Christos Faloutsos is the Fredkin Professor of Computer Science at Carnegie Mellon University, with a courtesy appointment in Electrical and Computer Engineering. He holds a B.Sc. from the National Technical University of Athens and M.Sc./Ph.D. from the University of Toronto. His research focuses on data mining, graph analysis, fractals, and database systems. Notable contributions include foundational work on R-trees, graph mining laws (e.g., Kronecker graphs), and applications in medical imaging, network security, and fraud detection. Key projects include PEGASUS (petascale graph mining), fraud detection in online auctions (NetProbe), and tools for human trafficking analysis (TrafficVis). He has led NSF-funded projects on tensor mining, network anomaly detection, and bioinformatics. Over 300 refereed publications highlight his contributions across databases, data mining, and networks. Awards include the KDD Best Paper (2005, 2016), SIGMOD Test-of-Time Award, and recognition as a top nurturer in IT. His lab collaborations span the Parallel Data Lab (PDL), Machine Learning Department, and Computational Biology.
Dr. Andre Kahles is a Lecturer in the Department of Computer Science at ETH Zürich, specializing in biomedical informatics. His research focuses on computational methods for analyzing large-scale genomic and transcriptomic data, with applications in cancer genomics, metagenomics, and precision medicine. He has contributed to the development of tools such as SplAdder for alternative splicing analysis, MetaGraph for petascale genomic data exploration, and SECEDO for subclone detection in cancer genomes. His work bridges algorithmic innovation with biological insights, addressing challenges in single-cell analysis, genome graph alignment, and multi-omics integration. Key research themes include: Developing scalable algorithms for processing nanopore sequencing and metagenomic data Characterizing somatic mutations and non-coding drivers in cancer genomes Advancing genome graph-based alignment and annotation methods Integrating multi-omics data for clinical decision-making and tumor profiling His publications span topics like RNA-seq analysis, chromothripsis in cancers, and global urban microbiome tracking through the MetaSUB consortium. Kahles has collaborated on landmark projects including the Pan-Cancer Analysis of Whole Genomes (PCAWG) and the Tumor Profiler Study.
William Gropp is the Grainger Distinguished Chair and Director of the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign. He holds a Ph.D. in Computer Science from Stanford University (1982) and has contributed extensively to parallel computing, software for scientific computing, and numerical methods for PDEs. His research focuses on high performance computing, programming models, and scalable algorithms. Education: B.S. Mathematics (Case Western Reserve, 1977), M.S. Physics (University of Washington, 1978), Ph.D. Computer Science (Stanford, 1982). Research Interests: Gropp's work spans HPC, parallel computing, and numerical methods. He co-developed the MPI standard and MPICH implementation, and contributed to the PETSc library. His current projects include the Delta and DeltaAI supercomputers, Illinois Computes, and exascale initiatives. Scientific Awards: AAAS Fellow (2018), ACM/IEEE-CS Ken Kennedy Award (2016), SIAM/ACM Prize (2015), and 2024 ACM Software System Award. Member of the National Academy of Engineering. Grants & Leadership: Director of NCSA, leader of the Midwest Big Data Hub, and contributor to NSF-funded projects. His teams support AI/ML infrastructure and exascale computing. He advises on HPC policy and serves on committees like the Computing Community Consortium. Labs/Teams: NCSA, Siebel School research groups, collaborations with DOE, NSF, and industry partners. His work drives advancements in cyberinfrastructure and computational science.
