Find Academic Supervisors

Rafal P. WojdaView profile

Researcher

• High-Frequency Magnetics
• Medium-Voltage Magnetic Components
• Loss Optimization in Magnetic Devices
• +5 more

Rafal P. Wojda is a Research and Development Staff Member at Oak Ridge National Laboratory (ORNL), Knoxville, TN, since 2018. His professional journey includes a Senior Scientist role at ABB Corporate Research Center (2012–2018) and academic training in Electronics Engineering from Warsaw University of Technology (B.S./M.S.) and Wright State University (Ph.D., 2012). As a Fulbright Scholar (2009–2010), he specialized in microwave engineering, VLSI, and nanotechnology. Current Role: Leads research on advanced magnetic technologies for power flow control and energy-efficient hardware for exascale HPC systems at ORNL. Research Focus: High-frequency magnetics, loss optimization, insulation coordination, solid-state transformers, wireless power transfer, and partial discharge phenomena. His publications span magnetic component design, thermal management, and power conversion systems, with recent work on flexible PCB windings , digital twins for supercomputers , and additive manufacturing of copper windings . Awards include IEEE Senior Member (2020), IET Power Electronics Premium Award (2014), and Fulbright Commission Award (2009). Professional Service: IEEE East Tennessee PELS/PES Joint Chapter Secretary (2018–2022) and Chair (2022–present). Expertise: Holds multiple patents in magnetic component optimization and collaborates extensively with academia and national laboratories.

Gregory GangerView profile

Professor

• Computer Systems
• Cloud Computing
• Distributed Systems
• +6 more

Gregory Ganger is the Stephen J. Jatras Professor at Carnegie Mellon University's College of Engineering, holding joint affiliations with the Computer Science Department and CyLab. He has served as Director of the Parallel Data Lab (PDL) since 2000 and teaches Storage Systems (Fall) and Advanced Cloud Computing (Spring) courses. Education: Ph.D. in Computer Science & Engineering (1995, University of Michigan) M.S. in Computer Science & Engineering (1993, University of Michigan) B.S. in Computer Science (1991, University of Michigan) Dr. Ganger's research spans computer systems, focusing on: cloud computing, distributed systems, storage architectures, and machine learning infrastructure. His work addresses challenges in resource scheduling, non-volatile memory optimization, and scalable infrastructure design through projects like BigLearning, CILES, HeART, and Zoned Storage systems. Recent publications demonstrate expertise in: DNN training optimization (GraphPipe, Nonuniform-Tensor-Parallelism), sustainable storage (Storage Emissions, FairyWREN), and cloud efficiency (MACARON Cache). These works intersect machine learning, storage systems, and hardware-software co-design. Scientific Awards: 2021 OSDI Best Paper 2021 SOSP Best Paper 2021 SoCC Test of Time Award 2021 R&D 100 Award As advisor to 8 graduate students, he contributes to training the next generation of systems researchers. His lab (PDL) collaborates with industry partners on cutting-edge storage and cloud infrastructure problems.

Yu LiuView profile

Associate Professor

• Computer Architecture
• High Performance Computing
• Real-Time Systems
• +3 more

Dr. Yu Liu is an Associate Professor in the Department of Electrical and Computer Engineering at Clarkson University, Potsdam, NY. He holds a Ph.D. from Southern Illinois University Carbondale (2011) and M.S./B.S. degrees from Sichuan University (2003/2000). Prior to academia, he worked at Canadian Nuclear Laboratories (2013–2017), IBM (2011–2013), and Motorola (2003–2007). His research focuses on computer architecture, high-performance computing, thermal simulation, and wireless sensor networks , with interdisciplinary applications in nuclear science. He has secured over $1.7M in NSF funding as PI/co-PI and published >50 peer-reviewed papers. Education: Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2011) M.S., Power System and Automation, Sichuan University (2003) B.S., Communication Engineering, Sichuan University (2000) Research Interests: Physics-based thermal simulation methodologies for multi-core CPUs/GPUs Data-driven and machine learning approaches in high-performance computing Interdisciplinary applications in nuclear science and environmental monitoring Real-time embedded systems and scratchpad memory architectures Grants & Awards: 5 NSF grants (e.g., REU Site: High Performance Computing with Engineering Applications) Best Paper Award at 2022 IEEE ITherm Conference Teaching & Advising: Undergraduate/Graduate courses: Software Engineering, High-Performance Computing, Computer Networks Advised 10+ graduate/undergraduate students in thermal simulation, HPC, and nuclear engineering projects Key Projects: Development of PODTherm-GP thermal simulation framework NSF-funded REU programs training over 50+ students

Jan FostierView profile

Researcher

• Bioinformatics
• High Performance Computing (HPC)
• Big Data Analytics
• +3 more

