Find Academic Supervisors

• Internet of Things
• Human-Computer Interaction
• Ambient Intelligence
• +5 more

Fulvio Corno is a Professor in the Department of Control and Computer Engineering at the Polytechnic University of Turin, Italy. With a research career spanning over three decades since 1992, he has established himself as a prominent researcher in Internet of Things, Human-Computer Interaction, and Ambient Intelligence. His research focuses on making technology accessible to end-users, particularly through work on End-User Development in IoT systems, assistive technologies for people with disabilities, and security challenges in IoT environments. Corno's work bridges the gap between technical complexity and user needs, developing tools that simplify interaction with smart environments. His publication trends show a consistent focus on IoT systems, with recent work emphasizing security issues for novice programmers, computational notebooks for prototyping, and natural language interfaces for configuring smart environments. His research has evolved from foundational work on semantic approaches to IoT to practical tools addressing real-world implementation challenges. Corno has collaborated extensively with researchers including Luigi De Russis, Alberto Monge Roffarello, and Dario Bonino, producing significant contributions to the field of smart environments and end-user programming. He has contributed to education in Ambient Intelligence, developing courses that prepare engineers for the challenges of intelligent environments, and has supervised numerous students in this emerging field. His laboratory work centers on practical implementations of IoT systems, with projects spanning healthcare support systems, notification management across devices, and accessibility solutions for people with motor disabilities.

• Scheduling
• Multi-criteria Optimization
• Operational Research
• +1 more

Dr. Vincent T'KINDT is a Researcher in the Computer Science Department at the Polytechnic School of the University of Tours. He serves as Head of the "Scheduling and Management" axis at the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT, UR 6300) and as Head of Corporate Relations for his department. His research focuses on Scheduling , Multi-criteria Optimization , and Operational Research , contributing to theoretical and applied advancements in these fields. Recent publications highlight his work on Multi-objective scheduling for parallel machines Heuristic algorithms for dynamic scheduling Just-in-Time scheduling models Rescheduling with setup times His research combines exact and approximate methods to solve complex industrial scheduling challenges. He has authored key works in journals like the Journal of Scheduling , Computers & Operations Research , and European Journal of Operational Research , with notable contributions including the book Multicriteria Scheduling: Theory, Models and Algorithms (Springer, 2006). Dr. T'KINDT collaborates with the LIFAT laboratory (UR 6300) and has participated in international conferences such as PMS'08, MISTA'07, and MAPSP2007. His methodological approaches include Integer Programming, Beam Search algorithms, and Pareto optima enumeration.

Petre BirteaView profile

Associate Professor

• Differential Geometry
• Optimization
• Geometric Mechanics
• +3 more

Petre Birtea is an Associate Professor at the Faculty of Mathematics and Computer Science, West University of Timisoara, Romania, where he conducts cutting-edge research at the intersection of differential geometry, optimization theory, and geometric mechanics. His work fundamentally advances mathematical frameworks for constrained systems on manifolds. His research portfolio spans: Differential Geometry of Lie groups and homogeneous spaces Optimization algorithms on Stiefel and orthogonal manifolds Geometric mechanics of rigid bodies and underwater vehicles Quantum control landscapes and stability analysis Geometric dissipation in dynamical systems Applications to neural network training and compositional data analysis Analysis of his 15 most recent publications (2015-2024) reveals a cohesive research trajectory centered on developing geometric optimization techniques for matrix Lie groups. His work systematically addresses critical points characterization, second-order optimality conditions, and Newton-type algorithms on Stiefel manifolds, with direct applications in quantum control, mechanical systems, and machine learning. A distinctive feature is his use of ambient Euclidean coordinates to derive intrinsic geometric operators, enabling novel computational approaches to constraint optimization problems. Birtea's theoretical contributions include foundational results on Laplace-Beltrami operators, double bracket flows, and stability criteria for nongeneric equilibria. His 2024 work on parametric Lie group structures for probabilistic simplices demonstrates the expanding scope of his geometric framework into statistical applications, while his neural network training method (2016) exemplifies cross-disciplinary impact. The consistent application of symplectic and Riemannian geometry across his publications establishes a unified methodology for solving complex constrained optimization problems in physics and engineering.

