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Kaihang ShiView profile

Assistant Professor

• Quantum Chemical Modeling
• Molecular Simulation
• Multiscale Modeling
• +6 more

Dr. Kaihang Shi is an Assistant Professor in the Department of Chemical and Biological Engineering at the University at Buffalo (UB), School of Engineering and Applied Sciences. He leads the Digital Porous Materials Laboratory (DP Lab) and serves as an affiliate member of the Acceleration Consortium. His research integrates machine learning, atomistic simulations, statistical mechanics, and mathematical modeling to study molecular adsorption, reaction, and transport in porous media for energy, sustainability, and healthcare applications. Education: PhD in Chemical Engineering (North Carolina State University, 2020), BS in Polymer Materials and Engineering (East China University of Science & Technology, 2015) Professional Affiliations: American Institute of Chemical Engineers (AIChE), American Association for the Advancement of Science (AAAS), International Adsorption Society (IAS) Dr. Shi's research focuses on computational discovery of nanoporous materials, particularly metal-organic frameworks (MOFs), using quantum chemical modeling, molecular simulations, and interpretable machine learning. His group develops tools like gRASPA for GPU-accelerated simulations and MOFX-DB for computational adsorption data sharing. Recent trends in his publications span: Machine learning for adsorption prediction in nanoporous materials Molecular transport mechanisms in MOFs and composites Microscopic pressure tensor analysis in confined systems CO2 capture and methane activation catalysts GPU algorithms for efficient simulation Scientific honors include: ACS PRF Doctoral New Investigator Award (2024) Best Poster Award, Diffusion Fundamentals XI (2025) Team Science Contest Winner, US Department of Energy (2021) James K. Ferrell Outstanding Ph.D. Graduate Award (NCSU 2020) Multiple teaching and conference presentation awards Dr. Shi mentors students in computational materials science, with Master’s graduates Asritha and Asha contributing to molecular transport studies. His group actively collaborates on CO2 capture projects and has secured grants from NSF, UB, and ACS for advanced materials research.

Lu PengView profile

Professor

• Computer Architecture
• GPU Computing
• Quantum Computing
• +2 more

Lu Peng holds the Yahoo! Founder Chair in Science and Engineering at Tulane University. His research spans computer architecture with focus on GPU optimization, quantum computing, and blockchain acceleration. Peng develops specialized hardware architectures for emerging computing paradigms, including quantum processors, blockchain processing units (BPUs), and efficient GPU memory systems. Recent innovations include GeauxTrace blockchain contact tracing and high-throughput GPU database indexing techniques. Awarded the ORAU Powe Award (2007) and IEEE conference best papers (2019, 2001), Peng maintains active research in computer systems design. He teaches graduate and undergraduate courses in computer architecture while directing research on next-generation computing paradigms.

• X-ray imaging and simulation
• Non-destructive testing by ionising radiation
• Inverse problems
• +9 more

Dr. Franck Vidal is a Senior Lecturer and Acting Director of Research at the School of Computer Science & Electronic Engineering at Bangor University, where he is also a member of the Visualization and Medical Graphics (VMG) Group and the Research Institute of Visual Computing (RIVIC). His educational background includes: PhD in Computer Science from Bangor University (2008) Postgraduate Certificate in Higher Education (2016) Diplôme d'études approfondies Images et Systèmes from Institut National des Sciences Appliquées de Lyon (2003) Master of Science in Computer-Aided Graphical Technology Applications from Teesside University (2002) Dr. Vidal's research focuses on X-ray imaging and simulation, non-destructive testing by ionising radiation, inverse problems, optimisation and artificial evolution, high performance computing, computer vision, image analysis and machine learning, and image-based simulation. His work has significant applications in medical imaging and medical physics, including X-ray, CT, MRI, PET and radiotherapy. He applies evolutionary algorithms to solve complex inverse problems in medical imaging. His recent publications demonstrate a strong focus on GPU-accelerated X-ray simulation technologies, particularly through the gVirtualXRay library, which enables real-time simulation of X-ray images and CT volumes. His work bridges the gap between computational physics, medical imaging, and practical clinical applications, with significant contributions to PET reconstruction algorithms using the Fly algorithm. Among his notable achievements: 2nd place in the Eurographics 2009 Medical Prize for ImaGINe-S David Duce Prize for the Best Short Paper (2019) Best Poster Presentation award (2022) Development of the gVirtualXRay open source library Multiple publications on evolutionary algorithms for medical imaging Dr. Vidal has supervised multiple research students including Julien Lavauzelle and Adrien Dutertre from ENSTA ParisTech. He has secured funding for significant projects including Fly4PET (focused on PET reconstruction for radiotherapy) and RAMPVIS (visualization for pandemic response). He actively collaborates with institutions worldwide, including INRIA in France and the University of California, San Diego. He leads the development of several innovative projects including gVirtualXRay (a GPU-based X-ray imaging library), Fly4Arts (evolutionary art using the Fly algorithm), and RASimAs (a regional anaesthesia simulator and assistant), demonstrating his ability to translate theoretical research into practical applications across medical and non-medical domains.

