Find Academic Supervisors

• Calculus of variations
• Partial differential equations
• Geometric measure theory
• +4 more

Filippo Cagnetti is an Associate Professor (Professore di II fascia) in the Department of Mathematical, Physical and Computer Sciences at the University of Parma , Italy. He regularly teaches foundational courses such as Calculus 2A and Mathematical Analysis A to first- and second-year students enrolled in Mathematics, Geological Sciences (Earth Sciences), and Management Engineering degree programmes, spanning academic years from 2023/2024 through 2025/2026. His research lies at the intersection of calculus of variations , partial differential equations , and geometric measure theory . A recurrent theme is the rigorous derivation of effective models for materials with microstructures via deterministic and stochastic homogenisation , particularly for free-discontinuity problems that describe brittle fracture. Parallel efforts focus on symmetrisation inequalities (Pólya–Szegö, Steiner, Schwarz, Gaussian) and their rigidity properties: under which geometric or analytic conditions equality in an inequality forces the domain to be symmetric or even trivial. Over the past decade his publication portfolio shows a clear evolution from foundational results on viscosity solutions and large-time behaviour of Hamilton–Jacobi equations to highly specialised contributions on Korn- and Poincaré-Korn-type inequalities in spaces of special functions of bounded deformation (GSBD), and on optimal regularity of free boundaries in cohesive fracture models. The techniques employed combine Γ-convergence, fine properties of BV and GSBD spaces, measure-theoretic arguments, and stochastic two-scale convergence. Contact: Email: filippo.cagnetti@unipr.it

• Engineering Geology
• Geotechnics
• Numerical Modeling
• +3 more

BÖGÖLY Gyula is an Associate Professor affiliated with the Department of Engineering Geology and Geotechnics at the Budapest University of Technology and Economics, College of Engineering. His academic contributions focus on geotechnical engineering, rock mechanics, and numerical modeling, with a particular emphasis on stability analysis of rock slopes and masonry structures. Research Interests : Engineering geological characterization, mechanical degradation of aggregates, slope stability analysis using deterministic and probabilistic methods, application of UAV and FEM in structural geological mapping, and preservation of stone masonry arch bridges. Teaching : Courses include Engineering Geological and Geotechnical Project, Geodynamics, Rock Mechanics, and Tunneling. Publications : Recent works address joint network effects in rock slopes, empirical relations between aggregate degradation and strength parameters, UAV-based topographic modeling, and GSI determination for boreholes. Awards : Recipient of the #építő250 scholarship. Contact: bogoly.gyula@emk.bme.hu . Consultations held Thursday 12-14.

• Autonomous Vehicles
• Reinforcement Learning
• Motion Planning
• +3 more

Árpád Fehér is an Assistant Professor at the Department of Transport and Vehicle Control, Faculty of Transportation Engineering and Vehicle Engineering at Budapest University of Technology and Economics. He also maintains an affiliation with the Systems and Control Theory Research Laboratory at the Hungarian Academy of Sciences Research Institute for Experimental Computer Science (SZTAKI) since 2022. Having completed his PhD in 2023 with a dissertation on "Reinforcement learning-based motion planning methods," Dr. Fehér has established himself as a promising researcher in the field of autonomous vehicle technology. His educational background includes: PhD in Engineering from Budapest University of Technology and Economics (2023) Prior academic work at Budapest University of Technology and Economics since 2013 Dr. Fehér's research focuses on the intersection of artificial intelligence and vehicle control systems. His work primarily addresses motion planning, trajectory control, and reinforcement learning applications for autonomous vehicles. His research has evolved from foundational work on trajectory planning algorithms to more complex implementations involving real-world testing and mixed reality environments. He has made significant contributions to developing practical AI solutions that can be implemented in actual vehicle control systems, bridging the gap between theoretical AI approaches and automotive engineering requirements. His publication record demonstrates a consistent focus on advancing autonomous vehicle technology through innovative applications of machine learning. The trends in his work show progression from basic trajectory planning algorithms to more sophisticated implementations involving closed-loop control systems, real-world testing, and integration with simulation environments. His research spans both theoretical algorithm development and practical implementation challenges. Dr. Fehér has received recognition through citations of his work, with his 2020 paper "Hierarchical Evasive Path Planning using Reinforcement Learning and Model Predictive Control" in IEEE Access accumulating 24 citations, indicating significant impact in the field. His advising approach appears to be collaborative and hands-on, with numerous co-authored publications involving students and junior researchers. His work on educational platforms for control education suggests a commitment to developing practical teaching methods that connect theoretical concepts with real-world applications. Dr. Fehér is actively involved in the Systems and Control Theory Research Laboratory at SZTAKI, where his team works on advancing autonomous vehicle technologies through a combination of AI research and practical engineering solutions. His laboratory focuses on implementing and testing advanced control algorithms in both simulated and real-world environments.