• Mathematics Education
• Computing Education
• STEM Equity
• +3 more

Sarah D Castle serves as Assistant Professor of Mathematics Education at the University of Idaho with dual appointments in the Department of Mathematical and Statistical Sciences (College of Science) and Curriculum and Instruction Department (College of Education, Health and Human Sciences). Her research bridges computational methods and mathematics education to advance equity and foster creativity in undergraduate STEM contexts. Her educational background includes: B.A. in Mathematics, Whitworth University (2012-2016) B.S. in Engineering Physics, Whitworth University (2012-2016) M.S. in Mathematics, Michigan State University (2019-2022) Ph.D. in Mathematics Education, Michigan State University (2018-2023) Castle's research centers on computational approaches to uncover systemic inequities in STEM education while creating environments that nurture mathematical creativity, particularly for marginalized students. She investigates how computational modeling—such as Jupyter Notebook implementations in linear algebra—can transform mathematical engagement and understanding. Her work critically examines power structures in educational spaces and develops pedagogical frameworks that leverage coding to deepen conceptual learning and promote equitable participation. Analysis of her 13 publications (2020-2024) reveals three dominant trends: (1) computational tools as catalysts for mathematical creativity and conjecture, (2) quantitative critical analyses of systemic advantages in STEM courses, and (3) investigations of student agency development in proof-based mathematics. These works span ACM computer science education conferences, mathematics education research forums, and interdisciplinary learning sciences venues. Castle actively contributes to major research initiatives: SEISMIC Project: Multi-institutional analysis of equity in introductory STEM courses Transition to Proof Project: Studying sense-making in advanced mathematics Computational Education Research Lab: Developing computational modules for mathematics learning Her teaching portfolio includes Secondary Mathematics Methods (EDCI 434), Proof and Viable Argumentation (MTHE 410), and Calculus I (MTH 132), reflecting her dual expertise. She has extensive experience as instructor of record, teaching assistant, and developer of STEM curricula including high-altitude balloon launch modules for K-12 students.

Aysegül KahramanView profile

Research Fellow

• Wind Power
• Microgrids
• Reinforcement Learning
• +5 more

Aysegül Kahraman serves as a Postdoctoral Researcher (Research Fellow) at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), with active projects running through 2028. Her work directly contributes to UN Sustainable Development Goals in renewable energy and climate action through advanced energy systems research. Her research expertise spans Wind Power integration , Microgrid optimization , and Machine Learning applications for energy systems. She specializes in deploying Reinforcement Learning and Deep Learning techniques to solve critical challenges in load forecasting, home energy management, and multi-energy system operations under uncertainty, with particular focus on transactive control frameworks and EV scheduling. Analysis of her 2022-2024 publications reveals a strong trend toward AI-driven solutions for renewable energy integration , with consistent emphasis on real-world implementation in microgrids and distribution systems. Her work demonstrates increasing sophistication in handling system constraints while optimizing energy flexibility across multiple domains. As academic supervision, she currently serves as supervisor for PhD candidate Sundhu, A. A. in the Real-Time Digital Twin for Active Distribution Networks project, building on her own recently completed PhD research at DTU.

Tessa ConsoliView profile

Research Fellow

• Digital Transformation in Secondary Education
• Technology Integration
• Teaching Quality in Technology-Supported Learning
• +3 more

Tessa Consoli serves as a Research Fellow at the Institute of Educational Science, University of Zurich, within the Chair of General Didactics and Media Didactics. Her work focuses on the intersection of digital technologies and educational practices in upper secondary contexts. Her research program centers on: Digital transformation processes in Swiss upper secondary education Technology integration quality and its measurable impact on student outcomes Digital citizenship education frameworks Generative AI's role in learning environments Teacher profiles and professional development needs Recent publications reveal strong methodological diversity including large-scale comparative studies, qualitative case analyses, and policy evaluations. Her work consistently examines both technical implementation and human factors affecting educational technology adoption. Current projects investigate BYOD (Bring Your Own Device) implementations, media literacy instruction patterns, and transformational leadership approaches for technology integration. Professional trajectory shows progressive research engagement since 2020 through multiple funded projects including DigiTraSII and SCATT, complemented by practical teaching experience in Italian language education. Her scholarly contributions primarily appear in high-impact educational technology journals with significant Swiss policy relevance.

