Find Academic Supervisors

Vinod KumarView profile

Professor

• Mechanical Engineering
• Fluid Dynamics
• Computational Fluid Dynamics (CFD)
• +4 more

Dr. Vinod Kumar is a Professor and Chair in the Department of Mechanical and Industrial Engineering at Texas A&M University-Kingsville. He holds a PhD in Mechanical Engineering from Rice University (2005) and has completed postdoctoral research at Princeton University/NOAA/GFDL (2007-2008) and Rice University (2005-2007). His expertise spans computational fluid dynamics (CFD), finite element methods (FEM), and high-performance computing (HPC). Professional roles include Senior Scientist at Calysta Inc. (2019-2020) and prior faculty positions at The University of Texas, El Paso (2008–2023). Research interests focus on fluid dynamics, nanomaterials, and interdisciplinary applications in energy, biomedical engineering, and environmental systems. His work has produced over 50 peer-reviewed articles, including studies on unit commitment optimization, microbial fuel cells, and biomedical device materials. Teaching responsibilities include graduate courses in CFD, FEM, and numerical methods, along with undergraduate fluid mechanics and differential equations. Notable contributions include advancements in computational modeling for renewable energy systems and collaborations on biocontrol agents for agricultural pests. His professional experience bridges academia and industry, emphasizing applied engineering solutions to real-world challenges.

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.

Sreepathi PaiView profile

Associate Professor

• Compilers
• Heterogeneous Architectures
• GPU Algorithms
• +3 more

Sreepathi Pai is an Associate Professor of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the Indian Institute of Science. His research focuses on experimental computer systems, particularly the performance of computer programs, with emphasis on heterogeneous architectures combining CPUs and GPUs. He develops optimizing compilers for irregular/graph algorithms on GPUs and creates performance models for these systems. Key research interests include compilers, heterogeneous architectures, GPU algorithms, and performance modeling. His work addresses challenges in large-scale automata processing, graph processing frameworks like Groute, and modular GPU emulation (Horus). He collaborates on projects such as TriForce for multiphysics simulations and explores efficient electromagnetic algorithms for plasma simulations. Recent projects include formal methods for scientific computing (DANUBE), lightweight GPU performance simulation, and autotuning for volunteer computing. His research also spans statistical caching, compiler-assisted memory management, and parallel triangle counting algorithms. His work bridges theory and practice, with contributions to frameworks like PLASMA for heterogeneous accelerators and tools for analyzing memory utilization in shared-memory systems. He has published extensively on topics ranging from tensor core modeling to preemptive thread block scheduling in GPUs.

Qiong CaiView profile

Professor

• Sustainable Energy Storage
• Materials Science
• Electrochemical Engineering
• +3 more

Professor Qiong Cai is a faculty member at the University of Surrey's School of Chemistry and Chemical Engineering, serving as a Sustainability Fellow and Theme Leader for Chemicals for Net-Zero within the Institute for Sustainability. She holds a PhD and is a Fellow of the Royal Society of Chemistry (FRSC) and Fellow of the Higher Education Academy (FHEA). Her research focuses on multiscale materials design for sustainable energy storage and conversion, combining experimental and computational approaches to address challenges in fuel cells, batteries, and electrolysers. Key research areas include nano-scale materials synthesis, meso-scale electrode engineering, and system-level modeling. Academic roles include Academic Integrity Officer (2013-2016, 2020–present) and Director of Postgraduate Research (2016–2017). She leads modules such as 'Advanced Electrochemical Systems' (MSc) and 'Engineering Materials and Sustainability' (Undergraduate). Her work spans collaborations with physics, mechanical engineering, and computer science disciplines. Notable contributions include advancements in solid oxide electrolysis, lithium-sulfur batteries, and aluminum-ion battery technologies. Research interests emphasize understanding material-structure-process relationships through computational methods (e.g., DFT, molecular dynamics) and experimental validation. Her team addresses energy storage challenges at the interface of materials science and chemical engineering. Current projects include optimizing electrolytes for solid oxide systems and developing high-performance cathodes for next-generation batteries. Teaching and supervision activities include mentoring MEng and MSc students, with a focus on fostering innovation in sustainable energy solutions. She actively contributes to the University HPC Cluster Stakeholder Group, leveraging computational resources for research.