Jan Fostier is a researcher at Ghent University, affiliated with the Internet Technology and Data Science Lab (IDLab). His work focuses on High Performance Computing (HPC) and Big Data Analytics in life sciences and bioinformatics, leveraging supercomputers and GPUs to accelerate genomic data processing. He collaborates across disciplines with bioscience engineers, biologists, medical doctors, and industry partners like Janssen Pharmaceutica and Agilent. His research interests include: Bioinformatics Sequencing Data Analysis High-Performance Computing Stochastic Modelling Genomic Data Compression Recent publications highlight advancements in approximate pattern matching, de Bruijn graph construction, and nanopore signal analysis. Awards include an Outstanding Presentation Award (2015) and an Outstanding Oral Poster Presentation Award (2015). He has developed tools like Halvade , Jabba , and BLSSpeller for genomic applications.

William ThackerView profile

Professor

• Numerical Methods
• Parallel Processing
• Computer Science
• +3 more

William I. Thacker is a Professor of Computer Science at Winthrop University's College of Business and Technology. He holds a Ph.D. from Virginia Tech. His research focuses on the intersection of computer science, mathematics, and engineering, particularly in solving complex mathematical models for engineering applications using advanced numerical methods. He teaches courses in Numerical Methods, Parallel Processing, Data Structures & Algorithms, and an unconventional SCUBA Diving course through the P.E. department. Education Ph.D., Virginia Tech Research Interests Dr. Thacker's work emphasizes numerical methods, parallel processing, and high-performance computing. He applies these techniques to solve real-world engineering problems, often integrating interdisciplinary approaches from mathematics and computational science. His contributions include algorithm development for optimization and interpolation of scattered data. Publications His recent publications highlight advancements in numerical algorithms for optimization (e.g., QNSTOP, vtmop) and interpolation (SHEPPACK), as well as contributions to high-performance computing (HPC) symposium proceedings. These works reflect his expertise in computational methods, parallel processing, and algorithmic innovation. Awards No scientific awards explicitly mentioned in the provided texts. Teaching and Service Regular courses: CSCI 271, CSCI 311, CSCI 371 SCUBA Diving course (PESH 128) for the P.E. department Labs/Teams No dedicated lab or team explicitly mentioned, though his research may involve collaborations within the College of Business and Technology and interdisciplinary projects.

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 .

Torsten HoeflerView profile

Professor

• High-Performance Computing
• Parallel Computing
• Computer Networks
• +3 more

Prof. Torsten Hoefler is a Full Professor at the Department of Computer Science, ETH Zürich. His research focuses on High-Performance Computing (HPC), parallel systems, networking, and AI-infrastructure. He leads projects on scalable interconnects, network topology design, and cloud computing benchmarks like SeBS. His work bridges theoretical foundations with practical implementations in distributed systems. Research Interests : High-Performance Computing & Networking Parallel Algorithms & Architectures AI Infrastructure & Distributed Systems Chiplet Interconnects & Topology Optimization Key Contributions : Developed tools like ATLAHS (AI/HPC network simulation) Advanced RDMA-based communication protocols (SDR-RDMA) Benchmarks for serverless computing (SeBS-Flow) His recent articles (2025) emphasize adaptive networks, low-precision AI models, and energy-efficient HPC systems. He also explores ethical computing and sustainability in supercomputing through initiatives like Core Hours & Carbon Credits.

Andrew A. ChienView profile

Professor

• Sustainable Computing
• Cloud Software
• Hardware and System Architecture
• +7 more

Andrew A. Chien is the William Eckhardt Distinguished Service Professor of Computer Science at the University of Chicago and a Senior Computer Scientist at Argonne National Laboratory. He leads the Large-Scale Sustainable Systems Group and directs the CERES Center for Unstoppable Computing. His research focuses on sustainable computing, cloud software/hardware, supercomputing architectures, and energy-efficient systems. Chien holds ACM, IEEE, and AAAS Fellowships and has served on advisory boards for NSF, DARPA, and multiple universities. He previously led Intel’s Research division, founded Entropia, and held professorships at UC San Diego and UIUC. His educational contributions include courses on sustainable computing, data-intensive systems, and computer architecture for scientists. Education: Sc.D., S.M., S.B. in Computer Science/Electrical Engineering from MIT Affiliations: UChicago, Argonne, NSF CISE Advisory Committee, DARPA ISAT Study Group Key Projects: Zero-Carbon Cloud, CERES Center, Recode/UDP Architecture, Global View Resilience Chien’s research bridges computational sustainability and scalable systems, addressing energy grids, data center efficiency, and low-carbon computing. His work has been recognized through awards like the NSF Young Investigator Award (1994) and the Argonne Impact Award (2020).

