Find Academic Supervisors

Paul FranzonView profile

Professor

• Quantum Information Science
• Machine Learning & AI Hardware
• 3D Integration
• +4 more

Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)

Sabin TabircaView profile

Senior Lecturer

• Artificial Intelligence
• Data Analytics
• Algorithmics
• +6 more

Dr. Sabin Tabirca is a Senior Lecturer at the School of Computer Science and Information Technology, University College Cork (UCC). He holds a BSc from Bucharest University and a PhD from Brunel University. His research focuses on Artificial Intelligence, Data Analytics, Algorithmics, Interactive Media, and HCI, with applications in computational cancer modeling, mobile health (mHealth), and parallel computing. He coordinates MPT Activities and is affiliated with the CRR Group and BCRI Centre. Notable awards include UCC's President Award for Innovation in Teaching (2007), IT@Cork Leader Award (2008), and UCC Staff Recognition Award (2013). His teaching includes modules like Parallel and Grid Computing, Mobile Application Design, and Graphics for Interactive Media. Dr. Tabirca has supervised over 70 MSc students and numerous PhD candidates, many of whom now hold academic and industry roles globally. His research includes developing mHealth apps for cystic fibrosis patients, 3D cancer visualization tools, and frameworks for mobile parallel computing. Publications span mHealth design pipelines, cancer prediction models, and mobile gaming for health education. He actively engages in interdisciplinary projects, blending computer science with medical and biological applications.

• Control Theory
• Applied Mathematics
• Computational Mechanics
• +7 more

Goong Chen is a Professor at Texas A&M University (TAMU) within the College of Arts & Sciences. His research focuses on Control Theory, Applied Mathematics, and interdisciplinary applications in computational mechanics, fluid dynamics, and quantum systems. He holds a Ph.D. from the University of Wisconsin (1977) and a B.S. from National Tsing-Hua University (1972). His research interests span computational biomechanics, forensic modeling, nanofluidics, and mathematical physics. Recent work includes modal analysis of animal motion, crash mechanics of aircraft, and forensic reconstruction of disasters. He has contributed to theoretical advancements in PDEs, nonlinear dynamics, and quantum computing. Chen’s articles address cutting-edge topics like thermoelastic plates, Volterra equations, and DFIM systems. He has pioneered numerical methods for complex systems using OpenFOAM and finite element techniques. His computational models have been applied to real-world scenarios such as submarine implosions and chemical warfare forensics. No academic awards are explicitly listed, but his extensive publication record reflects significant contributions to applied mathematics and engineering. He leads research teams focused on interdisciplinary challenges in computational science and engineering.

Matthias S. MaierView profile

Associate Professor

• Multiscale Methods
• Computational Fluid Dynamics
• Finite Element Methods
• +3 more

Matthias S. Maier is an Associate Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on multiscale methods, computational fluid dynamics, and finite element software development, particularly with the deal.II library. He organizes an annual undergraduate summer school on PDE modeling and simulation. Research Interests: Multiscale effects in Maxwell’s equations, computational fluid dynamics, finite element methods, and numerical analysis. His work includes studies on surface plasmon-polaritons, homogenization theory, and high-performance computing for hyperbolic systems. Developed ryujin, a high-performance finite-element solver for compressible flows. Contributed to deal.II, a widely used open-source finite element library. Recipient of NSF awards (DMS 1912847, DMS 2045636) and AFOSR funding. Teaching includes courses on numerical methods (Math 417, 610), mathematical modeling (Math 442), and finite element methods (Math 676). He advises graduate students in applied mathematics and computational science. Labs/Teams: Core developer of deal.II and contributor to the ryujin framework. Collaborates with interdisciplinary teams in physics and engineering.

Roberto BertolussoView profile

Senior Lecturer

• Systems Biology
• Cancer Research
• Computational Biology
• +3 more

Roberto Bertolusso is a Senior Pfeiffer Lecturer in the Department of Statistics at Rice University. He has held this position since 2013, focusing on teaching probability, statistics, and data science courses. His research integrates computational and systems biology approaches to study cancer mechanisms. Bertolusso earned his Ph.D. and M.S. in Statistics from Rice University and holds a graduate degree in Industrial Engineering from Universidad de Buenos Aires. His academic background includes significant contributions to cancer biostatistics and systems biology, highlighted by his participation in the AACR Methods in Cancer Biostatistics Workshop (2015) and a CPRIT-funded training program (2013–14). His publications span epidemiological modeling, orthopedic outcomes, and computational cancer research, reflecting interdisciplinary expertise. Education: Ph.D. & M.A. in Statistics, Rice University Graduate Level in Industrial Engineering, Universidad de Buenos Aires Teaching Focus: Probability theory, regression analysis, SAS programming, and data science methodologies. Research interests emphasize systems biology applications in cancer, stochastic modeling of biological systems, and computational methods for medical data analysis. His work bridges statistical theory with clinical and biomedical challenges, as seen in studies on viral infection dynamics and tumor growth modeling. Recent publications reflect both his cancer research and collaborative work in orthopedic surgery outcomes, showcasing a commitment to interdisciplinary problem-solving. Awards include recognition for methodological contributions to cancer clinical trial design and biostatistical training initiatives.

