جست‌وجوی استاد و پژوهشگر

Aydın Buluçمشاهده پروفایل

استاد مدعو

• High-performance graph analysis
• Parallel sparse matrix computations
• Communication-avoiding algorithms
• +۳ مورد دیگر

Aydın Buluç is a Senior Scientist at the Lawrence Berkeley National Lab (LBNL) in the Applied Mathematics and Computational Research Division and an Adjunct Professor in the Electrical Engineering and Computer Sciences (EECS) department at UC Berkeley. At LBNL, he leads the Performance and Algorithms group, and at UC Berkeley, he is part of the SLICE lab. He also serves as the Director of Sparsitute, a DOE Mathematical Multifaceted Integrated Capability Center (MMICC) focused on sparse computations. Dr. Buluç's research focuses on high-performance graph analysis, parallel sparse matrix computations, and communication-avoiding algorithms with applications in machine learning and computational genomics. His work bridges theoretical computer science with practical applications in scientific computing, particularly in bioinformatics and large-scale data analysis. He has made significant contributions to the development of parallel algorithms for sparse linear algebra operations, which form the foundation for many graph analytics frameworks. His recent publications demonstrate a strong trend toward optimizing sparse computations for modern hardware architectures, particularly GPUs and distributed systems. The research spans theoretical algorithm design, practical implementation challenges, and applications in computational biology. A notable pattern is the increasing focus on communication-avoiding techniques for distributed graph neural network training and large-scale genomic analysis. Dr. Buluç has been actively involved in numerous professional activities, serving on program committees for major conferences including EuroSys (2026), ALENEX (2019, 2026), SPAA (2025), and IPDPS (2013-2019, 2021-2022, 2025). He has also served on the SIAM George Pólya Prize for Mathematical Exposition Selection Committee (2025) and as Founding Associate Editor for ACM Transactions on Parallel Computing (2013-2020). He leads the PASSION Lab research group, which focuses on parallel algorithms and systems for irregular numerical workloads. The lab develops several important open-source software packages including Combinatorial BLAS, HipMCL, PASTIS, CAGNET, and BELLA. These tools address challenges in large-scale graph analysis, protein sequence alignment, and distributed machine learning.

Han Mao Kiahمشاهده پروفایل

استادیار

• Coding Theory
• Combinatorics
• Information Theory
• +۱ مورد دیگر

Han Mao Kiah is an Assistant Professor at Nanyang Technological University , specializing in Coding Theory and Combinatorics . He earned his Ph.D. in Mathematics at NTU under Yeow Meng Chee and held a postdoctoral position at the Coordinated Science Lab, University of Illinois at Urbana-Champagne with Olgica Milenkovic . Current Role: Assistant Professor, NTU, Department of Mathematics Education: Ph.D. in Mathematics (NTU), Postdoc (University of Illinois) His research focuses on Coding Theory for applications in DNA-based data storage , Reed-Solomon codes , and combinatorial designs . Recent work includes Private Information Retrieval , Sequence Reconstruction , and Error Correction in distributed systems. Key trends in his publications involve Reed-Solomon codes , Private Information Retrieval , and DNA sequence profiling with a focus on Efficient Algorithms and Constrained Coding for error control in emerging storage systems. ISITA Early Career Researcher Paper Award (2022, Researcher: D. T. Dao) Memorable Paper Award Finalist (2021, Student: J. Chrisnata) Best Student Paper Award (2020, Student: J. Chrisnata) Student Paper Award Finalist (2012)

