aug. 2, 2012.
4. Gary W. Johnson, Jr. and Robert R. Snapp, “Modelling ecosystem under uncertainty with stochastic SPAN,” National Academies Keck Futures Initiative Conference on Ecosystem Services, UC Irvine, Irvine, CA, November
11–13, 2011.
5. Lingbo Yu, Robert Snapp, Teresa Ruiz, Michael Radermacher, “Probabilistic principal component analysis with
expectation maximization (PPCA - EM) facilitates volume classification and estimates the missing data,” 66th
Annual Meeting of the Microscopy Society of America, 2010.
6. Lingbo Yu, Robert Snapp, Teresa Ruiz, Michael Radermacher, “Multivariate statistical analysis of volumes with
missing data,” 35th Annual Meeting of the Microscopical Society of Canada, 2008.
Invited Talks
• Vermont Council of Teachers of Mathematics, Spring Meeting, Vermont Technical College, Randolph Center,
VT, March 28, 2011.
• Computing Educators Meeting, Vermont Technical College, Williston, VT, May 23, 2007.
• Department of Industrial Engineering, The Technion, Haifa, Israel, April 24, 1999.
• Department of Computer Science, The Technion, Haifa, Israel, February 28, 1999.
• Computer Science Department Colloquium, SUNY Buffalo, October 6, 1995.
• Siemens Corporate Research, Princeton, NJ, June 16, 1995
• Computer Science Department Colloquium, Concordia University, Montreal, Canada, May 11, 1995.
• Electrical and Computer Engineering Department Colloquium, Rensselaer Polytechnic Institute, Troy, NY, April
13, 1995.
• St. John’s College, Cambridge University, England, October 3, 1994
• Computer Science and Electrical Engineering Department Colloquium, University of Vermont, Burlington, VT,
March 4, 1994.
• Electrical Engineering Department, California Institute of Technology, Pasadena, CA, February 16 and 18, 1994.
• Mathematics Department Colloquium, University of Vermont, Burlington, VT, March 1993
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Software Developed
• blobify: A clojure project that uses component trees to identify chromocenters in laser scanning microscope
image stacks of DAPI-stained cell nuclei, and quantify their size and spatial distribution (2011). Available for
download at github.com/RobertSnapp/blobify.
• nuclearShape: A C++ subroutine library that enables universal access to image data contained stored in the
Zeiss laser scanning microscope image format (2006). Available for download at
github.com/RobertSnapp/nuclearShape.
• pstool: An X Window program that classifies pixels in Landsat thematic mapper satellite images. Developed in
1995.
• knncToolkit.m: A Mathematica notebook that graphs the decision boundaries of a k-nearest-neighbor classifier,
given a reference sample of labeled, two-dimensional feature vectors. Developed in 1994.
• IPToolkit: A C language subroutine library that supports a variety of image processing operations (e.g., image
enhancement, edge detection, and segmentation) for TIFF image files under the X Window System. Developed
with Robert L. Stevenson in 1993.
Grants & Contracts
Proposals Under Review
• “CS 10K: A Partnership for Innovation in Computer Science Education and 21st Century Learning in Vermont
(PICSE-21),” with Regina E. Toolin (PI), Cynthia Gerstl-Pepin, Maureen Neumann, and Alan Tinkler. National
Science Foundation, $974,948, 2012–2015 (Declined. Will reapply in 2013).
Current Grants
• “Wavelets and the Ising Problem—A Gateway to Monte Carlo and Molecular Dynamic Simulations,” with
Gagan Mirchandani (PI) and Richard Foote, NASA-EPSCoR, $28,000, 2009–2012.
Completed Grants & Contracts
• “Towards a Multiscale Wavelet Transform—Using Wavelet Solutions for Multiscale Modeling,” with Gagan
Mirchandani (PI) and Richard Foote, UVM Graduate College, $30,000, 2009–2011.
• “Verification and Validation of Feedforward Neural Networks for Safety-Critical Applications,” Vermont-NASA
EPSCoR Small-Scale Grant, $19,386, 2005.
• “Development of CS 5, ‘Puzzles and Games:’ A Novel Introduction to Computer Science,” University of Vermont Instructional Incentive Grant, $5,000, 2003–2004
• “Verification and Validation of Neural Networks for Safety-Critical Applications,” Goodrich Aerospace, $12,000,
2001–2002.
