Find Academic Supervisors

• Geometric Topology
• Geometric Group Theory
• Manifold Topology
• +7 more

Craig R. Guilbault is a Professor in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee (UWM), where he also serves as Department Chair. He is a leading researcher in geometric topology and geometric group theory, with a focus on the large-scale structure of spaces and groups. He is actively involved in the topology community through co-organizing the Annual Workshop in Geometric Topology and serving on editorial and steering committees. PhD, University of Tennessee, Knoxville (1988) BS, Northland College, Ashland, Wisconsin (1982) His research centers on geometric topology and geometric group theory, particularly the study of ends of manifolds, group boundaries, Z-compactifications, and proper homotopy theory. He investigates how the topology at infinity influences global structure and develops tools like EZ-structures and coarse Z-boundaries to analyze noncompact spaces and groups. His work bridges algebraic, geometric, and topological methods to solve deep structural problems. The recent publications highlight a sustained focus on boundaries of groups, compactifications of manifolds, and the interplay between large-scale geometry and homotopy theory. Themes include non-uniqueness phenomena, tameness at infinity, and the construction of topological invariants for noncompact spaces. His collaborations span multiple institutions and have advanced understanding in areas such as Baumslag-Solitar groups and Croke-Kleiner spaces. He has received recognition through editorial roles and long-term leadership in conference organization, including: Co-organizer, UWM Topology Seminars Co-organizer, Annual Workshop in Geometric Topology (1991–Present) Editor, Topology Proceedings Member, Steering Committee, Spring Topology and Dynamics Conference Guilbault has advised several graduate students, including Brendan Healy, Molly Moran, and Carrie Tirel, and has secured research support through NSF funding for workshops. He leads a vibrant research group in topology at UWM and continues to publish in top journals such as Algebraic & Geometric Topology , Fundamenta Mathematicae , and Geometry & Topology . He is a key figure in the UWM Topology Research Group and contributes to national and international topology initiatives through editorial and organizational leadership. His work fosters collaboration and training in geometric and algebraic topology.

• Fractal Geometry
• Number Theory
• Dynamical Systems
• +3 more

Jörg Thuswaldner is a Professor at the Chair of Mathematics, Statistics and Geometry at Montanuniversität Leoben. His research focuses on fractal geometry, number theory, dynamical systems, and combinatorics, with a particular emphasis on topics such as self-affine tiles, continued fraction algorithms, and digit systems. He has published extensively in top-tier journals such as Advances in Mathematics and Journal of Number Theory , contributing to the understanding of fractal structures, metric number theory, and symbolic dynamics. His editorial roles include serving on the boards of Lithuanian Mathematical Journal and Combinatorics and Number Theory , highlighting his influence in academic publishing. He actively collaborates internationally, hosting visiting scholars and participating in conferences to advance interdisciplinary research. Recent work explores applications of fractal geometry to permeable sets and the analysis of Weyl sums with digital restrictions. Thuswaldner’s research spans over two decades, with publications ranging from foundational studies in tiling theory to cutting-edge analyses of non-standard digit systems. His contributions bridge pure mathematics with applications in algebraic geometry and ergodic theory, making him a leading figure in modern mathematical research.