Garofalakis Minosمشاهده پروفایل

استاد

• Database Systems
• Data Streams
• Big Data Analytics
• +۷ مورد دیگر

Minos Garofalakis is a Professor of Computer Science at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete (TUC), where he directs the Software Technology and Network Applications Laboratory (SoftNet). He is also the Director of the Information Management Systems Institute (IMSI) at the Athena Research and Innovation Centre in Athens. Previously, he held roles at Yahoo! Research, Intel Research Berkeley, and Bell Laboratories, and was an Adjunct Associate Professor at UC Berkeley. Education: He earned a BSc in Computer Engineering from the University of Patras (1992), followed by MSc (1994) and PhD (1998) in Computer Science from the University of Wisconsin-Madison. Research Interests: His work focuses on Big Data analytics , including database systems, data streams, approximate query processing, probabilistic databases, and secure/private data analytics. Key areas include distributed stream processing, data synopses, and machine learning applications. He has authored over 150 papers and holds 29 patents, with an h-index of 63 and 13,500+ citations. Recent Work Trends: His recent articles emphasize scalable stream analytics (e.g., OmniSketch), privacy-preserving techniques, and distributed event processing. He explores challenges in handling high-velocity data streams, uncertainty in databases, and real-world applications like healthcare analytics. Scientific Awards: ACM Fellow (2018), IEEE Fellow (2017), TUC Excellence Award (2015), and multiple patents from Bell Labs/Yahoo/AT&T. Advising & Grants: He led EU projects such as FERARI, LEADS, and The Human Brain Project. His lab, SoftNet, develops tools for extreme-scale analytics and declarative networking. Current work includes interactive cross-platform analytics (Infore) and AI-driven medical data systems. Labs/Teams: Director of SoftNet Lab and IMSI. Collaborates with industry partners on distributed systems and privacy-preserving technologies.

David McDanielمشاهده پروفایل

استاد پژوهشی

• Computational Fluid Dynamics (CFD)
• Aircraft Stability and Control
• High-Fidelity Simulations
• +۵ مورد دیگر

David McDaniel is a Research Professor (Not-In-Residence) at the University of Alabama at Birmingham (UAB), affiliated with the Department of Aerospace Engineering within the College of Engineering. He holds a B.S. in Aeronautical Engineering from the United States Air Force Academy, an M.S. in Mechanical Engineering from The George Washington University, and a Ph.D. in Aerospace Engineering from the University of Colorado at Colorado Springs. Dr. McDaniel’s career spans military service and academic research. He began as a US Air Force flight tester analyzing stability and control of aircraft like the B-1A and B-2A bombers. Later, he taught aerodynamics and thermodynamics at the US Air Force Academy while pioneering computational aerodynamics research in the Aeronautics Lab. Currently, he leads development of the Kestrel fixed-wing simulation tool under the DoD’s High Performance Computing Modernization Program (HPCMP CREATE™-AV). He is a senior member of the American Institute of Aeronautics and Astronautics (AIAA) and maintains an active DoD security clearance. His research focuses on high-fidelity CFD simulations for multidisciplinary aerospace problems, including hypersonic flows, aircraft stability/control modeling, and unstructured grid methods. His work supports defense acquisition efficiency through advanced simulation tools like Kestrel, which integrates aerodynamics, propulsion, and structural dynamics for fixed-wing aircraft analysis. Publications highlight advancements in surrogate modeling, machine learning for aerodynamic predictions, and high-performance computing optimizations. His contributions address challenges in CFD accuracy, scalability, and real-world applications for fighter aircraft and hypersonic systems.

• Computational Neuroscience
• Machine Learning
• Cognitive Science
• +۳ مورد دیگر

Qiong Zhang is an Assistant Professor jointly appointed in the Psychology Department and Computer Science Department at Rutgers University. Her research bridges human learning and machine learning, focusing on comparing human memory systems with AI solutions and informing intelligent machine design through cognitive insights. She holds a Ph.D. from Carnegie Mellon University's Machine Learning Department under the School of Computer Science and completed postdoctoral training at Princeton University. Education: Ph.D., Machine Learning, Carnegie Mellon University (School of Computer Science) Postdoctoral Training, Princeton University Her research investigates cognitive constraints in memory systems and their neural bases. She employs computational modeling, behavioral experiments, and neural imaging to explore how memory optimizes performance under constraints. Recent work includes an NSF-funded project on external cue benefits/detriments in memory search. Her lab's findings aim to enhance memory interventions and inform AI design. Grants and funding include support from the National Science Foundation and Rutgers Brain Health Institute. She explores interdisciplinary topics like episodic memory in storytelling (comparing humans and LLMs), and cognitive resource modeling during memory encoding.

• Electromagnetic Fields
• RFID Technology
• Internet of Things (IoT)
• +۳ مورد دیگر

