Find Academic Supervisors

Shahram RahimiView profile

Professor

• Computational Intelligence
• Machine Learning
• Artificial Intelligence
• +10 more

Shahram Rahimi is a Professor and Department Head in the Department of Computer Science at the University of Alabama, College of Engineering. He concurrently holds an Adjunct Professor position at Mississippi State University. His research spans computational intelligence, machine learning, healthcare AI, cybersecurity, and quantum computing. He leads the PATENT Lab, focusing on predictive analytics, decision support systems, and AI-driven healthcare solutions. His educational background includes a Ph.D. in Computer Science. Key research areas include multi-agent systems, generative models, and predictive maintenance. He has served as an editor for journals like Scalable Computing: Practice and Experience and Informatica . Rahimi’s recent work emphasizes secure MLOps, quantum algorithms, and patient-centric medical systems. His publications address challenges in explainable AI, anomaly detection, and healthcare informatics. He actively contributes to conferences and journals in AI, cybersecurity, and computational intelligence. Editorial Roles: Scalable Computing, Engineering Letters, Informatica Labs: Predictive Analytics & Technology Integration (PATENT) Lab Key Focus Areas: Healthcare AI, Quantum Computing, Cybersecurity, Explainable Machine Learning

Karim SabraView profile

Professor

• Acoustics
• Structural Health Monitoring
• Biomechanics
• +7 more

Dr. Karim Sabra is a Professor at the George W. Woodruff School of Mechanical Engineering , Georgia Institute of Technology, specializing in Acoustics and Dynamics . He holds a Ph.D. from the University of Michigan (2003) and joined Georgia Tech in 2007 as an Assistant Professor. His research integrates theoretical and experimental approaches to study wave propagation in diverse fields including structural health monitoring, biomechanics, and ocean acoustics. Key areas of focus include passive imaging techniques using ambient noise and diffuse wave fields, with applications in non-invasive monitoring of mechanical systems and seismoacoustic environments. Education: Ph.D., University of Michigan, 2003 M.S., University of Michigan, 2000 M.Sc., École Nationale Supérieure de Techniques Avancées (France), 2000 Research Interests: Dr. Sabra’s work spans acoustics, structural health monitoring, biomechanical systems evaluation, underwater acoustics, and geophysics . Recent projects include developing passive elastography techniques for soft tissues using physiological vibrations and exploring ambient noise-based tomography for ocean environments. His interdisciplinary approach bridges multi-scale engineering challenges with multi-wave tools (acoustical, electrical, optical). Publications: His work focuses on advanced acoustic technologies, including underwater communication systems, passive acoustic identification tags, and ray-based tomography methods. Themes include seamount effects on sound propagation, machine learning for acoustic modeling, and environmental sensing using shipping noise. Awards: R. Bruce Lindsay Award (2011) Fellow of the Acoustical Society of America (2007) Institute of Acoustics A.B. Wood Medal (2009) Advising & Grants: Dr. Sabra mentors graduate students in acoustics and wave phenomena, emphasizing interdisciplinary collaboration. His research is supported by grants focused on underwater acoustics, environmental sensing, and biomedical applications. Labs/Teams: His research group develops novel sensors and algorithms for oceanographic and biomedical applications, collaborating with industry and academic partners.

Daniel A. McAdamsView profile

Professor

• Design Theory
• Bio-inspired Design
• Product Architecture
• +3 more

Daniel A. McAdams is the Robert H. Fletcher Professor in Mechanical Engineering at Texas A&M University and serves as the NSF Program Director of Convergent Activities. His research develops design theory and methodology with focus on functional modeling, bio-inspired design, and technology evolution. Educational Background: PhD in Mechanical Engineering from University of Texas at Austin MS in Mechanical Engineering from California Institute of Technology BS in Mechanical Engineering from University of Texas at Austin His research investigates innovation in concept synthesis through computational methods, bio-inspired design approaches, and technology evolution applied to product development. Current projects include function-sharing principles in biological systems, digital twin architectures, and patent mining for technology forecasting. Recent publications explore applications of speculative fiction in design ideation, graph-theoretic approaches for digital twins, and automated assessment in engineering education, demonstrating cross-disciplinary innovation across design science. Awards and Honors: ASME Design Theory and Methodology Award Distinguished Achievement Award for Student Relations Multiple Faculty Fellow awards Design Studies Best Paper Award Outstanding Faculty Mentor Award He leads the Product Synthesis Engineering Lab, advancing design methodologies for complex engineered systems through computational approaches and biological analogies.