• Machine Learning
• Artificial Intelligence
• Speech Processing
• +6 more

Saeed Amizadeh is a prominent researcher specializing in artificial intelligence and machine learning, currently affiliated with Microsoft's research division. His work spans multiple domains of AI including speech processing, natural language understanding, computer vision, and time series analysis, with a particular focus on developing novel frameworks that bridge symbolic reasoning with neural approaches. Over the past decade, he has established himself as a significant contributor to the field through publications in top-tier conferences including ICASSP, ICLR, AAAI, KDD, and IJCAI. Dr. Amizadeh's research interests primarily center around advancing the theoretical foundations and practical applications of machine learning systems. His work on neuro-symbolic visual reasoning has contributed to understanding how to effectively disentangle visual perception from logical reasoning in AI systems. He has made significant contributions to differentiable programming, particularly in making classical machine learning pipelines fully differentiable, which enables end-to-end optimization of complex ML workflows. His research on speech enhancement using GANs represents cutting-edge work in audio processing, while his time series anomaly detection frameworks have practical applications in numerous industry settings. Analysis of his publication trends reveals a consistent trajectory from theoretical machine learning foundations toward increasingly applied research with practical industrial relevance. His early work (2010-2015) focused on fundamental algorithms and probabilistic modeling approaches, while his more recent publications (2019-2025) demonstrate a shift toward practical AI systems with direct applications in speech processing, audio separation, and enterprise machine learning. A notable theme throughout his career is the development of frameworks that enable more efficient, scalable, and interpretable AI systems. As a key contributor to Microsoft's ML.NET framework, Dr. Amizadeh has played an important role in developing tools that make machine learning more accessible to enterprise developers. His collaborative work spans academia and industry, with notable partnerships with researchers from various institutions as well as within Microsoft Research.

• Symbolic Computation
• Numerical Analysis
• Computer Algebra
• +3 more

Marc Mezzarobba is a Researcher at École Polytechnique , affiliated with the LIX laboratory and MAX team. His work bridges symbolic computation, numerical analysis, and algorithm design for differential and recurrence equations. Key research themes: D-finite functions, rigorous numerical evaluation, symbolic-numeric methods, Mahler equations, precision analysis Recent publications focus on error bounds in linear recurrences, Stokes matrices computation, and high-precision differential equation solutions Active developer of tools like ore_algebra-analytic and NumGfun , with significant contributions to SageMath 2025 talk on "Solving Linear Differential Equations to High Precision" highlights ongoing work in numerical stability

Lena Hansen MalnesView profile

Assistant Professor

• Active Travel
• Adolescent Physical Activity
• School-based Physical Activity Interventions
• +5 more

Lena Hansen Malnes serves as an Assistant Professor in the Department of Health and Nursing Science at the University of Agder, where she actively contributes to teaching, research, and academic leadership. Her work bridges clinical nursing practice with public health research, focusing on physical activity interventions across different population groups. Her educational background includes: Nursing Assistant certification Clinical Nurse training Master in Public Health Science Ongoing Ph.D. in Health and Sports Science Dr. Malnes specializes in active travel research, particularly examining adolescent walking and cycling behaviors to school through the multi-center "School In Motion" (ScIM) project. Her work investigates environmental determinants of physical activity using geographic mapping data, with implications for sustainable transportation and chronic disease prevention. Earlier research focused on e-biking interventions among inactive adults, demonstrating approximately 7% improvement in physical fitness over 8 months. Her methodological expertise includes applied statistics and objective physical activity measurement. Analysis of her publication record reveals a consistent trajectory in transportation and physical activity research, with increasing methodological sophistication from pilot studies to cluster randomized controlled trials. Her work bridges public health, sports science, and urban planning, with growing emphasis on objective measurement techniques and environmental determinants of behavior. As an educator, Dr. Malnes supervises bachelor's and master's students in clinical practice studies and research methods. She leads a university-wide SPSS course package for staff and students, demonstrating commitment to research methodology education. Her clinical nursing experience with elderly patients in hospital and community settings informs her holistic approach to health promotion. Dr. Malnes collaborates within two research groups: Health, Behavior, Diversity, and Social Inclusion; and Physical Activity and Health across the LifeSpan (PAHLS), reflecting her interdisciplinary approach to population health challenges.

• Health Economics
• Economic Evaluation
• Health Care Priority Setting
• +5 more