Jia-Liang LeView profile

Professor

• fracture mechanics
• probabilistic mechanics
• scaling
• +6 more

Jia-Liang Le is the James L. Record Professor and Associate Department Head at the University of Minnesota in the Department of Civil Engineering. His research focuses on fracture mechanics, probabilistic mechanics, and structural reliability, particularly for quasibrittle materials like concrete and fiber composites. Email: jle@umn.edu Office: 254 Civil Engineering Building, 500 Pillsbury Drive SE, Minneapolis, MN 55455 His work provides deterministic and probabilistic models for fracture and fatigue in structures, aiming to enhance understanding of quasibrittle behavior and improve design standards for buildings, infrastructure, aircraft, and medical implants. Recent publications emphasize stochastic fracture simulations, scaling effects, and advanced material modeling. Research trends highlight applications in aerospace, geomechanics, and sustainable pavement design, with a strong focus on computational modeling and experimental validation. Current projects include probabilistic failure criteria for composites, fishnet models, and asphalt material optimization using graphene nanoplatelets.

Birsen YaziciView profile

Professor

• Statistical signal processing
• Inverse problems in imaging
• Image reconstruction
• +7 more

Birsen Yazici is a Full Professor in the Department of Electrical, Computer and Systems Engineering and the Department of Biomedical Engineering at Rensselaer Polytechnic Institute. She has made significant contributions to statistical signal processing, inverse problems in imaging, and radar remote sensing. Her work spans theoretical developments in microlocal analysis for imaging applications to practical implementations in synthetic aperture radar and biomedical optics. Her educational background includes: Ph.D in Electrical Engineering from Purdue University (1994) MS in Mathematics from Purdue University (1990) Dual BS degrees in Electrical Engineering and Mathematics from Bogazici University, Istanbul, Turkey (1988) Professor Yazici's research focuses on statistical signal processing, inverse problems in imaging, and image reconstruction techniques. She has pioneered work in synthetic aperture radar imaging, particularly with ultra-narrowband waveforms and passive radar systems. Her biomedical imaging research includes advances in diffuse optical tomography and fluorescence imaging for cancer detection. She has made significant contributions to understanding artifacts in imaging systems and developing robust reconstruction algorithms that account for noise and clutter. Her recent publications demonstrate a clear trajectory toward integrating deep learning with traditional signal processing techniques for phase retrieval and phaseless imaging. This represents a significant shift in her research focus toward leveraging machine learning to solve challenging inverse problems in radar and medical imaging where phase information is unavailable or corrupted. Her scientific achievements have been recognized with several prestigious awards: Elevated to IEEE Fellow (2020) Distinguished Lecturer of the IEEE Aerospace and Electronics Systems Society (2020-2022) Rensselaer Polytechnic Institute School of Engineering Research Excellence awards (2007, 2013) 2nd Best paper award from IEEE Transactions in Industrial Applications Professor Yazici has advised numerous graduate students throughout her career, with current Ph.D. students including Samia Binte Kazemi, Sean Thammakhoune, and Nazia Afroz Choudhury. Her research has been generously supported by multiple federal agencies including NSF, DARPA, NIH, and various defense research organizations (AFOSR, AFRL, ONR, NRL). She has served as an associate editor for several prestigious journals including IEEE Transactions on Computational Imaging, IEEE Transactions on Geoscience and Remote Sensing, SIAM Journal on Imaging Science, and IEEE Transactions on Image Processing. Her laboratory focuses on developing advanced signal processing algorithms for radar imaging systems and biomedical applications. The research group maintains strong collaborations with government laboratories and industry partners to translate theoretical advances into practical imaging systems.