• “Finite Sample Analysis of Nearest Neighbor Algorithms,” US Army Research Office, $172,436, 1997–2001.
• “CISE Research Instrumentation: A High-performance Computing Facility for Experimental Algorithms,” with
Sanjoy Baruah (PI), Charles Colbourn, Yuanyuan Yang, and Guoliang Xue, National Science Foundation,
$50,000, 1997–1998.
• US Air Force, “Pattern Recognition and Image Analysis Extensions to the IE2000 IPToolkit,” US Air Force,
$128,735, 1994–1997.
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• UVM Research Advisory Council Equipment Grant, with Jeffrey Siskind (PI) and Charles Colbourn), $10,000,
1996.
Doctoral Dissertations Supervised
• Gary W. Johnson, TBA, Ph.D. Computer Science, University of Vermont, in progress.
• Lingbo Yu, 3D Reconstruction in Electron Microscopy: Novel Alignment and Statistical Analysis Algorithms
for Volumes with Missing Data, co-supervised with Michael Radermacher, Ph.D. Computer Science, University
of Vermont, Dec. 2012.
• Alessandro Palau, Implementing Nearest Neighbor Classifiers, Ph.D. Electrical Engineering, University of Vermont, May 1997.
Master’s Theses Supervised
• John T. Evans, A Wavelet-Based Accelerated Monte Carlo Algorithm for Multiscale Simulation of the Ising
Model, (co-advisor with Gagan Mirchandani and Richard Foote), M.S. Electrical Engineering, University of
Vermont, January 2011.
• Duane Compton, TWiGS: A New Algorithm for Denoising Liquid-Chromatogrphic-Mass-Spectrometric Data,
M.S. Computer Science, University of Vermont, May 2008.
• Dan Nardi, Finding Every Occurrence of a Given Device in a VLSI Layout, M.S. Computer Science, University
of Vermont, February 2004.
• Gabriel Kontrovitz, Real-Time Three-Dimensional Line-Art Rendering, M.S. Computer Science, University of
Vermont, May 2000.
• Chaoyu Jin, The Dependence of Approximation Error on the Sample Size for Feedforward Neural Networks,
M.S. Electrical Engineering, University of Vermont, October 1997.
• Xinguan Li, Controlling Traffic Signals at Isolated Intersections using Q-Learning, M.S. Electrical Engineering,
University of Vermont, October 1997.
• Tong Xu, Estimating the Infinite-Sample Risk for the k Nearest Neighbor Classifier, M.S. Electrical Engineering,
University of Vermont, October 1995.
• Ross Deming, Neural Networks for Selective Edge Detection, M.S. Electrical Engineering, University of Vermont, October 1993.
Master’s Projects Supervised
• Ron Magnuson, The 3D Graphical Neural Network Toolkit, M.S. Computer Science, University of Vermont,
March 2007.
Undergraduate Honors Theses Supervised
• Sam Brown, Data Mining from Nonverbal Characteristics of Computer Mediated Communication, B.S. Computer Science, University of Vermont, May 2012.
• Jonathan Parker, Reinforcement Learning and Poker, B.S. Computer Science, University of Vermont, May 2008.
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Courses Taught at the University of Vermont
The following table lists courses taught at the University of Vermont, along with enrollments, and quantitative ratings by students
polled by written or online questionnaires. The ratings for question #6, “overall rating of instructor” are defined on scale 1 = poor;
2 = unsatisfactory; 3 = satisfactory; 4 = good; and 5 = excellent.