Elvin ÇobanView profile

Associate Professor

• Healthcare and Humanitarian Operations
• Optimization
• Service Systems
• +3 more

Elvin Çoban serves as Associate Professor and Chair of the Industrial Engineering Department within Ozyegin University's Faculty of Engineering, specializing in optimization applications for healthcare systems, humanitarian logistics, and service operations. Her research bridges theoretical operations research with real-world implementation in critical infrastructure domains. Education Background: PhD in Operations Management and Manufacturing, Carnegie Mellon University (2012) MS in Operations Management and Manufacturing, Carnegie Mellon University (2010) MS in Industrial Engineering, Sabanci University (2008) BS in Manufacturing Systems Engineering, Sabanci University (2006) Dr. Çoban's research program focuses on developing mathematical models for complex decision-making under uncertainty, with significant contributions to disaster response logistics using drone technology and healthcare operations management. Her work integrates machine learning with combinatorial optimization to solve scheduling problems in medical facilities and vaccine distribution networks, while also exploring energy systems through electric vehicle charging infrastructure modeling. Current projects emphasize predictive analytics for post-disaster damage assessment and resource allocation during pandemics. Analysis of her 15 most recent publications (2019-2024) reveals three dominant research thrusts: (1) humanitarian logistics (35% of output), particularly drone routing for disaster assessment; (2) healthcare operations (50%), including operating room scheduling and vaccine distribution; and (3) energy systems (15%), focused on electric vehicle infrastructure optimization. Methodologically, her work combines stochastic programming, metaheuristics, and machine learning to address data-scarce environments. Research Funding and Advisory: Principal Investigator for TUBITAK 1001 project (2021-2024): Information Gathering and Damage Prediction for Post-Disaster Drone Assessment Researcher for TUBITAK 3501 project (2013-2016): Blood Supply Chain Optimization Consultant for TEA Networks (2018-2021) and TUBITAK 1507 workforce management project (2014-2016) Advises three graduate students: Kian Farajkhah (PhD candidate), Birce Adsanver (MSc 2020), and Gulsah Alper (MSc 2015) on optimization applications in disaster response and healthcare She leads an active research group at Ozyegin University that collaborates with government agencies and industry partners on projects involving real-time decision support systems. Current initiatives focus on integrating real-world constraints into optimization models for medical logistics and developing scalable algorithms for drone-based disaster assessment networks, with ongoing recruitment for TUBITAK-funded humanitarian logistics research positions.

Lillian J. RatliffView profile

Associate Professor

• Game Theory
• Economics
• Optimization
• +4 more

Lillian J. Ratliff is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Washington, with adjunct appointments in the Paul G. Allen School of Computer Science & Engineering and the Department of Aeronautics and Astronautics. Her research bridges theoretical foundations with practical applications in intelligent systems, focusing on strategic decision-making in complex environments. Dr. Ratliff earned her PhD in Electrical Engineering and Computer Sciences from UC Berkeley in 2015. She completed dual Bachelor of Science degrees in Electrical Engineering and Mathematics from the University of Nevada, Las Vegas (UNLV) in 2008, followed by a Master of Science in Electrical Engineering from UNLV in 2010. Her research expertise spans the intersection of game theory , economics , optimization , machine learning , and control theory . She develops theoretical frameworks for decision-making in intelligent systems with learning-enabled components and strategic agents. Her work addresses fundamental questions about convergence properties of learning algorithms in game-theoretic settings, equilibrium analysis in complex multi-agent systems, and the development of efficient algorithms for strategic decision-making. Her theoretical contributions have significant practical implications for understanding strategic interactions in systems ranging from transportation networks to human-AI collaboration. Dr. Ratliff's recent publications reveal a strong focus on matrix games, Stackelberg games, and performative prediction, with applications spanning from human-computer interaction to multi-agent reinforcement learning. Her work demonstrates increasing sophistication in handling decision-dependent distributions and analyzing convergence properties of learning dynamics in complex strategic environments. Her scientific achievements have been recognized with prestigious awards including: NSF Graduate Research Fellowship (2009) NSF CISE Research Initiation Initiative award (2017) NSF CAREER award (2019) ONR Young Investigator award (2020) UW College of Engineering Junior Faculty Award (2021) Dhanani Endowed Faculty Fellowship (2020) Invited speaker at the National Academy of Engineering China-America Frontiers of Engineering Symposium (2019) Dr. Ratliff leads a research program funded by multiple National Science Foundation grants (CNS-1736582, CNS-1836819, CNS-1931718, CNS-1907907, CNS-1844729, CNS-1952011, CNS-1634136, CNS-1646912, CNS-1656873) and the Office of Naval Research Young Investigator Program. Her research group investigates theoretical foundations for strategic decision-making with applications to intelligent transportation systems, human-machine interaction, and multi-agent reinforcement learning. Her work is organized around theoretical foundations for decision-making in strategic environments, with applications to intelligent transportation systems, human-AI collaboration, and network congestion management. She maintains active collaborations across disciplines, including with researchers in computer science, aeronautics, control theory, and economics, demonstrating the interdisciplinary nature of her research program.

