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Paola Cascante-Bonilla

PhD candidate in Computer Science
Graduate Research Assistant in The Vision, Language and Learning Lab
2023-24 Future Faculty Fellow

WEBSITE(S)| The Vision, Language and Learning Lab at Rice University

I received a Master of Computer Science at the University of Virginia and a B.S. in engineering at the Tecnológico de Costa Rica. I spent one Summer at Mitsubishi Electric Research Laboratories (MERL) and two consecutive Summers at the MIT-IBM Watson AI Lab. I am the recipient of the Ken Kennedy Institute SLB Graduate Fellowship (2022/23). My research has been featured in the IBM Research Blog, TechXplore, Rice CS News, and MIT News. My work has been published in several vision and language conferences (CVPR, ICCV, NeurIPS, AAAI, BMVC, NAACL).

Nurturing the next generation of researchers and guiding them to success while learning from them is, to me, a great deal. After spending some years working as a Senior Software Engineer in several industrial settings in my home country, I found myself willing to explore exciting new technology and face new challenges. Academia provides a vibrant and stimulating environment that is constantly evolving. When re-evaluating my career status, I found some lacking aspects in my development that were inevitably embedded in all academic settings. I thrive on the constant quest for knowledge, and I am passionate about both advancing research in my field and contributing to it through mentoring, research, writing, collaboration, and service. I cherish the research freedom academia can offer, alongside additional perks involved in peer review processes, innovation, creativity, and effective communication through impactful publications.

Preparation, communication, and networking are principal aspects that could aid in the process of becoming a competitive professional. Determination is not a lone runner, and companionship is key to success. The Future Faculty Fellows (FFF) program is designed to help explore multiple academic career paths, with a large body of successful academics willing to help strong candidates in the process. This is a great opportunity to learn and grow while getting mentored by future peers, and I am looking forward to fostering new connections and strengthening my academic profile.

Research Areas

I work on Computer Vision, Natural Language Processing, and Machine Learning. I have been exploring ways to train deep learning models with large amounts of data but limited labels, or in cases where the data lacks informative value. Particularly, I have been focusing on multi-modal learning, few-shot learning, semi-supervised learning, representation learning, and synthetic data generation for compositionality and privacy protection.

Education

Master of Computer Science, University of Virginia

B.S. in Engineering, Tecnológico de Costa Rica

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