Semester
Fall 2013
Spring 2013
Fall 2012
Spring 2012
Fall 2011
Spring 2011
Fall 2010
Spring 2010
Fall 2009
Spring 2009
Fall 2008
Spring 2008
Fall 2007
Spring 2007
Fall 2006
Summer 2006
Spring 2006
Fall 2005
Spring 2005
Fall 2004
Spring 2004
Fall 2003
Course
CS 32: Puzzles, Games & Algorithms (with TAP)
CS 295: Computer Vision
CS 195: Probability Models & Inference
CS 295: Machine Learning
CS 32: Puzzles, Games & Algorithms (with TAP)
CS 276: Integrative Computing
CS 195: Probability Models in Computer Science
CS 295: Machine Learning
CS 32: Puzzles, Games & Algorithms (with TAP)
CS 276: Integrative Computing
CS 195: Probability Models in Computer Science
CS 256: Neural Computation
CS 32: Puzzles, Games & Algorithms(with TAP)
HCOL 196: Computers of the Future
CS 294: Information Theory
CS 32: Puzzles, Games & Algorithms (with TAP)
CS 64: Discrete Structures
CS 294: Photorealistic Computer Graphics
EE 270: Stochastic Processes
CS 32: Puzzles, Games & Algorithms (with TAP)
CS 274: Computer Graphics
CS 256: Neural Computation
CS 294: Algorithms for Wind Velocity Forecasting
EE 295: Image Processing (team taught)
CS 32: Puzzles, Games & Algorithms (with TAP)
CS 251: Artificial Intelligence
CS 295: Information & Complexity
CS 32: Puzzles, Games & Algorithms
CS 274: Computer Graphics
CS 32: Puzzles, Games & Algorithms
HCOL 196: Computers of the Future
CS 294: Algorithmic Interpretations of Conceptual Dependency Graphs
CS 32: Puzzles, Games & Algorithms
CS 355: Statistical Pattern Recognition
CS 256: Neural Computation
CS 294: Professional Ethics
CS 294: Real-time Rendering Algorithms
CS 32: Puzzles, Games & Algorithms
CS 274: Computer Graphics
CS 294: Advanced Rendering Algorithms
CS 265: Computer Networks
CS 294: Reinforcement Learning
CS 394: Pattern Recognition
CS 5: Puzzles, Games & Algorithms
CS 274: Computer Graphics
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Enrollment
TBA
TBA
50
9
31
12
13
16
31
7
9
7
20
6
1
34
36
3
6
26
9
16
1
8
27
13
6
17
6
3
10
1
29
7
10
1
1
24
8
1
11
1
1
13
9
Rating
4.63
4.57
4.24
4.50
4.00
5.00
4.33
3.83
4.46
3.83
—
4.56
3.57
4.67
4.20
4.45
4.62
4.92
—
—
4.68
3.91
5.00
4.62
4.60
—
4.80
—
4.57
5.00
4.78
—
—
4.28
4.67
5.00
4.12
5.00
—
3.40
4.22
Semester
Spring 2003
Fall 2002
Summer 2002
Spring 2002
Fall 2001
Spring 2001
Fall 2000
Spring 2000
Fall 1999
Spring 1998
Fall 1997
Spring 1997
Fall 1996
Spring 1996
Fall 1995
Spring 1995
Fall 1994
Spring 1994
Fall 1993
Spring 1993
Fall 1992
Spring 1992
Fall 1991
Spring 1991
Fall 1990
Course
CS 256:
CS 381:
CS 251:
CS 274:
CS 294:
CS 265:
CS 295:
CS 274:
CS 394:
CS 251:
CS 265:
CS 274:
CS 256:
CS 265:
CS 274:
Neural Computation
Graduate Seminar on Machine Learning (1 credit)
Artificial Intelligence
Computer Graphics
Computer Vision
Computer Networks
Statistical Pattern Recognition
Computer Graphics
Reinforcement Learning
Machine Intelligence
Computer Networks
Computer Graphics
Neural Computation
Computer Networks
Computer Graphics
Sabbatical Leave (Fall 1998—Spring 1999)
CS 103: Programming Languages
CS 274: Computer Graphics
CS 251: Machine Intelligence
EE 142: Electromagnetic Field Theory II
CS 295: Computer Graphics
EE 141: Electromagnetic Field Theory I
EE 142: Electromagnetic Field Theory II
EE 395: Theory of Neural Computation
EE 141: Electromagnetic Field Theory I
EE 270: Probability Theory & Stochastic Processes
EE 142: Electromagnetic Field Theory II
EE 141: Electromagnetic Field Theory I
EE 277: Image Analysis & Pattern Recognition
EE 142: Electromagnetic Field Theory II
EE 378: Statistical Communication
EE 141: Electromagnetic Field Theory I
EE 4: Engineering Analysis II (Linear Circuit Theory)
EE 3: Engineering Analysis I (Linear Circuit Theory)
EE 295: Neural Computation
EE 242: Electromagnetic Field Theory II
EE 241: Electromagnetic Field Theory I
EE 242: Electromagnetic Field Theory II
EE 295: Artificial Neural Networks (team taught)