Professor Cam Donaldson is a part-time professor of health economics in the Department of Health Services Research & Policy at the Australian National University's National Centre for Epidemiology and Population Health. He also holds the Yunus Chair at Glasgow Caledonian University as Distinguished Professor of Health Economics. His career spans multiple prestigious institutions including the Universities of York, Sydney, Aberdeen, Calgary, and Newcastle. His educational background includes a BA (Hons) in Economics from the University of Nottingham (1982), an MSc in Health Economics from the University of York (1984), and a PhD in Economics from the University of Aberdeen (1996). Professor Donaldson is internationally renowned for his work on developing methods and frameworks for health care priority setting and evaluation. His research focuses on the development and application of methods of economic evaluation, economics and health care priority setting, and conceptualizing microfinance and social business/enterprise as complex, community-based public health initiatives. His work has significantly advanced the field of health economics, particularly in the areas of Quality-Adjusted Life Years (QALYs) and discrete choice experiments. An analysis of his recent publications reveals a consistent focus on health economic evaluation methods, with particular attention to pediatric health valuation, pandemic preparedness economics, health care priority setting frameworks, and the social value of different health outcomes. His work demonstrates a progression from methodological development to practical applications across diverse health contexts including chronic disease management, child health, and pandemic response. Fellowship of the Royal Society of Edinburgh (2022) Professor Donaldson has published over 300 papers in refereed journals and authored 8 books. He has attracted approximately £30 million in competitive research funding (around £11 million as principal investigator). His research has been supported by senior investigatorships from the Canadian Institutes of Health Research and the UK's National Institute for Health Research. While specific student supervision details aren't provided in the source material, his extensive publication record and leadership positions suggest significant mentorship of junior researchers. His work connects with multiple research groups including the Health Economics group at ANU and collaborative networks across Europe and North America, particularly evident in his multi-national pandemic research and European health care priority setting studies.

• Combinatorics
• Graph Theory
• Discrete Mathematics
• +4 more

Jaume Martí Farré is a researcher at the Technical University of Catalonia (Universitat Politècnica de Catalunya) , affiliated with the School of Mathematics and Statistics (FME) and the Department of Mathematics . He is a member of the COMBGRAPH (Combinatorics, Graph Theory, and Applications) and OMGRAPH (Optimization Methods on Graphs) research groups. Education: PhD in Mathematics from Universitat de Barcelona , thesis on symbolic power filtrations and their algebraic properties. Research Interests: Martí Farré's work spans combinatorics, graph theory, and commutative algebra, focusing on structures like matroids, clutters, and dominating sets. His studies include network optimization, immune sets in infection dynamics, and symbolic power algebra. He applies graph theory to telecommunications and uses algebraic methods to analyze discrete structures. Article Trends: His recent publications (since 2023) emphasize graph decomposition, matroid theory, and network optimization. Earlier works (2015–2019) explore hypergraphs, uniform clutters, and their connections to dominating sets. Older research (1994–1999) delves into algebraic topics like symbolic powers and local cohomology. Scientific Awards: Recognition of Special Quality Teaching Merits (2023) Advising & Projects: He supervised the PhD thesis of José Luis Ruiz Muñoz (2015). Martí Farré participates in competitive R+D+I projects like AGRUPS 2024 and OMGRAPH , focusing on graph-based optimization and network analysis. Collaborations include researchers such as Josep Fàbrega and Marisa Mora .

Olivier BuffetView profile

Researcher

• Artificial Intelligence
• Automated Planning and Scheduling
• Reinforcement Learning
• +7 more

