David Haglin's Brief Resume

Professional Experience

2007-      Interim Associate Dean, College of Science, Engineering, and Technology

2005-2007  Department Chair

2002-      Professor, Computer & Information Sciences Dept.,
           Minnesota State University, Mankato

2002-2005  Editor of ACM SIGACT News.

1994-2002  Associate Professor, Computer & Information Sciences
           Dept., Mankato State University.

1991-1994  Assistant Professor, Computer & Information Sciences
           Dept., Mankato State University.  Graduate Coordinator
           in Computer Science September, 1992 -- August, 1994.

1990-1991  Adjunct Professor, Computer Science Department,
           University of Minnesota.

1990-1991  Senior Programmer, Data Communication Software, Unisys.

1986-1990  Systems Programmer, Data Communication Software, Sperry
           Corp/Unisys.

1983-1986  Associate Programmer, Data Communication Software,
           Sperry Corp.


Journal Publications

  1. Haglin, D. J., K. R. Mayes, A. M. Manning, J. Feo, J. R. Gurd, M. Elliot and J. A. Keane, “Factors Affecting the Performance of Parallel Mining of Minimal Unique Itemsets on Diverse Architectures,” Concurrency and Computation: Practice and Experience, to appear.
  2. Manning, Anna M., David J. Haglin and John A. Keane, “A Recursive Search Algorithm for Statistical Disclosure Assessment,” Data Mining and Knowledge Discovery, vol 16 no 2, pp. 165-196, April 2008. ISI Impact Factor (2006): 2.295
  3. Jon Hakkila, Timothy W. Giblin, Kevin C. Young, Stephen P. Fuller, Christopher D. Peters, Chris Nolan, Sarah M. Sonnett, David J. Haglin, and Richard. J. Roiger, “A Gamma-Ray Burst Database of BATSE Spectral Lag and Internal Luminosity Function Values,” The Astrophysical Journal Supplement Series, Vol. 169 no. 1, pp. 62-74, 2007.
  4. D. Haglin, R. Roiger, J. Hakkila and T. Giblin, “A Tool for Public Analysis of Scientific Data,” Data Science Journal, vol 4, pp. 39-53, August 2005.
  5. Hakkila, J., T. W. Giblin, K. C. Young, D. J. Haglin, R. J. Roiger, J. P. Norris, and J. D. Bonnell, “Probing GRB Jet Structure from Prompt Emission,” Chinese Journal of Astronomy and Astrophysics, vol 5 Suppl., pp. 171-176, 2005.
  6. Hakkila, J., T. W. Giblin, R. J. Roiger, D. J. Haglin, W. S. Paciesas, and C. A. Meegan, “How Sample Completeness Affects Gamma-Ray Burst Class Classification,” Astrophysical Journal, vol. 582, pp. 320-329, 2003.
  7. David J. Haglin and Rupert W. Ford, “The Message-Minimizing Load Redistribution Problem,” Journal of Universal Computer Science, vol. 7 no. 4, pp. 291-306, 2001.
  8. Hakkila, J., D. J. Haglin, G. N. Pendleton, R. S. Mallozzi, C. A. Meegan, and R. Roiger, “Gamma-Ray Burst Class Properties,” Astrophysical Journal, vol. 538, pp. 165-180, 2000.
  9. Wolf, Marty J., and David J. Haglin, “ An Optimal Algorithm for Finding All Convex Subsets in Tournaments,” Ars Combinatoria, vol. 52, pp. 173-179, 1999.
  10. Kaliski, J.A., D.J. Haglin, C. Roos, and T. Terlaky, “Logarithmic Barrier Decomposition Methods for Semi-Infinite Programming,” International Transactions in Operational Research, vol. 4 no. 4, pp. 285-303, 1997.
  11. Haglin, D.J., and M.J. Wolf, “On Convex Subsets in Tournaments”, SIAM Journal on Discrete Mathematics, vol. 9 no. 1, pp. 63-70, February, 1996.
  12. Haglin, David J., “Bipartite Expander Matching is in NC”, Parallel Processing Letters, vol. 5 no. 3, pp. 413-420, 1995.
  13. Fischer, T., A.V. Goldberg, D.J. Haglin, and S. Plotkin, “Approximating Matchings in Parallel”, Information Processing Letters, vol. 46, pp. 115-118, June, 1993.
  14. Haglin, D.J., “Approximating Maximum 2-CNF Satisfiability”, Parallel Processing Letters, vol. 2 no 2&3, pp. 181-187, 1992.
  15. Haglin, D.J., and S.M. Venkatesan, “Approximate and Intractability Results for the Maximum Cut Problem and its Variants”, IEEE Tr. on Computers, vol. 40, pp. 110-113, 1991.
  16. Haglin, D.J., W.D. McCuaig, and S.M. Venkatesan, “Contractible Edges in 4-Connected Maximal Planar Graphs”, ARS Combinatoria, vol. 31, pp. 199-203, 1991.
  17. Haglin, D.J., and S.M. Venkatesan, “Approximation Results for the Two-Layer Constrained-Via-Minimization Problem”, Computer-Aided Design, pp. 463-466, 1989.


