Find Academic Supervisors

Carlo BaldassiView profile

Associate Professor

• Machine Learning
• Computational Neuroscience
• Statistical Mechanics
• +4 more

Carlo Baldassi is an Associate Professor at Bocconi University, where he has served as Director of the BSc in Mathematical and Computing Sciences for Artificial Intelligence (BAI) since 2023/24. He holds a background in Theoretical Physics from the University of Trieste and a PhD in Computational Neuroscience from the University of Turin. His research focuses on applying Statistical Mechanics to Machine Learning and Neural Networks, particularly studying loss landscapes, optimization problems, and the role of quantum annealing in nonconvex learning. He teaches courses in Machine Learning, Artificial Intelligence, and Computer Science, emphasizing Python and Julia programming. His work bridges theoretical physics and AI, exploring topics like synaptic stochasticity in low-precision neural networks and the efficiency of quantum vs. classical annealing. He has published extensively in top journals such as Physical Review Letters and Proceedings of the National Academy of Sciences , contributing to foundational understanding of neural network dynamics and optimization techniques. Teaching includes Machine Learning and Artificial Intelligence Computer Science I Machine Learning II Machine Learning and Artificial Intelligence Lab Research emphasizes large-scale inference problems, with a focus on the interplay between statistical mechanics and modern AI architectures.

Giorgio ScorzelliView profile

Researcher

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +6 more

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

• Hydroinformatics
• Watershed Hydrology
• Surface Water Quality
• +8 more

Jeffery Horsburgh is a Professor in the Department of Civil and Environmental Engineering and the Utah Water Research Laboratory at Utah State University, where he conducts research and teaches in hydroinformatics, GIS, and environmental data systems. He is affiliated with the College of Engineering and actively contributes to advancing cyberinfrastructure for hydrologic science. His educational background includes a PhD (2009), MS (2001), and BS (1999), all in Civil and Environmental Engineering from Utah State University. Horsburgh's research focuses on watershed hydrology, surface water quality, environmental sensor networks, data models, and cyberinfrastructure for environmental observations. He leverages GIS for data analysis and develops modeling techniques for hydrologic and water quality prediction. His work emphasizes open, reproducible science and data interoperability. His recent publications highlight a strong trend in hydroinformatics, with emphasis on open data platforms (e.g., HydroShare), sensor data integration, metadata standards, and reproducible modeling. The articles span topics from edge computing in smart water meters to national-scale hydrologic modeling frameworks. Outstanding Researcher, USU CEE, 2024 Reproducibility Author Award, 2023 Outstanding Teacher, USU CEE, 2019 Early Career Excellence Award, iEMSs, 2014 Outstanding Reviewer Award, Environmental Modelling & Software, 2014 Horsburgh has mentored over a dozen graduate students in civil and environmental engineering, focusing on hydroinformatics and water systems. He leads multiple research projects funded by federal and state agencies, including HydroShare, I-GUIDE, and Logan River Observatory. He is also involved in developing open-source software for environmental data management and has contributed to national standards for hydrologic data sharing. He leads or participates in key research initiatives such as the Logan River Observatory, HydroShare, and the Critical Zone Collaborative Network, emphasizing interdisciplinary collaboration, real-time data integration, and next-generation water infrastructure.

Martin SjölundView profile

Associate Professor

• Software Engineering
• Cyber-Physical Systems
• Modelica Compiler Development
• +3 more

Martin Sjölund is an Associate Professor at Linköping University's Department of Computer and Information Science (IDA), part of the Software and Systems (SAS) division. His work focuses on software engineering and cyber-physical systems, with a specialization in Modelica compiler development and open-source simulation tools. He contributes to the OpenModelica project, advancing compiler frameworks, integration with Julia, and standardization efforts. Research interests include compiler design, formal methods, and domain-specific languages. Recent work emphasizes modular compiler architectures, structural variability handling, and interoperability between Modelica and other systems like Julia and Python. He has co-authored over 30 peer-reviewed publications since 2017, with a focus on compiler optimization, co-simulation, and educational applications of Modelica. As part of the SAS division, he collaborates with researchers like Lena Buffoni, Adrian Pop, and Peter Fritzson on projects funded by the MODPROD Center and other initiatives. His contributions to open-source tools have been recognized through conference proceedings and industry partnerships.

