Find Academic Supervisors

• Optimization applied to complex problems
• Predictive modeling in healthcare
• Interpretable Machine Learning
• +9 more

Sara Lumbreras Sancho is a Senior Associate Professor at the ICAI School of Engineering, Universidad Pontificia Comillas, where she graduated in 2006 as the Graduation Prize Winner. She serves as Deputy Director of Research Results at the Technological Research Institute and co-leads the Chair of Science, Technology, and Religion with Jaime Tatay. Her research focuses on decision support techniques for complex problems in energy, healthcare, and finance, leveraging optimization methods like Benders decomposition, heuristics, and AI. She also explores the philosophy of technology, transhumanism, and AI's impact on anthropology. With over 70 academic publications and leadership in over 20 projects, she bridges technical innovation with ethical and anthropological considerations. Education: Bachelor’s Degree in Engineering from ICAI School of Engineering (2006) PhD in Decision Support Methods for Large-Scale Flexible Transmission Planning (2014) Research Interests: Optimization techniques (stochastic, Benders, metaheuristics) Predictive healthcare modeling Interpretable machine learning AI ethics and responsible science Energy systems planning (wind farms, transmission networks) Philosophical implications of technology Grants & Projects: Lead researcher in EU-funded initiatives like DIAMOND and OPEN_ENTRANCE Collaborations with industries like Iberdrola, Enel, and Pharoes Advisory Transnational projects addressing energy transition challenges Awards: Global Shaper (World Economic Forum) Marshall Memorial Fellow Outreach: Chair of Cátedra de Ciencia, Tecnología y Religión Keynote speaker at conferences like EURO 2025 and Science and Religion Forum Author of books and book chapters on AI ethics, transhumanism, and science-religion dialogue

Amine MezghaniView profile

Assistant Professor

• Wireless Communication
• Digital Signal Processing
• Communication Systems
• +12 more

Dr. Amine Mezghani is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on wireless communication systems, signal processing, and antenna engineering, particularly under hardware constraints like 1-bit quantization and phase-only DACs. Education: PhD (2015) in Electrical Engineering, Technical University of Munich; Dipl.-Ing. (Electrical Engineering), Technical University of Munich; Diplôme d'Ingénieur, École Centrale Paris His work explores sustainable wireless infrastructure, with emphasis on reconfigurable intelligent surfaces (RIS), millimeter-wave Massive MIMO, V2X communications, and physically consistent modeling combining Shannon and Maxwell theories. Publications include over 100 papers on hardware-constrained systems. Scientific contributions have earned him the 2021 IEEE Signal Processing Society Best Paper Award and the 2016 Rohde & Schwarz Outstanding Dissertation Award . He serves as an Associate Editor for IEEE Transactions on Wireless Communications . Current research includes electromagnetic consistency in multi-band MIMO, unlabeled compressed sensing, and VAMP-based algorithms. He advises graduate students in wireless communications and signal processing at the Intelligent Communication and Sensing Lab .

Puyan MojabiView profile

Professor

• Applied Electromagnetics
• Antennas and Propagation
• Electromagnetic Inverse Problems
• +5 more

Puyan Mojabi is a Professor and a Canada Research Chair (Tier 2) in the Department of Electrical and Computer Engineering at the University of Manitoba within the Price Faculty of Engineering. His research integrates electromagnetic inverse problems with metasurface design for applications in wireless communication and non-destructive testing. Education BSc Electrical Engineering, University of Tehran (2002) MSc Electrical Engineering, Iran University of Science and Technology (2004) PhD Electrical Engineering, University of Manitoba (2010) His work focuses on two key themes: systematic metasurface design for next-generation wireless systems and metasurface-enhanced electromagnetic imaging for cost-effective non-destructive testing. Articles demonstrate expertise in deep learning integration , implicit solvers , and phaseless inversion techniques . Scientific Awards University of Manitoba Falconer Emerging Researcher Award University of Manitoba Excellence in Teaching Award (x2) University of Manitoba Graduate Students Association Teaching Award International Union of Radio Science (URSI) Young Scientist Award (x3) Mojabi actively seeks graduate students in applied electromagnetics and contributes to professional communities as an Early Career Representative of URSI and Chair of the IEEE Winnipeg Waves Chapter.

