Assistant Professor of Computer Science
Professor Bejarano earned her doctoral degree from the State University of New York at Binghamton, where she also pursued a Master's in Computer Science thanks to a Fulbright Scholarship. She has lectured at Universidad Peruana Cayetano Heredia and Pontificia Universidad Católica del Perú. After her doctoral studies, she worked as a Postdoctoral Researcher at Baylor University in Texas. Her research has focused on machine learning for sequential data, such as sign language processing and forecasting in smart city problems. She has been awarded the AI2050 Early Career Fellowship from Schmidt Futures to explore iconicity in sign language and gestures through artificial intelligence methods. She has experience in the financial and insurance industries, as well as serving in the Peruvian Government in topics related to Artificial Intelligence. Bejarano is a member of the LatinxInAI community and the Research Experience for Peruvian Undergrad program (REPU).
PhD, Computer Science, Binghamton University (SUNY), 2021
MS, Computer Science, Binghamton University (SUNY), 2017
BS, Informatics Engineering, Pontificia Universidad Católica del Perú, 2009
Research Interests / Areas of Focus
Sign Language Processing
Data Science and Artificial Intelligence
Bejarano, G., Huamani-Malca, J., Cerna-Herrera, F., Alva-Manchego, F., & Rivas, P. (2022, June). PeruSIL: A Framework to Build a Continuous Peruvian Sign Language Interpretation Dataset. In Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources (pp. 1-8).
Jui, T. D., Bejarano, G. M., & Rivas, P. (2022, June). A machine learning-based segmentation approach for measuring similarity between sign languages. In sign-lang@ LREC 2022 (pp. 94-101). European Language Resources Association (ELRA).
Bejarano, G., DeFazio, D., & Ramesh, A. (2019). Deep Latent Generative Models for Energy Disaggregation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 850-857. https://doi.org/10.1609/aaai.v33i01.3301850
Bejarano, G., Kulkarni, A., Raushan, R., Seetharam, A., & Ramesh, A. (2019, November). Swap: Probabilistic graphical and deep learning models for water consumption prediction. In Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (pp. 233-242).
Bejarano, G., Jain, M., Ramesh, A., Seetharam, A., & Mishra, A. (2018, June). Predictive analytics for smart water management in developing regions. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 464-469). IEEE.
First Peruvian Sign Language – Spanish Dictionary: www.dvlsp.link
Awards and Honors
AI2050 Early Career Fellow. Schmidt Futures, 2022
Teaching Assistant Award. Department of Computer Science, SUNY Binghamton 2021
Google Anita Borg Scholarship for Grace Hopper Conference (GHC). Orlando, USA 2017
Google Travel Scholarship for Lisbon Machine Learning Summer School (LxMLS). Portugal 2017
Fulbright Scholarship from Peru for master program at SUNY Binghamton. USA 2015-2017