Grants and Awards

  1. Tebbe, Bates and Haglin, $140,000, “MRI: Acquisition of a Linux Cluster to Fulfill High Performance Computing Needs Within Engineering and Science,” National Science Foundation, CTS-0619641 from August 15, 2006 to July 31, 2009.
  2. Guerra-Salcedo, César M. and David J. Haglin, $30,828, “Churn Management in Telephony,” Midwest Wireless, from January 15, 2003 to August 31, 2003.
  3. Hakkila, Giblin, Haglin, and Roiger, $130,279, “RUI: Guiding Gamma-Ray Burst Classification with the KDD Process,” National Science Foundation, AST-0098499, from August 1, 2001 to July 31, 2004.
  4. Hakkila, Haglin, Roiger, Mallozzi, and Pendleton, $222,100, “An Artificial Intelligence Classification Tool and Its Application to Gamma-Ray Bursts”, NASA AISR 20121324, from February 1, 1999 to January 31, 2002.
  5. Slack, J., C. Azarbod, D. Christianson, and D. Haglin, $172,979, “Strengthening the CS/IS Curriculum with a Client/Server Lab”, National Science Foundation Instrumentation and Laboratory Improvement, DUE-9552312, from September 1, 1995 to August 31, 1997. (Partially funded by matching money supplied by Mankato State University.)
  6. Haglin, D.J., and Leon Tietz, $92,615, “Bringing Parallel Processing Experiences into the Undergraduate Curriculum”, National Science Foundataion Instrumentation and Laboratory Improvement, DUE-9451919, from September 1, 1994 to August 31, 1996. (Partially funded by matching money supplied by Mankato State University.)


