Keynote Speakers for 2024

Professor Stephanie Farrell, (IFEES President and Rowan University, New Jersey, USA.)

Stephanie is past president of the American Society for Engineering Education, and she has served two terms on the IFEES Executive Committee. For the last decade, she has been actively involved in the Indo-US Collaboration for Engineering Education, serving as an international partner, teaching certification cluster leader, and IFEES representative.

Stephanie leads Rowan’s Revolutionizing Engineering Diversity organizational change initiative, funded through NSF’s Revolutionizing Engineering Departments (RED) Program. She is on Rowan’s ADVANCE team for organizational change to promote intersectional gender equity in STEM, and she leads ASEE’s national initiative to promote LGBTQ+ inclusion in engineering, both funded by NSF.

Stephanie’s contributions to engineering education have been recognized with numerous national and international awards. She has been honored by the American Society of Engineering Education (ASEE) with several teaching awards such as the National Outstanding Teaching Medal and the Quinn Award for experiential learning. She received the highest award from the International Society for Engineering Pedagogy (IGIP), the Nikola Tesla Award for outstanding achievements in engineering pedagogy. She received AIChE’s national award for Service to Chemical Engineering Education. Stephanie was the 2014-2015 Fulbright Scholar in Engineering Education at Dublin Institute of Technology (Ireland).

Yakut Gazi, Ph.D. (Duke University, Durham, North Carolina, USA)

Yakut Gazi is the Vice Provost for Learning Innovation and Digital Education at Duke University. In this role, she oversees the Office of Learning Innovation and Lifetime Education (LILE), with responsibility for the development and execution of pedagogical innovation and digital education strategies, youth academic enrichment programs, and continuing education and lifelong learning offerings. Previous to this position, she was the Associate Dean for Learning Systems at Georgia Tech Professional Education. Her higher education experience spans over 30 years in four countries.

She served on the Academic Advisory Council for Quality Matters©, is an elected council member and First Vice President of the International Association for Engineering Continuing Education (IACEE), and is an at-Large Board Member of the University Professional and Continuing Education Association (UPCEA). Dr. Gazi has her Ph.D. in Educational Psychology from Texas A&M University, and an M.A. in Educational Sciences and a B.S. in Teaching Chemistry, both from Bogazici University in Turkey. A native of Istanbul, Dr. Gazi is married, with a daughter.

Davide Buscaldi (USPN, France)

Davide Buscaldi is associate professor at Université Sorbonne Paris Nord and “chargé d’enseignement” (part-time) at DIX, Ecole Polytechnique. He has acquired an important experience with text mining for the construction of Knowledge Graphs for the construction of the Computer Science Knowledge Graph (CS-KG) , a joint project with University of Cagliari (Italy) and The Open University (UK). For this project he supervised the construction of a pipeline that extracts triples from scientific texts and verify their consistency with the help of a neural network.

He is the author of more than 120 works related to NLP and text mining, published in peer-reviewed international and national venues. He co-directed 4 Ph.D. at LIPN and 1 Ph.D. at LIX and he is currently co-directing 2 more Ph.D. at LIX on Large Language Models and a Ph.D. at LIPN on Graph Neural Networks.

Message from Prof. Buscaldi:

In this talk, I will show some preliminary results on the surge of the use of chatGPT by students in French Computer Science BUT at Villetaneuse. I will discuss the capabilities and the reliability of AI detection tools, then I will highlight how these tools can be used to estimate the use of AI and chatGPT to write reports, and finally I will give some insights regarding which parts of these reports tend to be generated automatically.