• Drug Discovery
• Computational Chemistry
• Protein-Ligand Interactions
• +5 more

Michael Ashley Spies is a Professor of Drug Discovery and Experimental Therapeutics, and Biochemistry and Molecular Biology at the University of Iowa's College of Pharmacy. His research focuses on protein-ligand interactions, enzymology, and computational chemistry applied to drug discovery. He leads the Ashley Spies Laboratory which develops novel computational methods (HPC docking, molecular dynamics) and validates findings through biochemical assays. Key areas include allosteric inhibition mechanisms, antimicrobial agents, and DNA repair proteins in cancer therapy. Research highlights include pioneering work on caspase inhibitors featured in Angewandte Chemie as a 'hot paper' and ChemMedChem cover article. His group combines quantum mechanical/molecular mechanical (QM/MM) simulations with experimental validation. Awards include JSPS Fellowship and recognition for impactful publications in JACS and eLife. Expertise: Computational drug design, enzyme kinetics, free energy calculations Techniques: High-performance computing, steered molecular dynamics, QM/MM simulations Clinical focus: Antimicrobial development, cancer therapeutics targeting DNA repair pathways Recent work explores small-molecule effectors for DNA repair proteins (Burger's Medicinal Chemistry) and covalent inhibitors of bacterial enzymes. His lab actively collaborates across disciplines to translate computational insights into therapeutic leads.

Michael Georg BaderView profile

Professor

• High-Performance Computing
• Scientific Computing
• Seismic Simulations
• +4 more

Univ.-Prof. Dr. Michael Georg Bader is a professor at the Department of Computer Science at the Technical University of Munich (TUM), leading the research group on hardware-aware algorithms and software for high-performance computing (HPC). He holds a professorship within the TUM School of Computation, Information and Technology. Bader's academic career includes a PhD from TUM (2001), followed by roles in the Elite Master’s Program in Computational Engineering and the Munich Centre of Advanced Computing. His work focuses on challenges in supercomputing platforms, scalable algorithms for simulation tasks in science/engineering, and earthquake/tsunami modeling. Education: PhD in Computer Science, TUM (2001) Research Interests: Bader's research emphasizes hardware-aware algorithms, parallel numerical methods, and large-scale simulations in geosciences. Key areas include seismic wave propagation, adaptive mesh refinement, and exascale computing. His group develops simulation frameworks like SeisSol and ExaHyPE, addressing multi-physics problems at extreme scales. Key Contributions: His team achieved the SC17 Best Paper Award for tsunami-earthquake coupling simulations. Current projects include exascale computing in solid earth (ChEESE) and mixed-precision algorithms for hyperbolic PDEs. Bader's work integrates cutting-edge HPC techniques with geophysical modeling. Scientific Awards: SC17 Best Paper Award (2017) Finalist for ACM Gordon Bell Prize (2014) PRACE ISC Award (2014) Grants & Advising: Supervised 13 doctoral students, including leading researchers in seismic simulation. Active in EU-funded projects like ChEESE-2P and ScalaMIDA. Teaches courses on high-performance computing, numerical algorithms, and scientific computing. Labs/Teams: Research group based at the Leibniz Supercomputing Center (LRZ), collaborating with global institutions in computational seismology and HPC.

• High-Performance Computing
• Parallel Computing
• Scientific Computing
• +3 more

Siegfried Benkner is a full Professor at the Vienna University of Technology (TU Wien) within the Faculty of Computer Science and leads the Research Group for Scientific Computing. His work focuses on high-performance computing (HPC), parallel programming models, runtime systems, and performance optimization for heterogeneous architectures. He has actively contributed to EU-funded projects such as TROCI (2024–2027) and PEPPHER, addressing resilience in critical infrastructures and programmability for exascale systems. His research spans topics like task-based runtime systems (OCR-Vx), autotuning frameworks (Periscope PTF), and performance portability for GPUs/Xeon Phi architectures. Recent interests include accelerating graph neural networks via novel matrix compression formats and cloud-edge continuum systems for eHealth applications. Prof. Benkner has published over 270 articles, with a focus on runtime systems, parallel patterns, and HPC infrastructure. His work emphasizes practical applications, including semantic data management for medical research and cloud-based analytics frameworks for big data processing in cellular networks. He has led multiple EU projects (9 total), including the 2024 initiative on exascale computing and resilience, and frequently presents at conferences like Euro-Par and Supercomputing events. His activities include media engagement on topics like exascale hardware trends and HPC challenges.

