Find Academic Supervisors

• Applied Mathematics
• Probabilities and Statistics
• Econometrics
• +4 more

Assoc. Prof. Mariyan Milev is a dedicated academic at the Department of Statistics and Econometrics within the Faculty of Economics and Business Administration at Sofia University "St. Kliment Ohridski." With extensive international experience including doctoral studies at the University of Trento, Italy and postdoctoral research at the University of Venice, Dr. Milev brings global perspective to his teaching and research activities at the university. His academic journey reflects a strong commitment to both theoretical mathematics and practical applications in economic and financial contexts. Dr. Milev's research interests span multiple interconnected domains including applied mathematics, probability theory, statistics, econometrics, financial derivatives, numerical methods, and fractional equations. His scholarly work demonstrates consistent focus on developing mathematical frameworks that address real-world economic and financial challenges, particularly in option pricing and stability analysis of complex systems. His research bridges pure mathematics with practical applications across diverse fields from finance to medicine and oenology. The publication record of Assoc. Prof. Milev reveals a clear trajectory of increasingly sophisticated mathematical modeling, with recent work focusing on fractional calculus applications and advanced numerical methods. His research shows strong international collaboration patterns, particularly with scholars from Italy, Iran, and other European institutions. The publications demonstrate expertise in both theoretical mathematics (fractional systems, moment problems) and applied contexts (financial derivatives, medical modeling, wine analysis). Dr. Milev has successfully co-supervised doctoral students including Anna Nikolova (Sofia Technical University, 2021) and Irina Naskinova (Southwest University "Neofit Rilski", 2023), demonstrating his commitment to academic mentorship. His educational contributions extend beyond supervision to include authoring textbooks such as "Application of MATLAB in Economics" and co-authoring several other educational materials on statistics and mathematical modeling. Within the university structure, Dr. Milev contributes significantly to the Department of Statistics and Econometrics through teaching courses in mathematical economics, statistics, and econometrics. His office is located in Block 3, Office 516 at 125 "Tzarigradsko Shausse" Boulevard, where he holds regular office hours on Thursdays from 13:00-15:00 by appointment.

Yilmaz GuneyView profile

Professor

• Corporate Finance
• Corporate Governance
• Behavioral Finance
• +7 more

Professor Yilmaz Guney is a prominent academic at Swansea University's School of Management, where he serves as Professor and contributes to the Centre for Resilient Business and Society. With expertise in corporate finance and governance, Professor Guney has built an impressive research portfolio spanning two decades, evidenced by an h-index of 18 and 1,584 citations. He actively supervises PhD students and teaches across undergraduate, postgraduate, and executive education programs internationally. Professor Guney's educational background includes: Doctorate in Finance, University of Durham (1999-2002) MSc in Finance, University of Durham (1998-1999) Bachelor's in Economics, Middle East Technical University (1994-1998) His research focuses on corporate financing decisions, examining how firms determine their debt-equity mix, cash holdings, and hedging strategies. Professor Guney has extensively investigated the relationship between corporate governance, social responsibility, and financial performance, with recent work expanding into sustainable finance and machine learning applications in finance. Professor Guney's recent publications reveal a sophisticated evolution in research methodology while maintaining core finance interests. His work increasingly incorporates behavioral perspectives and advanced analytics, with growing attention to sustainable finance topics and international comparative studies. Notably, his research on CEO characteristics and their impact on corporate financial policies has gained significant scholarly attention. Professor Guney's academic impact is evidenced by: 39 research outputs documented in Pure Multiple publications in top finance journals Regular presentations at international conferences including the Multinational Finance Society and British Accounting and Finance Association Expert commentary on major economic events including the Global Financial Crisis and Covid-19 pandemic As an educator, Professor Guney teaches a wide range of finance courses across multiple levels and supervises doctoral research with a focus on corporate finance and governance. His professional service includes editorial roles such as Regional (European) Editor for the International Journal of Behavioural Accounting and Finance since 2008, and External Examiner positions at several international institutions. Professor Guney contributes to the Centre for Resilient Business and Society at Swansea University, focusing on business approaches to societal challenges. His research demonstrates extensive international collaboration, with co-authored papers spanning multiple continents and economic regions, reflecting his global perspective on finance and business issues.