Jacob FishView profile

Professor

• Multiscale Computational Science
• Computational Mechanics
• Homogenization Methods
• +5 more

Jacob Fish is the Robert A.W. and Christine S. Carleton Professor and Chair of the Department of Civil Engineering and Engineering Mechanics at Columbia University. He directs the Multiscale Science and Engineering Center and leads Columbia's Computational Science and Engineering initiative (iCSE), coordinating 65+ faculty. With 35 years of pioneering research, he specializes in multiscale computational methods bridging aerospace, automotive, and healthcare industries. His research integrates multiscale computational science with applications in: Homogenization and reduced-order methods for complex materials Stochastic modeling of heterogeneous systems Coupled thermo-chemo-electro-mechanical processes Data-physics driven frameworks for industrial processes Recent work emphasizes AI-enhanced modeling for composites, porous media, and environmental systems. His 15 most recent publications (2023-2025) demonstrate strong trends toward: Data-physics integration in manufacturing (e.g., resin transfer molding) Multiscale environmental applications (canopy flows, CO2 mineralization) Advanced numerical methods (discontinuous Galerkin, solver-free homogenization) Digital twin development for composite lifecycle management Scientific Awards & Honors: 2018 JSCES Grand Prize 2010 IACM Computational Mechanics Award 2005 USACM Computational Structural Mechanics Award 2003 Rensselaer Research Award Fellowships: AAM, USACM, IACM Two Best Paper awards He founded the commercial Multiscale Designer software suite (250+ global clients) and secured major grants including an NSF-DFG collaboration on thermoplastic interfaces. His textbooks are used in 200+ universities worldwide. Leads the Multiscale Science and Engineering Center focusing on industrial-scale computational challenges and mentors researchers through Columbia's iCSE initiative. Former President of USACM and current IACM Vice-President for the Americas.

• Marketing Management
• International Marketing
• Competitive Analysis
• +2 more

CHIEN-WEI CHEN is Professor and Chairman of the Department of International Business at National Chengchi University's College of Commerce. With over two decades of service at NCCU, he progressed from Assistant Professor (2000) to Associate Professor (2012) and full Professor (2019), concurrently serving as Department Chair (2023-2026). His research specializes in marketing strategy, international business, and competitive analysis, with particular focus on technology product launches and consumer behavior dynamics. Education: Ph.D. in Business Administration, University of Warwick (1996-2000) M.S. in Business Administration, National Taiwan University (1988-1990) B.S. in Electrical Engineering, National Taiwan University (1983-1987) Research Focus: Dr. Chen's work bridges marketing strategy and behavioral economics, examining how technological disruption influences consumer decisions (e.g., smartphone upgrades), how firms signal value through product preannouncements, and how co-branding strategies shape market perceptions. His research consistently applies empirical methods to high-technology contexts in international markets. Publication Trends: His 15 most recent articles (2002-2023) reveal three dominant themes: 1) Strategic signaling in product launches and financial markets, 2) Psychological drivers of technology adoption/upgrading, and 3) Cross-border marketing efficiency. Methodologically, they combine behavioral experiments, event studies of stock reactions, and empirical analyses of firm strategies. Awards & Honors: 20-Year Outstanding Teacher Award (NCCU, 2020) 10-Year Outstanding Teacher Award (NCCU, 2010) International Research Excellence Award (NCCU, 2006) Research Leadership: As Principal Investigator for 10+ National Science and Technology Council projects (2013-2021), he secured funding for studies on consumer technology adoption patterns, signaling theory applications in marketing, and service innovation frameworks. Projects include 'Fear of Product Obsolescence' (2013-2015) and 'Upgrade Decisions for High-Tech Durables' (2015-2017).