Olivier Buffet is a Researcher at INRIA, working at the INRIA Center at Université de Lorraine / LORIA since November 2007. He is affiliated with the LORIA laboratory (Lorraine Laboratory of Computer Science and its Applications), which focuses on computer science research. His work spans multiple institutions, having previously held positions at NICTA's Statistical Machine Learning program (2004-2006), RSISE at ANU (2004-2006), and LAAS at CNRS (2006-2007). Dr. Buffet received his engineering degree from Supélec and a DEA (Diplôme d'Etudes Approfondies) from Henri Poincaré University. He completed his PhD in computer science under the supervision of François Charpillet and Alain Dutech at LORIA / INRIA Nancy Grand-Est, defended on September 10, 2003. He later defended his habilitation to supervise research (HDR) on December 18, 2017. Dr. Buffet's research focuses on artificial intelligence, particularly in the areas of automated planning and scheduling, reinforcement learning, and decision-making under uncertainty. His work extensively explores Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and Decentralized POMDPs (Dec-POMDPs), with applications ranging from multi-agent systems to traffic management and adaptive conservation strategies. His research often bridges theoretical foundations with practical applications, developing algorithms that can handle complex decision problems in uncertain environments. His publication record demonstrates a consistent focus on advancing methods for planning and decision-making under uncertainty. Over the past decade, his work has increasingly addressed decentralized and multi-agent settings, developing novel approaches for coordination among multiple decision-makers with partial information. More recently, his research has explored interpretable solutions for adaptive management problems, particularly in environmental contexts, and advanced theoretical understanding of properties like Lipschitz continuity in POMDP value functions. Dr. Buffet has received recognition for his contributions to the field, including: Winner of the probabilistic track in the Fifth International Planning Competition (IPC-06) Best Paper award at AAMAS-14 for "Exploiting separability in multi-agent planning with continuous-state MDPs" Best Paper award at JFSMA-13 for "Synchronisation de véhicules autonomes aux croisements d'un réseau de routes" Best Paper award at CAp'11 for "Une extension des POMDP avec des récompenses dépendant de l'état de croyance" As an educator and mentor, Dr. Buffet has supervised numerous PhD students including Arnaud Glad, Mauricio Araya-Lòpez, Mohamed Tlig, Arsène Fansi, and Manel Tagorti. He has also guided many interns and research projects. His teaching experience includes tutored sessions on discrete and deterministic optimization, decision making under uncertainty, and computer science for industrial engineering at École des Mines de Nancy, as well as courses on Unix shell and C programming at Université Henri Poincaré. Dr. Buffet has been actively involved in the academic community, serving as Co-Conference Chair of the 30th International Conference on Automated Planning and Scheduling (ICAPS 2020) in Nancy. He has organized multiple meetings of the French workgroup JFPDA (formerly PDMIA) and chaired several workshops on planning and scheduling under uncertainty. He previously served on the editorial boards of Revue d'Intelligence Artificielle (RIA) and Journal of Artificial Intelligence Research (JAIR), and has been a reviewer for numerous prestigious journals and conferences in artificial intelligence.

Hubertus Th. JongenView profile

Professor

• Nonlinear Optimization
• Parametric Optimization
• Semi-Infinite Programming
• +10 more

Hubertus Th. Jongen is a full Professor of Mathematics at RWTH Aachen University and has held an ALMA Professorship at Maastricht University since 2001. He is affiliated with the Department of Mathematics within the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen. His academic career includes professorships at the University of Hamburg and visiting positions at institutions such as the Humboldt University of Berlin, University of Rome, Tokyo Institute of Technology, and ICSI Berkeley. Full Professor, RWTH Aachen University (since 1987) ALMA Professor, Maastricht University (since 2001) Professor, University of Hamburg (1980–1982) Special Professor, University of Hamburg (from 1983) Guest Professor, Humboldt University of Berlin (1985) His primary research interests lie in nonlinear optimization, parametric optimization, semi-infinite and generalized semi-infinite programming, bilevel optimization, and global optimization. His work integrates deep theoretical analysis with applications in operations research and mathematical programming. He has made foundational contributions to the structural analysis of optimization problems, stability of feasible sets, and critical point theory. The most recent articles show a consistent focus on advanced topics in optimization, particularly in generalized semi-infinite programming, bilevel problems, and equilibrium models. His publications emphasize structural analysis, stability, smoothing techniques, and computational aspects of nonconvex and constrained optimization. There is a strong trend toward understanding the geometry and topology of feasible sets and the behavior of solution concepts under perturbations. Doctor honoris causa, Shanxi University (2003) Honorary Foreign Member, Mongolian Mathematical Society Miembro Honorario, Sociedad Colombiana de Investigación de Operaciones Visitante Distinguido de la Ciudad Puebla, Mexico Special Issue in Optimization journal (2008) honoring his 60th birthday Professor Jongen has advised numerous PhD students and collaborators, including Oliver Stein, Jan-J. Rückmann, and Vladimir Shikhman. He has been deeply involved in research grants and editorial work, serving as co-editor of major journals such as Journal of Global Optimization , Optimization , and Mathematical Methods of Operations Research . He co-founded the SIGOPT interest group and has organized multiple international conferences. He has also authored influential books on nonlinear optimization and optimization theory. He has led significant academic initiatives, including serving as Chairman of the Department of Mathematics at RWTH Aachen (1992–1994, 1996–1998) and as a Senator of the university (1998–2000). His research group and collaborations form a well-established network in parametric and global optimization.