• High Performance Computing
• Cloud Computing
• Interconnection Networks
• +3 more

Ernst Gunnar Gran is an Associate Professor at the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), where he leads the communication technology discipline. He also holds an adjunct research scientist position at Simula Research Laboratory, focusing on High Performance Computing (HPC) and cloud computing. Education: M.Sc. and Ph.D. in Computer Science from the University of Oslo (2007, 2014) Research: Intersections of HPC, interconnection networks, enterprise data centers, cloud computing, and data-intensive multi-cloud processing His recent work addresses real-time anomaly detection in IoT systems, traffic speed prediction for transportation networks, and adaptive routing in HPC environments. Articles span machine learning, network security, and distributed computing, with a focus on lightweight, self-adaptive algorithms. Current projects include leadership of the RCN-funded eX3 infrastructure project and contributions to the EU H2020 Melodic initiative. He has designed the NorNet Core testbed and holds patents in network reconfiguration and cloud architecture.

Tolulope OlugbojiView profile

Assistant Professor

• Earth imaging
• Seismic signal processing
• Mathematical geophysics
• +8 more

Tolulope Olugboji is an Assistant Professor of Earth Science at the University of Rochester, with joint appointments in Electrical and Computer Engineering and affiliation with the Georgen Institute of Data Science. He leads the Earth Imaging Lab, focusing on planetary structure and dynamics using seismic data and computational methods. His research integrates ground vibrations, high-performance computing, AI algorithms, and signal processing to study lithosphere architecture and seismic signals. Education: PhD in Geology and Geophysics (2014), Yale University M.Phil. (2011), Yale University B.Sc. in Computer Science (2008), Obafemi Awolowo University Research Interests: Earth imaging with intelligent algorithms, lithosphere structure, seismic signal processing, mathematical geophysics, marine geophysics, and machine learning applications in computational imaging. His work emphasizes probabilistic inversion, CRISP-RF denoising filters, and global mantle stratification studies. Publications: Recent work includes ADAMA (Africa’s crustal architecture), CRISP-RF discontinuity detection, and oceanic lithosphere imaging. His articles span seismic tomography, mantle dynamics, and algorithm development for noisy data. Awards: NSF CAREER Award (2024) CIG Distinguished Lecturer (2021-2022) Carnegie Visiting Fellowship (2022) Grants: NSF Geophysics+MG&G ($701k, 2024-2029) Georgen Institute Seed Funding ($20k, 2023) XSEDE HPC Resources ($3.5M SU allocation) Teaching: Courses include Nature’s Fury, Earth Data Analysis, Seismology, and Signal Processing. He emphasizes computational methods and geophysical pedagogy. Labs/Teams: The Earth Imaging Lab develops algorithms like CRISP-RF and SHARP-SS for seismic imaging. Collaborations include Carnegie, MIT, and global institutions.

Murali Krishna EmaniView profile

Researcher

• High Performance Computing
• Scalable Machine Learning
• Parallel programming models
• +3 more