James SweeneyView profile

Professor

• Machine Learning
• Time Series Analysis
• Geospatial Statistics
• +3 more

James Sweeney serves as Professor in the Department of Mathematics and Statistics at the University of Limerick, concurrently holding memberships in the Centre for Battery and Energy Materials Research and the Mathematics Applications Consortium for Science and Industry (MACSI). Actively accepting PhD students, his research bridges theoretical mathematics with practical industry applications across diverse sectors including energy materials, real estate, and public health. His research portfolio demonstrates exceptional interdisciplinary range, with core expertise in machine learning algorithms (particularly time series classification and neural networks), geospatial statistics for property valuation, and epidemiological modeling for disease surveillance. Key methodological contributions include evolutionary algorithms for optimization, dissimilarity-preserving representation learning, and flexible geospatial smoothing techniques that address complex real-world data challenges. Analysis of his 23 publications (2015-2024) reveals accelerating scholarly output since 2020, with 2024 being particularly prolific. His work consistently targets high-impact applications: developing diagnostic thresholds for bovine tuberculosis, modeling COVID-19 transmission dynamics in Dublin, and creating neural network solutions for geodemographic clustering. This trajectory reflects deepening engagement with computational approaches to solve pressing societal problems through mathematical innovation. As a PhD supervisor, he cultivates next-generation researchers in advanced computational methods. His collaborative framework extends through MACSI's industry partnerships and the Centre for Battery and Energy Materials Research, where mathematical modeling directly informs energy technology development. These dual affiliations position him at the critical intersection of academic research and industrial application, particularly in Ireland's growing tech and energy sectors. His laboratory activities center around computational mathematics teams within MACSI, focusing on applying statistical learning to battery materials research and real-world data challenges. Current projects involve time series analysis for sensor data, geospatial modeling for economic forecasting, and optimization algorithms for veterinary epidemiology – demonstrating remarkable methodological versatility across traditionally disparate domains.

Coen TeulingsView profile

Professor

• Labor Economics
• Macroeconomics
• Urban Economics
• +6 more

Prof. Coen Teulings is a Professor of Economics at Utrecht University's Department of Economics, part of the Law, Economics, Governance and Organisation faculty. He holds concurrent roles as a Fellow at CEPR, IZA, and CES-Ifo. Previously, he served as Professor of Economics at the University of Cambridge (2013–2017) and part-time Professor at the University of Amsterdam (2004–2017). His professional history includes seven years as President of the Netherlands Bureau for Economic Policy Analysis (CPB), CEO of SEO Economic Research (2004–2006), and Director of the Tinbergen Institute (1998–2004). Teulings earned a Master's degree in Economics (cum laude, 1985) and a PhD (1990) from the University of Amsterdam. He has advised governmental and international bodies, including the Dutch House of Commons Economic Advisors and the UK's Office for Budget Responsibility. His research focuses on labor economics, macroeconomics, urban economics, and policy analysis, with notable work on minimum wages, income inequality, and secular stagnation. He co-edited the 2014 E-book on Secular Stagnation with Richard Baldwin. Teulings' recent publications address topics like agglomeration effects, urban retail dynamics, and wealth distribution. He contributes to policy debates through roles on the AFM Capital Markets Committee and supervisory boards of institutions like the Boekmanstichting. His ancillary activities include a biweekly column in the Financial Daily (FD) and academic governance roles in the Netherlands and Europe.

Marco SerafiniView profile

Assistant Professor

• Database Systems
• Distributed Systems
• Data Science
• +3 more

Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.

