Find Academic Supervisors

Eirik KeilegavlenView profile

Researcher

• Mathematical Modeling
• Numerical Methods
• Porous Media
• +5 more

Eirik Keilegavlen is a Researcher at the Department of Mathematics, University of Bergen. His primary research focuses on developing mathematical models, numerical methods, and simulation tools for multiphysics processes in porous media, particularly in geothermal energy, CO 2 storage, and subsurface energy systems. He leads the development of the open-source software PorePy, designed for simulating processes in fractured porous media. His work emphasizes coupled problems involving fluid flow, heat transfer, and mechanical deformation. Key research interests include: Mathematical modeling of coupled thermal-hydro-mechanical processes Numerical discretization methods for fractured media Development of open-source simulation tools Applications in geothermal energy extraction and carbon sequestration Recent publications highlight advancements in: Uncertainty quantification for CO 2 leakage Viscous fingering in fractured reservoirs Automated solver selection for multiphysics systems Collaborations involve interdisciplinary teams addressing challenges in geothermal reservoir stimulation, fault mechanics, and high-performance computing. His work bridges theoretical developments with practical applications in energy and environmental systems.

Peter JansenView profile

Associate Professor

• Natural Language Processing
• Artificial Intelligence
• Cognitive Science
• +5 more

Peter Jansen is an Associate Professor at the University of Arizona's College of Information Science with a joint appointment at the Allen Institute for Artificial Intelligence (Ai2). He specializes in natural language processing (NLP), cognitive artificial intelligence, and automated scientific discovery. His research focuses on virtual environments for scientific reasoning, such as ScienceWorld and DiscoveryWorld, and methods for explainable AI through projects like the Explanation Bank. Education: PhD in Psychology and Neuroscience from McMaster University, and a Bachelor of Independent Studies from the University of Waterloo. His interdisciplinary background combines NLP, computer science, physics, and electrical engineering. Key projects include open-source hardware initiatives like the science tricorder (featured in over 50 media outlets and exhibited at the German Museum of Technology in Berlin) and TextWorldExpress, a text-game simulator. His work emphasizes grounding science education through sensing and developing AI systems capable of systematic reasoning and explanation generation. Current roles include advising students in automated scientific discovery and contributing to cross-listed courses in Computer Science and Linguistics. He maintains an active presence in academic outreach through teaching, conferences, and publications.

Jarek NabrzyskiView profile

Professor

• Quantum Computing
• Blockchain Technology
• Distributed Ledger Technologies
• +4 more

Jarek Nabrzyski is the founding and current director of the Center for Research Computing (CRC) at the University of Notre Dame and a concurrent Professor in the Department of Computer Science and Engineering. He co-directs the Blockchain Research Lab with Ian Taylor and leads the Quantum Computing Lab. His research focuses on distributed ledger technologies, quantum computing, and resource management in distributed, cloud, and exascale systems. He has overseen over 50 national and international research projects developing cyberinfrastructure for science and industry, emphasizing collaboration and team-building in complex projects. Key areas of research include blockchain applications in decentralized systems, quantum algorithm optimization for near-term hardware, and cybersecurity protocols for data integrity. He actively engages in interdisciplinary initiatives like the Center for Social Science Research and has pioneered frameworks for verifiable credentials and reproducible scientific data. His work bridges theoretical advancements with practical industry partnerships. Notable contributions include hybrid cross-chain protocols, quantum Poisson solvers, and frameworks for blockchain trust analytics. His recent publications highlight advancements in AI-driven blockchain stabilization, suspicious transaction detection, and scalable quantum computing solutions. Despite no explicit mentions of awards, his leadership roles and project count underscore significant contributions to computational science and infrastructure. As a mentor, he fosters student engagement in cyberinfrastructure through initiatives like the Cyberinfrastructure Center of Excellence. His labs and collaborations drive innovation in quantum computing, blockchain interoperability, and high-performance computing systems. Current efforts include transparent data tracking for aerospace supply chains and secure scientific data reproducibility.