Hermann EberlView profile

Professor

• Mathematical Biology
• Engineering Mathematics
• Applied Differential Equations
• +5 more

Hermann Eberl is a Professor in the Department of Mathematics & Statistics at the University of Guelph, affiliated with the College of Engineering and Physical Sciences. His research focuses on Mathematical Biology and Engineering, emphasizing modeling, analysis, and simulation of biological systems and bio/environmental engineering processes using differential equations and numerical methods. He leads the Computational Biomathematics Laboratory, which houses a diverse research group of undergraduates, graduate students, and postdocs. Key research areas include microbial biofilms, honeybee health dynamics, agricultural systems, and environmental bioprocesses. His work is supported by funding agencies such as NSERC, CFI, and OMAFRA. Students under his supervision have excelled in interdisciplinary journals and secured careers in academia, industry, and government. Dr. Eberl holds full supervisory privileges in the Mathematics and Biophysics graduate programs and actively mentors students pursuing MSc/PhD degrees. His lab is located in MacNaughton Building, with a focus on integrating mathematical modeling with real-world applications in biotechnology and environmental engineering. Research outputs span biofilm reactor modeling, quorum sensing mechanisms, and gut microbiome interactions, with notable contributions to the ADM1jl computational tool for anaerobic digestion simulations. Collaborations emphasize interdisciplinary problem-solving, reflecting his commitment to bridging mathematical theory with practical engineering challenges.

Giuseppe RagusaView profile

Associate Professor

• microeconometrics
• time series analysis
• economic forecasting
• +2 more

Giuseppe Ragusa is an Associate Professor of Economics at the Department of Economics and Law, Sapienza University of Rome, where he also coordinates the PhD in Economics program. Previously, he held positions at the University of Pisa, Rutgers University, and the University of California, Irvine. He earned his PhD in Economics from the University of California, San Diego, and an undergraduate degree from Bocconi University. His research focuses on microeconometrics, time series analysis, economic forecasting, and machine learning applications in economics and finance. He has also served as a Senior Economist at the European Central Bank's Monetary Strategy Division from 2015 to 2017. Ragusa teaches courses in econometrics, time series, and machine learning, utilizing programming tools like Julia, R, and Python. His work has been published in leading journals such as the Journal of Monetary Economics, Journal of Econometrics, and Review of Economics and Statistics. He maintains an active research agenda centered on monetary policy analysis and the application of advanced statistical methods to economic problems.

Daniele MarinazzoView profile

Professor

• Computational Neuroscience
• Complex Systems
• Neurophysics
• +4 more

Daniele Marinazzo is a Full Professor in the Department of Data Analysis at Ghent University (Belgium), with a background in statistical physics and a focus on computational neuroscience and complex systems. He serves as Deputy Editor at PLOS Computational Biology , Co-Editor-in-Chief at Neurons, Behavior, Data Analysis, and Theory , and academic editor at Network Neuroscience and Imaging Neuroscience. His work spans theoretical physics, neuroscience methodology, and experimental neurophysiology. Academic Affiliation: Ghent University (Belgium) Editorial Roles: 3+ journals Research Initiatives: EBRAINS representative Research Interests combine computational neuroscience, complexity science, and network analysis. He investigates higher-order interactions in brain networks, synergistic information processing, and methodological advancements in neuroimaging. Recent work includes MEG/EEG analysis, gene expression modeling, and developing open-source tools like SPM-Python. Scientific Contributions include 15+ recent publications on topics ranging from individual brain fingerprinting to multisensory integration models. His work appears in venues like Nature Communications , Physics of Life Reviews , and Entropy , with methodological innovations in hidden factor localization and network synchronization analysis. Current projects include PhD/postdoc recruitment for fast-sampled fMRI analysis and organizing the Erice workshop Higher-Order Interactions: Mechanisms, Behaviors, and Networks (August 2025).

Simon ChristView profile

Researcher

• Computational Biology
• Biophysics
• Reproducible Science
• +4 more

Dr. Simon Christ is a Researcher at the Department of Computational Biology within the Institute of Cell Biology and Biophysics at Leibniz University Hannover's Faculty of Natural Sciences. His office is located in Building 4160, Room 110 at Herrenhäuser Str. 2, 30419 Hannover. He can be contacted via phone (+49 511 762 16174) or email (christ@cell.uni-hannover.de). Christ holds a Doctor rerum naturalium (PhD) from the University of Potsdam (2020) and a Master of Science from Humboldt University of Berlin (2014). His doctoral research focused on morphological transitions of vesicles under nonuniform conditions, while his master's thesis examined non-Markovian systems in statistical physics. His research focuses on computational approaches to biological systems, including: Development of reproducible scientific workflows and open-source tools Agent-based simulations of biological processes Numerical solutions of differential equations in biological contexts Computational modeling of membrane dynamics and vesicle behavior Research software engineering practices and tools He is proficient in multiple programming languages including Julia, C/C++, MATLAB, Mathematica, Python, and R, and is a certified instructor for The Carpentries. Christ's recent publications demonstrate a strong focus on research software engineering, computational biology methods, and biophysical modeling of cellular systems. His work frequently appears in computational biology, biophysics, and software engineering journals, with consistent contributions to open science practices and reproducible research methodologies. No specific awards or student advising relationships are mentioned in the available documentation. His current work appears to be conducted within the computational biology research group at Leibniz University Hannover, with collaborations extending to other institutions including the University of Potsdam and Max Planck Institute.