Nikos SpanoudakisView profile

Assistant Professor

• Multi-Agent Systems
• Artificial Intelligence
• Agent-Oriented Software Engineering
• +11 more

Nikos Spanoudakis is an Assistant Professor at the Department of Electronic Engineering of Hellenic Mediterranean University , with concurrent research collaboration at Technical University of Crete . He holds a PhD in Computer Science (Artificial Intelligence) from Paris Descartes University (2009, "Très Honorable"), an MSc in Organization and Administration from Technical University of Crete, and a Diploma in Computer Engineering and Informatics from University of Patras. Research Focus: Multi-Agent Systems (AOSE, Computational Argumentation), Model-Driven Engineering , Smart Buildings , IoT , and Artificial Intelligence Applications in Ambient Intelligence, Finance, and Education Key Contributions: Created ASEME Methodology and AMOLA Language for agent modeling, developed Gorgias-B argumentation framework, and designed Kouretes Statechart Editor for robotic behavior specification His recent publications reveal a strong trend in Explainable AI (2023: Explainable Argumentation as a Service ), Smart Energy Systems (2025: Engineering IoT-Based Open MAS for Large-Scale V2G/G2V ), and EdTech Innovations (2024: Role Assignment in Programming Courses ). He has received prestigious ACM Senior Member (2023) and IEEE Senior Member (2012) distinctions, along with teaching recognition (2021) from Technical University of Crete. Academic Leadership: Serves as Editor for Springer Nature's Computer Science journal and has reviewed for 15+ top-tier publications including IEEE Intelligent Systems and Journal of Web Semantics Conference Involvement: Program Committee Member for 20+ international conferences (IJCAI, ECAI, AAMAS, AAAI) and organizer of multiple European Agent Systems Summer Schools

Aliaa AlnaggarView profile

Assistant Professor

• Operations Research
• Optimization under Uncertainty
• Supply Chain and Logistics
• +7 more

Dr. Aliaa Alnaggar is an Assistant Professor of Industrial Engineering at the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. Her research focuses on operations research, optimization under uncertainty, supply chain logistics, sharing economy, and healthcare operations management , leveraging mathematical modeling and probability theory to address complex decision-making challenges. She holds a PhD from the University of Waterloo (2021) and completed a postdoctoral fellowship at the Rotman School of Management (University of Toronto, 2022). Education details include a BSc from Kuwait University (2010), MASc from the University of Waterloo (2017), and a PhD in Industrial Engineering (2021). She teaches IND 604: Operations Research II and actively contributes to professional organizations like INFORMS, CORS, and WORMS. Her research addresses societal challenges such as hospital resource allocation during surges, gig-economy workforce management, and energy storage optimization. Key achievements include the NSERC Postdoctoral Fellowship and collaborative work on probabilistic driver repositioning in crowdsourced delivery systems. She emphasizes interdisciplinary approaches to balance technical rigor with real-world applicability.

Gurram GopalView profile

Professor

• Business Strategy
• Supply Chain Management
• Corporate Social Responsibility
• +8 more

Dr. Gurram Gopal serves as an Industry Professor at Illinois Institute of Technology and a Visiting Professor at ISM. He has held roles as a strategy consultant, manager at ZS Associates, and supply chain executive at Tellabs. His research focuses on business strategy, supply chain optimization, sustainability, and corporate social responsibility. He has published over 50 papers and presented globally at conferences like PRME. Dr. Gopal has received prestigious honors including the Fulbright Scholar Award (2011-2012) and selection as a Fulbright Specialist Candidate (2013-2018). He teaches courses in operations management, supply chain analytics, and global business processes. His work integrates technology applications with ethical business practices, emphasizing innovation in process management and B2B marketing. Notable contributions include studies on blockchain in humanitarian logistics, AI adoption in retail, and sustainable marketing's impact on customer loyalty. He leads educational initiatives in Ireland, India, and Turkey, advocating for global supply chain resilience and data synchronization strategies.

