Operations Research

Main research directions


Operations Research Group (Business Analytics Research Group)

Generate new knowledge and solutions via research to improve the wellbeing of people and the planet.

Business Analytics focuses on transforming data into insights by applying advanced analytical methods to improve the performance of an organization.  

The BARG at Pompeu Fabra University focuses on developing data-driven analytical approaches, as mathematical models, methods and algorithms from the areas of Operations Research, Artificial Intelligence, Computer Science, and Statistics, to help making better decisions in businesses and organizations in general.

The BARG has a large experience in applying the Business Analytics approaches to several large-scale and complex optimization problems arising in different fields, as for example in Transportation, Retailing, Healthcare, Logistics and Manufacturing industries.

The group draws researchers from different areas at UPF and other universities experts on different fields (from Operations Research to Marketing or Computer Science) leading to Multidisciplinary approach to solve real problems. This multidisciplinary approach provides a key factor to obtain excellent results and make an impact on the business and the society.

The main focus of the research of this group is: “OPERATIONS RESEARCH FOR SOCIAL GOOD“.

O. Papaspiliopoulos and N. Chopia. Springer, 2020

An Introduction to Sequential Monte Carlo

This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics.

An Introduction to Sequential Monte Carlo

LP. Bartlett, P.L. Long, G. Lugosi, and A. Tsigler. PNAS, 2020.

Benign overfitting in linear regression

The phenomenon of benign overfitting is one of the key mysteries uncovered by deep learning methodology: deep neural networks seem to predict well, even with a perfect fit to noisy training data. Motivated by this phenomenon, Gábor and his co-authors consider when a perfect fit to training data in linear regression is compatible with accurate prediction. Their analysis shows that overparameterization is essential for benign overfitting in this setting: the number of directions in parameter space that are unimportant for prediction must significantly exceed the sample size. (link to the article)

S. Lauritzen, C. Uhler and P. Zwiernik

Total positivity in exponential families with application to binary variables

Annals of Statistics, to appear

  • Location of a new warehouse for Serveto transportation firm (2019)
  • Mathematical Models and Algorithm to solve Logistics Optimization Problems in SEAT-Volkswagen (2016-2019)
  • Crew Scheduling for Taxi Drivers – Application to Cabify. Funding: Job and Talent (2018)
  • Solving Optimization Problems for e-commerce in INDITEX. Funding: OESIA and INDITEX (2016-2017)
  • On the Improvement of Blood Sample Collection at a Clinical Laboratory, CATLAB (2013)
  • Tecnology Transder Software for Vehicle Route Optimization. Collaborating entities: Universitat de la Laguna and Ingeniería Electronica Canarias S.L. (2012)
  • Locating lottery retail stores in Spain (2009-2012). Funded by Loterías y Apuestas del Estado (LAE).
  • Local Fuel Markets. Funded by Comisión Nacional de Energía.
  • The demand for lottery in Spain: a territorial study. Funded by Loterías y Apuestas del Estado (LAE).
  • Economic impact of FGC tourism activities, Ferrocarrils de la Generalitat de Catalunya.
  • The costs of labor claims in Catalonia. Departament de Treball, Generalitat de Catalunya.
  • Study on the logistics of LAE warehouses. Funded by Sistemas Técnicos de Lotería (STL)
  • The logistics of fire fighting services in Barcelona, funded by Barcelona City Council
  • Economic Evaluation of the network of Access Highways to Buenos Aires, funded by OCRABA (Organo de Control de la Red de Accesos a B.A.)
  • The rescue of motorway concessions in Catalonia, funded by Dept. de Política Territorial I Obres Públiques, Generalitat de Catalunya
  • Barcelona Crane Logistics, funded by Barcelona City Council
  • Budget allocation of the Barcelona Primary Care Centers, funded by Servei Català de Salut
  • The economic impact of infrastructure investments made by the Generalitat de Catalunya, funded by Cable y Televisión de Catalunya
  • “The economic impact of infrastructure investments made by the Generalitat de Catalunya”, funded Generalitat de Catalunya
  • A. Corral, F. Udina and E. Arcaute, Truncated lognormal distributions and scaling in the size of naturally defined population clusters. Physical Review E, 2020, 101, No. 4.

  • On choosing mixture components via non-local priors by J. Fúquene, M.F.J. Steel, and D. Rossell. Journal of the Royal Statistical Society B, 2019, 81, 5, 809-837.

  • Maximum likelihood estimation in Gaussian models under total positivity by S. Lauritzen, C. Uhler, and P. Zwiernik. Annals of Statistics, 2019, Vol. 47, No. 4, 1835-1863.

  • Sub-Gaussian estimators of the mean of a random vector by G. Lugosi, and S. Mendelson. Annals of Statistics, 2019, Vol. 47, No. 2, pp 783-794.

  • Auxiliary gradient‐based sampling algorithms by Titsias, Michalis K., and O. Papaspiliopoulos. Journal of the Royal Statistical Society: Series B, (Statistical Methodology) 80.4, 2018, pp 749-767.

  • Tractable Bayesian variable selection: beyond normality by D. Rossell and F.J. Rubio. Journal of the American Statistical Association, 2018, pp 1-17.

  • Nonlocal priors for high-dimensional estimation by D. Rossell and D. Telesca. Journal of the American Statistical Association, 2017, 112.517, pp 254-265.