Are RaklevView profile

Professor

• Theoretical Physics
• Particle Physics
• Dark Matter
• +4 more

Are Raklev is a Professor at the University of Oslo's Department of Physics. His research focuses on theoretical and computational physics, particularly dark matter, particle physics at CERN's LHC, and applications of machine learning. He holds leadership roles in the GAMBIT Collaboration and its Collider Working Group. Education: PhD (University of Bergen, 2007), Postdoc (University of Cambridge, 2009), and Associate Professor at the University of Oslo (2010-2013) before becoming a full Professor in 2013. His teaching includes classical mechanics and electrodynamics (FYS3120) and supersymmetry (FYS5190/9190). Research Interests: Dark matter detection, Higgs boson studies, software for computational physics, and machine learning techniques like Gaussian processes. He leads projects like GAMBIT (a global inference tool) and PLUMBIN' (to address computational bottlenecks in high-energy physics). Recent publications span superconducting magnet design for colliders, quantum computing error correction, and nuclear isotope production. His work bridges theoretical insights with experimental applications at CERN and other facilities.

Seda OgrenciView profile

Professor

• Design Automation
• Real-Time Edge AI/ML for Science
• Thermal-Aware Circuit Design
• +3 more

Seda Ogrenci is a Professor of Electrical and Computer Engineering and Computer Science at Northwestern University. She holds affiliations with the McCormick School of Engineering and leads the Ogrenci-Memik Lab. Her research focuses on thermal-aware design, edge AI/ML acceleration, and energy-efficient computing systems. She teaches courses like EECS 303 (Advanced Digital Design), EECS 355 (FPGA Design), and EECS 459 (VLSI Algorithmics). Education includes a PhD in Computer Science from UCLA, MS in Electrical and Computer Engineering from Northwestern, and BS from Bogazici University. Her work spans thermal management, 3D stacked memory systems, and reconfigurable architectures. She authored the book Heat Management in Integrated Circuits (2016) and holds patents on thermal sensors and energy harvesting. Recent projects include ML-based real-time control at the edge, FPGA-accelerated ML monitoring, and in-pixel AI for X-ray detectors. Awards include the NSF CAREER Award (2006) and EECS Best Teacher Award (2013). She advises students like Dawei Li and Yingyi Luo and contributed to grants on thermal-aware HPC systems.

• Multiphysics Modeling
• Thermal-Hydraulics
• Monte Carlo Transport Methods
• +3 more

April Novak is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC) in the Department of Nuclear, Plasma, and Radiological Engineering. She holds a joint appointment at the National Center for Supercomputing Applications (NCSA). Her research focuses on multiphysics modeling, thermal-hydraulics, and advanced reactor design, particularly using the MOOSE computational framework. She earned her PhD in Nuclear Engineering from UC Berkeley (2020) and a BS from UIUC (2015). Dr. Novak’s work emphasizes porous media modeling for pebble bed reactors (PBRs), conjugate heat transfer, and Monte Carlo transport methods. She has validated models against experiments like SANA and contributed to tools like Pronghorn and Cardinal. Her research integrates high-performance computing (HPC) for reactor simulations, with recent projects on lead-cooled fast reactors and sodium fast reactor bypass flows. Recipient of the R&D 100 Award (2023) and the Innovations in Nuclear Technology Award (2018), her teaching accolades include the Students' Award for Excellence in Undergraduate Teaching (2024). She advises on multiphysics coupling between neutronics and thermal-hydraulics and collaborates on the Virtual Test Bed (VTB) for advanced reactor models.

Daniel S. KatzView profile

Academic

• Parallel and Distributed Computing
• High-Performance Computing (HPC)
• Fault Tolerance
• +6 more

Daniel S. Katz is a Research Professor and Chief Scientist at the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign. He holds Research Associate Professor appointments in the Siebel School of Computing and Data Science and the School of Information Sciences (iSchool). His work focuses on advancing scientific software sustainability, open science practices, and high-performance computing (HPC). Katz earned his B.S., M.S., and Ph.D. in Electrical Engineering from Northwestern University (1988–1994). Roles and Affiliations: Chief Scientist, NCSA (100%, 2020–Present) Research Associate Professor, School of Information Sciences (0%, 2016–Present) Research Associate Professor, Computer Science (0%, 2016–Present) Editor-in-Chief, Journal of Open Source Software (JOSS) Co-founder, US Research Software Engineer Association (US-RSE) Research Interests: Katz's research spans parallel/distributed computing, fault tolerance, software sustainability, and open science policies. He advocates for FAIR (Findable, Accessible, Interoperable, Reusable) principles in research software and promotes equitable software practices. His work includes developing frameworks for software citation, improving reproducibility in AI, and fostering collaboration through community-driven initiatives. Awards: 2024 US-RSE Impact Award 2023 IEEE Computer Society Golden Core recognition R&D 100 Award (2018) for Swift/T workflow system Grants and Projects: Katz leads efforts in strategic initiatives like the FARR Research Coordination Network for FAIR and AI readiness. He collaborates on projects funded by the NSF and NASA, including work on sustainable HPC software and AI training programs at NCSA. Labs and Teams: He contributes to NCSA's research initiatives and is affiliated with the Better Scientific Software (BSSw) Fellowship program. His work integrates with teams like the Research Software Alliance (ReSA) to advance software engineering practices in academia.