Birgit RudloffView profile

Professor

• Multivariate risks
• Set-valued risk measures
• Markets with transaction costs
• +4 more

Birgit Rudloff is a Full Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. With a PhD in Financial Mathematics from Martin-Luther-University Halle-Wittenberg, her research focuses on multivariate risks, set-valued risk measures, optimization algorithms, and game theory applications in finance. She leads significant research projects in financial mathematics and vector optimization. Her research interests span dynamic set-valued risk measures, markets with transaction costs, systemic risk measurement, and algorithms for solving vector optimization problems. Recent work explores Nash equilibria computation and convex projections in unbounded spaces. Research outputs demonstrate strong focus on convex optimization techniques in financial mathematics, risk measurement methodologies, and game theory applications. Recent publications show growing interest in machine learning approaches for optimization problems. 2021 SIAM-FME Conference Paper Prize 2021 WU Star Journal Award Advises doctoral students and postdoctoral researchers in financial mathematics and optimization theory. Current research group includes postdocs and PhD students working on vector optimization and risk measures.

Massimo RiccaboniView profile

Professor

• Economics
• Innovation Studies
• Health Economics
• +5 more

Massimo Riccaboni is a Professor of Economics and Director of LIME (Laboratory for Innovation, Management and Economics) at the IMT School for Advanced Studies in Lucca, Italy. His research focuses on innovation economics, technological complexity, machine learning applications in economics, and global networks. He leads interdisciplinary projects analyzing urban development, healthcare policy, climate change, and corporate digital divides. His work bridges economic theory with computational methods, addressing real-world challenges like pandemic impacts, pharmaceutical reimbursement strategies, and entrepreneurial ecosystems. Research interests include: Innovation networks and global cities' technological competitiveness Machine learning-driven economic analysis (e.g., matrix completion for trade/emissions predictions) Health economics: drug pricing, clinical outcomes, and healthcare technology assessment Environmental policy: CO2 emission modeling and climate change mitigation Regional economics: talent mobility, entrepreneurial ecosystems, and urban tourism impacts Recent work highlights include: Quantifying pandemic policy effects (stay-at-home mandates, vaccination campaigns) Assessing biotechnology industry dynamics (M&A impacts, R&D productivity) Developing AI tools for meme stock monitoring and breast cancer detection As Director of LIME, he oversees initiatives integrating big data, networks analysis, and experimental economics to address complex societal challenges. His contributions span over 100 peer-reviewed articles, with active engagement in policy discussions on innovation governance, digital transformation, and sustainable development.

Hans AuerView profile

Professor

• Energy Economics
• Energy Market Design
• Energy System Modeling
• +4 more

Hans Auer serves as Professor in Energy System Economics and Head of the Energy Economics Group (EEG) at Vienna University of Technology (TU Wien) since 2024, with additional appointment as Adjunct Professor at the Norwegian University of Science and Technology (NTNU) since 2023. His leadership spans academic research, European project coordination, and energy policy development. Education: MSc in Electrical Engineering (1996) PhD in Energy Economics (2000) Venia Docendi in Energy Economics (2012) Research Focus: Professor Auer specializes in energy market design, system modeling, and decarbonization strategies with emphasis on renewable integration, sector coupling, and grid flexibility. His work bridges theoretical modeling with practical policy applications across European energy systems, addressing challenges from local communities to continental-scale transitions through advanced computational methods and economic analysis. Publication Trends: Recent publications (2023-2026) reveal concentrated research on European decarbonization pathways, hydrogen storage economics, electric vehicle integration, and peer-to-peer energy trading. Key thematic clusters include scenario modeling for 2050+ transitions, optimization of flexible resources in congested grids, and socio-technical analysis of local energy communities within circular economy frameworks. Academic Leadership: With over 25 years of supervision experience for bachelor, master, and PhD students, Professor Auer coordinates major European initiatives including PSS2030+ (power system security), iDesignRES (renewable integration tools), and RENVELOPE (building energy retrofits). His project portfolio demonstrates sustained funding success across EU Horizon programs and national research agencies. Operational Framework: Leading the EEG research unit within TU Wien's Institute of Energy Systems and Electrical Drives, he directs a multidisciplinary team conducting policy-relevant analysis for European Commission bodies, national regulators, and industry stakeholders through evidence-based modeling of energy transitions.