Murali Krishna Emani is an Assistant Computer Scientist in the Data Science group at Argonne Leadership Computing Facility (ALCF) within Argonne National Laboratory. Previously, he served as a Postdoctoral Research Staff Member at Lawrence Livermore National Laboratory. His research spans High Performance Computing , Scalable Machine Learning , and Emerging HPC architectures . Key interests include parallel programming models, runtime systems, and online adaptation for scientific applications. At ALCF, he co-leads the AI Testbed initiative exploring AI accelerator performance for scientific machine learning, and chaired the MLPerf HPC group at MLCommons for benchmarking large-scale ML on HPC systems. His recent publications (2023-2025) reveal strong focus on LLM optimization (MoE inference, KV cache management), AI accelerator benchmarking , and scientific applications (climate modeling, protein design). The work demonstrates cross-cutting themes in hardware-software co-design and performance modeling for emerging architectures. ACM Gordon Bell Prize finalist for climate modeling (2025) Winner of ACM Gordon Bell Special Prize for HPC-based Covid-19 research (2022) Emani actively mentors PhD students and postdocs, with advisees now faculty at Binghamton University, California State University, and researchers at NVIDIA, Microsoft, and national labs. His service includes program committees for SC, IPDPS, and AAAI conferences. Current projects focus on performance modeling for ML/DL frameworks on supercomputers, co-design of hardware architectures for ML algorithms, and benchmarking ML/DL frameworks on HPC systems.

Hans Michael GerndtView profile

Professor

• Computer architecture
• Parallel computing
• Cloud computing
• +4 more

Prof. Dr. Hans Michael Gerndt is a leading academic at the Technical University of Munich (TUM) , affiliated with the TUM School of Computation, Information and Technology . Since 2000, he has headed the Parallel Computer Architecture group within the Faculty of Computer Science, focusing on tools for optimizing parallel supercomputers and cloud applications. His research emphasizes automatic performance analysis and optimization , particularly through the Periscope Tuning Framework . He has contributed to energy efficiency in computing systems, autoscaling mechanisms for cloud environments, and multi-aspect tuning frameworks for HPC applications. His work bridges theoretical advancements with practical tools for computational efficiency. Key publications include studies on energy tuning (2018), autoscaling measurement tools (2018), and MIMD-parallelization for supercomputing (1987). Notable awards include the Virtual Institute for High Productivity Supercomputing (VI-HPS) admission (2009) and Eclipse Innovation Award (2005) .

• High Performance Computing
• Parallel I/O Optimization
• Data Compression
• +4 more

Dr. Julian Kunkel is a Researcher at the University of Reading specializing in High Performance Computing (HPC) with 44 publications spanning 2014-2024. His work focuses on parallel I/O optimization, data compression, and machine learning applications for scientific computing and financial systems. Research interests include: High Performance Computing infrastructure optimization Parallel I/O monitoring and prediction using machine learning Data compression techniques for climate and scientific datasets Energy efficiency in data centers and HPC systems Financial time series forecasting with deep learning Tropical cyclone detection through satellite imagery analysis Recent publications (2020-2024) demonstrate a strong trend toward integrating machine learning with HPC challenges, particularly in I/O optimization and cross-domain applications like financial forecasting and meteorology. His work on the HPC Certification Forum shows leadership in standardizing HPC education globally. Dr. Kunkel collaborates internationally with researchers from Germany, Japan, and the USA on projects like AIMES (Advanced Computation and I/O Methods for Earth-System Simulations). His development of tools like SIOX for I/O monitoring and TrioFlow for financial prediction highlights practical applications of his research.

Dimitrios FytanidisView profile

Researcher

• Environmental Fluid Mechanics
• Computational Fluid Dynamics
• Turbulence Modeling
• +8 more

Dimitrios Fytanidis is a Computational Scientist at the Environmental Science Division of Argonne National Laboratory. He earned his Ph.D. in Civil Engineering from the University of Illinois at Urbana-Champaign in 2020. Research Focus: Environmental Fluid Mechanics, Computational Fluid Dynamics (CFD), Turbulence Modeling, High-Performance Computing (HPC), and Numerical Modeling with physics-based and data-driven techniques. Applications: Resilient infrastructure, Renewable Energy, Urban Air Quality, and Sediment/Water Quality Management. His recent work emphasizes GPU-accelerated climate and urban flow simulations, including studies on heatwaves, distributed wind energy, and hyporheic zone dynamics. He collaborates with organizations like the Computing, Environment, and Life Sciences directorate. Key trends in his publications include: High-fidelity simulations (DNS, LES) for environmental flows Low-order modeling for urban wind and energy systems GPU acceleration in climate and coastal engineering

