Education

Jeanfranco David Farfan Escobedo is currently pursuing a PhD in Computer Science at the University of Campinas (UNICAMP), Brazil. He earned his Master of Science degree in Computer Science from UNICAMP, focusing on Conversational Systems. Previously, he obtained a Bachelor of Science in Computer and Systems Engineering from Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Peru, with a thesis in Computer Vision.

Research Focus

Jeanfranco’s research primarily revolves around applying deep learning techniques to reduce uncertainty in oil reservoir simulations. Additionally, he explores topics in natural language processing, focusing on conversational systems, and computer vision for tasks like image recognition.

Professional Journey

Jeanfranco has accumulated diverse professional experiences. He currently works as a researcher at Shell Oil Company in Brazil, specializing in utilizing deep learning for improving oil reservoir simulations. He has also served as a Teaching Assistant at UNICAMP, where he supported courses in Algorithms and Computer Programming. Furthermore, he has taught Machine/Deep Learning at the Artificial Intelligence University of Engineering and Technology (UTEC) in Peru.

Honors & Awards

Jeanfranco has received several notable awards, including the Shell Oil Company Industry Research Scholarship in 2021, the Sinch Latin America Industry Research Scholarship in 2019, and first place in the AgroHack hackathon for developing a plant disease monitoring app in 2018.

Publications Noted & Contributions

Jeanfranco has contributed significantly to academic publications, including:

• “Towards accurate building recognition using convolutional neural networks” (2017) at IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing, showcasing advancements in image recognition through convolutional neural networks.
• “A new method of classification with rejection applied to building images recognition based on Transfer Learning” (2018) at IEEE XXV International Conference on Electronics, Electrical Engineering and Computing, presenting innovative techniques in classification using transfer learning.
• “Deep hierarchical distillation proxy-oil modeling for heterogeneous carbonate reservoirs” (2023) published in Engineering Applications of Artificial Intelligence, focusing on improving oil reservoir modeling through deep learning techniques.
• “Cross-Domain Feature learning and data augmentation for few-shot proxy development in oil industry” (2023) published in Applied Soft Computing, exploring methods to enhance proxy development across different domains within the oil industry.
• “Active Learning Approach for Intent Classification in Portuguese Language Conversations” (2021) at IEEE 15th International Conference on Semantic Computing, presenting methodologies for enhancing intent classification in conversational systems using active learning.

Research Timeline

Jeanfranco’s research journey spans from his undergraduate studies through to his current doctoral research. He has consistently explored cutting-edge topics in deep learning, natural language processing, and computer vision, contributing to advancements in these fields.