Find Academic Supervisors

Carl TapeView profile

Professor

• Seismology
• Computational Seismology
• Earthquake Hazard Assessment
• +4 more

Carl Tape is a Professor at the University of Alaska Fairbanks (UAF), affiliated with the Geophysical Institute (GI) and the Department of Geosciences. His research focuses on seismology, computational modeling of seismic wavefields, and imaging Earth’s internal structure using adjoint tomography and waveform inversion techniques. Current Students: Aakash Gupta, Nealey Sims, Amanda McPherson, Bella Seppi Former Students: Ulrika Cahayani Miller (MS 2014), Celso Alvizuri (PhD 2016), Vipul Silwal (PhD 2018), Kyle Smith (PhD 2020), Cole Richards (MS 2020) His work leverages UAF’s high-performance computing resources to simulate 3D seismic wavefields for earthquakes in Alaska and global subduction zones. These simulations improve ground motion predictions and tectonic interpretations, particularly for regions like the Cook Inlet Basin and the Hikurangi Subduction Margin in New Zealand. Carl also develops open-source tools like adjTomo for automating seismic waveform inversion and adjoint tomography workflows. Collaborations include projects with the Alaska Earthquake Center, the Alaska Volcano Observatory (AVO), and NASA-supported GNSS ground motion studies. He contributes to interdisciplinary initiatives like the NSF-funded Arctic Observing Network, which integrates seismic data with meteorological and geodetic observations to track climate-induced geophysical changes.

Matt SinclairView profile

Assistant Professor

• Computer Architecture
• Heterogeneous Computing
• GPU Architecture
• +7 more

Matt Sinclair is an Assistant Professor in the Computer Sciences Department at the University of Wisconsin–Madison, with affiliate appointments in the Electrical and Computer Engineering Department and the Teaching Academy. He leads the Heterogeneous Architectures Lab (HAL) and is actively involved in the gem5 Project Management Committee, focusing on modern heterogeneous computing systems. His research interests include computer architecture, GPU design, parallel programming, operating systems, and high-performance computing. He develops tools and methodologies for programming and optimizing future heterogeneous systems, particularly focusing on GPUs. His work spans architectural design, efficient software implementation, and simulation frameworks. Recent publications highlight advancements in gem5 simulation accuracy, GPU energy modeling, synchronization mechanisms, and scheduling policies for ML workloads. His research has led to improvements in GPU memory bandwidth modeling, extensible power modeling, and full-system heterogeneous simulation capabilities. Scientific awards include the NSF CAREER Award, the David J. Kuck Outstanding PhD Thesis Award, and a Qualcomm Innovation Fellowship. He has also received honors such as the ACM SIGARCH–IEEE TCCA Outstanding Dissertation Award Honorable Mention and multiple fellowships. He mentors a diverse group of graduate and undergraduate students, many of whom have published at top venues and moved on to roles at NVIDIA, AMD, Microsoft, and leading graduate programs. He is involved in teaching and curriculum development, particularly through the Excel Initiative and as a former Madison Teaching & Learning Fellow. He also stewards the ISCA Hall of Fame, contributing to the academic community beyond research.

Phil CarnsView profile

Adjunct Associate Professor

• High-Performance Computing
• HPC Storage Architectures
• HPC System Software
• +7 more

Phil Carns is a computer scientist at Argonne National Laboratory's Mathematics and Computer Science Division and an Adjunct Associate Professor in the Department of Electrical and Computer Engineering at Clemson University. He is also a fellow of the Northwestern-Argonne Institute for Science and Engineering and serves as deputy director of the Software Tools Ecosystem Project. Education: Ph.D. in Computer Engineering, Clemson University, 2005 His research focuses on high-performance computing (HPC), particularly in the areas of HPC storage architectures, system software, and I/O workload analysis. He has pioneered advancements in scalable data storage and management through projects such as Mochi (composable data services), Darshan (I/O characterization), PVFS (parallel file system), and CODES (storage simulation). His work supports exascale computing initiatives and scientific data infrastructure. The 15 most recent publications reflect a consistent focus on HPC storage, I/O analysis, and system software design. Broad keywords include Computer Science, High-Performance Computing, Distributed Systems, and Simulation. These works explore subfields such as composable data services, I/O profiling, parallel file systems, performance monitoring, and exascale data libraries, demonstrating a trajectory toward modular, scalable, and efficient data management solutions for scientific computing. Scientific Awards: R&D 100 Award R&D 100 Award R&D 100 Award Phil Carns has served as principal investigator, technical lead, and developer on numerous federally funded HPC projects, including the Exascale Computing Project. While no formal students are listed, his leadership roles imply mentorship and collaboration within research teams. He is actively involved in advancing software tools and data ecosystems for large-scale scientific computing. He leads and contributes to several key research initiatives, including the Mochi Project , Darshan Project , and the Software Tools Ecosystem Project (STEP) , all aimed at improving data services, monitoring, and software sustainability in HPC environments.

