Faculty & Research
Directory
Rodriguez Serrano, Jose A.
Senior Lecturer, Department of Operations, Innovation and Data Sciences at Esade
Contracted Doctoral Professor URL
Director acadèmic MSc in Business Analytics, Master Business Analytics
Education
- Ciencias de la Computación. Universitat Autònoma de Barcelona
- Licenciado en Física. Universitat de Barcelona
Areas of interest
- SDG 9 - Industry, Innovation, and Infrastructure
Biography
José A. Rodríguez Serrano PhD joined Esade in September 2022.
With over 16+ years of experience in the field of Artificial Intelligence AI, his past background mixes academic research, industrial work, and management practice. Before joining Esade, he held a Program Manager position at BBVA AI Factory, and prior to that a Machine Learning Manager position at Xerox, where he had developed a career as a corporate researcher in AI. His trajectory includes years of international experience in France and the United Kingdom.
Jose has published papers in top AI conferences and journals such as CVPR, ICCV, NeurIPS, IEEE PAMI, IJCV, holds over 20 patents and has participated, both as a contributor and as manager, in delivering machine learning functionalities both into functioning prototypes as well as products that are commercially available.
Jose is passionate about innovation with machine learning and AI, and over the years has been involved in the different stages of that process, from research to business impact. Moreover, he is also interested in better understanding the challenges, complexities, and best practices of AI innovation cycles. He has participated in managing innovation strategies and likes to contribute to the public debate on innovation with machine learning.
Selected publications
- Rodriguez-Serrano, J. A. (2024). Prototype-based learning for real estate valuation: a machine learning model that explains prices. Annals of Operations Research. DOI: https://doi.org/10.1007/s10479-024-06273-1.
- Akoglu, L., Chawla, N., Domingo-Ferrer, J., Kurshan, E., Kumar, S., Naware, V., Rodriguez-Serrano, J. A., Chaturvedi, I., Nagrecha, S., Das, M. & Faruquie, T. (2024). Machine Learning in Finance. KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 6703). Association for Computing Machinery. DOI: https://doi.org/10.1145/3637528.3671488.
- Bueno, A., Rodriguez-Serrano, J. A., Torrens, M., Agell, N., Nguyen, J. T. V. & Agell, N. (2024, May). Poster: Using Deep Attention Multiple Instance Learning To Improve Malpractices Detection In The Health Sector [Paper presentation]. https://convin.gr/assets/files/misc/IALM2024_Abstract%CE%92ook.pdf
- Rodriguez-Serrano, J. A. (E-pub ahead of print). (2024). Prototype-based models for real estate valuation: a machine learning model that explains prices. Annals of Operations Research. DOI: https://doi.org/10.2139/ssrn.4695079.
- Brando, A., Gimeno, J., Rodriguez-Serrano, J. A. & Vitrià, J. (2022). Deep Non-Crossing Quantiles through the Partial Derivative. Proceedings of Machine Learning Research, 151, pp. 7902-7914.
- Kumar, S., Akoglu, L., Chawla, N., Rodriguez-Serrano, J. A., Faruquie, T. & Nagrecha, S. (2021). Machine Learning in Finance. KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4139-4140). Association for Computing Machinery. DOI: https://doi.org/10.1145/3447548.3469456.
- Brando, A., TORRES-LATORRE, C., Rodriguez-Serrano, J. A. & Vitrià, J. (2020). Building Uncertainty Models on Top of Black-Box Predictive APIs. IEEE Access, 8, pp. 121344-121356. DOI: https://doi.org/10.1109/ACCESS.2020.3006711.
- Brando, A., Rodriguez-Serrano, J. A., Ciprian, M., Maestre, R. & Vitrià, J. (2019). Uncertainty modelling in deep networks. In Brefeld, U., Marascu, A. & Pinelli, F., Curry, E., Macnamee, B., Hurley, N., Daly, E. (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings (pp. 325-340). Springer Verlag. DOI: https://doi.org/10.1007/978-3-030-10997-4_20.
- Brando, A., Rodriguez-Serrano, J. A., Vitrià, J. & Rubio, A. (2019). Modelling heterogeneous distributions with an uncountable mixture of asymmetric laplacians. Advances in Neural Information Processing Systems, 32.
- Tariq, U., Rodriguez-Serrano, J. A. & Perronnin, F. (2017). Unsupervised fisher vector adaptation for re-identification. Advances in Computer Vision and Pattern Recognition (9783319583464th ed., pp. 213-225). Springer London. DOI: https://doi.org/10.1007/978-3-319-58347-1_11.
- Dai, Z., Larlus, D. & Rodriguez-Serrano, J. A. (2016). Data-Driven Detection of Prominent Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, pp. 1969-1982. DOI: https://doi.org/10.1109/TPAMI.2015.2509988.
- Gordo, A., Perronnin, F. & Rodriguez-Serrano, J. A. (2015). Label embedding: A frugal baseline for text recognition. DOI: https://doi.org/10.1007/s11263-014-0793-6.
