Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University , focusing on Data Management , System Efficiency (e.g., Scalable Data Platforms), and Human Efficiency (e.g., LLM-Based Query Interfaces). His work bridges Database Systems , Machine Learning , and Human-Computer Interaction . Co-founder of four startups: Paradise (1997), Locomatix (2007), Quickstep (2015), and DataChat (2017). Member of SIGMOD 2025 (AE) , CIDR 2024 (Co-Chair) , and other program committees. Research Interests include efficient data analysis algorithms , LLM-based data interaction , and systems security . His group develops platforms combining scalability and user productivity . Scientific Awards include Best Paper Awards at SIGMOD and VLDB, and Fellowships from AAAS, ACM, and IEEE. He also received Teaching Awards at CMU. Professional Activities feature co-founding startups , serving on program committees , and teaching courses like Database Systems and Advanced Database Systems at CMU.
Per Christian Hansen is a Professor at the Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark (DTU), where he leads the Section for Scientific Computing. He is a VILLUM Investigator and heads the CUQI (Computational Uncertainty Quantification for Inverse Problems) research initiative, aiming to develop accessible computational platforms for uncertainty quantification in inverse problems. His expertise lies in numerical analysis, numerical linear algebra, iterative reconstruction methods, and computational inverse problems, with applications in tomography, signal analysis, and plasma physics. His research integrates theoretical analysis—such as perturbation and convergence analysis—with the development of robust, adaptive, and efficient computational methods. He has co-authored five books, over 100 scientific papers, and several widely used MATLAB software packages, including IR Tools and Regularization Tools. His recent work (2023–2025) emphasizes uncertainty quantification, Bayesian inversion, and high-dimensional tomography in fusion plasmas, reflecting a strong trend toward probabilistic and robust modeling in inverse problems. He is a SIAM Fellow (2015) for his contributions to computational methods for rank-deficient and discrete ill-posed problems and regularization techniques. His scientific leadership is evident in both theoretical advances and practical software implementations. He actively collaborates across disciplines, particularly in nuclear fusion and medical imaging, and continues to supervise PhD students and publish in top-tier journals such as Inverse Problems , SIAM Journal on Scientific Computing , and Nuclear Fusion . SIAM Fellow (2015) VILLUM Investigator He advises PhD and Master’s students in computational mathematics and inverse problems, and his research is supported by major grants, including the VILLUM Investigator award. He leads the CUQI team, which develops open-source tools for non-experts to apply uncertainty quantification in inverse problems. His lab focuses on creating modeling frameworks that bridge theory, computation, and real-world applications in materials science, imaging, and plasma diagnostics.
Faez Ahmed is an Associate Professor at the Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), where he serves as the Doherty Chair in Ocean Utilization. He leads the Design Computation and Digital Engineering (DeCoDE) Lab, focusing on integrating machine learning and optimization with engineering design to enhance human-AI collaboration and accelerate design processes. Ph.D., Mechanical Engineering, University of Maryland College Park (2019) B.Tech.-M.Tech., Mechanical Engineering, Indian Institute of Technology Kanpur (2012) His research interests include generative design methodologies, AI-driven optimization techniques, and the development of algorithms that facilitate collaboration between human designers and artificial intelligence systems. This interdisciplinary work spans applications in automotive design , ship hull synthesis , and wind turbine optimization , with a strong emphasis on creating open-source tools and datasets for the engineering community. Recent publications demonstrate his lab's leadership in fields such as 3D CAD generation , multimodal design datasets , and constraint-aware generative models . These works often address challenges in design space exploration , performance prediction , and data-driven design frameworks . Scientific Awards NSF CAREER Award (2025) ASME Young Investigator Award (2024) Google Research Scholar Award (2024) 3M Non-Tenured Faculty Award (2022) University of Maryland Alumni Research Award (2022) Faez Ahmed's lab has trained numerous Ph.D. candidates and postdoctoral researchers, fostering a collaborative research environment that bridges mechanical engineering , artificial intelligence , and computational methods . The DeCoDE Lab actively engages with industry partners and academic institutions, contributing to large-scale datasets and benchmarks that power the next generation of engineering design research.