• Computer Science
• Artificial Intelligence
• Smart Cities
• +5 more

Sebastian Ernst is a Lecturer at the Department of Applied Informatics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology in Kraków. His research focuses on Computer Science , Artificial Intelligence , and Smart Cities , with a strong emphasis on Geographic Information Systems (GIS) and Energy Efficiency in urban environments. University: AGH University of Science and Technology Faculty: Electrical Engineering, Automatics, Computer Science and Biomedical Engineering Department: Applied Informatics Position: Lecturer Contact: ernst@agh.edu.pl Office: C-2, 4th floor, room 424 Phone: +48 12 617 51 95 His recent publications highlight trends in Graph-Based Modeling , Smart Lighting Systems , and Robust Algorithm Design for urban infrastructure. He explores Pairwise Comparisons , GIS Applications , and Energy Conservation through computational methods.

Rebecca Hartman-BakerView profile

Researcher

• Applied Mathematics
• Numerical and Computational Mathematics
• Computation Theory and Mathematics
• +7 more

Rebecca Hartman-Baker is a computational scientist and the User Engagement Group Lead at the National Energy Research Scientific Computing Center (NERSC) under Lawrence Berkeley National Laboratory. She specializes in scalable parallel algorithms for petascale systems and focuses on inverse problems, numerical optimization, and HPC user training. PhD in Computer Science – University of Illinois at Urbana-Champaign BS in Physics – University of Kentucky Her research spans applied mathematics, distributed computing, and computational physics, with a strong emphasis on software sustainability and checkpointing techniques for large-scale systems. Recent publications highlight her work in HPC education, fault tolerance, and the IDEAS Productivity Project. Scientific awards include the R&D100 Award (Oak Ridge), James Corones Award (2019), and the Director's IDEA Mentorship Award (2023). Professional activities range from leading the Student Cluster Competition to board membership at Griffin Technology Academies.

Ali ButtView profile

Professor

• Cloud Computing
• High-Performance Computing (HPC)
• Distributed Systems
• +6 more

Ali Butt is a Professor of Computer Science at Virginia Tech's College of Engineering and ECE by courtesy. He serves as Associate Department Head for Faculty Development and directs the Distributed Systems & Storage Laboratory (DSSL) and the stack@cs Center for Computer Systems . His research focuses on cloud and high-performance computing systems , systems support for machine/deep learning , storage systems , and distributed systems . He earned his Ph.D. in Electrical and Computer Engineering from Purdue University (2006). He has received prestigious awards including the NSF CAREER Award (2008) IBM Faculty Awards (2008, 2015) VT College of Engineering Dean's Award for Outstanding New Assistant Professor (2009) NetApp Faculty Fellowships (2011, 2015) VT College of Engineering Faculty Fellow (2013) His recent work explores federated learning optimizations , I/O load balancing in parallel file systems , and containerized HPC workflows . As Associate Editor for journals like IEEE Transactions on Cloud Computing and ACM Transactions on Storage, he contributes to advancing systems research. His leadership in the DSSL lab drives innovations in large-scale experimental computer systems and data-intensive computing .

Norbert EickerView profile

Professor

• Modular Supercomputer Architecture
• HPC Middlewares
• System Architecture

Norbert Eicker serves as Professor at Bergische University of Wuppertal and Head of the Research Group "Software for Modular Supercomputers" at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich. His research specializes in advanced computing infrastructure with core competencies in: Modular Supercomputer Architecture HPC Middlewares development and optimization High-performance System Architecture design As a Principal Investigator in Helmholtz Information Program 1, Topic 2, he contributes to Germany's national strategy for computational science advancement through the Helmholtz Association framework. His work addresses critical challenges in creating flexible, scalable supercomputing environments that can adapt to diverse scientific workloads while maintaining performance efficiency. Based at Forschungszentrum Jülich's facilities in Building 16.3, Room 203, he operates within one of Europe's premier supercomputing environments that supports multidisciplinary research across the continent.