Brian Vermeire is an Associate Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on computational fluid dynamics, aerodynamics, high-performance computing, turbulence modeling, numerical methods, and optimization. He leads the Computational Aerodynamics Laboratory, emphasizing scale-resolving simulations and high-order numerical techniques. Key interests include large eddy simulation (LES), direct numerical simulation (DNS), and gradient-free optimization. His work often involves developing advanced algorithms for unstructured grids and high-performance computing platforms. Research Interests: High-order numerical methods Implicit/explicit time integration schemes Polynomial adaptation for adaptive meshing Aeroacoustic shape optimization Large eddy simulation (LES) and direct numerical simulation (DNS) Software development for CFD (e.g., PyFR) Recent work trends show strong focus on hybridized flux reconstruction methods, energy-conservative algorithms, and industrial adoption of high-fidelity simulations. Major contributions include scalable implementations for petascale computing and open-source tools like PyFR. His group collaborates on applications such as wind turbine aerodynamics and low-pressure turbine design. Labs/Teams: Computational Aerodynamics Laboratory (website: link )
Xiaoming Li is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Delaware , focusing on compiler optimization, GPU computing, and hardware-software interaction. His work bridges machine learning with code generation to enhance program efficiency. B.S. and M.E. from Nanjing University (1998, 2001) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2006) Research interests include: Compiler optimizations for static and dynamic code transformation Machine learning-driven code generation techniques FFT algorithms for sparse and hybrid systems Non-traditional compilers for SAT solvers and virtual machines GPU acceleration for large-scale computational problems His publications span 15 years , emphasizing: FFT optimization across GPU/CPU architectures Compiler techniques for heterogeneous systems Adaptive scheduling and error resilience Integration of empirical and model-driven approaches Notable awards: NSF CAREER Award (2008) Best Paper Award at ADAPT Workshop (2013) Advising highlights: Current students: Ryan Taylor, Sha Li, Shuo Chen, Yuanfang Chen, Chao Yang, Chaoyu Chen Graduates: Liang Gu (FFT Libraries), Jakob Siegel (GPGPU Frameworks), Murat Bolat (Context-Aware Compilation)
Ryan Giordano is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. He holds a PhD in Statistics from UC Berkeley (2019), advised by Michael Jordan, Tamara Broderick, and Jon McAuliffe, an MSc in Econometrics and Mathematical Economics from the London School of Economics (2009), and undergraduate degrees in Mathematics and Theoretical/Applied Mechanics from the University of Illinois at Urbana-Champaign. Prior to academia, he worked as an engineer at Google and HP and served as a Peace Corps volunteer in Kazakhstan. His research focuses on variational methods , Bayesian robustness , sensitivity analysis , and statistical computing , with applications in machine learning, environmental science, and astronomy. He is particularly known for developing scalable Bayesian inference techniques and quantifying the robustness of statistical models to data perturbations. Giordano’s recent work includes studies on Laplace approximation accuracy, MCMC sensitivity to data removal, and robustness metrics for differential expression analysis. He has contributed to open-source statistical software and collaborates with Tamara Broderick’s group at MIT on postdoctoral work (pre-2019 position). His academic trajectory combines theoretical innovation with practical applications, emphasizing reproducibility and computational efficiency in statistical methodology.
Andreas Wicenec is a Professor and Senior Principal Research Fellow at the University of Western Australia (UWA), leading the Data Intensive Astronomy Program (DIA) at the International Centre for Radio Astronomy Research (ICRAR). He specializes in data-intensive astronomy, high-performance computing, and large-scale data management systems. His work supports the Square Kilometre Array (SKA) and other major observatories. Education: PhD in Astronomy from the University of Tübingen (1994), Physics Diploma (1989). Professional roles include Archive Scientist at the European Southern Observatory (ESO) and leadership in the International Virtual Observatory Alliance (IVOA). Research focuses on petascale data flows, reproducible science workflows, and next-generation archive systems like NGAS. Current projects include the DALiuGE engine, SKA data handling, and gravitational wave detection pipelines using deep learning. Key Projects: SKA Science Data Processing (7M AUD contract), Data Activated Flow Graph Engine (DALiuGE), and NGAS archive system Awards: ACM Gordon Bell Prize 2020 finalist Grants: Includes SKA Bridging Design (2019–2021), ICRAR IV (2025–2030) Labs/Teams: Active in ICRAR's Data Intensive Astronomy group, collaborating internationally on large-scale astronomy initiatives.
Valerio Pascucci is a Professor at the University of Utah's School of Computing and a DOE Laboratory Fellow at Pacific Northwest National Laboratory. He directs the Center for Extreme Data Management Analysis and Visualization (CEDMAV) and previously led projects at Lawrence Livermore National Laboratory and University of Texas at Austin. PhD in Computer Science (Purdue University, 2000) MSc in Electrical Engineering (University 'La Sapienza', Rome, 1993) As a pioneer in Big Data Management , Scientific Visualization , and Computational Topology , his work connects topological methods with progressive algorithms to enable interactive exploration of petascale datasets. His research spans climate modeling , neuroscience , materials science , and precision agriculture , focusing on multi-resolution techniques and geometric compression . Recent publications show specialization in web-based visualization and AI-driven analytics for climate data, with emphasis on equity in data access and FAIR data principles . His ViSUS project enables real-time data streaming from supercomputers to desktops, while NAPA explores GPU-based architectures for streaming algorithms. Scientific Awards : Best Paper Award, IEEE Pacific Visualization 2011 Best Application Paper Award, IEEE VIS 2006 DOE Laboratory Fellow He advises numerous graduate students and leads collaborations across national laboratories , universities , and industry . Funded by NSF Grant #2127548 , he develops technologies for exascale computing and geospatial intelligence .