  • Maximum likelihood estimation for linear Gaussian covariance models by P. Zwiernik, C. Uhler, and D. Richards. Journal of the Royal Statistical Society: Series B, 79(4), 2017, 1269–1292.

  • Total positivity in Markov structures by S. Fallat, S. Lauritzen, K. Sadeghi, C. Uhler, N. Wermuth, and P. Zwiernik. Annals of Statistics 2017, Vol. 45, No. 3, 1152-1184.

  • Set estimation from reflected Brownian motion by A. Cholaquidis, R. Fraiman, G. Lugosi, and B. Pateiro-López. Journal of the Royal Statistical Society: Series B, 2016, 78:1057–1078.

  • Sub-Gaussian mean estimators by L. Devroye, M. Lerasle, G. Lugosi, and R. Imbuzeiro Oliveira. Annals of Statistics, 2016, 44:2695-2725.

  • Almost optimal sparsification of random geometric graphs by N. Broutin, L. Devroye, and G. Lugosi, Annals of Applied Probability, 2016, 26:5, 3078-3109.

  • On probability laws of solutions of differential systems driven by fractional Brownian motion by F. Baudoin, E. Nualart, C. Ouyang, and S. Tindel, Annals of Probability, 2016, 44, pp 2554-2590.

  • Exact sampling of diffusions with a discontinuity in the drift by O. Papaspiliopoulos, G. Roberts, and K. Taylor, Advances in Applied Probability, 2016, 48(A), 249-259.

  • Exponential varieties by M. Michałek, B. Sturmfels, C. Uhler, and P. Zwiernik, Proceedings of the London Mathematical Society (3) 112 (2016), no. 1, 27–56.

  • Empirical risk minimization for heavy-tailed losses by C. Brownlees, E. Joly and G. Lugosi, Annals of Statistics, 2015, 43(6), 2507-2536.

  • Gavard R, Jones H, Palacio Lozano D, Thomas M, Rossell D, Spencer S, Barrow M (2020). KairosMS: A new solution for the processing of hyphenated ultrahigh resolution mass spectrometry data. Analytical Chemistry, 92.5 3775-86

  • Gavard R, Palacio Lozano D, Guzman A, Rossell D, Spencer S, Barrow M (2019). Rhapso: Automatic stitching of mass segments from Fourier transform ion cyclotron resonance mass spectra. Analytical Chemistry, 91:15130-37

  • M. Greenacre. Variable selection in compositional data analysis using pairwise logratios. Mathematical Geosciences, 2018, 1-34.

  • Marty R, Kaabinejadian S, van de Haar J, Rossell D, Ideker T, Hildebrand W, Engin HB, Font-Burgada J, Carter H. (2017) MHC-I genotype restricts the oncogenic mutational landscape. Cell, 171, 1272-1283

  • Font-Burgada J, Shalapour S, Ramaswamy S, Hsueh B, Rossell D, Umemura A, Taniguchi K, Nakagawa H, Valasek MA, Ye L, Kopp JL, Sander M, Carter H, Deisseroth K, Verma IM, Karin M. (2015) Hybrid Periportal Hepatocytes Regenerate the Injured Liver without Giving Rise to Cancer. Cell, 162(4):766-79.

  • Calon A, Lonardo E, Berenguer A, Espinet E, Hernando-Momblona X, Iglesias M, Sevillano M, Palomo-Ponce S, Tauriello DVF, Byrom D, Cortina C, Morral C, Barceló C, Tosi S, Riera A, Stephan-Otto Attolini C, Rossell D, Sancho E, Batlle E. (2015) Stromal gene expression defines poor prognosis subtypes in colorectal cancer. Nature Genetics, 47, 320-329. doi:10.1038/ng.3225

Christian Brownlees:

Annals of Financial Economics, Econometrics, Journal of Network Theory in Finance, Journal of Risk and Financial Management

Gábor Lugosi:

Annals of Applied Probability, Journal of Machine Learning Research, Probability Theory and Related Fields

Eulàlia Nualart:

Stochastic Processes and their Applications (Associate Editor)

Omiros Papaspiliopoulos:

Biometrika (Deputy Editor), SIAM Journal of Uncertainty Quantification

David Rossell:

Bayesian Analysis (Associate Editor)

Piotr Zwiernik:

Biometrika, Journal of Algebraic Statistics, Scandinavian Journal of Statistics

“Prediccion, Inferencia y Computacion en Modelos Estructurados de Alta Dimension”

  • Reference: PGC2018-101643-B

  • Financing entity: Ministerio de Economía y Competitividad (MINECO)

  • Dates: 2019-2021

  • Principle investigators: Gábor Lugosi, Omiros Papaspiliopoulos

  • Amount: € 141,812

“Algorithms and Learning for AI”

  • Financing entity: Google

  • Dates: 2018-2020

  • Principle investigator: Gábor Lugosi

  • Amount: USD 150,000

“High-dimensional problems in structured probabilistic models”

  • Financing entity: Fundación BBVA

  • Dates: 2018-2020

  • Principle investigator: Gabor Lugosi

  • Amount: € 100,000

“Estimación de redes latentes”

  • Reference: MTM2015-67304-P

  • Financing entity: Ministerio de Economía y Competitividad (MINECO)

  • Dates: 2016-2018

  • Principle investigators: Gabor Lugosi, Omiros Papaspiliopoulos

  • Amount: € 52,998

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