• Computer Security
• Cryptography
• Distributed Systems
• +3 more

Nickolai Zeldovich is the Joan and Irwin M. Jacobs Professor of Electrical Engineering and Computer Science at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from Stanford University in 2008 and focuses on building practical secure systems, including encrypted databases, undefined behavior detection tools, formally verified file systems, and cryptocurrency protocols. His work spans both theoretical and applied aspects of computer security and distributed systems. Research interests include: Secure system design Distributed consensus mechanisms Formal verification of software/hardware Cryptographic protocols Privacy-preserving web technologies Publications & Verification Tools Recent work focuses on modular verification of complex systems, including: Shipwright (2025): Byzantine-fault-tolerant distributed system verification PoWER (2025): Crash consistency verification framework K2 Architecture (2023): Trustworthy hardware security modules Grove (2023): Separation-logic-based verification library Tiptoe (2023): Private web search protocols Major Awards Best paper award, ACM SOSP (2011, 2015, 2017) Sloan Research Fellowship (2010) NSF CAREER award (2011) MIT Jamieson Award for Teaching (2024) Active in multiple startup ventures including Algorand (cryptocurrency), MokaFive (virtualization), and PreVeil (end-to-end encryption).

• Materials Science
• Computational Physics
• Electronic Structure Theory
• +3 more

Deyu Lu is a Physicist with continuing appointment at the Center for Functional Nanomaterials (CFN), Brookhaven National Laboratory, a position held since 2018, and concurrently serves as an Adjunct Professor in the Department of Materials Science and Engineering at Stony Brook University since 2012. His work bridges theoretical physics and materials engineering through advanced computational methodologies. Dr. Lu's educational background includes: B.S. in Physics, Tsinghua University, China, 1997 M.S. in Physics, Chinese Academy of Sciences, 2000 Ph.D. in Physics, University of Illinois at Urbana-Champaign, 2000 His research centers on developing first-principles computational methods including density functional theory and many-body perturbation theory to investigate materials properties. Current focus areas encompass catalytic behavior of 2D zeolites, computational modeling of X-ray spectroscopy (XPS/XAS/XES) for catalysis and battery systems, and machine learning applications for structure-property relationship analysis. This work positions him at the intersection of computational physics, materials characterization, and data science. Analysis of his 2017-2024 publications reveals a progressive integration of machine learning with spectroscopic techniques, particularly in X-ray absorption analysis. Key contributions include the Lightshow Python package for computational spectroscopy inputs and methods for decoding structure-spectrum relationships using physically constrained latent spaces, demonstrating significant advancement in data-driven materials characterization. Within Brookhaven's CFN, Dr. Lu actively contributes to the Theory/Computation group and has organized multiple workshops at NSLS-II and CFN User Meetings, including the 2023 Workshop on X-ray Absorption Spectroscopy Curation, the 2022 Symposium on Electronic Structure of Nanomaterials honoring Dr. Mark Hybertsen, and 2021-2022 workshops on machine learning for battery development and X-ray scattering.

Mark BattyView profile

Professor

• Concurrent Systems
• Formal Verification
• Programming Languages
• +3 more

Mark Batty is a Professor in the School of Computing at the University of Kent, specializing in formal methods for concurrent systems. His work bridges hardware-software interfaces, focusing on memory models for C/C++, OpenCL, and architectures including x86, ARM, POWER, and GPUs. As a member of the Programming Languages and Systems Research Group, he develops mathematical specifications and verification tools for real-world concurrency challenges. His research centers on empirical testing of hardware/compiler behavior, formal modeling of system components, and verification of fine-grained concurrent algorithms. Key contributions address relaxed memory semantics, transactional memory, and compositional reasoning for concurrent data structures. His work combines theoretical rigor with practical tool development to ensure correctness in complex concurrent environments. Analysis of his 2015-2025 publications reveals consistent focus on memory consistency models, formal verification of weak memory concurrency, and compiler optimizations. Dominant themes include C/C++11 standards, GPU concurrency semantics, and mechanized verification techniques. His research demonstrates strong industry relevance through collaborations with hardware vendors and contributions to language standards. Mark Batty has received significant recognition: John C. Reynolds Doctoral Dissertation Award (2015) from ACM SIGPLAN CPHC and BCS Distinguished Dissertation Award (2015) Lloyds Register Foundation and Royal Academy of Engineering Research Fellowship (2016) He actively leads major research initiatives and mentors next-generation researchers: Current Funding: EPSRC Standard Grant 'Verifiably Correct transactional memory' (2018), VeTTS Grant 'Specification and verification of C++ data structure libraries' (2018), EPSRC First Grant 'Compositional, dependency-aware C++ concurrency' (2018) PhD Recruitment: Actively seeking candidates for UKRI-funded studentship in Verified Trustworthy Software Systems Batty drives community engagement through Kent Concurrency Workshop (2016) and South of England Programming Language Seminars, fostering national collaboration in programming languages research. His leadership in organizing Royal Society discussions underscores his influence in trustworthy systems verification.