Pawan GoyalView profile

Researcher

• Machine Learning
• Physics-Informed Neural Networks
• Generative AI
• +4 more

Dr. Pawan Goyal is a Senior AI Engineer/Researcher at appliedAI Initiative GmbH, focusing on generative AI and physics-informed machine learning. Previously, he led the Physics-Enhanced Machine Learning team at the Max Planck Institute for Dynamics of Complex Technical Systems, where he developed surrogate modeling techniques integrating physics into AI. His research spans dynamical systems, model reduction, and materials science, emphasizing stability and interpretability of models. Education: PhD in Applied Mathematics (Max Planck Institute, 2018), M.Tech and B.Tech in Engineering Design (IIT Madras). His work bridges computational science and AI, addressing challenges in engineering design and materials discovery. Key interests include generative AI for design, physics-informed neural networks, and reduced-order modeling for complex systems. Awards: Dr.-Klaus-Körper Prize (GAMM, 2019), Best Ph.D. Thesis (Otto-von-Guericke University, 2018).

Mengyu ChuView profile

Assistant Professor

• Physics-Enhanced Deep Learning
• Fluid Dynamics Simulation
• Computer Graphics
• +3 more

Mengyu (Rachel) Chu is a tenure-track Assistant Professor at the School of Intelligence Science and Technology, Peking University, affiliated with the Visual Computing and Learning Lab. Previously, she was a Lise Meitner Postdoctoral Research Fellow at the Max Planck Institute for Informatics (2020–2022), and completed her Ph.D. in Computer Science at Technical University of Munich (2014–2020). She holds a B.Eng. in Software Engineering from Southeast University (2007–2011) and an M.Eng. in Computer Science from Zhejiang University (2011–2014). Her research focuses on Physics-Enhanced Deep Learning , combining deep learning with physical simulations to enhance fluid dynamics modeling, video synthesis, and neural network training. Key areas include fluid reconstruction, manipulation, synthesis, and temporally coherent video generation. Her work bridges physics-based principles with modern AI techniques for realistic simulations and generative models. Notable contributions include Physics Informed Neural Fields for sparse data reconstruction (SIGGRAPH 2022), Learning Meaningful Controls for Fluids (SIGGRAPH 2021), and TempoGAN for fluid flow super-resolution (SIGGRAPH 2018). Her research emphasizes real-world applications in graphics, visualization, and computational physics. She has been recognized with the Lise Meitner Postdoctoral Fellowship (2020) and Summa Cum Laude distinction for her Ph.D. (2020). Her work spans academic publications, patents, and open-source projects, with collaborations across institutions like TU Munich and Max Planck Institute.

• Partial Differential Equations
• Fluid Dynamics
• Calculus of Variations
• +3 more

Maria Colombo is an Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL). Her research focuses on nonlinear partial differential equations (PDEs), particularly in fluid dynamics, calculus of variations, and optimal transport. She investigates fundamental questions related to existence, uniqueness, and regularity of solutions in contexts such as Euler and Navier-Stokes equations, nonlocal conservation laws, and branched transportation networks. Her work spans theoretical analysis of PDEs, including studies on non-uniqueness phenomena in transport equations, stability of optimal traffic plans, and singular limits of nonlocal models. She also explores geometric aspects of variational problems, such as free boundary regularity and epiperimetric inequalities. Colombo's contributions bridge pure mathematics and applied analysis, addressing challenges in both classical and modern settings like semiclassical limits in quantum mechanics. Her research often involves collaboration with leading mathematicians in PDEs and analysis, resulting in over 50 publications in top-tier journals. While no specific awards are listed, her prolific output reflects recognition in her field. She maintains an active presence in academic communities through conference organization and speaking engagements.