• High-Performance Computing
• Scientific Workflows
• Distributed Computing
• +14 more

Justin M. Wozniak is a computational scientist at Argonne National Laboratory’s Mathematics and Computer Science Division within the Computing, Environment and Life Sciences directorate. He is a key contributor to advanced scientific workflow systems such as Swift/T and Parsl, enabling scalable, distributed, and many-task computing for data-intensive science. His work supports major initiatives in cancer research (CANDLE), epidemiological modeling, and exascale computing (ExaWorks). He collaborates extensively with leading researchers including Ian T. Foster, Michael Wilde, and Kyle Chard. His research focuses on high-performance computing, scientific workflows, distributed systems, and machine learning applications in science. He has pioneered techniques in workflow automation, fault tolerance, in-situ data analysis, and performance optimization. His work enables robust, scalable execution of complex computational pipelines across heterogeneous environments, from supercomputers to cloud platforms. His recent publications (2021–2025) emphasize workflow interoperability, resilience, benchmarking, and applications in cancer and epidemic modeling. Themes include automated model comparison, job management portability (PSI/J), adaptive workflow steering, and exascale-ready workflow toolkits. These works reflect a strong trend toward reproducibility, scalability, and real-world scientific impact. Justin M. Wozniak has no listed scientific awards in the provided text. However, his leadership in major DOE-funded projects and high-impact publications in top venues (SC, HPDC, e-Science) underscores his significant contributions to computational science. He has mentored or collaborated with numerous researchers, though specific advisees are not listed. His work is supported by large-scale computing grants and initiatives such as the ExaWorks project and CANDLE, which aim to accelerate scientific discovery through advanced computing infrastructure. He contributes to open science through tools like Parsl and Swift/T, which are widely used in the scientific community. He is a core developer in the ExaWorks ecosystem and contributes to workflow frameworks that integrate with AI/ML pipelines, containerization, and real-time data analysis. His work on Braid-DB and provenance tracking supports AI-driven science with full reproducibility. These efforts are central to modern computational laboratories aiming for autonomous, data-intensive discovery.

Andreas HertenView profile

Researcher

• High-Performance Computing (HPC)
• GPU Programming
• Parallel Computing
• +6 more

Andreas Herten is a Researcher at the Jülich Supercomputing Centre (JSC) within Research Center Jülich GmbH. He serves as Co-Lead of the Novel System Architecture Design division and heads the ATML Accelerating Devices group, focusing on GPU programming, parallel computing, and high-performance computing (HPC) optimizations. Key Expertise: GPU programming, parallel algorithms, HPC systems, benchmarking, Python, LaTeX Research Focus: Accelerating scientific applications on GPUs, European exascale initiatives, AI workload evaluation, and heterogeneous computing architectures His recent publications highlight advancements in GPU-accelerated materials science, AI training on HPC systems, and exascale benchmarking. Herten contributes to projects like OpenGPT-X, JUPITER, and CARAML, with work spanning computational physics, environmental science, and machine learning applications.

Angelos BilasView profile

Professor

• Computer Systems
• Computer Architecture
• Storage Systems
• +4 more

Angelos Bilas is a Professor of Computer Science at the University of Crete, Greece, and a collaborating researcher at FORTH-ICS (Foundation for Research and Technology-Hellas). He received his PhD in Computer Science from Princeton University in 1998 and has held academic positions at the University of Crete since 2002, including Department Chair (2016-2020). His research focuses on systems software for storage systems, computing architectures, and parallel systems with emphasis on flash storage optimization. PhD, Princeton University (1998) MA, Princeton University (1995) BEng, University of Patras (1993) His research interests span computer systems, storage architectures, and parallel computing , with notable contributions in memory-mapped I/O optimization, LSM key-value store design, and heterogeneous accelerator integration. He has developed frameworks like TeraHeap for DRAM pressure mitigation in big data systems and Arax for decoupling applications from accelerators. Prominent trends in his recent publications include flash storage optimization (TeraHeap), LSM tree efficiency (Tebis), and cloud-HPC convergence (EVOLVE, KNoC). His work appears in top venues like ASPLOS, USENIX ATC, and ACM Transactions on Storage. Scientific recognitions include: Marie Curie Excellent Teams Award (2005-2009) Best Presentation Award, USENIX HotStorage'20 He has served on editorial boards (ACM Transactions on Storage) and program committees (ACM HotStorage, HPCA). His research involves 35 master’s and 8 PhD students, with 45+ EU/national projects and 4 patents licensed to industry.