Luca Catarinucci is an Associate Professor in Electromagnetic Fields (ING-INF/02) at the University of Salento's Department of Innovation Engineering. He leads courses in Microwaves and Electromagnetic Solutions for Hi-Tech , focusing on IoT, RFID, and 3D-printed electronic devices. His research spans electromagnetic interactions with biological systems, RFID sensor integration, and additive manufacturing for high-frequency applications. He has co-authored over 200 publications, including 80+ journal articles and 4 book chapters, and holds patents in RFID technology. His work bridges academic research and industry applications, emphasizing practical solutions for wireless communication and environmental monitoring. Education: Laurea in Electrical Engineering from the University of Perugia (1998). Professional roles include Researcher at the University of Salento (2004–2015) and collaboration with ICEMB (Interuniversity Center for Electromagnetic Field Studies). His expertise includes electromagnetic simulation, antenna design, and IoT-enabled systems. Research interests: RFID technology, IoT infrastructure, microwave engineering, 3D-printed antennas, and electromagnetic characterization of novel materials. Projects include smart waste management systems, thermal comfort monitoring, and worker safety solutions using RFID and sensor networks. Labs/Teams: Part of the Department of Innovation Engineering's research groups focused on electromagnetic design, IoT, and additive manufacturing. Collaborates with industry partners on projects involving 3D-printed RF devices and energy-autonomous sensors.

Shukai Duمشاهده پروفایل

استادیار

• Numerical Analysis
• Scientific Machine Learning
• Inverse Problems
• +۶ مورد دیگر

Shukai Du is an Assistant Professor of Mathematics at Syracuse University , within the College of Arts and Sciences . Previously, he served as a Visiting Assistant Professor at the University of Wisconsin-Madison (2020–2024) and earned his Ph.D. in Applied Mathematics from the University of Delaware (2020). His research focuses on numerical analysis , scientific machine learning , and inverse problems , with expertise in discontinuous Galerkin methods , radiative transfer , and adaptive mesh refinement . He is also affiliated with his research website . Education: Ph.D., Applied Mathematics, University of Delaware (2020) M.S., Computational Mathematics, Wuhan University (2015) B.S., Pure Mathematics, Wuhan University (2012) Research Highlights: Dr. Du’s work emphasizes accelerating numerical methods via machine learning (element learning), developing goal-oriented hp-adaptive mesh refinement for inverse radiative transfer, and creating symplectic Hamiltonian finite element methods for electromagnetics. His methods prioritize accuracy, computational efficiency, and preservation of physical invariants. Recent Contributions: Recent publications address operator learning for finite element acceleration, adaptive-mesh inversion for inverse problems, and energy-conserving discretizations for Hamiltonian systems. His work bridges classical numerical methods and modern data-driven techniques. Awards: Wenbo Li Prize (2020, University of Delaware) Grants: "Breaking the 1D Barrier in Radiative Transfer: Fast, Low-Memory Numerical Methods for Enabling Inverse Problems and Machine Learning Emulators" (NSF, 2023–2026) "Convective Processes in the Tropics Across Scales" (NSF, 2024–2026) Labs/Teams: His research collaborates with computational scientists and applied mathematicians, focusing on interdisciplinary challenges in computational physics and data-driven modeling.

Gordon Kindlmannمشاهده پروفایل

دانشیار

• Biomedical Image Analysis
• Scientific Visualization
• Systems
• +۵ مورد دیگر

Gordon Kindlmann is an Associate Professor of Computer Science at the University of Chicago, affiliated with the Systems Group research community. His work bridges computational imaging science and visualization theory, focusing on biomedical applications and machine learning integration. He leads projects in diffusion MRI analysis, surgical planning tools like SlicerDMRI, and theoretical advancements in visualization design. Research Interests: Biomedical Image Analysis Scientific Visualization Theory High Performance Computing Medical Imaging Algorithms Machine Learning Applications Recent Articles Trends: His work emphasizes cardiovascular modeling (e.g., aortic dissection prediction) and visualization validation techniques. Recent collaborations include optimizing visualization tools for scalability and accuracy in threaded data exploration. Awards: None explicitly listed in provided text. Grants/Advising: Involved in CDAC Discovery Grants (2019) and actively supports student research, though specific advisees are not listed here. His lab develops open-source tools like Diderot for tensor field visualization. Labs & Teams: Member of the Systems Group, a collaborative environment advancing systems research, programming languages, and software engineering.