Rinaldo SchinaziView profile

Professor

• Probability Models
• Population Biology
• Mathematical Biology
• +5 more

Rinaldo Schinazi is a Professor in the Department of Mathematics at the University of Colorado Colorado Springs (UCCS), part of the College of Letters, Arts & Sciences. His research focuses on applying probability models to investigate questions in population biology, emphasizing stochastic processes in ecological and evolutionary contexts. He holds a Ph.D. in Statistics from the University of São Paulo (1988). Education: Ph.D. in Statistics, University of São Paulo (1988). Research Interests: Schinazi uses probability models to study population dynamics, including stochastic processes like Markov chains, branching processes, and random walks. His work addresses survival mechanisms under catastrophes, genetic diversity impacts, and migration effects in population viability. He explores theoretical frameworks to model ecological and evolutionary phenomena, bridging mathematical rigor with real-world biological challenges. Recent Work Trends: His articles (2023–2024) highlight studies on species survival under environmental stress, stochastic models of population dynamics, and the interplay between randomness and biological systems. Key themes include Markov chain analysis, ruin problems, and evolutionary paths under catastrophic events. Awards: No scientific awards explicitly mentioned in the provided texts. Advising & Grants: No student advisees or grants listed in the available information. His contributions focus on theoretical and applied mathematical biology research. Labs/Teams: No specific laboratory or collaborative team affiliations noted in the text.

• Operational Research
• Optimization Methods
• Logistics and Supply Chain Management
• +7 more

Manuel Iori is a Full Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia (UNIMORE), Italy. His primary research focuses on operational research, optimization methods, and logistics systems. He specializes in vehicle routing problems, scheduling algorithms, and decision support systems with applications in industrial automation, healthcare, and service industries. Iori is actively involved in teaching advanced optimization courses for engineering students, emphasizing practical applications in data-driven decision-making and simulation. Research Interests: His work addresses complex optimization challenges such as multi-trip vehicle routing with time windows, scheduling under resource constraints, and tool switching in manufacturing systems. He integrates machine learning and metaheuristics to develop innovative solutions for logistics, production planning, and healthcare operations. Collaborations with industry partners (e.g., pharmaceutical distributors, printing companies) ensure practical relevance of his research. Teaching: Iori teaches courses like Optimization Methods for Data-Driven Engineering Processes , Models for Logistics and Production Optimization , and Methods and Algorithms for Optimization in Digital Industries . These courses combine theoretical foundations with hands-on labs using tools like Python, Xpress, and Anylogic. Key Contributions: He developed decision support systems for multi-trip routing in pharmaceutical distribution and supplier selection in facility management. His research on satellite scheduling and attended home delivery systems advances both theoretical and applied domains. As a member of CIRRELT (Canada), he collaborates on logistics optimization projects. Professional Activities: Iori’s work is reflected in over 80 peer-reviewed publications and contributions to conferences. He advises graduate students on optimization challenges and serves as a reviewer for top journals in operations research.

• Operations Research
• Healthcare Logistics
• Closed-Loop Supply Chains
• +3 more

Pierre Desport is a Researcher in the Department of Computer Science at the University of Tours, affiliated with the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT, UR 6300). He is associated with the UFR of Sciences and Technology and the Polytechnic School of the University of Tours (EPU). His research focuses on optimization challenges in healthcare logistics, supply chain management, and telecommunications, emphasizing operational efficiency and sustainability. Key research areas include minimizing patient waiting times in outpatient settings, optimizing short local food supply chains, and developing combinatorial optimization methods for tactical distribution problems. His work often integrates advanced algorithms like Benders Decomposition and metaheuristics to address real-world logistical constraints. Desport’s publications span interdisciplinary applications of operations research, with a strong emphasis on practical solutions for healthcare systems, agricultural logistics, and telecommunications infrastructure. His research contributes to both theoretical advancements in optimization and actionable strategies for industry and public health sectors. He holds a position in the LIFAT laboratory, where his collaborations and projects address complex logistical challenges in dynamic environments. While no specific awards are listed, his prolific publication record highlights sustained contributions to optimization methodologies across multiple domains.