Paolo SerenaView profile

Associate Professor

• Fiber-optic communications
• Nonlinear optics
• Space-division multiplexing
• +3 more

Paolo Serena is Associate Professor of Telecommunications at the University of Parma , Department of Engineering and Architecture. Since 2018 he has held this academic rank after serving as a researcher at the same university from 2005. He teaches Optical Communications in the Master’s program in Communication Engineering and Telecommunication Systems in the Master’s program in Computer Science, Electronics and Telecommunications. In addition to his university duties, he maintains active third-mission collaborations with Nokia Bell-Labs and Alcatel Submarine Networks in Paris, France. Education Ph.D. in Information Technology, University of Parma, 2000-2003 Laurea (M.Sc.) in Electronic Engineering, University of Parma, 1999 Thesis: “Theoretical Study of the Interaction between Chromatic Dispersion and Nonlinear Effects in Fiber Optic Systems” Research Interests Prof. Serena’s research spans the full spectrum of fiber-optic communication systems . His work focuses on understanding and mitigating nonlinear effects in both single-mode and space-division multiplexed fibers, developing numerical models for signal propagation, exploiting digital signal processing techniques for coherent receivers, and creating performance monitoring tools. He is the original author of Optilux , an open-source simulation platform widely used in the optics community. Recent investigations include modal dispersion in few-mode and multi-core fibers, machine-learning-assisted optimization of launch powers, and quantum-classical co-propagation in field-deployed multi-core links. These studies converge toward increasing the capacity, reach, and energy efficiency of next-generation optical networks. Scientific Service & Editorial Activity Associate Editor, Optics Express (OSA Publishing) Technical Committee Member, European Conference on Optical Communications (ECOC) , since 2018 Technical Committee Member, Fotonica (Italian National Conference on Photonic Technologies), since 2013 Collaborations & Tools Within the framework of university–industry collaboration, Prof. Serena works closely with Nokia Bell-Labs and Alcatel Submarine Networks on modeling and performance optimization of long-haul and submarine links. His open-source simulator Optilux is continuously extended to include the latest advances in nonlinear propagation modeling and machine-learning-enhanced system design.

• Operations Research
• Combinatorial Optimization
• Vehicle Routing Problems
• +6 more

Demetrio Salvatore LAGANA' is an Assistant Professor in Operations Research at the Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria , Italy. He has been teaching courses in Operations Research, Optimization, and Transport Systems since 2003 and has held visiting researcher positions at Georgia Tech, HEC Montréal, and University of Valencia. He supervises multiple PhD and Master’s students, collaborates internationally, and serves on scientific committees and editorial boards. Education : Master’s in Engineering (1992, Università Mediterranea di Reggio Calabria); Graduate School in Transport Terminal Infrastructures (1996, Università Federico II di Napoli); PhD in Operations Research (2006, University of Calabria). Research interests focus on combinatorial optimization and logistics , particularly arc routing problems , general routing problems , and inventory routing problems under deterministic and stochastic conditions. He develops algorithms for vehicle routing, supply chain optimization, and sustainable logistics, addressing constraints like capacity, time windows, and stock-out penalties. Recent publications highlight trends in multi-vehicle inventory routing , dynamic programming , stochastic optimization , and green logistics , including applications in bike-sharing systems, autonomous delivery robots, and integrated supply chain models. His work integrates exact methods, heuristics, and hybrid approaches. Academic responsibilities include membership in PhD committees (Mathematics and Computer Science, University of Calabria) and editorial boards (Advances in Operations Research, Hindawi). He has co-authored over 20 peer-reviewed articles and led research projects funded by national grants.

• Nonlinear Dynamical Systems
• Seismology
• Geodesy
• +8 more

Adriano Gualandi is a researcher at the University of Cambridge , affiliated with the Department of Earth Sciences and associated with the Cambridge NERC Doctoral Landscape Awards program as a DLA supervisor. His work focuses on nonlinear dynamical systems in geophysics, extreme events, and their hazards. Research Areas : Seismology, Geodesy, Fluid-Solid Earth interactions, and machine learning applications to tectonic data. Techniques : Combines classical statistical methods with modern machine learning to extract tectonic insights, using deterministic and stochastic models. Collaborations : Linked to institutions like INGV (Italy), EPOS, and British Antarctic Survey via the C-CLEAR DTP. His publications highlight a focus on slow earthquakes, seismic cycles, and causal analysis in complex systems. Current projects explore machine learning for geophysical data and fluid-solid-earth interplay.