• Distributed Systems
• Computer Networks
• Middleware
• +13 more

Antonio Corradi is a Full Professor of Computer Networks and Infrastructures supporting Cloud and Big Data at the University of Bologna's School of Engineering, within the Department of Computer Science - Science and Engineering. His roles include President of the regional CLUSTER RER for service innovation, President of the Alma Mater FAM Foundation, and Director of the UNIBO High Studies Center in Buenos Aires. He previously served as Director of the DISI department (2018–2021) and International Delegate for Latin America (2014–2018). His research focuses on distributed systems, middleware for pervasive computing, cloud solutions, mobile systems, smart cities, Industry 4.0/5.0, and 5G communication standards. Education: Laurea cum laude in Electrical Engineering (University of Bologna, 1979) and a Master's in Computer Engineering from Cornell University (1981, supported by a Fulbright-Hayes grant). He joined the University of Bologna as a Researcher in 1983 and became Full Professor in 2000. Research interests span distributed/parallel systems, middleware for mobile agent systems, cloud computing, smart city monitoring, and Industry 4.0 protocols. He emphasizes QoS-aware solutions, edge computing, and IoT integration. His work includes designing frameworks for big geospatial data and novel architectures for serverless and fog computing environments. Selected scientific awards include the 1980 'Cavalieri del Lavoro' prize for his thesis. He actively contributes to institutional duties, including the CCIB (Computer Services Centre of the Engineering School) and CINI Bologna University Section (Italian Interuniversity Consortium). In advising and grants: He coordinates PhD programs and has led projects funded by MIUR, CNR, and European initiatives. Notable collaborations include industry grants with Jakala, OTConsulting, ENAV-Sicta, and the Zefiro Consortium. His projects address challenges in energy efficiency, smart manufacturing, and healthcare management during crises. Labs/Teams: Developed platforms like SOMA and REDMAN middleware. Involved in initiatives such as ParticipAct (mobile crowdsensing), COLOMBO (vehicular traffic monitoring), and the Audit4Cloud platform for cloud performance auditing.

Takao SasakiView profile

Associate Professor

• Collective animal behavior
• Collective learning
• Animal cognition
• +3 more

Takao Sasaki is an Associate Professor in the Department of Brain and Cognitive Sciences at the University of Rochester. His research focuses on collective cognition in animal groups, particularly using homing pigeons and acorn ants as model organisms. He explores how groups achieve higher cognitive abilities than individuals through information pooling and decision-making processes. His work combines field observations with mathematical modeling and computational simulations to uncover principles of collective intelligence. Research interests include collective animal behavior, collective learning mechanisms, and decision-making strategies in social insects. He investigates how environmental cues, colony size, and quorum thresholds influence group decisions during tasks like nest relocation and foraging. His methodologies involve cutting-edge tools such as GPS tracking, high-resolution video analysis, and hidden Markov models. Recent articles highlight studies on fire ant pontoon bridge dynamics, reversal learning in ant colonies, and the impact of tagging on recruitment behavior. These studies emphasize local information processing and self-organization principles in swarm systems. Sasaki’s contributions bridge behavioral ecology, cognitive science, and systems biology. No scientific awards are explicitly listed in the provided materials. His research also examines the role of individual diversity (e.g., ‘naïve’ individuals) in enhancing group exploration efficiency in homing pigeons. He explores historical influences on decision-making and the anchoring effect’s role in nest site selection.

Kate CooperView profile

Associate Professor

• Consumer Health Informatics
• Nutrition Informatics
• Microbiome
• +9 more

Kate Cooper is an Associate Professor and Undergraduate Program Chair for BIOI, CYBR, and ITIN in the School of Interdisciplinary Informatics at the University of Nebraska at Omaha (UNO), College of Information Science & Technology. She has been a member of the UNO Bioinformatics Research Group since 2005 and officially joined the faculty in 2015. Her work bridges computational science and biomedical applications, with a strong focus on network modeling and data interpretation. Research Interests: Dr. Cooper's research centers on applying network science to biomedical data, particularly in modeling gene expression and protein-protein interaction networks. She investigates how graph-theoretic properties can reveal functional insights in biological systems. Her recent focus includes consumer health informatics, exploring how diet impacts the microbiome to prevent disease. She also examines the use of graphs in modeling infectious disease spread and food product label co-occurrence. Teaching and Service: She is passionate about bioinformatics education, modular curriculum design, and promoting reproducibility in research. She actively contributes to academic governance as Chair of multiple committees, including the College of IS&T Advisory Committee and the Bioinformatics Undergraduate Curriculum Committee. Education: BS in Bioinformatics, University of Nebraska at Omaha, 2007 PhD in Pathology & Microbiology (Bioinformatics Specialty Track), University of Nebraska Medical Center, 2013 Scientific Awards: No awards listed in the provided text. Advising and Grants: While no students or grants are explicitly listed, Dr. Cooper plays a significant leadership role in curriculum development and academic service. Her long-standing involvement with the Bioinformatics Research Group since 2005 suggests sustained research engagement and potential grant activity, though specific funding sources are not mentioned. Labs and Teams: She is a core member of the UNO Bioinformatics Research Group, where she collaborates on interdisciplinary projects involving network analysis, high-performance computing, and health informatics applications.