EE 241: Electromagnetic Field Theory I
Enrollment
3
16
14
18
1
26
8
21
1
13
30
8
10
20
12
Rating
4.50
4.23
4.56
4.07
—
4.27
4.43
4.46
—
3.90
3.59
4.29
3.00
—
3.67
26
11
11
4
18
9
16
5
19
4
14
12
2
8
1
9
25
34
9
4
3
3
8
7
3.31
4.25
3.88
—
4.00
3.50
4.00
5.00
4.00
5.00
3.55
3.00
5.00
3.50
—
4.22
3.44
3.76
4.70
4.50
5.00
5.00
4.43
4.67
(Note: From Fall 1990 through Fall 1995 the College questionnaire evaluated teaching using a numerically decreasing
scale, from 1 (excellent) to 5 (poor). For the sake of consistency, values reported above during from 1990–1995 have
been inverted by subtracting each original value from 6.00.)
Other Instruction
• Summer 2011, Instructor of “Combinatorial Game Theory,” a five-day course, for the Governor’s Institute on
Mathematics, UVM.
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• Fall 2004, delivered five lectures on the Ethics of File Sharing to students enrolled in HCOL 95, the first-year
seminar course of the Honors College.
• Summer 1997, Instructor for the 1997 Summer Enrichment Institute, sponsored by the Vermont State Mathematics Coalition, UVM. Summer course on mathematical games.
• Summer 1996, Instructor for the 1996 Summer Enrichment Institute, sponsored by the Vermont State Mathematics Coalition, UVM. Course on pattern recognition and neural networks.
• At Caltech in late 1980s, I co-instructed EE 124, Statistical Pattern Recognition; and presented two lectures for
CS 286, Theory of Neural Networks.
• At Austin Community College in mid 1980s, I instructed Calculus I, Precalculus, Trigonometry, and College
Algebra.
• At the University of Texas in mid 1980s, I taught introductory laboratory courses in mechanics, electronics, and
optics.
• At UCSD in mid 1970s, I tutored students in mathematics and science for the OASIS program, and was a teaching
assistant for Physics 2A& B.
Professional Service
• Refereed manuscripts for
– Annals of Statistics.
– IRE Proceedings on Science, Measurement, & Technology.
– IEEE Transactions on Computers.
– IEEE Transactions on Reliability.
– IEEE Transactions on Circuits and Systems.
– IEEE Transactions on Information Theory.
– IEEE Transactions on Neural Networks.
– Neural Networks.
– Physical Review A.
• Member of program committees for
– Conference on Innovation and Technology in Computer Science Education (ITiCSE06, ITiCSE07).
– IEEE Workshop on Neural Networks for Signal Processing in 2000, 2001, 2002, 2003, and 2004.
– Annual Technical Meetings of ACM SIGCSE.
• Referee for the National Science Foundation.
• Referee for the U.S. Army Research Office.
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University Service
University Committees and Offices
Term
Department
2012–13
2011–12
CS Graduate Program Coordinator
CS Curriculum, Chair
2010–11
CS Curriculum, Chair
2009–10
CS Curriculum, Chair
2008–09
CS Curriculum, Chair
2007–08
CS Curriculum, Chair
2006-07
CS Curriculum, Chair
2005-06
CS Curriculum, Chair
CS Faculty Search (Bongard)
CS Curriculum
EE Ph.D. Comp. Exam
CS Curriculum
CS Graduate Program
CS Curriculum
CS Graduate Program
2004–05
2003–04
2002–03
2001–02
2000–01
1999–00
College
CEMS Curriculum
CEMS Honors & Awards, Chair
Faculty Marshal (5/20/12)
CEMS Parliamentarian
CEMS Curriculum
CEMS Honors & Awards, Chair
Faculty Marshal
CEMS Parliamentarian
CEMS Curriculum
CEMS Honors & Awards, Chair
CEMS Parliamentarian
CEMS Curriculum
CEMS Parliamentarian
CEMS Ad Hoc Bylaws Revision
CEMS Faculty Marshal
CEMS Faculty Standards, Chair
CEMS Parliamentarian
CEMS Ad Hoc Bylaws Revision
CEMS Faculty Advisory
CEMS Faculty Marshal
CEMS Parliamentarian
CEMS Faculty Advisory
Curriculum 21 Math
Curriculum 21 Science
CEMS Faculty Marshal
CEMS Faculty Marshal
CEMS Faculty Standards, Chair
CEMS Faculty Marshal
CEMS Faculty Standards, Chair
CEMS Faculty Marshal
CEM Faculty Standards
Dorothean Search (Wang)
CAS Commencement Dept. Rep.