Stephen M. Wattمشاهده پروفایل

استاد

• Computer Algebra
• Symbolic Computation
• Programming Languages
• +۹ مورد دیگر

Stephen M. Watt is a Distinguished University Professor and Dean of the Faculty of Mathematics at the University of Waterloo, holding a position in the David R. Cheriton School of Computer Science. He earned his PhD and DSc (h.c.) in computer science and has contributed significantly to fields such as computer algebra, programming languages, and mathematical computing interfaces. His research focuses on symbolic computation, pen-based mathematical recognition, and compiler optimization. He is a co-creator of Maple and Aldor, and a key contributor to standards like MathML and InkML. Watt’s academic journey includes roles at the University of Western Ontario and IBM Research. His awards include the IBM Outstanding Innovation Award (1992), NSERC Synergy Award (2004), and IWAY Award for New Technology Development (2002). He has supervised numerous graduate students, advancing research in areas like approximate polynomial algorithms, mathematical collaboration tools, and handwriting recognition. His leadership extends to directing the Ontario Research Center for Computer Algebra (ORCCA) and pioneering projects like MONET (Mathematical Web Services) and PenMath (pen-based interfaces). Current research explores AI applications in STEM assessment, GPU-optimized arithmetic, and hybrid symbolic-numeric methods.

• Optimizing Compilers
• Program Analysis
• Embedded Systems
• +۴ مورد دیگر

Jens Knoop is a Full Professor at TU Wien, leading the Department of Information Systems Engineering (E194) and the Research Unit Compilers and Languages (E194-05). His roles include Head of Institute for Information Systems Engineering and membership in the Curriculum Commission for Informatics. He specializes in compiler design, program analysis, and embedded systems, with a focus on worst-case execution time (WCET) analysis and formal methods. His research interests span optimizing compilers, programming languages, and real-time computing. Notable contributions include the Platin tool suite for WCET analysis and contributions to the T-CREST project on time-predictable multi-core architectures. He has authored and edited multiple influential publications, including proceedings of international conferences such as Code Generation and Optimization (CGO) and ARCS. Professor Knoop has supervised numerous theses, including works on effect systems in Haskell, mutation testing in Rust, and robotic systems. He has received the Most Influential PLDI Paper Award (2002) for foundational work in compiler optimization.

• High-Energy Physics
• Particle Physics
• Proton-Proton Collisions
• +۵ مورد دیگر

Muammer Altan Çakır is a Professor in the Department of Physics Engineering at Istanbul Technical University, actively engaged in high-energy physics research with significant contributions to the CMS Collaboration at CERN. His work focuses on proton-proton collisions, Higgs boson physics, and top quark studies using Large Hadron Collider data. His research interests span experimental particle physics with emphasis on proton-proton collisions , Higgs boson characterization , lepton physics , and transverse momentum analysis . Recent work explores charm baryon production, Drell-Yan processes, and muon identification techniques using advanced multivariate methods in the CMS detector. Analysis of his 15 most recent publications reveals a strong focus on precision measurements in high-energy collisions, particularly in Higgs physics (3 publications), top quark physics (2), and detector optimization (2). His work consistently addresses fundamental questions in the Standard Model through advanced statistical techniques and large-scale data analysis. Doktora sonrası araştırma bursu (2010) Landes Baden Württenberg Doktorarbeit Stipendium (2006) LPC Fall Guest Visitor Faculty (2017) Multiple Performans ödülü awards (2012, 2013, 2015) Professor Çakır has supervised 15 students and leads multiple research projects including GPU-based distributed computing platforms for big data processing, real-time predictive maintenance systems, and deep learning applications for top quark spin correlation studies. His current projects integrate generative AI with domain-specific physics research while developing scalable open-source big data architectures for industrial applications. His laboratory work centers on CMS detector data analysis, with recent focus on online data visualization tools and multivariate techniques for particle identification. Current projects explore GPU-accelerated computing frameworks and generative AI applications for physics-informed design systems.

• Sustainable Energy Systems
• Numerical Optimization
• Deep Learning
• +۳ مورد دیگر

Dr. Manuel Dahmen serves as Head of Department at the Institute of Climate and Energy Systems (ICE-1) within the Research Center Jülich. His research focuses on designing sustainable and cost-efficient energy systems through numerical optimization and deep learning techniques. He leads efforts in advancing energy system technology, particularly in optimizing renewable energy integration, process network analysis, and the co-design of fuels and engines. His work emphasizes innovative applications of machine learning, such as physics-informed neural networks and reinforcement learning, to address challenges in energy system design and operation. Key areas include reducing greenhouse gas emissions in industrial processes, optimizing energy networks, and developing data-driven models for dynamic process control. Dr. Dahmen’s contributions span algorithm development (e.g., MUSE-BB decomposition algorithms), energy system scenario generation, and robust design methodologies under uncertainty. His research bridges computational methods with practical energy solutions, aiming to achieve decarbonization in industries like copper production and transportation. Publications highlight advancements in renewable energy integration, fuel design for spark-ignition engines, and the application of graph neural networks for molecular property prediction. His interdisciplinary approach fosters collaboration across chemical engineering, computer science, and energy economics to tackle global sustainability challenges.