Conference Papers

  1. Paraskevas Yiapanis, David J. Haglin, Anna M. Manning, Ken Mayes, and John Keane, “Variable-grain and Dynamic Work Generation for Minimal Unique Itemset Mining,” in 2008 IEEE International Conference on Cluster Computing (Cluster 2008), Tsukuba, Japan, 29 Sep - 1 Oct 2008.
  2. David J. Haglin and Anna M. Manning, “On Minimal Infrequent Itemset Mining,” in 2007 International Conference on Data Mining (DMIN'07), Las Vegas, June 25-38, 2007, pp. 141-147.
  3. Dan Singer, David J. Haglin and Anna M. Manning, “Towards Average Case Analysis of Itemset Mining,” in 2007 International Conference on Data Mining (DMIN'07), Las Vegas, June 25-38, 2007, pp. 127-133.
  4. K.R. Mayes, M.J. Elliot, A.M. Manning, D. Haglin and J.R. Gurd, “A distributed search infrastructure for statistical disclosure on a grid,” in Second International Conference on e-Social Science, Manchester, UK, June, 2006, pp. 28-30.
  5. Manning, A. M., and D. J. Haglin, “A new algorithm for finding minimal sample uniques for use in statistical disclosure assessment,” in IEEE International Conference on Data Mining (ICDM05), pp. 290-297, November, 2005, pp. 290-297.
    [Acceptance Rate] = 69/630 = 11%
  6. Giblin, T.W., J. Hakkila, D. Haglin, and R. Roiger, “The Gamma-Ray Burst ToolSHED is Open for Business,” in Gamma-Ray Burst Symposium 2003, Sante Fe, NM, pp. 585-588, 2003.
  7. Hakkila, J., T. W. Giblin, W. S. Paciesas, R. J. Roiger, D. J. Haglin, & C. A. Meegan, “Comments on Anisotropic Distributions of Faint BATSE GRBs,” in Gamma-Ray Burst and Afterglow Astronomy 2001, eds. G. R. Ricker and R. K. Vanderspek (AIP: New York), pp. 144-146, 2003.
  8. Hakkila, J., T. W. Giblin, T. M. Freismuth, K. C. Young, A. J. Sprague, A. D. Stallworth, R. J. Roiger, & D. J. Haglin, “The Internal Luminosity Functions of BATSE 5B GRBs”, in Gamma-Ray Burst and Afterglow Astronomy 2001, eds. G. R. Ricker and R. K. Vanderspek (AIP: New York), pp. 147-149, 2003.
  9. Hakkila, J., R. J. Roiger, D. J. Haglin, T. W. Giblin, & W. S. Paciesas, “A Comparison of Unsupervised Classifiers on BATSE Catalog Data”, in Gamma-Ray Burst and Afterglow Astronomy 2001, eds. G. R. Ricker and R. K. Vanderspek (AIP: New York), pp. 179-182, 2003.
  10. Hakkila, J., D. J. Haglin, R. J. Roiger, T. W. Giblin, & W. S. Paciesas, “An Update on the GRB ToolSHED Project Status”, in Gamma-Ray Burst and Afterglow Astronomy 2001, eds. G. R. Ricker and R. K. Vanderspek (AIP: New York), pp. 556-558, 2003.
  11. Haglin, D. J., R. J. Roiger, J. Hakkila, T. Giblin, “Data Mining from a Web Browser,” in International Conference on Advances in Infrastructure for Electronic Business, Science, and Education, L'Aquila, Italy, ISBN 88-85280-63-3, August, 2002.
  12. Hakkila, J., T. W. Giblin, R. J. Roiger, D. J. Haglin, W. S. Paciesas & C. A. Meegan, “The Dual Timescale Peak Flux and GRB Classess,” in GAIA Spectroscopy, Science and Technology, Vol XXX, 2002.
  13. Hakkila, J., T. W. Giblin, R. J. Roiger, D. J. Haglin & W. S. Paciesas, “How Data Mining Helps Expose Gamma-Ray Burst Properties and Instrumental Biases,” in World Multiconference on Systemics, Cybernetics and Informatics, eds. N. Callaos, Y. He, and J. A. Perez-Peraza, pp. 479-484, July, 2002.
  14. Hakkila, J., R. S. Mallozzi, R. J. Roiger, D. J. Haglin, G. N. Pendleton, and C. A. Meegan, “Tools for Gamma-Ray Burst Data Mining,” in Gamma-Ray Bursts in the Afterglow Era: 2nd Workshop, ed. N. Callaos, Y. He, and J.A. Perez-Paraza, pp. 479-484, 2001.
  15. Hakkila, J., R. J. Roiger, D. J. Haglin, R. S. Mallozzi, G. N. Pendleton, & C. A. Meegan, “Mining Gamma-Ray Burst Data,” in Mining the Sky, eds. A.J. Banday, et al., pp. 487-493, 2000.
  16. Haglin, David J., Richard J. Roiger, Jon Hakkila, Geoffrey Pendleton, and Robert Mallozzi, “A GRB Tool Shed”, in Gamma-Ray Bursts: 5th Huntsville Symposium, ed. R.M. Kippen, et al., pp. 877-881, 2000.
  17. Hakkila J., C. A. Meegan, G. N. Pendleton, R. S. Mallozzi, D. J. Haglin, & R. J. Roiger, “The Fluence Duration Bias,” in Gamma-Ray Bursts: 5th Huntsville Symposium, ed. R.M. Kippen, et al., pp. 48-52, 2000.
  18. Roiger, Richard J., Jon Hakkila, David J. haglin, Geoffrey N. Pendleton, and Robert S. Mallozzi, “Unsupervised Induction and Gamma-Ray Burst Classification”, in Gamma-Ray Bursts: 5th Huntsville Symposium, ed. R.M. Kippen, et al., pp. 38-42, 2000.
  19. Hakkila, Jon, David J. Haglin, Richard J. Roiger, Robert S. Mallozzi, Geoffrey N. Pendleton, and Charles A. Meegan, “Properties of Gamma-Ray Burst Classes”, in Gamma-Ray Bursts: 5th Huntsville Symposium, ed. R.M. Kippen, et al., pp. 33-37, 2000.
  20. Roiger, R.J., M.W. Geatz, D.J. Haglin, and J. Hakkila, “ESX -- A Tool for Knowledge Discovery”, in Proceedings of the Federal Data Mining Symposium & Exposition '99, ed. W.T. Price, AFCEA International, Fairfax VA., pp. 109-120, March 9-10, 1999.
  21. Hakkila, Jon., David J. Haglin, Richard J. Roiger, Robert S. Mallozzi, Geoffrey N. Pendleton, and Charles A. Meegan, “AI Gamma-Ray Burst Classification: Methodology/Preliminary Results”, in Fourth Huntsville Gamma-Ray Burst Symposium, eds. C.A. Meegan, P. Cushman, pp. 77-81, 1998.
  22. Haglin, David J., and John A. Kaliski, “A Massively Parallel Transportation Solution”, in International Conference on Parallel and Distributed Processing, Techniques and Applications (PDPTA'95), pp. 231-239, 1995.
  23. Haglin, D.J., “Bipartite Expander Matching is in NC”, in Ninth IEEE International Symposium on Computer and Information Sciences, pp. 148-155, 1994.
  24. Haglin, D.J., “On A Fast Deterministic Parallel Approximate Matching Algorithm”, in Third IEEE Symposium on Parallel and Distributed Processing, pp. 774-777, 1991.

Education

1989 -- Ph.D., University of Minnesota
     Major emphasis:  Computer and Information Sciences
     Minor emphasis:  Mathematics
     Advisor:  Shankar M. Venkatesan
     Thesis:  Results on Matching, Maximum Cut and Related Problems

1987 -- M.S., University of Minnesota
     Major emphasis:  Computer and Information Sciences
     Advisor:  Sartaj Sahni

1982 -- B.A., magna cum laude, Concordia College, Moorhead, MN
     Majors:  Computer Science, Mathematics


Last Modified: by David J. Haglin (David.Haglin@mnsu.edu)