Robert CartwrightView profile

Professor

• Programming Languages
• Parallel Programming
• Software Engineering
• +3 more

Robert 'Corky' Cartwright is a Professor of Computer Science at Rice University, specializing in programming languages and software engineering. His research focuses on parallel programming extensions for Java/Scala/Swift, smart programming environments for error-free code, pedagogic tools like DrJava, and intent-driven programming in the FAST language. He has contributed to cyber-physical systems modeling through frameworks like Acumen and DrHJ. Education: PhD (Computer Science, Stanford University, 1976), BA (Applied Mathematics, Harvard College, 1971). Awards include ACM Fellow (1998). Teaching emphasizes principles of programming languages and program design. His work bridges theoretical foundations (domain theory, formal semantics) with practical tools for education and industry.

Xin GaoView profile

Professor

• Artificial Intelligence
• Machine Learning
• Statistical Genetics
• +5 more

Xin Gao is a Professor in the Department of Mathematics and Statistics at York University, Toronto. His research focuses on Artificial Intelligence , Machine Learning , and Statistical Genetics , with applications in biomedical data analysis and planetary science. He leads the Artificial Intelligence and Machine Learning Lab , which has developed impactful tools like an online Type 2 Diabetes risk predictor using logistic regression and Mars rock composition analyzers for NASA. His methodological work includes penalized composite likelihood and multi-task feature learning , implemented in R packages FusionLearn and lassoGEE . Scientific Awards NSERC Discovery Acceleration Award ($120,000, 2018-2020) Key Software Contributions FusionLearn : Correlated multi-task feature learning lassoGEE : High-dimensional clustered/longitudinal data analysis Notable Collaborations Vector Institute (AI scholarship mentoring) Fields Institute (committee roles) International genomic data integration projects

• Serious Games
• Healthcare Technology
• Gamification
• +4 more

Simon McCallum is an Associate Professor at NTNU’s Faculty of Information Technology and Electrical Engineering. Born in New Zealand, he transitioned from commercial game development in Norway to academia in 2009. His research focuses on Serious Games, particularly in healthcare contexts, gamification, and virtual reality applications. He holds a PhD from the University of Otago (2007), where he studied Computer Science, Mathematics, Psychology, and Philosophy. McCallum has contributed to over 30 publications, including works on cognitive health gaming, software engineering education, and AR applications. He teaches courses such as IMT4307 (Serious Games Research) and IMT3603 (Game Programming). His key projects include the Smartkuber AR game for cognitive screening and the Traction Trebuchet historical engineering study. McCallum has collaborated with institutions like the University of Otago and has engaged in outreach through conferences and media appearances, promoting game technology in healthcare and education.

• Applied Mathematics
• High Performance Computing
• Machine Learning
• +2 more

Prof. Panayot Vassilevski is a faculty member at the Faculty of Informatics of the Università della Svizzera italiana (USI) in Switzerland. He is engaged in collaborative research involving high-performance computing (HPC) and computational methods for partial differential equations (PDEs), with a focus on scalable discretization and solver development. His work intersects applied mathematics and computer science, particularly in the context of discrete networks and GPU-accelerated algorithms. Fields of Interest: Applied Mathematics, High Performance Computing, Machine Learning, Computational Science, Partial Differential Equations Recent work includes the GPU Accelerated Shifted Penalty Multigrid for Contact Elasticity (2024), which highlights his expertise in computational methods and HPC. This project builds on prior successful collaborations, such as the LLNL sabbatical of Prof. Rolf Krause and the internship of his student Patrick Zulian, which resulted in a publication. The collaboration also involves Dr. Zulian and the Swiss Supercomputing Center (CSCS) in Lugano. During the visit, Prof. Vassilevski and his PhD student Austen Nelson will work with Prof. Krause's team on reciprocal knowledge exchange. Advising: Austen Nelson, a PhD student under Prof. Vassilevski, is participating in the project. The visit project (Grant #229884, funded by the Swiss National Science Foundation with 10,500 CHF) emphasizes in-depth methodological collaboration and personnel exchange between USI and Portland University, as well as LLNL and CSCS. Labs and Teams: The project involves the Euler Institute at USI and the Swiss Supercomputing Center (CSCS) in Lugano. These institutions are pivotal for advancing the HPC-software project, which combines theoretical and algorithmic tasks in scalable discretization methods and PDE solvers.