Apostolos GeorgakisView profile

Associate Professor

• Biomedical Signal Processing
• Audio Engineering
• Electroglottography
• +7 more

Dr. Apostolos Georgakis is an Associate Professor in Electrical and Electronic Engineering at the School of Computing and Engineering, University of West London (UWL). He joined UWL in 2017 after serving at King’s College London’s Division of Engineering. His academic role involves teaching and research in engineering with a strong focus on signal processing and biomedical applications. University: University of West London School: School of Computing and Engineering Department: Department of Electrical and Electronic Engineering Academic Rank: Associate Professor Email: apostolos.georgakis@uwl.ac.uk Dr. Georgakis holds an MSc in Mathematics and Statistics and a PhD in Engineering. His research interests center on biomedical signal processing, audio engineering, and medical diagnostics using advanced signal analysis techniques. He has made significant contributions to the fields of electroglottography for vocal tract pathology detection and heart rate monitoring via tracheal audio signals. His work bridges engineering with healthcare applications, particularly in non-invasive monitoring and diagnostic systems. His recent publications (2022–2024) demonstrate a clear trend toward applying digital signal processing and machine learning methods to medical diagnostics. Topics include laryngeal pathology detection using MFCC/GTCC features, systematic reviews on electroglottography, and automatic heart rate estimation during sleep. Earlier works (2014–2015) focus on theoretical signal restoration and denoising techniques using fractional Fourier transforms and mask operations, showing a strong foundation in mathematical signal processing. A 2019 publication also highlights his interdisciplinary work in thermal engineering involving liquid desiccant systems. Dr. Georgakis teaches across multiple programs, including: MSc Electronic and Robotic Engineering BEng (Hons) Electrical and Electronic Engineering (with and without Foundation Year) BSc (Hons) Sound Engineering (with and without Foundation Year) BSc (Hons) Mathematics and Statistics (with and without Foundation Year) MSc Digital Audio Engineering PhD Engineering While no specific scientific awards are listed, his sustained publication record in high-impact journals such as Digital Signal Processing , Signal Processing , and MDPI Diagnostics reflects active research engagement. He collaborates with researchers internationally, particularly in biomedical signal analysis and veterinary monitoring. Dr. Georgakis advises postgraduate students and supervises PhD candidates in engineering disciplines, though specific student names are not listed. He has not received mention of external research grants in the provided text, but his involvement in applied research suggests potential grant-funded projects. His work in wireless acoustic sensors and medical diagnostics may involve future expansion into wearable health technologies and AI-driven diagnostic tools. There is no mention of a dedicated lab or research team in the provided text, but his research themes suggest potential affiliation with biomedical engineering or audio signal processing groups within the School of Computing and Engineering at UWL.

Cevdet AykanatView profile

Professor

• Parallel Computing
• High-Performance Computing
• Graph and Hypergraph Partitioning
• +4 more

Prof. Cevdet Aykanat is a Professor of Computer Engineering at Bilkent University, Ankara, Turkey. He earned his BS/MS in Electrical Engineering from METU and PhD from Ohio State University as a Fulbright scholar. His research focuses on parallel computing, sparse matrix algorithms, graph partitioning, and high-performance computing for big data. He has been affiliated with Bilkent since 1989 and has held roles like Associate Provost. Education: BSc/MSc (METU Electrical Engineering), PhD (Ohio State University Electrical & Computer Engineering). Research interests include parallel scientific computing, combinatorial optimization, distributed systems, and large-scale data analysis. His work spans over 100 publications in top journals like IEEE Transactions and SIAM, with 5,500 citations and an H-index of 40. Awards: 1996 TUBITAK Investigator Award, 2007 METU Parlar Science Award. He led 6 TUBITAK projects and participated in EU-funded projects like PRACE-1IP to PRACE-6IP. Publications emphasize efficient parallel algorithms for sparse computations, graph partitioning, and distributed systems. His work addresses latency reduction, load balancing, and scalable data processing in HPC environments. Grants: Funded by TUBITAK, Intel SSD, and EU programs. Academic service includes editorial roles at IEEE Transactions on Parallel and Distributed Systems.

