جستجوی استاد راهنمای دانشگاهی

Jean-François Côtéمشاهده پروفایل

استاد

• Combinatorial optimization
• Stochastic programming
• Mathematical programming
• +۵ مورد دیگر

Jean-François Côté is a Full Professor at the Department of Operations and Decision Systems within the Faculty of Business Administration at Laval University. His research focuses on combinatorial optimization, stochastic programming, and mathematical programming with applications in vehicle routing, cutting and loading problems, and logistics optimization. Dr. Côté holds a Doctorate in Computer Science, Operations Research from the University of Montreal, along with Master's and Bachelor's degrees in the same field from the same institution. His academic credentials provide a strong foundation for his interdisciplinary research at the intersection of computer science and operations research. His research spans several key areas in operations research including: Combinatorial optimization techniques for complex routing problems Stochastic programming approaches for handling uncertainty in logistics Mathematical programming models for integrated decision making Vehicle routing and delivery optimization in various contexts Cutting and loading problems in transportation and manufacturing Professor Côté's recent publication record demonstrates a strong focus on solving complex logistics and transportation problems with mathematical rigor. His work addresses challenging real-world problems including same-day delivery optimization, bike sharing systems, home healthcare routing, and automotive manufacturing sequencing. His methodological contributions include novel algorithmic approaches such as logic-based Benders decomposition, disaggregated integer L-shaped methods, and branch-and-regret algorithms. Dr. Côté has published extensively in top-tier operations research journals including: INFORMS Journal on Computing European Journal of Operational Research Transportation Science Computers & Operations Research Operations Research He actively collaborates with researchers across North America and Europe through the CIRRELT research center. His work addresses practical problems faced by industries including transportation, healthcare, manufacturing, and e-commerce. Professor Côté supervises graduate students working on cutting-edge optimization problems and contributes to advancing both theoretical and applied aspects of operations research. His laboratory, part of the broader research ecosystem at Laval University, focuses on developing innovative mathematical models and algorithms to solve complex decision-making problems in logistics and supply chain management.

Dora Demszkyمشاهده پروفایل

استادیار

• Natural Language Processing
• Education Data Science
• Educational Technology
• +۶ مورد دیگر

Dora Demszky is an Assistant Professor in Education Data Science at Stanford University's Graduate School of Education, with a courtesy appointment in Computer Science. She leads the EduNLP Lab, where she develops natural language processing tools to support equitable, student-centered instruction through analyzing educational discourse including student-teacher interactions, student group work, and textbooks. PhD in Linguistics from Stanford University (advised by Dan Jurafsky) BA summa cum laude in Linguistics with a minor in Computer Science from Princeton University Co-founder of Tarisznya Alapítvány (Knapsack Foundation), a nonprofit supporting underprivileged children in Hungary Dr. Demszky's research combines natural language processing, linguistics, and practitioner input to develop interpretable and scalable education measures. Her work focuses on creating tools that analyze classroom discourse to identify features of high-quality instruction and provide actionable feedback to educators. She has particular expertise in developing AI-powered systems that help teachers improve questioning quality, analyze textbook representation, measure dialect features, and scaffold curricula to meet diverse student needs. Her approach emphasizes the importance of human connections in teaching and learning, using technology to enhance rather than replace these critical interactions. Analysis of Dr. Demszky's recent publications reveals a strong trend toward practical applications of NLP in real educational settings, with increasing emphasis on randomized controlled trials to validate effectiveness. Her work spans multiple educational contexts from K-12 classrooms to higher education, with growing attention to equity considerations in AI-powered educational tools. Recent publications show expansion into mathematical education, speaker diarization for noisy classrooms, and open-source tools for the broader research community. MathemaTikZ dataset received the inaugural best dataset prize at Learning at Scale NCTE classroom transcript dataset received the best IEDMS Publicly Available Educational Dataset Prize Selected as a Leading Woman in AI at the ASU GSV AIR Show Dr. Demszky has secured significant research funding including grants from the Gates Foundation, NSF RAPID program, and Stanford HAI. Her work with teachers extends beyond research through initiatives like the Practitioner Voices Summit, which brought together 60 teachers from 22 states to inform AI research related to classrooms. She actively collaborates with educators to ensure her tools address real classroom needs while maintaining a focus on socially responsible edtech development. At Stanford, Dr. Demszky leads the EduNLP Lab, which operates at the intersection of education, computer science, and linguistics. The lab follows a three-pronged approach: gathering evidence through data science using NLP models, developing algorithms through co-design with domain experts, and piloting solutions that practitioners can use in real educational settings. Current projects include M-Powering Teachers, which provides automated feedback to educators, and tools for adapting mathematics curricula to support students with diverse learning needs.