CEM Faculty Marshal
CEM Faculty Standards
CAS Commencement Dept. Rep.
CEM Faculty Marshal
CS Curriculum
CS Faculty Search (Skalka), Chair
CS Faculty Search (Arslan)
CS Budget & Policy
CS Curriculum
CEM Faculty Standards
CS Senior Faculty Search (Wu)
CEM Faculty Marshal
CS Junior Faculty Search
(Eppstein, Ling)
CS Graduate Director
CS Acting Chair
CS Curriculum
CEM Faculty Standards
CS Faculty Search (Damon)
Faculty Marshal
CS Graduate Director
Honors Day Dept. Rep.
Sabbatical Leave, Fall 1998–Spring 1999.
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UVM
Honors College Council
Honors College Curriculum
Faculty Marshal (12/17/11)
Honors College Council
Honors College Curriculum
Honors College Council
Honors College Curriculum
Faculty Marshal
Honors College Council
Honors College Curriculum
Honors College Council
Honors College Curriculum
CS Faculty Senator
Honors College Council
Honors College Curriculum
CS Faculty Senator
ACCESS Faculty Advisor
CS Faculty Senator
ACCESS Faculty Advisor
CS Faculty Senator
ACCESS Faculty Advisor
CS Faculty Senator
CS Faculty Senator
Faculty Senate CS Rep.
Faculty Senate Technology
DOE-EPSCoR Comp. Bio.
Advisory
Faculty Senate CS Rep.
Faculty Senate Technology
Graduate Program Review
Faculty Senate CS Rep.
Faculty Senate Technology
Term
1997–98
1996–97
1995–96
1994–95
1993–94
1992–93
1991–92
1990–91
Department
CS Curriculum
CS Graduate Director
CS Faculty Search, Chair (∅)
CS Curriculum
EE Curriculum
EE Graduate Program Director
EE Curriculum
EE Graduate Program
EE Curriculum
CS Faculty Search
(Baruah, Pruesse)
EE Curriculum
EE Ph.D. Comp. Exam
CS Faculty Search (Yang, Xue)
EE Curriculum
EE Ph.D. Comp. Exam
EE Curriculum
College
UVM
CEM Commencement Speech
Chair Review (Math)
Dorothean Chair Search
(Colbourn)
Dorothean Chair Search (∅)
Dorothean Chair Search (∅)
Dorothean Chair Search (∅)
Dorothean Chair Search (∅)
Other University Service
• Wrote and initiated the new Code of Academic Integrity for UVM (2002–2005).
• Advisor of 10 first-year students deemed to be at “at risk” by the College of Arts and Sciences (2005–2006).
• Member of the Graduate College Program Review Committee, (2001).
• Member of the DOE EPSCoR Computational Biology Advisory Committee (2002).
Honors & Awards
• Nominee for the 2007 Kroepsch-Maurice Excellence in Teaching Award at the Associate Professor Category.
• Nominee for the 2006 Kroepsch-Maurice Excellence in Teaching Award at the Associate Professor Category.
• University of Vermont, Class of 2004 Appreciation Award.
• University of Vermont, Class of 2002 Appreciation Award.
• Chaim Weizmann Research Fellowship in Electrical Engineering, California Institute of Technology, (1987–
1989).
• Robert A. Welch Foundation Graduate Fellowship, University of Texas at Austin, (1980–1984).
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