David GamarnikView profile

Professor

• Discrete Probability
• Optimization
• Algorithms
• +4 more

David Gamarnik is a Professor of Operations Research at the Operations Research and Statistics Group within the Sloan School of Management at the Massachusetts Institute of Technology (MIT). He holds a PhD in operations research from MIT (1998) and a BA in mathematics from New York University (1993). Prior to joining MIT in 2005, he was a research staff member at IBM T.J. Watson Research Center. Research Interests : Discrete probability, optimization and algorithms, quantum computing, statistics, machine learning, and stochastic processes. His recent work focuses on algorithmic obstructions in random structures, quantum optimization, and high-dimensional statistical models. He has contributed to understanding the overlap gap property in combinatorial problems and the performance of gradient descent in neural networks. Scientific Awards : Fellow of the Institute for Mathematical Statistics (IMS) Fellow of the Institute for Operations Research and Management Science (INFORMS) Fellow of the American Mathematical Society (AMS) Erlang Prize Best Publication Award from the Applied Probability Society of INFORMS Franz Edelman Prize competition finalist He currently serves as an area editor for Mathematics of Operations Research and has previously held editorial roles in Operations Research , Annals of Applied Probability , Queueing Systems , and Stochastic Systems journals.

Jürgen HorbachView profile

Professor

• Crystallization
• Glass formation
• Phase transitions
• +7 more

Professor Jürgen Horbach is a W2 Professor at the Institute for Theoretical Physics II - Soft Matter at Heinrich-Heine-Universität Düsseldorf. His research focuses on theoretical aspects of soft matter physics, particularly glass formation, crystallization, and transport phenomena in complex systems. Prof. Horbach's research interests span a wide range of topics in theoretical physics, with a particular emphasis on the behavior of glass-forming systems. His work investigates crystallization from the melt , examining interfacial properties, growth kinetics, and nucleation processes. He also studies the nonlinear response of glassforming systems to shear , which has implications for understanding material failure and deformation. His research extends to phase transitions and critical dynamics in confined geometries, localization phenomena in ion-conducting silicates, and microfluidics with dense liquids. These diverse research areas are unified by a common thread of understanding complex behavior in disordered and soft materials through computational and theoretical approaches. Prof. Horbach's publication record demonstrates consistent contributions to the field of theoretical physics, with a particular focus on glass physics and soft matter. His recent work shows increasing integration of computational methods, including molecular dynamics simulations and machine learning approaches, to tackle fundamental questions about material behavior. The interdisciplinary nature of his research is evident in the range of journals where he publishes, spanning physics, materials science, and engineering publications. SFB-TR6 "Physics of Colloidal Dispersions in External Fields" (collaboration with Jülich, Konstanz, Mainz, Utrecht) Schwerpunktprogramm SPP 1296 "Heterogeneous nucleation and microstructure formation" DFG research group FOR 1394 "Nonlinear response to probe vitrification" Schwerpunktprogramm SPP 1594 "Topological Engineering of Ultra-strong Glasses" Throughout his career, Prof. Horbach has taught a variety of courses at Heinrich-Heine-Universität Düsseldorf, including Computational Physics, Theory of Phase Transitions, Quantum Mechanics, and Theoretical Mechanics. His teaching reflects his research expertise, with special lectures on Soft Matter, Non-equilibrium Thermodynamics, and the Dynamics of Dense Fluids.

• Optimal Control
• Tropical Mathematics
• Idempotent Analysis
• +8 more

Marianne Akian is a Researcher at INRIA Saclay – Île-de-France , affiliated with the Tropical team (joint with CMAP, École Polytechnique, IP Paris, and CNRS). Her research spans deterministic and stochastic optimal control, tropical mathematics, idempotent analysis, and nonlinear Perron-Frobenius theory. She has contributed to numerical methods for Hamilton-Jacobi-Bellman equations and mean payoff games. Education: PhD in Mathematics (1990), Université Paris IX-Dauphine Habilitation (HDR) in Mathematics (2007), Université Pierre et Marie Curie Her work integrates max-plus/tropical algebra with applications in portfolio optimization, dynamic programming, and epidemiological modeling. Recent articles focus on accelerating value iteration algorithms, tropical convexity, and entropy games. Scientific awards are not explicitly mentioned in the provided texts. She collaborates extensively with researchers like Stéphane Gaubert, Marouen B for Stochastic Control, and others in applied mathematics. Lab/Team: Tropical team at INRIA, which bridges tropical mathematics with control theory and optimization.