• Numerical Analysis
• Scientific Computing
• Data Science
• +4 more

Per-Gunnar Martinsson serves as Professor of Mathematics and Deputy Director of the Oden Institute at The University of Texas at Austin, holding the W. A. "Tex" Moncrief, Jr. Endowment in Simulation-Based Engineering and Sciences. He concurrently acts as Affiliated Professor of Mathematics at the Royal Institute of Technology (KTH) in Stockholm, where he chairs the MathDataLab scientific advisory board. Educational background: Ph.D. in Computational and Applied Mathematics, UT-Austin (2002) His research spans numerical analysis, scientific computing, and data science with emphasis on randomized linear algebra methods, accelerated direct solvers for elliptic PDEs, structured matrix computations, and applications in computational fluid dynamics and acoustics. Recent work extends to boundary integral equations, heterogeneous materials modeling, and lattice equations. Scientific awards: Germund Dahlquist Prize by SIAM (2017) Dr. Martinsson leads research initiatives through the Oden Institute's Center for Numerical Analysis and Center for Scientific Machine Learning, while providing strategic oversight to KTH's MathDataLab as chair of its scientific advisory board.

• Metal Forming
• Aluminum Alloys
• Product Development
• +3 more

Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.

Zoran BudimlićView profile

Associate Professor

• High Performance Computing
• Parallel Computing
• Compiler Optimizations
• +3 more

Zoran Budimlić is an Instructional Associate Professor in the Department of Computer Science and Engineering at Texas A&M University. He also serves as Director of Undergraduate Studies for Galveston. His roles include teaching and academic leadership in computer science education and high-performance computing. He holds a Ph.D. in Computer Science from Rice University (2001) and a B.S. in Computer Science and Engineering from the University of Belgrade (1994). His research interests focus on high-performance and parallel computing, compiler optimizations, programming languages, runtime systems, and high-level programming models. He emphasizes improving educational practices in computer science through innovative methods and curricula. His recent publications span parallel algorithms, task parallelism integration with MPI, and compiler optimizations for performance. Earlier work includes contributions to Java runtime optimization and static analysis techniques. Zoran Budimlić has no explicitly listed scientific awards or grants in the provided text, but his contributions to parallel computing and compiler design are notable. He advises students in these areas but no names are provided in the text.

Andrzej MurawskiView profile

Professor

• Semantics of Programming Languages
• Automata Theory
• Software Verification
• +4 more

Andrzej Murawski is a Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Worcester College . His research focuses on the semantics of programming languages and software verification, with applications in automata theory, probabilistic computation, and concurrency. Current affiliations: University of Oxford, SIGLOG Vice-Chair, FoSSaCS Steering Committee Research interests: Game semantics, higher-order recursion, probabilistic systems, differential privacy Recent publications address probabilistic verification, equivalence checking, and game semantics for concurrent systems. He has chaired program committees for conferences such as ESOP and PERR, and his work has earned recognition like the POPL 2025 Distinguished Paper Award . Current students : Benedict Bunting, Haoxuan Yin Past students : Conrad Cotton-Barratt, David Hopkins, Guanyan Li, Dominik Wagner, Fabian Zaiser