Michael G. KayView profile

Associate Professor

• Logistics Network Design
• Metaheuristics
• Freight Transportation
• +4 more

Michael G. Kay is an Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University's College of Engineering. Since joining in 1992, he has served as Director of the Integrated Manufacturing Systems Engineering graduate program and as Co-Director of the Operations Research Program. His educational background includes a Ph.D. in Industrial Engineering from NC State University (1992), an MS in Industrial Engineering from the University of Florida (1984), and a BA in Economics from the University of Florida (1981). Kay's research focuses on logistics engineering, particularly the design of public logistics networks using driverless delivery vehicles. His work explores innovative solutions for freight transportation, warehousing, and production system design, with applications ranging from military logistics to sustainable supply chains. Key research areas include metaheuristic optimization, material handling systems, and multimodal transportation networks. Analysis of Kay's recent publications reveals a strong interdisciplinary focus spanning operations research, logistics engineering, and supply chain innovation. His work increasingly incorporates emerging technologies like blockchain and additive manufacturing to solve complex industrial problems. Research trends demonstrate consistent application of stochastic optimization methods to real-world challenges in transportation, manufacturing, and military logistics. Throughout his career, Kay has developed significant software tools including: Logjam (Julia package for logistics engineering) Matlog (Logistics Engineering Matlab Toolbox) Lgpy (Logistics Engineering Python Package) GAOT (Genetic Algorithm Optimization Toolbox)

• Complex Dynamics
• Several Complex Variables
• Dynamical Systems
• +1 more

Mi Hu is a researcher at the Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo, Norway, since August 2023. She collaborates with Prof. Tuyen Trung Truong and Prof. John Erik Fornæss on projects involving dynamical systems, several complex variables, and algebraic geometry, focusing on optimization solutions and improved Newton's methods for solving systems of equations. Education: PhD in Mathematics from the University of Parma, Italy (2024) Thesis: Complex Dynamics Inside Fatou Sets Advisor: Prof. John Erik Fornæss Research Interests: Complex Dynamics in one and higher dimensions Interior dynamics of Fatou sets Parabolic basins Geometric complexity of Julia sets Hybrid applications of Newton's methods Algebraic geometry in dynamical systems Recent Work Trends: Her publications emphasize theoretical advancements in Newton's methods (e.g., Backtracking New Q-Newton's Method), connections to Schröder's theorem, and computational techniques like stochastic root finding. Articles also explore geometric and topological properties of complex dynamical systems. Conference Participation: On geometric complexity of Julia sets V (2024, Bedlewo, Poland) KAUS and Nordan (2024, Östanskär, Sweden) UiO seminar (2024, Oslo) Topics in Complex Dynamics (2023 & 2021, Barcelona) Let's Face Complexity (2017, Como, Italy) Recent developments on d-bar equations (2023, Oslo)

Elias Roland UdnæsView profile

Research Fellow

• Radiative Transfer
• Machine Learning
• Computational Physics
• +3 more

Elias Roland Udnæs is a Doctoral Research Fellow at the Rosseland Centre for Solar Physics, University of Oslo. His research focuses on radiative transfer in stellar atmospheres, leveraging machine learning and programming expertise in Python & Julia. He contributes to advancing computational methods for non-local thermodynamic equilibrium (NLTE) simulations in astrophysics. His academic work includes a notable publication in Astronomy and Astrophysics (2023), addressing irregular grid techniques for 3D radiative transfer models. Udnæs’ interdisciplinary approach bridges theoretical astrophysics with advanced computational tools, aiming to enhance accuracy in stellar atmosphere modeling. No scientific awards or grants are explicitly mentioned in the provided texts. His affiliation with the Rosseland Centre positions him within a leading institution for solar and stellar physics research.

Sascha HunoldView profile

Associate Professor

• Parallel Computing
• High-Performance Computing (HPC)
• MPI Collective Communication
• +4 more

Sascha Hunold is an Associate Professor at TU Wien, affiliated with the Department of Parallel Computing within the Faculty of Informatics. He holds roles as Vice Dean of Academic Affairs for Informatics Bachelor programs and Curriculum Coordinator for the Master's program in High-Performance Computing. His research focuses on parallel computing, MPI optimization, scheduling algorithms, and reproducible HPC experiments. Education: Habilitation (venia docendi) in Computer Science from TU Wien, PhD in Computer Science from the University of Bayreuth, and MSc in Computer Science from Martin Luther University Halle-Wittenberg. Research interests include MPI collective communication performance, HPC benchmarking, algorithm selection, and task scheduling. He has contributed to tools like ReproMPI and Scheduling.jl. Key awards include Best Paper awards at IEEE CLUSTER 2020 and EuroMPI/Asia 2014, and recognition for reproducible research methodologies. His work emphasizes practical applications of parallel computing in large-scale systems. Teaching responsibilities include courses on parallel algorithms, scientific programming with Python, and HPC project supervision. Active in program committees for major conferences like IPDPS and SC.