Giuseppe SavaréView profile

Professor

• Mathematical Analysis
• Optimal Transport
• Gradient Flows
• +4 more

Giuseppe Savaré is a Full Professor of Mathematical Analysis at the Department of Mathematics, University of Pavia, and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) since 2019. His research focuses on variational methods for evolution problems, including gradient flows, rate-independent models, nonlinear PDEs, and optimal transport theory in metric spaces. Education: PhD in Mathematics (1988) from University of Pavia. His work bridges optimal transport, metric measure spaces, and analysis in non-Euclidean geometries, with applications to PDEs, curvature bounds, and generalized gradient flows. Key contributions include foundational studies on Wasserstein gradient flows, entropy-transport problems, and abstract action spaces in dynamical systems. Scientific awards include the prestigious Ennio De Giorgi Prize (2011) and the Gioachino Japichino Prize (1994), alongside an invitation as a speaker at the 7th European Congress of Mathematics (2016). Collaborations with researchers like Massimo Fornasier and Alexander Mielke have advanced consensus-based optimization, multi-agent control, and quantum drift-diffusion models.

• Bayesian uncertainty quantification
• Computational mathematics
• Scientific computing
• +6 more

Nicholas J. Zabaras is a Professor in the College of Engineering at the University of Notre Dame and serves as director of the Warwick Centre for Predictive Modelling at the University of Warwick. He holds a Hans Fischer Senior Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2014. His academic journey began with a diploma in Mechanical Engineering from the National Technical University of Athens (1982), followed by an M.Sc in Material Science and Engineering from the University of Rochester (1983), and a PhD in Theoretical and Applied Mechanics from Cornell University (1987). His research spans computational mathematics, computational statistics, and scientific computing with focus on predictive modeling of complex multiscale and multiphysics materials systems. Key research themes include Bayesian uncertainty quantification, high-dimensional problem modeling, information-theoretic coarse graining, stochastic model reduction, and optimization under uncertainty. His work has significant applications in materials science, particularly in uncertainty propagation from ab initio to continuum simulations and modeling of random microstructures. His recent publications demonstrate strong activity in Bayesian coarse-graining techniques, deep Gaussian processes, and uncertainty quantification for multiscale materials systems. The research shows consistent focus on developing computationally efficient methods for high-dimensional problems with applications across materials science and engineering disciplines. Major Awards and Recognitions: Royal Society Wolfson Research Merit Award (2014) Research Fellow, Isaac Newton School of Mathematical Sciences, University of Cambridge (2011) Michael Tien'72 College of Engineering Teaching Award, Cornell University (2009) Fellow, American Society of Mechanical Engineers (2006) Presidential Young Investigator Award (1991) Zabaras leads the Scientific Computing and Artificial Intelligence (SCAI) Laboratory and the Computational Science and Engineering (CSE) Laboratory at Notre Dame, where his team develops innovative mathematical and statistical approaches addressing unique challenges in predictive modeling. His research integrates computational mathematics, machine learning, and multiscale/multiphysics modeling to address problems in materials physics, geological sciences, and climate modeling.

• Operations Research
• Optimization under Uncertainties
• Scheduling Algorithms
• +4 more

Faicel HNAIEN is a Full Professor at the Université de Technologie de Troyes (UTT), where he leads the Computer Science and Applied Mathematics Academic Department since December 2024. He also heads the Optimization Axis (LIST3N laboratory) with 30 members and oversees the International Master's Track in Logistics and Transport in Togo since 2023. His research focuses on optimization under uncertainties, scheduling algorithms, inventory control, wireless sensor networks, and quantum optimization. His work bridges theoretical advances with practical applications in manufacturing systems, energy management, and logistics. Responsibilities include academic leadership roles, strategic planning for research initiatives, and international program coordination. His recent projects address challenges like energy cost optimization in production scheduling, robust supply chain management under uncertainty, and deployment strategies for sensor networks in hazardous environments. He actively contributes to interdisciplinary research, integrating stochastic modeling, combinatorial optimization, and quantum computing approaches. His publications span high-impact journals such as Annals of Operations Research and European Journal of Industrial Engineering. Research trends emphasize hybrid methods (exact/ heuristic), stochastic programming for inventory systems, and multi-objective optimization in dynamic environments. Current efforts explore quantum formulations for scheduling problems and AI-driven solutions for supply chain resilience.