Kashinath BasuView profile

Senior Lecturer

• Network Virtualization
• Cloud Computing
• AI/ML in Networking
• +7 more

Dr. Kashinath Basu is a Senior Lecturer in Computer Science at Oxford Brookes University, affiliated with the School of Engineering, Computing and Mathematics. He holds a PhD in Computer Science and specializes in network virtualization, cloud computing, AI/ML for infrastructure management, and smart grid communication. His research explores SDN/NFV, cybersecurity frameworks, and quantum-resistant cryptography. Education: PhD in Computer Science, with academic and professional experience spanning over two decades. Research focuses on next-gen networks, cloud orchestration, and AI-driven security solutions. Current projects include softwarization of networks, smart grid communication protocols, and QoS-aware cloud services. Research interests span from foundational network architecture design to applied cybersecurity in smart cities. He has authored influential papers on topics like SD-WAN traffic prediction using ML, QoS-aware service selection in cloud environments, and control frameworks for smart grids. His work bridges theoretical network research with real-world infrastructure challenges. Professional contributions include editorial roles for journals like IEEE Transactions on Smart Grid and active participation in IEEE Working Groups on SDN/Quantum Communication. He has organized major conferences such as IEEE MobileCloud and ITNAC, reflecting his leadership in advancing network technologies. Labs/Teams: Member of the Cloud Computing and Cybersecurity Research Group, Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute, and the Dependable Systems Engineering Research Centre. Collaborates with industry partners on projects related to autonomous cyber defense and blockchain for smart city infrastructure.

• Computational Intelligence
• Financial Data Analytics
• Virtual Reality
• +3 more

Qi Cao is an Associate Professor in the School of Computing Science at the University of Glasgow, specializing in computational intelligence for financial analytics and technology-enhanced learning. His research bridges computing science with practical applications in finance, education, and immersive technologies. Education includes: PhD from Nanyang Technological University (2007) Bachelor of Engineering from HuaZhong University of Science & Technology (2000) Research spans: Intelligent systems for financial trading and portfolio management Virtual/Augmented Reality for educational enhancement Software engineering education methodologies Recent publications focus on educational technology innovations, including VR learning environments, blockchain applications, and AI-enhanced assessment tools. His work on generative AI in education and automated financial systems demonstrates interdisciplinary impact. Awards include IEEE MultiMedia Best Paper Award and recognition for applied research in industrial informatics. Developed open-source frameworks for financial analytics and VR education applications.

• Stochastic Calculus
• Numerical Analysis
• Scientific Computing
• +8 more

Antoine Tambue is a Full Professor in Mathematics at the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences (HVL), Norway, and an Honorary Professor at the University of Cape Town, South Africa. His research focuses on stochastic calculus, numerical analysis, computational finance, and AI, with a vision to develop efficient numerical algorithms for high-dimensional problems in energy, finance, and data science. He holds a PhD from Heriot-Watt University (UK), funded by the ORS Awards Scheme, and has held postdoctoral roles at the University of Bergen and NTNU. He also served as the AIMS ARETÉ junior research Chair, supervising 5 PhD and 12 master’s students across multiple institutions. Education: Bachelor in Mathematics – University of Dschang, Cameroon Master in Mathematics – University of Yaounde I, Cameroon Professional Master in Mathematics Education – Ecole Normale Supérieure de Yaoundé Postgraduate Diploma in Mathematical Sciences – AIMS South Africa & University of Cape Town PhD in Mathematics – Heriot-Watt University (UK) Research Interests: Tambue’s work bridges stochastic analysis, numerical methods, and AI. Key areas include: - Developing scalable algorithms for high-dimensional PDEs and uncertainty quantification - Stochastic optimal control and Bayesian inference in computational finance and engineering - Machine learning applications in glaciology and energy systems. Scientific Awards: ORS Awards Scheme PhD Scholarship AIMS ARETÉ Junior Research Chair (Robert Bosch Stiftung) Advising & Grants: Supervised 5 PhD and 12 MSc students across Africa and Europe. Research funded by interdisciplinary projects linking applied mathematics to energy and environmental challenges.

