University Directory

Rommel G. Regis, PhD

Professor and Director of Data Science Program

Rommel Regis Ph.D.

Department

Office

Barbelin Hall 247

Phone

610-660-1514
Contact

Dr. Regis earned his MS and PhD degrees in Operations Research from Cornell University and his M.S. degree in Mathematics from the University of Florida. He has been a faculty member in the Department of Mathematics at Saint Joseph’s University (SJU) since 2007 and he has taught over 20 courses in Data Science, Probability, Statistics, Optimization, Operations Research, Real Analysis, Linear Algebra, Differential Equations and Calculus. He is also an adjunct faculty member in the Department of Statistics and Data Science at the Wharton School of the University of Pennsylvania where he has been teaching a course on Data Analytics and Statistical Computing. Prior to SJU, he was a Postdoctoral Associate at the Cornell Theory Center, which is now the Cornell University Center for Advanced Computing.

For his research, Dr. Regis has published over 50 peer-reviewed papers in the areas of Derivative-Free Optimization, Global Optimization, Engineering Optimization, Surrogate-Based and Bayesian Optimization, and Computational Intelligence and Machine Learning. Moreover, he has delivered over 50 presentations at national and international conferences. He has also completed over 170 reviews for over 40 technical journals. In addition, since 2018, he has been a member of the Editorial Advisory Board of the Engineering Optimization journal.

Dr. Regis currently serves as the Director of SJU’s Data Science Program, which is a joint undergraduate program of the Departments of Mathematics, Computer Science, and Decision and System Sciences. He also previously served as the Director of SJU’s Actuarial Science Program from 2016 to 2020.

    • PhD in Operations Research, Cornell University, 2004
    • MS in Operations Research, Cornell University, 2002
    • MS in Mathematics, University of Florida, 1998
    • BS in Mathematics, Ateneo de Manila University, 1993
    • Saint Joseph’s University
      • Director, Data Science Program, 2022 – present
      • Professor, Department of Mathematics, 2018 – present
      • Director, Actuarial Science Program, 2016 – 2020
      • Interim Director, Actuarial Science Program, 2015
      • Associate Professor, Department of Mathematics, 2013 – 2018
      • Assistant Professor, Department of Mathematics, 2007 – 2013
    • University of Pennsylvania, The Wharton School
      • Adjunct Faculty, Department of Statistics and Data Science, 2022 – present
    • Cornell University
      • Postdoctoral Associate, Cornell Theory Center, 2004 – 2007
      • Research Assistant, School of Civil and Environmental Engineering, 2003 – 2004
      • Research Assistant, Department of Computer Science, 2000 – 2003
      • Teaching Assistant, School of Operations Research & Industrial Engineering, 1998 – 2000
    • University of Florida
      • Teaching Assistant, Department of Mathematics, 1996 – 1998
    • Ateneo de Manila University
      • Assistant Instructor, Department of Mathematics, 1993 – 1996
    • Faculty Merit Award for Teaching, Saint Joseph’s University, 2019, 2009
    • Faculty Merit Award for Research, Saint Joseph’s University, 2017, 2011
    • College of Arts and Sciences Advising Award, Saint Joseph’s University, 2017
    • Michael J. Morris '56 Grant for Scholarly Research, Saint Joseph’s University, 2019, 2014
    • Summer Research Grant, Saint Joseph’s University, 2016, 2012, 2008
    • Project NExT Fellow, Mathematical Association of America, 2008 – 2009
    • Section NExT Fellow, Eastern Pennsylvania and Delaware (EPADEL) Section of the Mathematical Association of America, 2008 – 2009
    • Derivative-Free Optimization
    • Engineering Optimization and Global Optimization
    • Surrogate-Based Optimization and Bayesian Optimization
    • Machine Learning and Computational Intelligence for Optimization
    • Optimization Applications in Data Science and in Engineering