Felix VoigtlaenderView profile

Professor

• Mathematics of Machine Learning
• Neural Networks
• Approximation Theory
• +11 more

Felix Voigtlaender is a Professor for Mathematics with a focus on Reliable Machine Learning at the Mathematical Institute for Machine Learning and Data Science, KU Eichstätt-Ingolstadt. He has held previous academic positions at TU Munich as an Emmy Noether Group Leader, at the University of Vienna as a Senior Scientist, and at KU Eichstätt and TU Berlin in research and postdoctoral roles. PhD: RWTH Aachen University, 2015 Master: RWTH Aachen University, 2013 Bachelor: RWTH Aachen University His research lies at the intersection of mathematics and machine learning, with a strong emphasis on the theoretical underpinnings of neural networks. He investigates approximation properties, expressiveness, and the existence of adversarial examples. His work also extends into harmonic analysis, functional analysis, and multiscale systems such as wavelets and shearlets. He is deeply interested in the mathematical foundations of data science, including sampling theory and information-based complexity. The recent publications highlight a consistent focus on the mathematical analysis of neural networks, particularly approximation capabilities and universal approximation theorems in both real and complex domains. His work often provides sharp theoretical bounds and deep insights into the behavior of deep learning models in high-dimensional settings. Friedrich-Wilhelm-Award 2016 for his PhD thesis Felix Voigtlaender has supervised students and is actively involved in teaching and academic development, notably contributing to the launch of a new BSc program in Data Science at KU Eichstätt-Ingolstadt. He has collaborated with prominent researchers such as Götz Pfander, Gitta Kutyniok, and Hartmut Führ, and has been funded through competitive grants like the Emmy Noether program. He leads a research group focused on rigorous mathematical approaches to machine learning challenges. He is affiliated with the Mathematical Institute for Machine Learning and Data Science, a newly founded institute at KU Eichstätt-Ingolstadt, which will be based in Ingolstadt. His team is involved in both theoretical exploration and practical applications of data science methods.

• Dynamical Systems
• Complex Analysis
• Metric Geometry
• +7 more

Sergiy Merenkov is a Professor in the Department of Mathematics at The City College of New York (CCNY) and a Doctoral Faculty member in the Mathematics Program at the CUNY Graduate Center. He holds offices in MR 316 at CCNY and 4213 at the Graduate Center, with phone numbers 212-650-5147 (CCNY) and 212-817-8561 (GC). His research focuses on dynamical systems, complex analysis, metric geometry, and fractal geometry. Key themes include quasisymmetric maps, conformal dynamics, hyperbolic groups, Sierpiński carpets, and Kleinian group actions. His work often bridges geometric topology, complex variables, and algebraic structures. Recent articles explore topics such as Carathéodory convergence, wandering domains in dynamical systems, and rigidity properties of fractal spaces. His research is supported by grants from the National Science Foundation (DMS-0400636, DMS-2247364, etc.). Merenkov has collaborated extensively with mathematicians like Mario Bonk, Misha Lyubich, and Oded Schramm on projects involving fractal geometry, conformal welding, and geometric group theory. His PhD thesis (Purdue University, 2003) addressed manifold classification via algebraic methods.

Vicenç TorraView profile

Professor

• Data Privacy
• Approximate Reasoning
• Decision Making
• +7 more

Vicenç Torra is a Professor at the Department of Computing Science, Umeå University, Sweden. He leads the NAUSICA: PrivAcy-AWare traNSparent deCIsions research group, focusing on artificial intelligence, data privacy, and approximate reasoning. His work bridges computer science and applied mathematics. Research Interests: Data Privacy (differential privacy, k-anonymity, secure multiparty computation) Approximate Reasoning (fuzzy sets, non-additive measures, Choquet integrals) Decision Making and Aggregation Operators Information Fusion and Clustering Recent Article Trends: His publications emphasize data privacy in machine learning, non-additive measure theory applications, and integrating privacy guarantees with mathematical rigor. 2024 works focus on Frechet manifold privacy and LLM knowledge distillation. Scientific Awards: EurAI Fellow (2010) ISI Elected Member (2013) IEEE Fellow (2017) Additional Contributions: Founder of Transactions on Data Privacy and MDAI conference series. Active in editorial boards of Fuzzy Sets and Systems, IEEE Transactions on Fuzzy Systems, and Information Sciences. Collaborates extensively with institutions in Japan and Spain.