Shervin Shahrokhi TehraniView profile

Assistant Professor

• Marketing Strategy
• Consumer Behavior
• Structural Modeling
• +5 more

Shervin Shahrokhi Tehrani is an Assistant Professor of Marketing at the Jindal School of Management (JSOM), University of Texas at Dallas. He holds a dual Ph.D. in Marketing from the University of Toronto's Rotman School of Management (2018) and a Ph.D. in Mathematics from the University of Toronto's Department of Mathematics (2012). His academic background also includes a B.A. in Mathematics from the University of Tabriz (2004) and an M.Sc. in Mathematics from Sharif University of Technology (2006). His research focuses on marketing strategy and consumer behavior, employing structural modeling, game theory, and machine learning methodologies. Key research topics include expert recommendation systems, social influence dynamics in online search, heuristic exploration strategies, and political fundraising mechanisms. His work has been published in top journals like Marketing Science and Management Science . Teaching responsibilities include Predictive Analytics (MKT/BUAN 6337), Marketing Model III (MKT 7316), and Principles of Marketing (MKT 3300). Professional memberships include the INFORMS Society for Marketing Science, The Econometrics Society, and the American Marketing Association. His academic contributions span both marketing and pure mathematics, reflecting his interdisciplinary expertise.

Nikola GradojevicView profile

Professor

• Empirical Asset Pricing
• Market Microstructure
• High-Frequency Finance/Big Data
• +11 more

Dr. Nikola Gradojevic is a Professor of Finance at the Department of Economics and Finance, University of Guelph, affiliated with the Gordon S. Lang School of Business and Economics. He holds dual appointments as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA) and the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI). His career includes roles at institutions such as the Bank of Canada, Federal Reserve Bank of St. Louis, and IÉSEG School of Management, where he served as Head of Finance, Audit & Control Department and Associate Dean (Academic). Education: Ph.D. in Financial Economics (University of British Columbia), M.A. in Economics (University of Essex and Central European University), and B.Sc./M.Sc. in Electrical Engineering (University of Novi Sad). Research focuses on empirical asset pricing, market microstructure, high-frequency finance, AI applications in finance, and energy commodities. He has pioneered work on non-linear forecasting models using deep learning and fuzzy logic, including patents on AI validation and model risk management systems. Key contributions include analyzing cryptocurrency volatility cascades, natural gas price dynamics, and the impact of crises like Brexit and the 2020 pandemic on financial markets. Publications appear in leading journals like IEEE Transactions on Neural Networks , Journal of Banking and Finance , and Annals of Operations Research . He frequently advises on model risk management and monetary policy, contributing to media outlets such as CBC News and Finder.com. Awards: Paul MacPherson Teaching Award (2020), CARE-AI Seed Fund (2022), SSHRC Grant (2008–2011). Funding: Institute for Sustainable Commerce, RCEA grants, and multiple institutional research awards. His research bridges finance, engineering, and AI, emphasizing ethical applications of machine learning in financial decision-making. Current work explores AI explainability in volatile markets and energy commodity forecasting under geopolitical instability.

• Emotion Recognition
• Facial Expression Analysis
• Machine Learning
• +5 more

Kamlesh Mistry is an Assistant Professor in the Computer and Information Sciences Department at Northumbria University. His research focuses on Emotion Recognition, Facial Expression Analysis, Optimization Algorithms, and Machine Learning applications in Computer Vision and Humanoid Robotics. He holds a PhD from his work on intelligent facial expression recognition, supervised by Professor Li Zhang. Dr. Mistry has contributed to over 34 research outputs since 2007, with notable recent work in 2025 introducing adaptive nature-inspired optimization frameworks and multimodal emotion recognition systems. His research interests span facial expression recognition under varying conditions, optimization algorithm design (e.g., Firefly, Differential Evolution), and deep learning applications for semantic segmentation, medical imaging, and anomaly detection. Collaborations include studies on privacy-preserving location-sharing and short-term forecasting models for energy and weather systems. Recent publications highlight advancements in multimodal emotion recognition using transformers and variational autoencoders, semantic segmentation via GAN-synthesized images, and pose-invariant facial expression systems. His work emphasizes practical applications in healthcare, robotics, and cybersecurity. Advising and grants are not explicitly detailed in the provided text, but his doctoral thesis demonstrates expertise in evolutionary feature optimization and facial point detection. No scientific awards are mentioned.