  • Derivative-Free Optimization and Trust Region Methods

    • R. G. Regis. On the Properties of the Cosine Measure and the Uniform Angle Subspace. Computational Optimization and Applications, Vol. 78, Issue 3, pp. 915-952, 2021.
    • G. Regis and S. M. Wild. CONORBIT: constrained optimization by radial basis function interpolation in trust regions. Optimization Methods and Software, Vol. 32, Issue 3, pp. 552–580, 2017.
    • R. G. Regis. On the properties of positive spanning sets and positive bases. Optimization and Engineering, Vo1. 17, Issue 1, pp. 229-262, 2016.
    • R. G. Regis. The calculus of simplex gradients. Optimization Letters, Vol. 9, Issue 5, pp. 845-865, 2015.
    • Wild, R. G. Regis, and C. A. Shoemaker. ORBIT: Optimization by radial basis function interpolation in trust-regions. SIAM Journal on Scientific Computing, Vol. 30, No. 6, pp. 3197-3219, 2008.

    Global Optimization Using Stochastic Search and Related Methods

    • Nuñez, R. G. Regis, K. Varela. Constrained global optimization using accelerated random search assisted by radial basis function surrogates. Journal of Computational and Applied Mathematics, Vol. 340, pp. 276-295, 2018.
    • R. G. Regis. On the convergence of adaptive stochastic search methods for constrained and multi-objective black-box optimization. Journal of Optimization Theory and Applications, Vol. 170, Issue 3, pp. 932–959, 2016.
    • J. C. Gouvea, R. G. Regis, A. C. Soterroni, M. C. Scarabello and F. M. Ramos. Global optimization using q-gradients. European Journal of Operational Research, Vol. 251, Issue 3, pp. 727–738, 2016.
    • R. G. Regis. Convergence guarantees for generalized adaptive stochastic search methods for continuous global optimization. European Journal of Operational Research, Vol. 207, Issue 3, pp. 1187-1202, 2010.

    Surrogate-Based and Bayesian Optimization and Machine Learning Methods for Optimization

    • A. Bouhlel, N. Bartoli, R. G. Regis, A. Otsmane, J. Morlier. Efficient Global Optimization for high-dimensional constrained problems by using Kriging models combined with the Partial Least Squares method. Engineering Optimization, Vol. 50, Issue 12, pp. 2038 – 2053, 2018.
    • R. G. Regis. Multi-objective constrained black-box optimization using radial basis function surrogates. Journal of Computational Science, Vol. 16, pp. 140–155, 2016.
    • R. G. Regis. Trust regions in kriging-based optimization with expected improvement. Engineering Optimization, Vol. 48, Issue 6, pp. 1037-1059, 2016.
    • Datta and R. R. G. Regis. A surrogate-assisted evolution strategy for constrained multi-objective optimization. Expert Systems with Applications, Vol. 57, pp. 270–284, 2016.
    • R. G. Regis. Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible starting points. Engineering Optimization, Vol. 46, Issue 2, pp. 218-243, February 2014.
    • R. G. Regis. Particle swarm with radial basis function surrogates for expensive black-box optimization. Journal of Computational Science, Vol. 5, Issue 1, pp. 12-23, January 2014.
    • R. G. Regis. Evolutionary programming for high-dimensional constrained expensive black-box optimization using radial basis functions. IEEE Transactions on Evolutionary Computation, Vol. 18, Issue 3, pp. 326-347, 2014.
    • G. Regis and C. A. Shoemaker. A quasi-multistart framework for global optimization of expensive functions using response surface models. Journal of Global Optimization, Vol. 56, Issue 4, pp. 1719-1753, 2013.
    • G. Regis and C. A. Shoemaker. Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization. Engineering Optimization, Vol. 45, Issue 5, pp. 529-555, 2013.
    • R. G. Regis. Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions. Computers and Operations Research, Vol. 38, Issue 5, pp. 837-853, 2011.
    • G. Regis and C. A. Shoemaker. Parallel stochastic global optimization using radial basis functions. INFORMS Journal on Computing, Vol. 21, No. 3 (Special issue: High-Throughput Optimization), pp. 411-426, Summer 2009.
    • Bliznyuk, D. Ruppert, C. A. Shoemaker, R. G. Regis, S. Wild and P. Mugunthan. Bayesian calibration of computationally expensive models using optimization and radial basis function approximation. Journal of Computational and Graphical Statistics, Vol. 17, No. 2, pp. 270-294, 2008.
    • G. Regis and C. A. Shoemaker. A stochastic radial basis function method for the global optimization of expensive functions. INFORMS Journal on Computing, Vol. 19, No. 4, pp. 497-509, Fall 2007.
    • G. Regis and C. A. Shoemaker. Parallel radial basis function methods for the global optimization of expensive functions. European Journal of Operational Research, Vol. 182, Issue 2, pp. 514-535, 2007.
    • G. Regis and C. A. Shoemaker. Improved strategies for radial basis function methods for global optimization. Journal of Global Optimization, Vol. 37, No. 1, pp. 113-135, 2007.
    • G. Regis and C. A. Shoemaker. Constrained global optimization of expensive black box functions using radial basis functions. Journal of Global Optimization, Vol. 31, No. 1, pp. 153-171, 2005.
    • G. Regis and C. A. Shoemaker. Local function approximation in evolutionary algorithms for costly black box optimization. IEEE Transactions on Evolutionary Computation, Vol. 8, No. 5, pp. 490–505, October 2004.