Jim Thatcher is an Assistant Professor at the College of Earth, Ocean, and Atmospheric Sciences (CEOAS) at Oregon State University. He serves as editor of Cartographic Perspectives , a leading journal in cartography. His research focuses on the intersection of technology, society, and environment, particularly examining how data and spatial technologies operate within capitalist systems. Key areas include Critical GIS, Critical Cartography, and Digital Political Ecology. Thatcher's current projects explore equitable coastal flood risk modeling using topological data analysis, mapping infrastructural legacies of slavery in the Pacific Northwest, and creating accessible disaster response maps for non-English speakers in Oregon. He also investigates spectral governance for rural internet access, cartography's societal role, climate education through board games, and geographic influences on electoral districting. His research emphasizes critical approaches to spatial data, questioning representational practices and power dynamics embedded in geospatial technologies. Recent work includes analyzing Bitcoin mining's environmental impacts on hydropower systems and examining the World Bank's renewable energy mapping through Critical Data Studies. Thatcher actively mentors graduate students and encourages interdisciplinary inquiries blending critical social theory with computational methods. Thatcher has authored numerous articles and co-authored Data Power (Pluto Press, 2022), an open-access exploration of data's role in societal control and resistance. His scholarship bridges critical geography with emerging technologies, advocating for ethical and equitable spatial practices in an increasingly data-driven world.
Azad J Naeemi is a Professor holding the Dean's Professorship in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He serves as Editor-in-Chief of the IEEE Journal on Exploratory Computational Devices and Circuits and Associate Director for Computation of the NSF-supported National Nanotechnology Coordinated Infrastructure (NNCI). His educational background includes a B.S. in Electrical Engineering from Sharif University (1994) and M.S./Ph.D. in Electrical and Computer Engineering from Georgia Tech (2001/2003). Prior to academia, he worked as a design engineer in Tehran (1994-1999) and as a research engineer at Georgia Tech's Microelectronics Research Center (2004-2008). Professor Naeemi's research spans nanotechnology with focus on emerging nanoelectronic devices, spintronics, ferroelectric devices, and design technology co-optimization for CMOS/beyond-CMOS technologies. His work bridges materials, devices, circuits, and systems, particularly investigating integrated circuits based on nanoscale devices and interconnects. Educational research includes experiential learning environments for engineering education. Recent publications (2024-2025) demonstrate strong emphasis on spin-orbit torque MRAM, ternary content addressable memories, ferroelectric/antiferroelectric devices, and plasmonic circuits. Key trends include energy-efficient hardware accelerators, neuromorphic computing applications, and compact modeling for advanced technology nodes. His scientific honors include: IEEE Solid-State Circuits Society James Meindl Innovators Award (2022) IEEE Electron Devices Society Paul Rappaport Award (2008) NSF CAREER Award (2013) SRC Inventor Recognition Award (2010) Multiple Georgia Tech teaching awards Professor Naeemi leads research supported by NSF (including NNCI infrastructure) and SRC. His editorial role with IEEE JXCDC positions him at the forefront of exploratory computational devices. He previously served as General Co-Chair for the IEEE International Interconnect Technology Conference (2013). His work connects with Georgia Tech's Microelectronics Research Center and national nanotechnology initiatives through the NNCI network, focusing on computational infrastructure for nanoscale device characterization and design.
Jonathan Shihao Ji is an Associate Professor in the School of Computing at the University of Connecticut (UConn), leading the Intelligent Systems Lab. He holds a Ph.D. in Electrical and Computer Engineering from Duke University and previously served as an Associate Professor at Georgia State University and Director of the DoD Center of Excellence (CiARE). His research focuses on deep learning applications in computer vision, NLP, robotics, and high-performance computing, with over 50 publications in top venues like CVPR, NeurIPS, and IEEE journals. He has secured grants from NSF, NIH, DoD, and industry partners including VMware and Nvidia. His work emphasizes efficient algorithms for large-scale data processing, parameter-efficient model fine-tuning (e.g., VB-LoRA), and 3D perception benchmarks for UAVs (UAV3D). Notable contributions include sparse network optimization (Dep-L0), energy-based models (M-EBM), and robust defenses against adversarial attacks (Defense-VAE). He is a Senior Member of IEEE and has developed open-source tools like Parallel Word2Vec and WordRank. Recent projects include accelerating Llama2 models on FPGAs (LlamaF) and improving text-to-image synthesis via contrastive learning. His research spans theoretical advancements and practical applications, with industry collaborations in healthcare, robotics, and embedded systems.