• Sub-Riemannian Geometry
• Mathematical Analysis
• Neurogeometry
• +3 more

Giovanna Citti is a Full Professor in the Department of Mathematics at the University of Bologna. Her research focuses on sub-Riemannian geometry, mathematical analysis, and neurogeometry, with a particular emphasis on modeling the functional architecture of the visual and motor cortices. She develops geometric frameworks to understand neural processing in the brain, including models of visual perception and motor control. Her work integrates concepts from differential geometry, partial differential equations (PDEs), and Lie group theory to analyze sub-Riemannian structures. She explores regularity properties of subelliptic PDEs and applies these to biological systems, such as the cortical organization of the visual cortex and motor pathways. Recent efforts include developing brain-inspired neural networks and studying neurogeometric models of stereo vision and 3D perception. Key contributions include sub-Riemannian models of perceptual completion, cortical-based optimal transport frameworks, and analysis of geometric optical illusions. Her research bridges pure mathematics and neuroscience, addressing challenges in both fields through innovative interdisciplinary approaches.

Shadi MohamedView profile

Associate Professor

• Computational Mechanics
• Finite Element Analysis
• Acoustic Wave Modeling
• +4 more

Shadi Mohamed is an Associate Professor at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, specifically within the Institute for Infrastructure & Environment. His research focuses on computational mechanics with applications spanning acoustic wave modeling, traffic flow simulation, and structural dynamics. He maintains an active research profile with over 100 publications and currently accepts PhD students for projects in computational mechanics, traffic modeling, and acoustic waves. His research interests center on advanced numerical methods for engineering problems, particularly finite element analysis for transient diffusion, wave propagation, and structural dynamics. Key focus areas include enriched finite element methods, partition of unity techniques, and machine learning integration for structural health monitoring and inverse problems. His work demonstrates strong alignment with UN Sustainable Development Goals through sustainable infrastructure development and environmental protection. Analysis of his recent publications reveals a clear trajectory toward hybrid computational approaches combining traditional numerical methods with machine learning, particularly for structural dynamics, fatigue analysis, and wave propagation problems. His work increasingly integrates convolutional neural networks for vibration analysis and employs isogeometric analysis for thermal and diffusion problems in composite materials. Best Post-graduate Research (2008) Japan Society for the Promotion of Science Invitational Fellowship (2019) Dr. Mohamed actively supervises PhD research and collaborates internationally, with recent work spanning vibration-induced fatigue assessment, dynamic load identification, and thermal analysis of composite materials. His research is supported by prestigious fellowships and demonstrates strong industry relevance, particularly in structural integrity assessment and sustainable infrastructure development. He is affiliated with the Institute for Infrastructure & Environment, which focuses on sustainable engineering solutions for global challenges.

Mohamed NasserView profile

Assistant Professor

• Boundary integral equations
• Numerical conformal mappings
• Riemann-Hilbert boundary value problems
• +4 more

Dr. Mohamed Nasser is an Assistant Professor of Applied and Computational Complex Analysis at the Department of Mathematics, Statistics & Physics, Wichita State University. His research focuses on boundary integral equations, numerical conformal mappings, Riemann-Hilbert boundary value problems, ideal fluid mechanics, and potential theory. Recent work includes computational methods for conformal capacity analysis in complex domains, applications of generalized Neumann kernels, and numerical simulations for Hele-Shaw flows. His publications demonstrate expertise in multiply connected regions, hyperbolic spaces, and polycircular domains. Email: mms.nasser@wichita.edu

• Operations Research
• Combinatorial Optimization
• Network Design
• +3 more

Ulrich Pferschy is a Professor affiliated with the Department of Algorithms and Complexity at Technische Universität Wien (TU Wien). His research focuses on Operations Research, Combinatorial Optimization, and Network Design, with particular expertise in algorithms for knapsack problems, Steiner tree problems, and network optimization. He has supervised numerous PhD students, including M. Ruthmair, A. M. Chwatal, and M. Leitner, among others. His work combines theoretical insights with practical applications in telecommunications infrastructure and algorithm design. Pferschy has contributed to leading journals and conferences, publishing influential papers on topics such as multidimensional knapsack problems and survivable network design. Despite his extensive contributions, no scientific awards are explicitly mentioned in the provided text.