• Software Engineering
• Software Testing
• Program Analysis
• +4 more

Alessandro Orso is the Dean and Professor of the University of Georgia College of Engineering. Previously, he served as a professor at Georgia Institute of Technology, where he directed the Scientific Software Engineering Center and held leadership roles, including interim dean of the College of Computing (2023–2024). He earned his Ph.D. and master’s in computer science and electrical engineering from Politecnico di Milano, Italy. Orso’s research focuses on software testing, program analysis, and debugging, with applications in improving software reliability and security. His work includes developing automated testing tools (e.g., AutoRestTest), methodologies for scientific software engineering, and techniques for program debloating. His research has secured funding from agencies and industry partners like Google, IBM, and Microsoft, including a $11M grant from Schmidt Futures. His scientific awards include being a Distinguished Member of the ACM and IEEE Fellow. He advocates for innovative education, such as Georgia Tech’s Online Master of Science in Computer Science and his virtual software engineering course. His publications span automated testing, API analysis, and security, reflecting his commitment to advancing both academic research and practical software solutions. Orso’s leadership includes directing the Scientific Software Engineering Center, which pioneers methodologies for scientific software improvement and trains a new generation of engineers. His career blends academic excellence with impactful industry collaborations, driving advancements in software engineering practices.

• Chromatography
• Separation Science
• Analytical Chemistry
• +3 more

Ken Broeckhoven holds a prominent academic position at Vrije Universiteit Brussel (VUB) as a Professor in the Department of Bio-engineering Sciences, within the Chemical Engineering and Industrial Chemistry school. His roles include leadership in research groups and administrative positions such as Dean and Department Chair. Specializing in chromatography and separation science, his work focuses on advancing techniques like liquid chromatography, supercritical fluid chromatography, and hydrophobic interaction chromatography for applications in protein analysis and biotherapeutics. He has authored over 236 research outputs and secured 13 major projects, including ones on column performance and sustainable chemical technologies. His contributions have earned 20 scientific awards, including top poster prizes and innovation recognitions. Broeckhoven actively participates in PhD committees and international conferences, emphasizing collaborative research and cutting-edge instrumentation development. Education: Not explicitly detailed in the provided text. Research Interests: Chromatography systems optimization, diffusion dynamics, column design, and analytical method development for complex biological samples. His work bridges theoretical modeling with practical applications in separation science. Recent Research Trends: Recent articles emphasize overcoming extracolumn band broadening, advancing HIC methods, and leveraging computational fluid dynamics (CFD) to enhance column efficiency. His studies often integrate experimental and numerical approaches to dissect separation mechanics. Awards: Includes 1st Prize Poster Award (HPLC 2008), Analytical Science Advances Best Poster (2020), and recognition for master’s thesis supervision (2012). Grants & Activities: Leads projects like the OZR Basisfinanciering voor Grote Onderzoeksgroepen (2024–2029) and participates in over 105 professional activities, including talks at global conferences. Supervises 12 students, including Timothy Januarius (2024) and T. Swier (2015).