Emma LjungView profile

Senior Lecturer

• Classical Archaeology
• Mediterranean Landscapes
• Roman Society
• +3 more

Emma Ljung is a Senior Lecturer in the Princeton Writing Program and a classical archaeologist specializing in Mediterranean landscapes and their influence on Greek and Roman societies. She directs the Santa Susana Archaeological Project, focusing on the history of a Roman villa in Portugal's Alentejo region. Research emphasizes smaller communities and economic problem-solving shaped by cultural/natural constraints Current fieldwork involves excavation and analysis of Roman villa complexes in Portugal

• Operational Research
• Combinatorial Optimization
• Heuristics
• +4 more

Maria Cândida Mourão is a full Professor in Operational Research at the Mathematics Department of ISEG - Lisbon School of Economics and Management, University of Lisbon. She serves on the Executive Committee and coordinates the Operational Research scientific area at ISEG, alongside active participation in CEMAPRE research center. PhD in Mathematics Applied to Economics and Management (1998) Aggregation in Mathematics, Operational Research (2010) Her research focuses on Combinatorial Optimization , Heuristics , and Mixed Integer Linear Programming to solve complex Arc Routing Problems in urban logistics, waste collection, and transportation. She has published extensively in top journals like European Journal of Operational Research , Networks , and Computers & Operations Research , contributing both theoretical models and real-world applications. Her work emphasizes bridging academic research with practical solutions for network design and logistics optimization . She has served on organizing committees for international conferences and acts as reviewer for major journals. Teaching responsibilities include coordinating curricular units in Operational Research and supervising over 25 Master's theses in optimization, scheduling, and routing applications across sectors like urban waste, energy distribution, and public safety.

Diarmuid O'DonoghueView profile

Assistant Professor

• Computational Creativity
• Analogical Reasoning
• Natural Language Processing
• +4 more

Diarmuid O'Donoghue is an Assistant Professor in the Faculty of Science & Engineering at Maynooth University, specializing in Computational Creativity and Analogical Reasoning . His research explores topological similarities between text and source code to develop cognitively inspired systems for problem-solving and bias detection. Co-PI of the Modelling implicit bias project (€21/FFP-P/10118) Senior Scientific Coordinator for the €2.6M EU-funded Dr Inventor project Key research areas include: Latent homomorphism detection in lexical data Comparative analysis of LLMs and analogical systems Formal specification generation from code/text His work has shaped undergraduate project frameworks with ethical GenAI integration . Publications span ICCC , GECCO , and journals like Artificial Intelligence Review .

• Theoretical Computer Science
• Computational Complexity
• Approximation Algorithms
• +6 more

Per Austrin is a Lecturer at the Royal Institute of Technology (KTH) in the Department of Theoretical Computer Science. He has been actively involved in teaching courses such as Algorithms and Complexity (DD2352) , Advanced Algorithms (DD2440) , and Problem Solving and Programming Under Pressure (DD2458) , serving as examiner and course coordinator for several programs. His research focuses on the foundations of computational complexity, approximation algorithms, and cryptographic barriers. Key areas include hardness of approximation for constraint satisfaction problems (CSPs), combinatorial optimization, and theoretical limits in cryptography and coding theory. His work often intersects with probabilistic methods and quantum computing challenges. Recent publications highlight his contributions to understanding lower bounds in optimization problems, inapproximability thresholds, and complexity in structured combinatorial problems. Detailed information about his work can be found on his personal website: Per Austrin's Website .

• IT innovation
• Organizational ambidexterity
• Digital transformation
• +5 more