Sudeep BhattacharyayView profile

Professor

• Computational Chemistry
• Biophysical Chemistry
• Protein Dynamics
• +5 more

Sudeep Bhattacharyay is a Professor in the Chemistry and Biochemistry Department at the University of Wisconsin-Eau Claire's College of Arts and Sciences. He directs computational research using the Blugold Super-Computing Cluster and mentors undergraduate researchers through discovery-guided projects in biophysical and physical chemistry courses. His educational background includes: Ph.D. in Chemistry from Indian Association for the Cultivation of Science, Jadavpur, India M.S. in Chemistry from Indian Institute of Technology, Kharagpur, India B.S. in Chemistry from Indian Institute of Technology, Kharagpur, India Bhattacharyay's research focuses on the intersection of computational chemistry and biophysics, particularly protein dynamics, enzyme catalysis, and molecular crowding effects. His lab employs molecular dynamics simulations to study intrinsically disordered proteins, SARS-CoV-2 mechanisms, and artificial intelligence applications in healthcare. He emphasizes active learning through computational projects that integrate theory, physical chemistry concepts, and advanced simulation methods, often collaborating with experimentalists for validation. His 15 most recent publications demonstrate a strong trend in computational biophysics, with increasing focus on viral mechanisms (particularly SARS-CoV-2), molecular crowding effects, and educational applications of high-performance computing. The work spans biophysical chemistry, structural biology, and science education, showing consistent integration of simulation methods with biochemical problems. Bhattacharyay actively mentors undergraduate researchers, as evidenced by numerous student co-authors across his publications. His advising extends to classroom research projects in biophysical chemistry courses, where students use computational methods to explore protein dynamics and enzyme mechanisms. He collaborates extensively on educational initiatives related to high-performance computing infrastructure. His research utilizes the Blugold Center for High Performance Computing, where he implements computational approaches to study protein systems. The lab maintains strong collaborations with experimental groups for validation of theoretical findings and focuses on translating computational insights into biochemical understanding.

Maarit Korpi-LaggView profile

Associate Professor

• High-Performance Computing
• Numerical Modelling
• Astroinformatics
• +3 more

Dr. Maarit Korpi-Lagg is an Associate Professor in the Department of Computer Science at Aalto University's School of Science. She specializes in High-Performance Computing (HPC), numerical modelling, and astroinformatics, with a focus on solar magnetic activity and dynamo theory. Her work bridges computational methods with astrophysical phenomena. External Positions: Corresponding Fellow, Nordic Institute for Theoretical Physics (2020–2025) Independent Max Planck Research Group Leader, Max Planck Institute for Solar System Research (2016–) Research interests include: High-Performance Computing for astrophysical simulations Solar magnetic field dynamics and sunspot analysis GPU-accelerated code development Stellar convection zones and atmospheric modeling Her recent publications highlight advancements in exascale astrophysics data analysis, solar dynamo simulations, and cross-disciplinary applications like epidemic modeling via computational methods. Key collaborations span Europe and global institutions. Scientific Awards: Grand Challenge Award (2018) for galactic dynamo research Nordita Corresponding Fellowship (2020) She leads multiple projects, including ERC UniSDyn and NEOSC, focusing on cosmic magnetism and GPU-based stencil computations. Her work contributes to UN Sustainable Development Goals, particularly quality education and planetary science.