Ramon RaveloView profile

Associate Professor

• Computational Materials Science
• High-Pressure Physics
• Shock Wave Physics
• +7 more

Ramon Ravelo is an Associate Professor in the Department of Physics at the University of Texas at El Paso (UTEP), with a strong focus on computational science and material behavior under extreme conditions. He is based in the College of Science and conducts research at the intersection of physics, materials science, and high-performance computing. His research interests lie in understanding material response to high pressures, temperatures, and strain rates, particularly those induced by shock waves. Employing advanced computational techniques, his work addresses: Shock-induced plasticity and material strength Stress-induced phase transformations and melting Development and validation of classical interatomic force-field models Large-scale atomistic simulations of extreme environments Applications of density functional theory and non-equilibrium statistical mechanics The body of work suggests a strong emphasis on predictive simulation methods in materials physics, leveraging advances in computational power to model complex physical phenomena. Although specific publications are not listed, the research keywords indicate active contributions in computational condensed matter physics and planetary science contexts. Scientific Awards: No awards listed in the provided text. Dr. Ravelo advises students in computational and materials physics, though specific advisees are not named. There is no mention of external grants or funding sources in the available content. He is involved in research networks related to planetary science and astrobiology, suggesting interdisciplinary collaborations. His work supports both fundamental science and potential applications in defense, geophysics, and space science.

Shrideep PallickaraView profile

Professor

• Machine Learning
• Deep Learning
• Large-Scale Systems
• +12 more

Shrideep Pallickara is a Professor in the Department of Computer Science at Colorado State University, where he also directs the Center for eXascale Spatial Data Analytics and Computing (XSD) . His research is funded by the National Science Foundation, Department of Homeland Security, Environmental Protection Agency, Department of Agriculture, and the UK's e-Science program. Research Interests: His research lies at the intersection of machine learning and large-scale systems, focusing on: Spatiotemporal data management and analytics Extreme-scale storage systems Stream processing for IoT and cyber-physical systems Deep learning over petabyte-scale, high-dimensional datasets Model construction for forecasting natural and urban phenomena His work addresses challenges in computational tractability, resource utilization, and convergence in distributed environments. Systems developed in his lab are deployed in domains such as urban sustainability, agriculture, epidemiology, environmental monitoring, healthcare, and defense. Research Trends in Publications: His recent publications demonstrate a strong focus on scalable analytics for geospatial and environmental data. Key themes include deep learning for soil moisture and salinity prediction, efficient visualization of massive satellite datasets, spatiotemporal search and summarization, and model performance profiling across spatial domains. The work integrates scientific domain knowledge with machine learning and systems innovation. Scientific Awards: NSF CAREER Award Board of Governors Award for Excellence in Undergraduate Teaching OLIE Award N. Preston Davis Award Monfort Professorship Best Paper Award at IEEE/ACM CCGrid 2019 Best Paper Award at BDCAT 2023 Best Paper Award at IEEE Cluster 2012 Best Student Paper Award at IEEE CloudCom 2010 Shortlisted for ACM DEBS-2015 Grand Challenge Award One of the Six Best Papers at ACM/IEEE GRID 2005 Advising and Grants: He advises numerous graduate students, many of whom are co-authors on his publications. His research is supported by major grants from NSF, DHS, EPA, USDA, and UK e-Science, enabling the development of open-source systems such as Granules, NaradaBrokering, Galileo, Funnel, and Spindle. Labs and Teams: He leads the XSD Center, which develops and maintains large-scale open-source software systems involving over 2500 classes and a million lines of code. These systems are used in academic, commercial, and defense applications.

• Sustainable and Resilient Supply Chain Management
• Supply Chain Network Design
• Multi-objective Optimization
• +11 more

Dr. Ahmed Mohammed is an Associate Professor in Supply Chain Management and Programme Director for the MSc Supply Chain Management at the University of Birmingham, Dubai Campus. He is affiliated with the Management Department and brings over 13 years of combined academic and industrial experience from the UK and the Middle East. Educational Background: Ph.D. in Engineering, University of Portsmouth, UK (2017) MSc in Communication Network Planning and Management, University of Portsmouth, UK (2010) Lean Six Sigma – Black Belt (2022) Fellow, Higher Education Academy, UK (2022) Dr. Mohammed's research focuses on the modeling and optimization of supply chains with an emphasis on sustainability and resilience. His work spans areas such as multi-tier supplier selection, circular economy, food waste management, Industry 4.0 integration, and last-mile logistics. He employs advanced methodologies including multi-criteria decision-making (MCDM) techniques like AHP, TOPSIS, and VIKOR, and develops hybrid optimization models for complex supply chain challenges. His recent publications, spanning from 2021 to 2023, reflect a strong trend in resilient and sustainable supply chain network design, pandemic-related disruptions, digital sourcing, and risk modeling under uncertainty. These works appear in high-impact journals such as International Journal of Production Research , Transportation Research Part E , Computers & Industrial Engineering , and Expert Systems with Applications . Scientific Awards and Recognitions: Fellow, Higher Education Academy, UK (2022) Lean Six Sigma Black Belt (2022) Fellow Member, Chartered Institute of Logistics and Transport (CILT) Member, MCDM Society Dr. Mohammed has secured multiple research grants from MOHERI, Oman, including projects on last-mile logistics sustainability, supply chain resilience, and online food waste management. He actively supervises research students and serves as a reviewer for leading journals including International Journal of Production Economics (IJPE) , Transportation Research Part D (TRD) , Omega , and International Journal of Production Research (IJPR) . He is also an external academic program reviewer for logistics-related programs and contributes to the academic community through peer review and collaborative research.