    Engineering Optimization Applications

    • Pandey, R. G. Regis, R. Datta and B. Bhattacharya. Surrogate-assisted multi-objective optimisation of the dynamic response of a freight wagon fitted with three-piece bogies. International Journal of Rail Transportation, Vol. 9, Issue 3, pp. 290-309, 2021.
    • Christelis, R. G. Regis, A. Mantoglou. Surrogate-based pumping optimization of coastal aquifers under limited computational budgets. Journal of Hydroinformatics, Vol. 20, Issue 1, pp. 164–176, 2018.
    • A. Shoemaker, R. G. Regis and R. C. Fleming. Watershed calibration using multistart local optimization and evolutionary optimization with radial basis function approximation. Hydrological Sciences Journal – Journal Des Sciences Hydrologiques, Vol. 52, Issue 3 (Special issue: Hydroinformatics), pp. 450-465, 2007.
    • Mugunthan, C. A. Shoemaker and R. R. G. Regis. Comparison of function approximation, heuristic and derivative-based methods for automatic calibration of computationally expensive groundwater bioremediation models. Water Resources Research, Vol. 41, W11427, 2005.

    Business Analytics and Operations Research

    • Glasser and R. R. G. Regis. A generalized survival function method for mixed distributions. American Journal of Mathematical and Management Sciences, Vol. 40, Issue 4, pp. 378-390, 2021.
    • M. Evangelista and R. R. G. Regis. A multi-objective approach for maximizing the Reach or GRP of different brands in TV advertising. International Transactions in Operational Research, Vol. 27, Issue 3, pp. 1664-1698, 2020.
    • M. Evangelista and R. R. G. Regis. Exploring the Suitability of Support Vector Regression and Radial Basis Function Approximation to Forecast Sales of Fortune 500 Companies. Advances in Business and Management Forecasting, Vol. 13, pp. 3-23, 2019.
    • P. Gomes, R. G. Regis and D. B. Shmoys. An improved approximation algorithm for the partial latin square extension problem. Operations Research Letters, Vol. 32, No. 5, pp. 479–484, 2004.