• Theoretical Computer Science
• Algorithms
• Parameterized Complexity
• +5 more

Yang Liu is a computer scientist specializing in theoretical computer science, particularly in the design and analysis of parameterized and exact algorithms. He has been affiliated with institutions such as Texas A&M University and has collaborated extensively with researchers like Jianer Chen and Songjian Lu. His work primarily focuses on NP-hard graph problems, including feedback vertex set, multiway cut, matching, and packing, where he has contributed improved fixed-parameter tractable algorithms and kernelization techniques. His research lies at the intersection of algorithms, complexity theory, and combinatorics. Key areas include: Fixed-parameter tractability (FPT) Kernelization and measure-and-conquer methods Graph partitioning and structural graph theory Randomized and deterministic exact algorithms Algebraic methods in polynomial testing His publications in top journals such as Journal of the ACM , Algorithmica , and Theoretical Computer Science demonstrate a consistent contribution to foundational algorithmic research. The article trends show a strong focus on solving hard combinatorial problems through novel algorithmic frameworks, often improving time complexity or kernel bounds. Notable scientific contributions include: A landmark 2008 JACM paper proving that the Directed Feedback Vertex Set problem is fixed-parameter tractable. Improvements in kernel sizes for feedback vertex and packing problems. Applications of color-coding and iterative expansion in 3D-matching. While no explicit information about advisees or grants is available, his long-term collaboration network suggests a role in mentoring and team-based research. He has not been associated with any lab or research center in the provided data.

• 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.

Günter LangView profile

Associate Professor

• Empirical Economics
• Environmental and Resource Economics
• Economic Policy
• +4 more

Prof. Dr. Günter Lang serves as Associate Professor for Applied Economics at Kühne Logistics University (KLU) in Hamburg, where he has been teaching since 2014. His academic journey includes professorial positions at the German University in Cairo, University of Augsburg, University of Klagenfurt, and Technical University Ilmenau, alongside teaching appointments at Warsaw School of Economics and University of Bratislava. With over two decades of academic experience, he has established himself as a prominent researcher in empirical economics. His educational credentials include: Habilitation and venia legendi in Economics from University of Augsburg (1998) Dr. rer. pol. (summa cum laude) from University of Augsburg (1993) Diploma in Economics and Management (M.Sc. equivalent) from University of Augsburg (1989) Prof. Lang's research centers on empirical economic analysis using sophisticated econometric methods to investigate firm technology, management inefficiency, environmental issues, technological change, and economic policy. His work spans environmental economics (particularly climate change impacts on agriculture), labor economics (focusing on immigrant earnings), banking efficiency, and increasingly, digital transformation. According to Google Scholar metrics, his publications have accumulated approximately 1,500 citations, reflecting substantial scholarly impact. His publication trajectory reveals an evolution from traditional economic topics toward contemporary digital economy issues while maintaining his environmental economics foundation. Early work focused on German banking, media markets, and agricultural impacts of climate change, while recent publications examine digitalization's effects on productivity, entrepreneurship, and ICT adoption in developing countries. Methodologically, he consistently employs advanced techniques including stochastic frontier analysis, panel data econometrics, and cost function estimation across diverse geographical contexts from Germany to Egypt and the broader EU. Prof. Lang teaches across KLU's academic programs including Microeconomics and Macroeconomics (Bachelor), Statistics and Econometrics, International Economics, and Economics of Business Strategy (Master), plus Business in a Global Economy and Strategic Decision Making: Game Theory in Business (MBA). His teaching philosophy emphasizes equipping students with analytical tools necessary for addressing complex economic challenges in both academic and professional settings.