Daniel PflugfelderView profile

Researcher

• Plant Biology
• Agricultural Science
• Medical Imaging
• +7 more

Daniel Pflugfelder is a Researcher at the Plant Sciences (IBG-2) department of the Institute of Bio- and Geosciences at Forschungszentrum Jülich. His work focuses on developing non-invasive imaging tools to study plant root systems, carbon dynamics, and water uptake using Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) . He is based in Jülich, Germany, and his research supports sustainable agriculture and bioeconomy goals. Expertise: Plant research, Magnetic Resonance, 3D Data analysis, Positron Emission Tomography, Root research His research integrates molecular, physiological, and ecological approaches to analyze plant-environment interactions and develop technologies for alternative biomass utilization. He has contributed to plant phenotyping tools like phenoPET and phenoVein , enabling precise studies of root architecture, carbon allocation, and water fluxes in crops such as sugar beet, maize, and wheat. Daniel's publications emphasize multimodal imaging techniques , particularly MRI-PET co-registration , to explore heterogeneous carbon distribution, root hydropatterning, and symbiotic interactions in legumes. His work addresses challenges in quantifying root water uptake, soil-root interactions, and climate adaptation strategies in agriculture. Key applications of his research include improving resource efficiency in crops and understanding plant responses to drought stress and warming scenarios. He has also contributed to computational tools for image processing and radiation therapy optimization in earlier works.

Ajay GuptaView profile

Professor

• High Performance Computing
• Proteogenomics
• Data Analytics
• +10 more

Ajay Gupta is a Professor in the Department of Computer Science at Western Michigan University's College of Engineering & Applied Sciences. He previously served as Department Chairman from 1998-2002 and directs the Wireless Sensor Network Laboratory (WiSe Lab). He holds a Ph.D. in Computer Science from Purdue University (1989), M.S. in Mathematics and Statistics from University of Cincinnati (1984), and B.E. in Electrical and Electronics Engineering from Birla Institute of Technology and Sciences, Pilani. His research spans high-performance computing, proteogenomics, data analytics, machine learning, sensor networks, cloud/mobile computing, web technologies, computer networks, evolutionary computation, and parallel/distributed algorithms. His published work includes significant contributions to computational methods and networking. Awards include: Best Paper nomination at HPCS 2016 Best Paper award at International Conference on High Performance Computing 1996 Honorable mention at CERIAS Symposium 2006 He holds joint copyright for parallel/distributed numerical integration software ParInt 1.1. As IEEE senior member, he participates in curriculum development and conference organization globally.

Vincent VoelzView profile

Professor

• Biomolecular Simulation
• Protein Folding
• Markov State Models
• +3 more

Vincent Voelz is a Professor and Associate Department Chair in Chemistry at Temple University. His research employs biomolecular simulation, statistical mechanics, and Bayesian inference to study protein folding mechanisms, ligand binding, and computational drug design. Voelz was promoted to Full Professor in 2024 and directs a research group developing advanced computational methods for biomolecular modeling. Education: Postdoctoral training at Stanford University (2007-2011) Ph.D. from University of California, San Francisco (2007) B.A. from University of Minnesota (1999) Research Focus: Markov state modeling of biomolecular dynamics Peptidomimetic design and optimization Distributed computing approaches Bayesian inference methods Free energy calculations His recent publications demonstrate innovations in enhanced sampling techniques, Folding@home distributed computing applications, and computational methods for protein design. Research trends include improving force field accuracy, advancing Markov state model methodologies, and applying computational approaches to drug discovery challenges including COVID-19 therapeutics. Scientific Awards: Temple University CST Distinguished Teaching Award NIH R01 grant for biomolecular simulation research As principal investigator, Voelz mentors graduate students in computational chemistry and bioinformatics. Recent PhD graduates include Tim Marshall, Dylan Novack, and Robert Raddi. Research is supported by NIH grants including an R01 from NIGMS. The Voelz Lab maintains the Owlsnest HPC cluster and contributes to the Folding@home distributed computing project. Laboratory Resources: Owlsnest HPC cluster Folding@home distributed computing Bayesian Inference of Conformational Populations (BICePs) Advanced Markov state modeling frameworks