    Peer-Reviewed Conference Proceedings and Book Chapters

    • R. G. Regis. A Bootstrap-Surrogate Approach for Sequential Experimental Design for Simulation Models. To appear in Computational Science and Its Applications – ICCSA 2022. Lecture Notes in Computer Science, vol 13377.
    • R. G. Regis. A Hybrid Surrogate-Assisted Accelerated Random Search and Trust Region Approach for Constrained Black-Box Optimization. In: G. Nicosia et al. (eds) Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science, vol 13164. Springer, Cham, 2022, pp. 162-177.
    • N. Iorio and R. R. G. Regis. Accelerated Random Search for Black-Box Constraint Satisfaction and Optimization. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, Florida, USA, 2021, pp. 1 – 8.
    • R. G. Regis. High-Dimensional Constrained Discrete Expensive Black-Box Optimization Using a Two-Phase Surrogate Approach. In: O. Gervasi et al. (eds) Computational Science and Its Applications – ICCSA 2021. Lecture Notes in Computer Science, vol 12953. Springer, Cham, pp. 366-381.
    • R. G. Regis. A two-phase surrogate approach for high-dimensional constrained discrete multi-objective optimization. In: F. Chicano (ed). Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2021): 1870-1878, ACM, New York, NY, USA, 2021.
    • R. G. Regis. Large-Scale Discrete Constrained Black-Box Optimization Using Radial Basis Functions. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia, 2020, pp. 2924-2931.
    • R. G. Regis. High-Dimensional Constrained Discrete Multi-Objective Optimization Using Surrogates. In: Nicosia G. et al. (eds) Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science, vol 12566. Springer, Cham, 2020.
    • Mandarano, R. G. Regis, E. Bloom. Machine Learning and Statistical Models for the Prevalence of Multiple Sclerosis. In: Nicosia G. et al. (eds) Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science, vol 12566. Springer, Cham, 2020.
    • R. G. Regis. A Survey of Surrogate Approaches for Expensive Constrained Black-Box Optimization. In: H. Le Thi, H. Le, T. Pham Dinh (eds), Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham, 2020.
    • S. Palar, Y. B. Dwianto, R. G. Regis, A. Oyama, and L. R. Zuhal. Benchmarking constrained surrogate-based optimization on low speed airfoil design problems. In: M. Lopez-Ibanez (ed). Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2019): 1990-1998, ACM, New York, NY, USA, 2019.
    • R. G. Regis. A hybrid between a surrogate-assisted evolutionary algorithm and a trust region method for constrained optimization. In: M. Lopez-Ibanez (ed). Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2019): 324-325, ACM, New York, NY, USA, 2019.
    • R. G. Regis. Surrogate-Assisted Particle Swarm with Local Search for Expensive Constrained Optimization. In: P. Korosec, N. Melab, E.G. Talbi (eds). Bioinspired Optimization Methods and Their Applications. BIOMA 2018. Lecture Notes in Computer Science, vol 10835. pp. 246-257. Springer, Cham, 2018.
    • R. G. Regis. A Surrogate-Assisted Approach for Expensive Equality Constrained Optimization. Proceedings of the 8th International Conference on Bioinspired Optimization Methods and their Applications (BIOMA 2018), Paris, France, 2018.
    • Bartoli, M. A. Bouhlel, I. Kurek, R. Lafage, T. Lefebvre, J. Morlier, R. Priem, V. Stilz, R. Regis. Improvement of efficient global optimization with application to aircraft wing design. 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. AIAA AVIATION Forum, (AIAA 2016-4001), 2016.
    • R. G. Regis. Trust regions in surrogate-assisted evolutionary programming for constrained expensive black-box optimization. In: R. Datta and K. Deb (eds). Evolutionary Constrained Optimization, pp 51-94. Springer India, 2015.
    • R. G. Regis. An initialization strategy for high-dimensional surrogate-based expensive black-box optimization. In: L.F. Zuluaga and T. Terlaky (eds). Selected Contributions from the MOPTA 2012 Conference Series: Springer Proceedings in Mathematics & Statistics, Vol. 62, pp. 51-85. Springer NY, 2013.
    • R. G. Regis. Surrogate-assisted evolutionary programming for high dimensional constrained black-box optimization. In: T. Soule and J.H. Moore (eds). Genetic and Evolutionary Computation Conference (GECCO 2012) Companion Material Proceedings: 1431-1432.
    • A. Shoemaker and R. R. G. Regis. MAPO: using a committee of algorithm-experts for parallel optimization of costly functions. Proceedings of the 15th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA ’03), San Diego, CA, 2003.
    • P. Gomes, R. G. Regis and D. B. Shmoys. An improved approximation algorithm for the partial latin square extension problem. Proceedings of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA ’03), Baltimore, Maryland, 2003.