Garth Gibson is a Professor in the Computer Science Department and Department of Electrical and Computer Engineering at Carnegie Mellon University's School of Computer Science. He serves as Co-Director of the Master of Computational Data Science program and as Associate Dean for Master's Programs. Gibson has been a faculty member at CMU since 1991, after receiving his Ph.D. and M.Sc. in Computer Science from the University of California at Berkeley and a Bachelor of Mathematics in Computer Science and Applied Mathematics from the University of Waterloo. Gibson's research focuses on large-scale parallelism in computer systems, secondary memory system technologies and optimization, scalable file and key-value storage systems, scalable machine learning, and systematic testing for large scale systems. His work bridges theoretical concepts with practical implementations, with a strong emphasis on shepherding technological advances from academic research to commercial reality. He has made significant contributions to RAID technology, network-attached secure disks (NASD), and parallel file systems that have shaped industry standards and products. Gibson's recent publications reveal a strong trend toward data-intensive scalable computing, with increasing focus on machine learning systems, distributed storage solutions, and high-performance computing infrastructure. His research has evolved from foundational storage technologies to address the challenges of petascale and exascale computing environments, with particular attention to the intersection of storage systems and machine learning workloads. The papers demonstrate a consistent theme of addressing system scalability challenges through innovative architectural approaches. Scientific Awards: 2014 Fellow of the IEEE for contributions to the performance and reliability of transformative storage systems 2012 Fellow of the ACM for contributions to the performance and reliability of storage systems 2012 Jean-Claude Laprie Award in Dependable Computing Industrial/Commercial Product Impact Category 2011 SIGOPS Hall of Fame for the SIGMOD88 RAID paper 1999 Reynold B. Johnson Information Storage Award 1999 Allan Newell Award for Research Excellence 1998 Test of Time Award 1991 A.C.M. Doctoral Dissertation Award (tied for second) Gibson has advised numerous graduate students who have gone on to influential positions in both academia and industry, including Swapnil Patil who won first place in the 2010 ACM Graduate Student Research Competition. He has secured significant research funding through initiatives like the DOE Petascale Data Storage Institute and the Intel Science and Technology Center for Cloud Computing. His research has been supported by collaborations with national laboratories including Los Alamos, Sandia, Oak Ridge, Pacific Northwest, and Lawrence Berkeley. Gibson founded CMU's Parallel Data Laboratory (PDL) in 1993, which has grown into a vibrant research community comprising 6-9 faculty members, 2-3 dozen students, and 4-10 staff. The PDL operates with guidance from the Parallel Data Consortium, which includes 15-25 companies interested in parallel data systems. He also founded Panasas Inc. in 1999, a scalable storage cluster company that has deployed technology in national laboratories, energy sectors, and other high-performance computing environments. More recently, Gibson established the Big Learning research group and created the Systems Major curriculum within CMU's Master of Computational Data Science program.
Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.
Zhiyuan Li is a Professor in the Department of Computer Sciences at Purdue University's College of Engineering. His primary research and teaching focus on program analysis, transformation, and run-time management for high-performance computing and multicore systems, as well as reliable software for networked embedded systems. Professor Li teaches graduate-level courses including CS502: Compiling and Programming Systems and CS591RS1: Research Seminar for First-year Graduate Students. Office: LWSN 3154H Contact: li@cs.purdue.edu Phone: +1 765-494-7822 Professor Li's research spans multiple areas within computer science, with particular emphasis on compiler design, program analysis, and parallel computing. His work addresses fundamental challenges in enabling efficient execution of applications on modern parallel architectures, including multicore processors and large-scale distributed systems. He has made significant contributions to techniques for data dependence analysis, loop parallelization, array privatization, and memory optimization in compilers. His research also extends to reliable software development for embedded and sensor network systems, where resource constraints and reliability requirements present unique challenges. Professor Li's publication record demonstrates consistent contributions to top-tier conferences and journals in computer science, particularly in the areas of parallel computing, compiler optimization, and high-performance numerical methods. His work shows a progression from foundational compiler techniques to applications in scientific computing domains such as computational fluid dynamics for jet engine noise simulation. This interdisciplinary approach connects low-level program analysis with real-world engineering applications requiring petascale computing resources. Principal Investigator for NSF/PetaApps project on jet engine noise simulation Principal Investigator for Intel-sponsored research on data dependence profiling Extensive service on program committees for major conferences including ICS, PPoPP, and LCTES Professor Li has been actively involved in mentoring graduate students through research projects and course instruction. His jet engine noise simulation project specifically mentions training three Ph.D. graduate students and involving undergraduate research assistants. As coordinator for the first-year graduate research seminar, he plays a significant role in guiding new students through the transition to graduate research work in computer science. His laboratory work focuses on developing compiler techniques and runtime systems for parallel and high-performance computing. The research infrastructure includes implementations in GCC for fast data dependence profiling and support for SIMD/SSE instructions, demonstrating practical applications of theoretical compiler techniques.