• Operator Algebras
• K-theory
• Symbolic Dynamics
• +4 more

Søren Eilers is a Professor at the Department of Mathematical Sciences, University of Copenhagen, affiliated with both the QA (Analysis & Quantum) and AG (Algebra & Geometry) sections. He holds editorial roles at Journal of Mathematical Analysis and Applications and contributed to Springer's Operator Algebra and Dynamics proceedings. Education : M.S. (1993), PhD (1995) in Mathematics from the University of Copenhagen. Positions : Assistant Professor (1996–1999), Associate Professor (1999–2008), Professor (2008–present). Research Interests : Focus on operator algebras, particularly C*-algebras associated with discrete structures. Active in symbolic dynamics, K-theory, and experimental mathematics. His work bridges algebraic structures and dynamical systems, with notable contributions to the classification of graph C*-algebras and the LEGO counting problem. Recent efforts emphasize computer-aided methods in mathematical research. Key Contributions : Pioneered geometric classification of graph C*-algebras, explored flow equivalence of shift spaces, and authored Introduction to Experimental Mathematics (2017). His research has been supported by grants such as the Villum Fonden's Experimental Mathematics initiative (2012–2016). Teaching & Mentorship : Supervised 28 PhD theses and numerous projects in analysis, discrete mathematics, and experimental mathematics. Taught courses in functional analysis, dynamical systems, and experimental methods. Scientific Leadership : Organized major programs like the 2016 Mittag-Leffler Institute's classification of operator algebras. Served as President of the Danish Mathematical Society (2006–2008) and led NordForsk's Operator Algebras and Dynamics network (2009–2012). International Collaboration : Extensive visiting appointments at institutions like MSRI (Berkeley), Fields Institute (Toronto), and Institut Mittag-Leffler (Stockholm). Maintains global research networks in operator algebras and dynamics.

Scot M. MillerView profile

Associate Professor

• Atmospheric Science
• Greenhouse Gases
• Air Pollution
• +11 more

Scot M. Miller is an Associate Professor in the Department of Environmental Health and Engineering at the Whiting School of Engineering, Johns Hopkins University. He leads the Greenhouse Gas Research Group, focusing on quantifying emissions of greenhouse gases and air pollutants using satellite, aircraft, and tower observations. His research spans global scales, from Arctic ecosystems to urban and industrial sources in the U.S. and China. His research interests include atmospheric science, greenhouse gas emissions, inverse modeling, big data analytics, and climate policy. He integrates tools from statistics, high-performance computing, and satellite remote sensing to improve emission estimates and inform environmental regulations. Recent publications highlight trends in methane, ethane, and sulfuryl fluoride emissions, carbon cycle dynamics, and innovative methods for analyzing massive satellite datasets. His work increasingly leverages OCO-2 and OCO-3 satellite data to study carbon dioxide and methane fluxes across diverse ecosystems. Scientific awards include the NSF CAREER Award, JHU Catalyst Award, and the Carnegie Distinguished Postdoctoral Fellowship. He was also recognized with Harvard’s Certificate of Excellence in Teaching. Miller advises multiple PhD students, including Mingyang Zhang, Dylan Gaeta, and Leyang Feng, and has secured grants from NASA, NSF, NOAA, and JHU. He is a member of the NASA OCO Science Team and collaborates with institutions such as Northern Arizona University, Carnegie Institution, and NOAA. His lab is involved in urban environmental monitoring in Baltimore and interdisciplinary climate policy research.

Giulia GuidiView profile

Assistant Professor

• High-Performance Computing
• Parallel Computing
• Sparse Linear Algebra
• +4 more

Giulia Guidi is an Assistant Professor of Computer Science at Cornell University, affiliated with the Cornell Ann S. Bowers College of Computing and Information Science. She leads the Cornell High-Performance Computing (HPC) Group and is an Affiliate Faculty at Lawrence Berkeley National Laboratory’s Performance and Algorithms Research Group. Her research focuses on high-performance computing for computational sciences, sparse linear algebra, and scalable software infrastructure for parallel systems. She holds a PhD in Computer Science from UC Berkeley (2022) and has been recognized with awards including the 2024 SIAG/Supercomputing Early Career Prize and the 2023 ISSNAF Young Investigator Award. Her work addresses challenges in genomics, population genetics, and scalable computational methods through collaborations like the NSF-funded 'ACED' project with April Wei’s Lab. Guidi mentors a diverse group of PhD, MEng, and undergraduate students, emphasizing parallel programming and HPC applications. Her lab’s research spans GPU-accelerated algorithms, sparse matrix computations, and bioinformatics tools like the Popcorn and BELLA aligners. She is also a Graduate Field Faculty in Computational Biology and Applied Mathematics at Cornell.