- Chidlovskii, B., Csurka, G. & Rodriguez-Serrano, J. A. (2014). Vehicle type classification from laser scans with global alignment kernels. 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 (pp. 2840-2845). Institute of Electrical and Electronics Engineers Inc.. DOI: https://doi.org/10.1109/ITSC.2014.6958145.
- Ferryman, J., Hogg, D., Sochman, J., Behera, A., Rodriguez-Serrano, J. A., Worgan, S., Li, L., Leung, V., Evans, M., Cornic, P., Herbin, S., Schlenger, S. & Dose, M. (2013). Robust abandoned object detection integrating wide area visual surveillance and social context. Pattern Recognition Letters, 34 (7), pp. 789-798. DOI: https://doi.org/10.1016/j.patrec.2013.01.018.
- Rodriguez-Serrano, J. A. & Perronnin, F. (2013, January). Label embedding for text recognition [Paper presentation]. 2013 24th British Machine Vision Conference, BMVC 2013, Bristol. http://www.scopus.com/inward/record.url?scp=84898493831&partnerID=8YFLogxK
- Mo, X., Monga, V., Bala, R., Rodriguez-Serrano, J. A., Fan, Z. & Burry, A. (2013). Practical methods for sparsity based video anomaly detection. 2013 16th International IEEE Conference on Intelligent Transportation Systems (pp. 955-960). DOI: https://doi.org/10.1109/ITSC.2013.6728355.
- Sandhawalia, H., Rodriguez-Serrano, J. A., Poirier, H. & Csurka, G. (2013). Vehicle type classification from laser scanner profiles. 2013 16th International IEEE Conference on Intelligent Transportation Systems (pp. 517-522). DOI: https://doi.org/10.1109/ITSC.2013.6728283.
- Perronnin, F. & Rodriguez-Serrano, J. A. (2012). A model-based sequence similarity with application to handwritten word spotting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, pp. 2108-2120. DOI: https://doi.org/10.1109/TPAMI.2012.25.
- Rodriguez-Serrano, J. A. & Singh, S. (2012). Trajectory clustering in CCTV traffic videos using probability product kernels with hidden Markov models. Pattern Analysis and Applications, 15 (4), pp. 415-426. DOI: https://doi.org/10.1007/s10044-012-0269-7.
- Rodriguez-Serrano, J. A. & Perronnin, F. (2012). Synthesizing queries for handwritten word image retrieval. Pattern Recognition, 45 (9), pp. 3270-3276. DOI: https://doi.org/10.1016/j.patcog.2012.02.015.
- Gordoa, A., Perronnin, F., Rodriguez-Serrano, J. A. & Valveny, E. (2012, June). Leveraging category-level labels for instance-level image retrieval [Paper presentation]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence.
- Rodriguez-Serrano, J. A. (2012). Handwritten word-spotting using hidden Markov models and universal vocabularies. Pattern Recognition, 42 (9), pp. 2106. DOI: https://doi.org/10.1016/j.patcog.2009.02.005.
- Bala, R., Zhao, Y., Burry, A., Kozitsky, V., Fillion, C., Saunders, C. & Rodriguez-Serrano, J. A. (2012). Image simulation for automatic license plate recognition. Proceedings of SPIE-IS and T Electronic Imaging - Visual Information Processing and Communication III (83050Z). DOI: https://doi.org/10.1117/12.912453.
- Rodriguez-Serrano, J. A., Sandhawalia, H., Bala, R., Perronnin, F. & Saunders, C. (2012). Data-driven vehicle identification by image matching. Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings (PART 2 ed., pp. 536-545). Springer Verlag. DOI: https://doi.org/10.1007/978-3-642-33868-7_53.
- Rodriguez-Serrano, J. A., Perronnin, F., Sánchez, G. & Lladós, J. (2010). Unsupervised writer adaptation of whole-word HMMs with application to word-spotting. Pattern Recognition Letters, 31 (8), pp. 742-749. DOI: https://doi.org/10.1016/j.patrec.2010.01.007.
- Rodriguez-Serrano, J. A., Perronnin, F., Lladós, J. & Sánchez, G. (2009). A similarity measure between vector sequences with application to handwritten word image retrieval. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 (pp. 1722-1729). IEEE Computer Society. DOI: https://doi.org/10.1109/CVPRW.2009.5206783.
- Rodriguez-Serrano, J. A. & Perronnin, F. (2009). Handwritten word-image retrieval with synthesized typed queries. ICDAR2009 - 10th International Conference on Document Analysis and Recognition (pp. 351-355). DOI: https://doi.org/10.1109/ICDAR.2009.201.
- Perronnin, F. & Rodriguez-Serrano, J. A. (2009). Fisher kernels for handwritten word-spotting. ICDAR2009 - 10th International Conference on Document Analysis and Recognition (pp. 106-110). DOI: https://doi.org/10.1109/ICDAR.2009.16.