Jiang Hu is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Eric D. Rubin '06 Endowed Professorship. He also serves as Co-Director of Graduate Programs and is affiliated with the Computer Science & Engineering department. His research focuses on VLSI design automation, machine learning applications, and hardware security. He has held roles as editor for IEEE Transactions on CAD and ACM Transactions on Design Automation, and chaired the 2012 ACM International Symposium on Physical Design. Education: B.S. in Optical Engineering (Zhejiang University, 1990), M.S. in Physics (1997), and Ph.D. in Electrical Engineering (University of Minnesota, 2001). He worked at IBM Microelectronics before joining Texas A&M in 2002. Research interests include energy-efficient VLSI circuits, on-chip communication fabrics, analog layout automation, and AI-driven EDA. Recent work emphasizes machine learning for design closure, privacy-preserving frameworks, and systolic array-based architectures. Awards: IEEE Fellow (2016) Humboldt Research Fellowship (2012) Multiple best paper awards at DAC, ICCAD, and ASPDAC Advising and grants: Leads initiatives like the SLICE project, NSF workshops on ML-EDA infrastructure, and serves as Editor-in-Chief of ACM TODAES since 2024. His work bridges academic research and industry applications in EDA and semiconductor design. Labs/Teams: Active contributor to open-source tools like ALIGN for analog layout generation and collaborations on machine learning for EDA commons.
Dr. Jing Li is an Associate Professor and Eduardo D. Glandt Faculty Fellow at the University of Pennsylvania , holding dual appointments in the Electrical and Systems Engineering and Computer and Information Science departments. As co-director of the CyberSavvy nationwide security research center and director of the Penn Computational Intelligence Lab (PennCIL) , she pioneers innovations in non-von Neumann computing paradigms. Her research spans post-CMOS technologies, in-memory computing, and hardware-software co-design for security and AI applications. PhD in Computer Engineering, Purdue University (2009) BSc in Electrical Engineering, Shanghai Jiaotong University (2004) Research Focus: Dr. Li's work addresses fundamental challenges in computer systems across the stack. Key areas include: In-Memory Computing: Liquid Silicon architecture combining RRAM with silicon CMOS through monolithic 3D integration Security Engineering: Transforming computer security from "Art" to formal "Engineering" discipline within CyberSavvy Virtualization: Cloud FPGA abstraction layers decoupling compilation from runtime resource management Graph Analytics: Degree-aware optimization techniques for massive-scale graph processing Deep Learning Systems: Roofline model extensions for FPGA-based CNN acceleration Scientific Impact: Awarded DARPA Young Faculty Award , NSF CAREER Award , and IBM CEO Milestone Award , her team has achieved world records in energy-efficient computing (ENIAD supercomputer). With 46 U.S. patents and over 80 publications, she leads ecosystem development for emerging computing architectures through initiatives like the open-source MEG simulation platform . Community Leadership: Dr. Li serves on program committees for flagship conferences ( ISCA , FPGA Symposium ), chairs the International Memory Workshop , and contributes to the MLsys conference's inaugural committee. She actively mentors through multiple PhD openings and industry collaborations.
Hank Childs is a Professor in the School of Computer and Data Sciences at the University of Oregon, specializing in scientific visualization and high-performance computing. He leads the Research Group on Computing and Data Understanding at eXtreme Scale (CDUX) and has held leadership roles including Interim Executive Director of the School of Computer and Data Sciences. His educational background includes a Ph.D. (2006) and B.S. (1999) in Computer Science from the University of California at Davis. Prior to academia, he worked for 14 years at Lawrence Livermore and Lawrence Berkeley National Laboratories, where he served as architect of the VisIt open-source visualization tool. Research interests center on visualizing extreme-scale scientific datasets from supercomputers, with a focus on in situ visualization for cosmology, seismology, and fluid dynamics. He has pioneered projects like VTK-m and Ascent, and his work explores power-performance tradeoffs and data-parallel algorithms for GPUs. Recent publications emphasize scalable visualization techniques for exascale computing, with 15 notable works from 2021-2020 covering particle advection, in situ triggering, and power-aware frameworks. His research has been honored with multiple best paper awards at IEEE LDAV, EGPGV, and SC conferences. DOE Early Career Award (2012) University of Oregon Faculty Excellence Award (2018) 4+ million dollars in research funding since 2013 5 Best Paper awards in 2021 alone As an educator, he received four consecutive CIS Best Teacher Awards (2014-2019). He has served as Associate Editor for IEEE Transactions journals and organized numerous visualization workshops including Dagstuhl seminars and Shonan workshops.