Dragos Vieru is a Full Professor of Information Technology at TÉLUQ University in Montreal, Canada. He joined the faculty in 2011 after accumulating more than 15 years of practical experience in information technology project management within the public health sector. Professor Vieru served as Director of the School of Business Administration from 2015 to 2018 and maintains an active research role as a regular researcher at the Information Systems Research Group (GReSI - HEC Montréal). His international academic engagement includes visiting professorships at the University of Münster (Germany), Budapest University of Technology and Economics (Hungary), Academy of Economic Studies (Romania), and Corvinus University of Budapest (Hungary). His academic credentials include: Ph.D. in Administration (major in Information Technologies; minor in Organizational Studies) from HEC Montréal M.Sc. in Management of Information Systems from John Molson School of Business, Concordia University M.Eng. in Industrial Engineering from Polytechnic University of Bucharest, Romania Professor Vieru's research spans IT innovation, organizational ambidexterity, digital transformation, digital ecosystems and platforms, inter-organizational information systems, global sourcing of IT services, and ethics of artificial intelligence. With over 50 publications in leading journals like the International Journal of Information Management and Information Systems Management, his work frequently examines the intersection of technology, organizational behavior, and management practices. His recent scholarship increasingly focuses on the ethical dimensions of emerging technologies, particularly artificial intelligence in organizational contexts. Professor Vieru has made significant contributions to the academic community through his leadership as chair of the Socio-Technical Issues in Organizational Information Technologies mini-track at the Hawaii International Conference on System Sciences since 2013. His certification as a LEGO Serious Play® Designer and Facilitator informs his innovative teaching and research approaches. His professional affiliations include: Member of the Association for Information Systems (AIS) Member of Groupe de recherche en systèmes d'information (GreSI) Professor Vieru actively bridges theoretical research with practical applications, leveraging his extensive industry experience to address real-world challenges in information technology management across various organizational contexts.

Götz PfanderView profile

Professor

• Numerical Harmonic Analysis
• Time-Frequency Analysis
• Gabor Systems
• +7 more

Götz Pfander is a Professor of Mathematics at the Catholic University of Eichstätt-Ingolstadt , holding the Chair of Mathematics - Scientific Computing . He has held previous academic roles at Philipps-Universität Marburg (W2 Numerical Analysis), Jacobs University Bremen (Associate/Assistant Professor), and visiting positions at institutions including MIT, NYU Courant, and TU München. His leadership roles include Dean and Vice Dean of the Faculty of Mathematics and Geography (2019-2023) and Speaker of the Mathematical Institute of Machine Learning and Data Science (MIDS) since 2022. PhD in Mathematics (University of Maryland, 1999), advised by John J. Benedetto Master of Arts in Mathematics (University of Maryland, 1998) Studies in Mathematics and Psychology (Johannes Gutenberg University Mainz, Freie Universität Berlin) Pfander's research focuses on numerical harmonic analysis, operator sampling theory, and time-frequency analysis, with applications in digital communications and signal processing. His work bridges Gabor frames, wavelet transforms, and uncertainty principles to solve problems in OFDM channel modeling, sparse signal recovery, and quantum information theory. Notable contributions include: Sampling theory for pseudodifferential operators with bandlimited Kohn-Nirenberg symbols Uncertainty principles for joint time-frequency representations on finite Abelian groups Design of robust Gabor systems for wireless communication channels Wavelet-based periodicity detection for biomedical signals His recent publications (2022-2024) emphasize exponential bases for interval partitions, cube tiling constraints, and complex-valued neural network approximation. Scientific awards include the Max Kade Fellowship and John von Neumann Visiting Professor title. He serves as Editor in Chief of Sampling Theory, Signal Processing, and Data Analysis and chairs the International Conference on Sampling Theory and Applications .

Tallys YunesView profile

Associate Professor

• Operations Research
• Optimization
• Mathematical Modeling
• +7 more

Tallys Yunes is an Associate Professor of Management Science at the Miami Herbert Business School, University of Miami. Originally from Rio de Janeiro, Brazil ("carioca"), he studied computer engineering and computer science at UNICAMP in Brazil before earning his Ph.D. from Carnegie Mellon University. His research focuses on applying optimization techniques to solve practical problems across various domains. Dr. Yunes's research interests span a wide range of applications in operations research and optimization: Healthcare operations and appointment scheduling Sports analytics and umpire scheduling (including Major League Baseball) Product line optimization and complexity cost analysis Information display on maps and proportional symbol maps Political districting and electoral systems Supply chain management and multi-supplier procurement Resource allocation and parallel task scheduling Hybrid optimization methods combining different techniques His recent publications demonstrate a consistent focus on applying mathematical optimization to real-world problems. He has published in top journals like Management Science, Production and Operations Management, INFORMS Journal on Computing, and European Journal of Operational Research. His work often involves collaboration with industry partners such as Caterpillar and MLB, demonstrating the practical impact of his research. Dr. Yunes is actively engaged in the academic community through his blog "O.R. by the Beach," where he discusses operations research concepts and applications. He has also developed educational content including games and YouTube videos to make optimization concepts more accessible.