Noah RhodesView profile

Research Fellow

• Power Grid Optimization
• Mathematical Optimization
• Energy Security
• +2 more

Noah Rhodes is a CNLS Postdoctoral Associate at Los Alamos National Laboratory's Center for Nonlinear Studies (CNLS), working within the Theoretical Division (T-Division). His research focuses on mathematical optimization applications for power grid challenges, particularly infrastructure flexibility and wildfire risk mitigation. Office contact: TA-3, Building 1690, Room 123; email: nrhodes@lanl.gov. Educational background includes: B.S. in Electrical Engineering, University of Wisconsin-Madison (2019) Ph.D. Minor in Energy Analysis and Policy, University of Wisconsin-Madison (2022) Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2024) Rhodes' research centers on enhancing power grid resilience through optimization techniques, with emphasis on reducing infrastructure needs and managing wildfire-related outages. His work bridges theoretical mathematics with practical energy security challenges, developing frameworks for public safety power shutoffs and grid restoration under extreme conditions. His 2021-2025 publications reveal a trajectory from foundational grid restoration algorithms toward integrated environmental considerations. Recent work combines carbon intensity analysis with grid operations while advancing open-source tools (PowerModelsRestoration.jl, ElectricityEmissions.jl) for academic and utility applications. Key trends include stochastic optimization for emergency response and topology-aware power flow formulations. No scientific awards are listed in the provided materials. No student advisement or grant information is documented in the source text. Rhodes operates within CNLS—a hub for nonlinear science research at LANL—which fosters interdisciplinary collaboration on complex systems. His work interfaces with T-Division's theoretical frameworks while addressing real-world energy infrastructure vulnerabilities through computational modeling and optimization.

Konrad WojdanView profile

Lecturer

• Artificial Intelligence
• Optimization Algorithms
• Energy Systems
• +5 more

Konrad Wojdan is a Lecturer at the Institute of Heat Engineering within the Faculty of Power and Aeronautical Engineering at Warsaw University of Technology. His research spans interdisciplinary applications of artificial intelligence, with particular expertise in optimization algorithms for energy systems and medical diagnostics. He maintains an active research program with publications extending into 2025, demonstrating ongoing scholarly contributions in both engineering and medical fields. Dr. Wojdan's research interests focus on developing and applying Artificial Intelligence and optimization algorithms in energy, heating and gas sectors, with significant contributions to district heating network optimization and combustion process control. His work also extends to medical applications, particularly in analyzing neurological conditions like tuberous sclerosis complex using advanced data science methods. This unique interdisciplinary approach bridges engineering and medical research, creating novel methodologies applicable across domains. Analysis of his recent publications reveals a consistent pattern of applying immune-inspired optimization techniques to complex engineering problems, while simultaneously developing AI-based diagnostic tools for medical applications. His work on district heating networks demonstrates practical implementation of AI for energy efficiency, while his medical research contributes to early diagnosis and intervention strategies for neurological conditions in infants. Active researcher with publications through 2025 Interdisciplinary work spanning energy engineering and medical diagnostics Specialized in immune-inspired optimization algorithms Develops practical AI applications for district heating systems Dr. Wojdan supervises diploma theses focused on Data Science methods in energy, heating and gas industries, requiring students to develop programming skills in Python, R, or Julia. His teaching emphasizes the practical application of computational methods to real-world engineering challenges, preparing students for careers at the intersection of traditional engineering and modern computational techniques.

• Complexity science
• Network science
• Social simulation
• +6 more

Dr. Cristina Chueca Del Cerro is a Postdoctoral Research Associate in computational social science at Durham University, affiliated with the Department of Sociology and the Durham Research Methods Centre (DRMC). Her work bridges political science, complexity science, and computational modeling. Education: BA in Politics with Quantitative Methods (University of Glasgow, 2018), MRes in Political Communication (University of Glasgow, 2019), PhD in Political Sciences (University of Glasgow, 2023) Research Interests: She specializes in social media research, polarisation, protest mobilisation, and agent-based modeling. Her PhD focused on how national identity polarisation and social media networks drive secessionist protest mobilisation, using Catalonia as a case study. She also contributes to interdisciplinary public health research via systems science. Scientific Awards: Best student paper at the Social Simulation Conference (SSC), Milan (2022) Professional Roles: Cristina co-organises events as a committee member of the European Association of Social Simulation (ESSA) and Social Network Analysis Scotland (SNAS), promoting computational social science and social simulation methodologies. Her technical expertise spans programming in R, NetLogo, Python, and Julia.