Daniel BüchelView profile

Researcher

• Neuroscience
• Motor Control
• Sports Psychology
• +7 more

Dr. Daniel Büchel is a researcher at the Institute for Movement Therapy and Movement-Oriented Prevention and Rehabilitation, Department of Movement-Oriented Prevention and Rehabilitation Sciences, at the German Sport University Cologne. His work bridges neuroscience, motor control, and sports performance, with a focus on brain-behavior interactions during athletic tasks and rehabilitation. His research interests lie at the intersection of neuroscience , motor-cognitive integration , biomechanics , and sports psychology . He investigates cortical activation, functional connectivity, and postural control using EEG and wearable sensors during complex motor tasks in elite athletes and during rehabilitation. His work emphasizes how cognitive demands influence motor performance and how injuries like ACL tears affect skilled movement. The recent trend in his publications reveals a strong focus on neural correlates of motor performance , particularly in team sports such as field hockey and handball. Using advanced neuroimaging and sensor technologies, his studies explore agility, training load, and postural instability, contributing to both performance optimization and injury rehabilitation. His work frequently applies methods from computational neuroscience and biomechanics to real-world sports contexts. Dr. Büchel actively collaborates with leading researchers in motor neuroscience and sports science, including Jochen Baumeister and Thorben Hülsdünker. While no formal grants or advising roles are mentioned in the provided text, his extensive publication record in high-impact journals indicates active involvement in funded research projects and scientific collaboration networks. He is part of a dynamic research environment at the German Sport University Cologne, contributing to the university's strong profile in movement and neuroscience. He is affiliated with the following research units: Institute for Movement Therapy and Movement-Oriented Prevention and Rehabilitation Department of Movement-Oriented Prevention and Rehabilitation Sciences

Trilce EstradaView profile

Associate Professor

• Machine Learning
• Big Data
• High Performance Computing
• +9 more

Trilce Estrada is an Associate Professor in the Department of Computer Science at the University of New Mexico (UNM), School of Engineering. She leads the Data Science Laboratory and is actively involved in research, teaching, and service. Her work focuses on solving data- and compute-intensive problems in science, health, and education, particularly in resource-constrained environments. Her research interests include: Machine Learning and scalable learning techniques Big Data analytics and distributed systems High Performance Computing and cyberinfrastructure In-situ data analytics for scientific workflows Pervasive healthcare using mobile and distributed systems Although no publication list is provided, her current research projects indicate a strong focus on distributed learning, in-situ analysis for molecular dynamics, cyberinfrastructure optimization, and robust science in high-throughput computing. These projects reflect interdisciplinary collaboration and a commitment to scalable, reproducible, and impactful computing solutions. Dr. Estrada has served in numerous leadership roles in major computing conferences, including as Program Co-Chair for IEEE Cluster 2022, Chair of the Mentor-Protégé Program at SC19, and Chair of the IPDPS PhD Forum and Student Program for multiple years. She has also been active in NSF panels and curriculum development, particularly in integrating Big Data into educational frameworks. She is committed to mentoring and improving diversity in computing, serving as faculty advisor for Women in Computing and CSGSA, and participating in outreach to attract underrepresented groups to computer science. She currently oversees a research group but is not accepting new students due to capacity. Independent study opportunities are available under strict eligibility criteria. Her lab, the Data Science Laboratory, supports interdisciplinary research in data-intensive domains. She is also involved in educational initiatives and curriculum development at UNM and nationally.