Gustavo Ovando-Montejo is an Assistant Professor in the Department of Environment & Society within the College of Natural Resources at Utah State University, based at the Blanding campus. He contributes to environmental and geospatial education and research, teaching undergraduate and graduate courses in GIScience, environmental data science, and natural resource management. Education: PhD in Geography (Geographic Information Science), Oklahoma State University, 2019 MS in Geography (Geographic Information Science), Oklahoma State University, 2015 BS in Geographic Information Systems, Brigham Young University, 2013 Research Interests: Dr. Ovando-Montejo's research centers on human-environment interactions, land cover change, and landscape ecology, with a focus on linking spatial patterns to ecosystem services. He explores issues of environmental equity, indigenous segregation, and applies advanced geospatial and data science techniques to environmental and natural resource problems. His work bridges technical GIScience with social and ecological applications. Publication Trends: His recent publication focuses on web-based visualization of large-scale geospatial data, emphasizing equitable access to advanced analytics. This reflects a growing trend in democratizing petascale computing for broader environmental research and education, combining technical innovation with social responsibility. Scientific Awards: QCNR Teacher of the Year, 2024 Advising and Grants: While no formal advisees or grants are listed in the provided text, Dr. Ovando-Montejo has been actively involved in teaching and extension since 2011. He teaches core courses such as Environmental Data Science, Geospatial Analysis, and Physical Geography, and contributes to USU Extension Sustainability efforts, indicating a long-standing commitment to outreach and applied environmental education. Labs and Teams: Specific research labs or collaborative teams are not mentioned in the available content. However, his work in geospatial analysis and environmental data science suggests potential affiliations with cyberinfrastructure or sustainability research initiatives at Utah State University.
Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.
Sanket Deshmukh is an Associate Professor in the Department of Chemical Engineering at Virginia Tech. His research emphasizes computational materials science, combining molecular dynamics simulations, machine learning, and Bayesian statistical methods to study structure-property relationships in polymers, biomaterials, and nanomaterials. He focuses on developing coarse-grained models for predictive simulations of complex systems and advancing applications in energy storage, drug delivery, and tribology. Education: Ph.D., University College Dublin, Ireland (2009) M.Sc., University of Pune, India (2004) B.Sc., University of Pune, India (2002) Research Interests: Molecular dynamics on petascale supercomputers Nano- and meso-scale tribology Smart polymers and bio-materials Corrosion mechanisms and surface chemistry Machine learning for materials design His work bridges computational theory and experimental validation, with notable contributions to metal-organic frameworks (MOFs), thermoresponsive polymers, and graphene-based systems. Recent articles highlight advancements in AI-driven drug discovery, high-entropy alloys, and self-folding nanomaterials. Deshmukh’s research group actively explores applications in sustainable energy, biomedical engineering, and advanced materials. Lab activities include the Research Group Google Scholar , emphasizing interdisciplinary collaboration in materials innovation.
Panagiotis Hadjidoukas is an Associate Professor and Head of the Laboratory for Computing at the Computer Engineering and Informatics Department, University of Patras, within the School of Engineering. His work focuses on high-performance computing systems and parallel programming models. His research spans parallel and distributed computing , runtime support for parallel programming models , and automation of AI/ML workloads . Key contributions include developing the torc runtime system for task parallelism and pioneering work in extreme-scale scientific simulations. His interests bridge theoretical computer science with practical applications in scientific computing and AI acceleration. Notable achievements include the ACM Gordon Bell Prize Winner (2013) for 11 PFLOP/s cloud cavitation simulations and Finalist (2015) for in-silico lab-on-a-chip microfluidics. His software tools ( torc_lite , torcpy ) enable efficient parallelism across diverse architectures. Doctor of Philosophy (2003), University of Patras Master of Science (2001), University of Patras Diploma in Computer Engineering (1998), University of Patras As Head of the Laboratory for Computing, he leads infrastructure development while maintaining active research collaborations with IBM Research and ETH Zurich. His teaching portfolio includes graduate courses on high-performance computing for data sciences and parallel processing principles.