Sanford R. StudentView profile

Assistant Professor

• Educational Measurement
• Psychometrics
• Quantitative Methods
• +3 more

Dr. Sanford R. Student is an Assistant Professor in the School of Education at the University of Delaware and a Resident Faculty member of the Data Science Institute. His research focuses on connecting psychometric methodologies with practical educational implications, particularly in academic growth measurement, large-scale science assessments, and instrument design. He holds a Ph.D. in Research and Evaluation Methodology from the University of Colorado Boulder (2023) and dual B.A.s in Philosophy and Computer Science from Brown University (2013). Dr. Student’s professional experience includes roles as a Research Associate at Lyons Assessment Consulting (2021–2023), Doctoral Researcher at the Center for Assessment Design, Research and Evaluation (2018–2023), and Lead Researcher for the American Bar Association’s Bar Exam study (2019–2020). Prior to academia, he worked as a Software Engineer at edX (2016–2018). His awards include selection for the 2020 AIR/NCES NAEP Data Training Workshop. Current research trends in his publications emphasize Bayesian methods, vertical scaling, and crosscutting concepts in science education. He actively contributes to educational policy discussions through work with state agencies and assessment developers. Dr. Student advises graduate students and collaborates on grants related to growth measurement and educational data systems. He is affiliated with the National Council on Measurement in Education and maintains a lab focusing on applied psychometric challenges in K-12 systems.

Gaja JaroszView profile

Professor

• Phonological Theory
• Computational Linguistics
• Language Acquisition
• +5 more

Gaja Jarosz is a Professor & Chair of Graduate Admissions in the Department of Linguistics at the University of Massachusetts Amherst, located in the Integrative Learning Center (ILC N410). She holds a PhD in Cognitive Science from Johns Hopkins University and a BA in Mathematics and Social Thought from Washington University in St. Louis. Her research focuses on phonological theory, computational linguistics, and language acquisition, particularly how sound systems and their acquisition can be modeled computationally. She develops formal systems informed by phonological theory and grounded in statistical learning, exploring hidden phonological structure, lexical-grammatical interactions, and learning biases. Since 2015, her work integrates experimental methods with computational modeling. Her publications span computational phonological learning, parametric stress systems, and experimental studies of phonological acquisition. Notable contributions include analyzing Polish phonotactics, stress learning mechanisms, and the role of distributional cues in word segmentation. She co-founded the Society for Computation in Linguistics (SCiL) and organizes conferences like ACL and SCiL workshops. Her research is supported by grants (not explicitly listed) and involves collaborations with scholars like Aleksei Nazarov, Joe Pater, and J. Alex Johnson. She contributes to software tools (e.g., NECPhon) and has taught courses on phonology and computational linguistics.

Man Ho Allen AuView profile

Professor

• Applied Cryptography
• Information Security
• Blockchain Technology
• +2 more

Dr. Man Ho Allen Au is an honorary professor at the Department of Computer Science, University of Hong Kong (HKU), with a career spanning cutting-edge research in applied cryptography, information security, and blockchain technology. His work bridges theoretical innovation and industrial applications. BEng, MPhil from the Chinese University of Hong Kong (2003, 2005) PhD from University of Wollongong (2009) A former associate professor at Hong Kong Polytechnic University, Dr. Au’s research drives advancements in blockchain technology , applied cryptography , and privacy-preserving techniques , with deployments in Hyperledger Fabric and discussions by privacy-focused currencies like Monero. His publications in top venues (CRYPTO, IEEE S&P, CCS) focus on lattice-based cryptography , secure protocols , and privacy-enhancing technologies . These works have attracted over 25 million HKD in grants, including RGC GRF and ITF projects. 2021 : New Frontiers of Lattice-Based Cryptography (RGC GRF), Secure Computation over Encrypted Data (ITF Seed), Blockchain Platform for Data Sharing (ITF Platform) 2020 : Practical Post-Quantum Zero-Knowledge Proofs (RGC GRF), NSFC project on quantum-resistant schemes 2019–2017 : Blockchain for logistics, anti-counterfeit systems, and post-quantum signature constructions Awarded the 2009 PET runner-up for privacy research, Dr. Au contributes to standards via ISO/IEC JTC 1/SC 27 working group and the Hong Kong Blockchain Society.