Anders Krogh is a Professor at the Department of Computer Science, University of Copenhagen, and also holds a position at the Department of Public Health in the Section for Health Data Science and AI. He serves as the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences. Previously, he was affiliated with the Department of Biology at the University of Copenhagen until 2020. Dr. Krogh earned his PhD in theoretical physics but transitioned into machine learning and bioinformatics during his doctoral studies. His research spans both theoretical foundations and practical applications in these fields. He is particularly renowned for his pioneering work on hidden Markov models for biological sequences, which has had significant impact in computational biology. In recent years, Krogh's research has focused on deep generative models applied to gene expression data and other biomedical applications. His work bridges computer science with healthcare, developing AI-driven approaches for precision medicine, cancer diagnostics, and analysis of complex biological systems. His current research integrates machine learning with quantum computing applications in biomolecular modeling. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence to healthcare challenges, particularly in rare diseases, cancer diagnostics, and personalized medicine. His work increasingly incorporates federated learning approaches to address privacy concerns while enabling collaborative research across institutions. There's also a growing emphasis on quantum computing applications in biomolecular modeling and drug discovery. As head of the Center for Health Data Science, Krogh leads interdisciplinary research efforts that bring together computer scientists, medical researchers, and clinicians. His team develops novel computational frameworks like MOSAIC for multimodal analysis of rare cancers and multiDGD for multi-omics data integration. These tools are designed to translate AI innovations into clinical practice while addressing the unique challenges of medical data.
Dr. Gary Scavone is a Professor and Department Chair in the Music department at McGill University's Schulich School of Music. He holds a PhD in Computer-Based Music Theory & Acoustics and MS in Electrical Engineering from Stanford University, alongside degrees from Syracuse University in Music and Electrical Engineering. His research focuses on music technology, including acoustic modeling, sound synthesis, and instrument design. He directs the Computational Acoustic Modeling Laboratory (CAML), which explores advanced techniques for simulating musical instruments and developing software tools. As a saxophonist, he specializes in contemporary concert music performance. Research interests include physically-based sound synthesis, wind instrument acoustics, and digital waveguide modeling. He has contributed to studies on brass and woodwind impedance measurements, violin soundpost dynamics, and free-reed instrument modeling. His work bridges engineering and artistry, with applications in music pedagogy, instrument design optimization, and virtual acoustic replication. Key contributions include open-source projects for wind instrument modeling and the development of tools for automated timbre assessment. His research often combines experimental methods with computational simulations, addressing challenges in both theoretical and applied music acoustics. Current projects focus on deep learning for friction modeling, impedance measurement systems, and cross-cultural instrument analysis.
Inga Berre is a Professor at the Department of Mathematics, University of Bergen, and serves as Director of the Center for Modeling of Coupled Subsurface Dynamics (CSD). She leads the Porous Media Research Group and was appointed Argyris Visiting Professor at the University of Stuttgart's SimTech Cluster of Excellence in 2023. Research Interests: Mathematical modeling, partial differential equations, numerical methods for coupled thermo-hydro-mechanical-chemical processes in subsurface systems, and fault reactivation induced by injection/production. Scientific Leadership: Member of SIAM Council (2022-2027), Chair of SIAM GS activity group (2021-2022), Co-Chair of SET-Plan Deep Geothermal Implementation Working Group (2019-2021), and Chair of the Joint Program Geothermal, European Energy Research Alliance (2018-2021). Awards: 2011 Meltzer Award for Young Researchers. Advisory Roles: Member of Scientific Advisory Boards for GFZ (2024-2027) and SFB1313 (2018-), among others. Teaching: Developed courses on calculus, functional analysis, mathematical modeling, and numerical methods at the Bergen Summer Research School.
Dr. Benjamin Scott Flavel is a Research Group Leader at the Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Germany. His research focuses on carbon nanotubes and their applications in photovoltaics and sensing technologies. He leads a dynamic research team conducting cutting-edge work on the separation, purification, and application of single- and double-walled carbon nanotubes. Education: Doctor of Philosophy (Chemistry), Flinders University, Australia, 2010 Bachelor of Science in Nanotechnology (Honors), Flinders University, Australia, 2005 Habilitation in Materials Science, Technische Universität Darmstadt, 2018 His research interests lie at the intersection of nanotechnology and energy materials, particularly in carbon nanotube-based solar cells , chirality-specific separation techniques , and nano-sensing devices . His group has pioneered methods such as ATPE for enantiomer sorting and developed innovative solar cell architectures with industrial relevance. The research spans fundamental charge transport mechanisms to applied device engineering. The publication record shows a strong trend in advancing carbon nanotube photovoltaics, with key contributions in interface engineering, scalable fabrication, and high-efficiency CNT:Si solar cells. Work also extends to spectroscopic characterization, alignment techniques, and software tools for data analysis. Scientific Awards: Heisenberg Program, DFG (2018) Emmy Noether Program, DFG (2013) Alexander von Humboldt Research Fellowship (2011) Australian Government Endeavor Research Fellowship (2009) Bloom-Gutmann Prize, Royal Australian Chemical Institute (2008) Hope Meeting, Japanese Society for the Promotion of Science (2008) Research Fellowship, Flinders University (2007) Dr. Flavel has successfully advised multiple PhD students, including Dr. Moritz Pfohl, Dr. Katherine Moore, and Dr. Daniel Tune. His research has been supported by significant grants from the German Research Foundation (DFG), including funding for a double-walled carbon nanotube project and an organic evaporation system (~320,000 EUR). Collaborations span institutions such as NIST, Freie Universität Berlin, University of Antwerp, McMaster University, and University of Sydney. The research group operates within the Institute of Nanotechnology at KIT, utilizing advanced infrastructure for nanomaterials synthesis, characterization, and device fabrication. The team is recognized internationally, with work featured in Open Access Government , Research Features , and Europhotonics , and covered by Nanotechweb.
Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He leads research in data-intensive AI systems as a member of the Data and Information Systems (DAIS) lab, focusing on novel data systems that bridge database theory and practical AI applications. His work emphasizes open-source contributions through GitHub and direct societal impact. Research interests center on systems for data-intensive AI , particularly efficient Retrieval-Augmented Generation (RAG) systems for exploratory AI, data science versioning, and in-storage computing. Key projects include Kishu (the world's first undoable Jupyter notebook with time-travel capabilities), CARE (a causal-relational system for structured/unstructured data), and AirDB/AirIndex (serverless transactions and automatic index optimization). His group develops tools enabling scalable, optimized AI workflows from storage layers to LLM inference. Recent publications reveal a strong focus on interactive data systems (85% of recent work), with significant contributions to notebook environments (Kishu), vector databases (ISCA'25), and RAG optimization. Awards highlight technical innovation, including SIGMOD 2025 Best Demo Award and NSF CAREER funding. His open-source philosophy drives GitHub releases of all major systems. SIGMOD 2025 Best Demo Award (Kishu) NSF CAREER Award (Novel data science systems) SIGMOD'23 Best Artifact Award Honorable Mention (DeepOLA) IBM-Illinois Project Selection (VectorDB/RAG) Mentorship spans 12 current PhD/MS students and 6 graduated advisees, including Supawit Chockchowwat (now Postdoc at Google, future Assistant Professor at CMKL University). He teaches advanced courses like CS511 (Advanced Data Management) and recruits 1-2 new PhD students annually, prioritizing data systems research. His lab emphasizes diversity, individual respect, and concrete outcomes in a collaborative workspace.
Johan Driesen is a full Professor at KU Leuven's Faculty of Engineering Sciences, where he serves as Department Head of Electrical Energy Systems and Applications (ELECTA) and Director of the KU Leuven Institute for Energy and Society (KIEM). He also holds leadership positions at EnergyVille as Division Head and Subdivision Head. His work spans both academic and applied research in electrical energy systems. His research interests focus on renewable energy integration, power electronics, electrical drives, electric vehicles, and smart grids. Driesen's work particularly emphasizes distributed generation of electricity, with significant contributions to floating photovoltaics, offshore wind integration, and low-voltage DC systems. His research group actively investigates the optimization of grid integration for renewable energy sources and the development of advanced power electronic converters. Driesen's recent publications reveal a strong emphasis on practical applications of renewable energy systems, with particular focus on reliability analysis of low-voltage DC systems, optimization of PV-battery systems, and integration of electric vehicles with renewable energy sources. His work bridges theoretical analysis with real-world implementation challenges. Laureate prize of the Belgian Royal Society of Electrotechnics (KBVE/SRBE) 'Research and Development' for master theses 1996 2nd place in final round IEEE Region 8 Student Paper Contest 1997 in München, Germany Laureate biannual prize 'R.Sinave' of the Belgian Royal Society of Electrotechnics (KBVE/SRBE) for best PhDs 2002 Driesen has supervised numerous students and researchers, including J. Despeghel who completed work on residential PV-battery system optimization. His current research portfolio includes substantial projects such as Flux50 (working on the future energy system), solar and wind energy in the Belgian marine zone, and smart charging solutions that integrate e-mobility with renewable energy. He serves as promoter or co-promoter on multiple major research initiatives with funding extending through 2028. At EnergyVille, Driesen leads research teams working on cutting-edge energy solutions, with particular focus on DC systems, grid integration challenges, and the development of innovative power electronic solutions for renewable energy applications. His work has significant industrial relevance and practical implementation potential.
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