• Neural Modeling
• Computational Neuroscience
• Neuroscience
• +۳ مورد دیگر

Michael Hines is a Senior Research Scientist in the Department of Neuroscience at the Yale School of Medicine. His work focuses on developing and optimizing computational tools for neural modeling, notably the NEURON simulation environment. This software enables researchers to model cellular and network mechanisms involved in neurological disorders and therapeutic interventions. Hines holds a PhD from the University of Chicago (1975) and has contributed extensively to computational neuroscience through software development and interdisciplinary collaboration. Education: PhD, University of Chicago, 1975 MS, Physics, University of Chicago, 1972 BS, Physics, Michigan State University, 1970 His research interests revolve around advancing computational methods for simulating neural systems, including the development of dynamic clamp techniques and real-time interfacing with living cells. He has collaborated with institutions globally to enhance the scalability and accuracy of NEURON for supercomputing environments. Hines' work bridges neuroscience, mathematics, and computer science, addressing challenges in modeling complex neural networks and their applications to disease mechanisms. Publications highlight his contributions to neural simulation software, error analysis in computational models, and interdisciplinary tools like ModelDB and SenseLab. His research underscores the integration of experimental and computational approaches to advance understanding of neurological disorders and therapeutic strategies. Labs/Teams: Hines leads the development of the NEURON Simulation Environment and contributes to collaborative projects in computational neuroscience at Yale.

Gunar Schirnerمشاهده پروفایل

دانشیار

• Embedded Systems
• Cyber-Physical Systems
• Hardware/Software Co-Design
• +۴ مورد دیگر

Gunar Schirner is an Associate Professor of Electrical and Computer Engineering at Northeastern University's College of Engineering . He holds a Ph.D. and M.S. from the University of California, Irvine, and a B.Sc. from Berufsakademie Berlin. His research focuses on embedded systems, cyber-physical systems, and hardware/software co-design, with emphasis on embedded vision and system-level methodologies. Education: Ph.D. & M.S. in Electrical and Computer Engineering, University of California, Irvine (2008, 2005) Bachelor's in Computer Engineering, Berufsakademie Berlin, Germany (1998) Research Interests: Embedded system modeling, real-time AI on edge devices, accelerator-rich computing architectures, and assistive robotics. His work bridges algorithm design with system-level implementation, including projects on neural-controlled prosthetics and marine mammal monitoring via passive acoustic sensing. Grants & Collaborations: Schirner leads/navigate grants totaling over $2M from the National Science Foundation (NSF), U.S. Army, and Office of Naval Research, including a $13M Army contract for distributed sensing research. He co-directs the Embedded Systems Laboratory , advancing heterogeneous platform design and embedded vision systems. Students & Impact: His advisees, including Mo Han and Yagmur Gunay, have won best paper awards at PETRA 2019. He actively integrates industry experience (e.g., Alcatel-Lucent) into teaching, mentoring students in both academia and industry.

Karoński Michałمشاهده پروفایل

استاد

• Random discrete structures
• Probabilistic combinatorics
• Graph theory
• +۱ مورد دیگر

Michał Karoński is a Professor of Mathematics at the Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznań, Poland, and serves as head of the Department of Discrete Mathematics. He has held visiting professorships at Emory University, The Johns Hopkins University, Purdue University, and Southern Methodist University since 1975, with ongoing engagements at Emory University since 1992. Research Focus: Discrete mathematics, random discrete structures, graph theory, and theoretical computer science. Leadership Roles: Editor-in-Chief of Random Structures & Algorithms (since 1990), Chairman of the Council of National Science Centre (2010–2016), and organizer of 17 bi-annual conferences on random structures. His research group in random structures gained international recognition in the 1970s. He has authored 60 publications, delivered over 30 plenary lectures, and collaborated globally at institutions like Microsoft Research, Bellcore, and BRICS. Scientific Awards: 2016 Medal 'Homini Vere Academico' (AMU Rector) 2013 Polonia Restituta Officer Cross (President of Poland) 1990 Minister of Science and Higher Education Prize His work spans probabilistic combinatorics, algorithm theory, and graph irregularity, with influential publications on stochastic Kronecker graphs, random intersection graphs, and distributed complexity in matchings.