Nizami GasilovView profile

Professor

• Fuzzy differential equations
• Interval analysis
• Numerical methods
• +3 more

Prof. Dr. Nizami Gasilov is a faculty member at Başkent University's Faculty of Engineering, specializing in computer engineering. His research focuses on fuzzy differential equations, interval analysis, numerical methods, and plasma physics. Teaches courses including Discrete Structures, Numerical Analysis Techniques, and Differential Equations Holds a PhD in Computational Mathematics and Cybernetics from Lomonosov Moscow State University (1989) Recent work analyzes fuzzy and interval differential equations for deterministic uncertainty modeling, with applications in tokamak plasma dynamics and recommender systems. His publications span journals like Kybernetika and Soft Computing. Active in editorial roles for multiple journals in 2024.

• Software Engineering
• Data Warehouses
• Big Data
• +9 more

Manuel Ángel Serrano Martín is a Full Professor at the University of Castilla-La Mancha (UCLM) since 2025, affiliated with the Ciudad Real School of Computer Science and the Department of Information Technology and Systems . With over 25 years of experience, he transitioned from roles as a programmer and secondary school teacher (1998-2000) to academia, where he has served as lecturer and researcher since 2000. PhD in Computer Engineering (cum laude), UCLM (2004) Technical Engineer & Computer Engineer, UCLM Current full-time researcher in the Alarcos Research Group Research Focus: Pioneering work in data quality , software quality , and business intelligence since 2000. Recently expanded into quantum software engineering and cybersecurity , including frameworks like MARISMA for risk analysis and QuantumUnit for quantum software testing. His work spans Big Data , I8K data quality models , and blockchain integration in security systems. Academic Impact: Over 100 publications, 24 in ISI-JCR journals, with 2,291 Google Scholar citations (H-index: 24, i10-index: 40). Key projects include QUASIMODO (quality & security in data warehouses), SDGear , DQIoT , and Di4SPDS . Collaborations with companies like INDRA through projects ORIGIN and LPS-BIGGER . Awards & Grants: Recipient of the JNIC 2018 Best Article Award . Holds 3 six-year research periods from Spanish Ministry of Science. Directed 2 regional projects (including PAC08-0157-0668) and participated in 20+ national/regional and 3 international research initiatives. International Engagement: Completed predoctoral (2003, University of Montreal, Canada ) and postdoctoral (2021, University of Bari, Italy ) stays. Active in global software development and swarm intelligence frameworks.

Hua ZhouView profile

Professor

• Numerical Optimization
• High-Dimensional Statistical Analysis
• Genomic Data Analysis
• +6 more

Hua Zhou is a Professor at the Fielding School of Public Health at the University of California, Los Angeles. His research focuses on developing scalable optimization algorithms for high-dimensional statistical problems, with applications to genomics and computational biology. Key contributions include deterministic annealing methods, quasi-Newton acceleration techniques, and GPU-based parallel computing frameworks. Dr. Zhou specializes in: Numerical optimization for maximum likelihood estimation Penalization methods in genome-wide association studies (GWAS) RNA-seq data analysis and microbiome statistics Development of the genetics analysis software Mendel Current work on QTL mapping in family designs and genotype imputation

Claudia CherubiniView profile

Senior Lecturer

• Hydrogeology
• Environmental Engineering
• Groundwater Management
• +7 more

Dr. Claudia Cherubini is an Honorary Senior Lecturer at the University of Queensland School of Civil Engineering, Faculty of Engineering, Architecture and Information Technology. She joined UQ in 2015 after serving as Associate Professor in Hydrogeology at Institute Polytechnique LaSalle Beauvais (France) and holding postdoctoral/visiting positions at CNR (Italy), Polytechnic University of Bari, Lawrence Berkeley National Lab, and USGS Menlo Park (USA). Her research focuses on hydrogeological modeling of heterogeneous aquifers , with particular emphasis on coastal fractured aquifers , unsaturated fracture flow , contaminant transport in fractured media , and application of advanced geostatistical techniques . She also investigates low-enthalpy geothermal energy exploitation through ground-source heat pump systems and environmental sustainability challenges related to climatic changes and desertification. Key methodologies include deterministic/stochastic hydrogeological modeling, non-Darcy flow analysis , non-Fickian transport characterization , and 3D fracture simulation . Her work supports groundwater protection, remediation of polluted sites, and sustainable coastal aquifer management. Supervision history includes associate advising roles for PhD projects on saltwater intrusion in fractured coastal aquifers (2020) and fluid flow in Enhanced Geothermal Systems (2020). Collaborations span institutions in Italy, France, UK, and USA.