Konstantin LäuferView profile

Professor

• Computer Science
• Programming Languages
• Software Architecture
• +7 more

Konstantin Läufer is a Professor of Computer Science at Loyola University Chicago, where he has held roles including Department Chairperson (2012–2018) and Associate Dean (2005–2006). He co-directs the Emerging Technologies Laboratory (ETL), focusing on experimental research in programming languages, distributed systems, and environmental science applications. His academic journey includes a PhD from New York University's Courant Institute (1992) and prior roles at institutions like Bell Laboratories and the University of Chicago. Education: PhD in Computer Science, New York University (1992) MS in Computer Science, New York University (1989) Studies at the University of Konstanz (Germany) and Rutgers University (USA) Research Interests: Programming languages and functional/reactive paradigms Mobile and pervasive computing, IoT Software quality metrics and digital humanities Environmental science applications Recent articles emphasize integrating large language models (LLMs) for software optimization (e.g., SysLLMatic ), formal methods education via TLA+, and energy-efficient code development. Grants and Awards: 2009 Faculty Fellowship from Loyola's Institute of Environmental Sustainability NSF grants for projects like IMPACTS (2008–2011) and PDC curriculum initiatives Advising and Labs: Supervised student projects in distributed systems, bioinformatics, and filesystem frameworks (e.g., RestFS). Co-directs ETL, which explores experimental technologies like NOFS and OLFS filesystems.

Thomas MarrinanView profile

Assistant Professor

• Computer Graphics
• Visualization
• Human-Computer Interaction
• +3 more

Thomas Marrinan is an Assistant Professor of Computer & Information Science at the University of St. Thomas, College of Arts and Sciences. His expertise spans computer graphics, visualization, human-computer interaction, and high-performance computing. He specializes in developing interactive tools for large-scale data analysis, including multi-platform visualization applications like VisAnywhere and immersive virtual reality experiences. His research focuses on bridging gaps between simulation analysis and user interaction through novel interfaces and algorithms. He has pioneered work in 3D Gaussian splatting, real-time image compression, and collaborative environments using 360 panoramas. Marrinan teaches courses such as Intro to Programming & Problem Solving and Web Development, emphasizing practical application of computational concepts. In 2024, his team won the IEEE Scientific Visualization Contest for VisAnywhere, a tool enabling cross-platform scientific visualization. His work spans over two decades, with contributions to scalable resolution displays (SAGE2), GPU-based rendering optimizations, and AI-driven audio generation for video games. He holds a strong commitment to data-intensive collaboration and immersive technologies. Recent projects include developing omnidirectional stereo imaging techniques for VR exploration of large datasets and leveraging AI to enhance user-generated content in interactive media. His research addresses challenges in distributed rendering, real-time data transmission, and improving accessibility to complex scientific datasets.

Franck CappelloView profile

Academic

• High-performance parallel and distributed computing
• Resilience and fault tolerance at extreme scale
• Lossy compression
• +3 more

Franck Cappello is a distinguished computer scientist currently serving as a Project Manager and Senior Computer Scientist at Argonne National Laboratory and as an Adjunct Research Professor at the University of Illinois at Urbana-Champaign. With over 30 years of research experience, he has made significant contributions to high-performance computing, particularly in the areas of resilience and fault tolerance at extreme scale, lossy compression of scientific data, and AI for science. Dr. Cappello received his Ph.D. from the University of Paris XI in 1994 with highest honors ("très honorable avec les félicitations du jury"). His academic journey includes positions as a Junior Researcher at CNRS (1994-2003), Senior Researcher at INRIA (2003-2013), and Visiting Research Professor at the University of Illinois (2009-2013). His research interests focus on high-performance parallel and distributed computing, resilience and fault tolerance at extreme scale, lossy compression of scientific data, and AI for science. Cappello has pioneered several high-impact software tools including XtremWeb (one of the first Desktop Grid software systems), MPICH-V fault tolerance MPI library, VeloC multilevel checkpointing environment, and SZ lossy compressor for scientific data. His work on the Grid'5000 project has enabled hundreds of researchers to conduct experiments in parallel and distributed computing, resulting in over 2000 research publications and supporting 300+ Ph.D. theses. Dr. Cappello's recent publication record shows a strong integration of AI techniques with traditional HPC approaches, particularly in lossy compression, workflow management, and energy efficiency. His research demonstrates a consistent focus on practical solutions for real-world scientific computing challenges with emphasis on maintaining data fidelity while achieving significant data reduction. The publications reveal growing interest in GPU acceleration, wafer-scale engines, federated learning, and energy trade-offs in compressed I/O systems. IEEE Fellow (2017) 2024 IEEE CS Charles Babbage Award 2024 Europar Achievement Award 2022 ACM HPDC Achievement Award 2021 IEEE Transactions of Computer Award for Editorial Service and Excellence 2018 IEEE TCPP Outstanding Service Award Two R&D 100 awards (2019 and 2021) for innovative software 12 Best papers Finalists/Awards Dr. Cappello has advised 22 Ph.D. students and served on 58 Ph.D. defense juries. He has secured over 60 research grants as main PI or Co-PI, including numerous DOE ECP projects, NSF grants, and European projects. His leadership extends to directing the Joint Laboratory on Extreme-Scale Computing (JLESC), which brings together seven prominent research centers in supercomputing. Currently, he leads the AuroraGPT Evaluation Group, focusing on evaluation methods for Large Language Models as research assistants, and continues to lead resilience and compression research at Argonne's Mathematics and Computer Science Division. Dr. Cappello directs several significant research initiatives including the Joint Laboratory on Extreme-Scale Computing and leads resilience and compression research at Argonne's Mathematics and Computer Science Division. His teams have developed groundbreaking software frameworks like SZ and VeloC that are deployed on exascale systems. Through his leadership of the Grid'5000 project and JLESC, he has fostered international collaboration among researchers working on the frontiers of supercomputing. His current work on error-bounded lossy compression, resilient workflow management, and energy-efficient computing represents the cutting edge of scientific computing research with practical applications across numerous scientific domains.

Tian GohView profile

Senior Lecturer

• Health Informatics
• Digital Health
• Chronic Pain Management
• +3 more

Tian Goh is a Senior Teaching Fellow in the Department of Electrical and Computer Systems Engineering at Monash University’s Faculty of Engineering in Melbourne, Australia. He focuses on first-year engineering education and health informatics, particularly digital health solutions and chronic pain management. He actively participates in interdisciplinary research, combining data science, clinical expertise, and patient-reported outcomes. His notable work includes developing a clinical decision support system for rural healthcare workers to improve non-communicable disease detection, and a high-frequency contextual data collection model for low back pain research. He collaborates with clinicians, industry partners, and organizations like The Alfred Hospital to bridge gaps in healthcare access and technology usability. Tian leads the Catch me before I fail project (2022–2024), an early intervention framework to enhance student academic performance and retention at Monash University. He also supervises PhD students in first-year education and health informatics as an associate supervisor. His research contributes to UN Sustainable Development Goals, emphasizing quality education and good health. His technical background spans systems analysis, Windows/Unix systems administration, and corporate management, underscoring his practical industry experience.

Arjun GuhaView profile

Associate Professor

• Programming Languages
• Program Synthesis
• Low-Resource Programming Languages
• +3 more

Arjun Guha is an Associate Professor of Computer Science at Northeastern University's Khoury College of Computer Sciences, where he also serves as Area Chair for Software. Additionally, he holds a Visiting Professor position at Roblox Research. His work spans programming languages, program synthesis, and the intersection of large language models with computer science education. Guha's research primarily focuses on two areas: program synthesis for "low-resource" programming languages (those not widely supported by existing AI code generation tools but widely used in specialized fields like scientific computing), and understanding how large language models impact computer science education. His work has led to significant contributions in benchmarking neural code generation across multiple languages and examining how students interact with AI-generated code. He is particularly interested in knowledge transfer between high-resource and low-resource programming languages for code LLMs. Guha's recent publications show a strong trend toward examining the practical applications and limitations of large language models in programming contexts. His work spans from developing benchmarks like MultiPL-E for cross-language code generation evaluation to studying how beginners understand and interact with AI-generated code. There's a clear emphasis on making code generation more accessible for specialized domains and understanding the educational implications of these technologies. Guha actively mentors students at all levels, leading a research group within Northeastern's Programming Research Laboratory. His current advisees include several PhD students and postdocs working on topics related to code LLMs, program synthesis, and computer science education. His research has been generously supported by the National Science Foundation, Department of Energy, Office of Naval Research, and industry partners including Google, JPMorgan Chase, MathWorks, Meta, Oracle, and Roblox. Guha is an active contributor to the programming languages research community, serving on program committees for major conferences including PLDI, POPL, SPLASH, and OOPSLA. He has chaired workshops and sessions at these conferences and continues to be an influential voice in the programming languages community.