Shaoyi HuangView profile

Assistant Professor

• Efficient machine learning algorithms
• Algorithm-system co-design for AI acceleration
• Large-scale machine learning for chip design
• +3 more

Shaoyi Huang is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology. She holds a PhD in Computer Engineering from the University of Connecticut (2024), where she was advised by Professors Caiwen Ding and Omer Khan. Her research focuses on advancing AI systems through algorithm-hardware co-design, with emphasis on efficient machine learning algorithms, energy-efficient privacy-preserving techniques, and large-scale machine learning for chip design. She has received multiple accolades including the 2024 Machine Learning and Systems Rising Star award and the 2022 DAC Publicity Paper Award. Education: PhD in Computer Engineering, University of Connecticut (2024) Appointments: Assistant Professor, Stevens Institute of Technology (2024–present) Key Research Areas: AI acceleration, graph neural networks, privacy-preserving ML, and EDA applications Her work spans theoretical contributions to practical implementations, with publications in top-tier conferences like HPCA, ASPLOS, and NeurIPS. She actively contributes to the academic community through program committee roles (e.g., DAC 2025, IPDPS 2025) and serves as a Guest Editor for special issues on high-performance computing. Huang mentors PhD students and oversees a team advancing interdisciplinary AI research, including funded projects from NSF and industry partners like AWS and OpenAI. Awards highlight her impact: Marion & Frederick Buckman Engineering Fellowship (2024), multiple fellowships from UConn, and recognition at WISE conferences for promoting women in hardware security. Her lab emphasizes scalable solutions for real-world challenges in AI and hardware systems.

Michael BerryView profile

Professor

• Data Science
• Machine Learning
• Text Mining
• +4 more

Michael Berry is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. He holds a PhD in Computer Science from the University of Illinois at Urbana-Champaign, an MS in Applied Mathematics from North Carolina State University, and a BS in Mathematics from the University of Georgia. His research focuses on data science, machine learning, text mining, nonnegative matrix factorization, parallel computing, and their applications in biomedical and environmental domains. Notable contributions include work on tensor decomposition for big data analysis, algorithms for text mining, and computational tools like PolyLens and CodeAssessor. Berry's publications emphasize interdisciplinary applications, such as using nonnegative tensor factorization for biomedical literature analysis and developing GPU-accelerated methods for traffic flow analysis. His work spans conferences like the International Conference on Soft Computing in Data Science (SCDS) and journals in computational science. He has contributed to software tools like FutureLens for text visualization and SHEPPACK for interpolation algorithms. His research also addresses environmental challenges via parallel ecosystem modeling and spatial control problems. No scientific awards or grants are explicitly mentioned in the provided text.

Hadjiefthymiades StathesView profile

Professor

• IoT Security
• Cloud Computing
• Machine Learning
• +7 more

Hadjiefthymiades Stathes is a Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His primary research division focuses on Computer Systems and Applications. He specializes in IoT security, cloud computing, machine learning, and edge computing, with notable contributions to data analytics, trust management, and cybersecurity in distributed systems. Research Interests: His work spans IoT security protocols, optimization of cloud storage systems, anomaly detection in Kubernetes environments, and predictive analytics for sensor networks. He has explored trust models in pervasive computing, energy-efficient node authentication, and hardware-accelerated mixed reality interfaces. His recent projects include frameworks for Earth observation data accessibility and next-generation internet experimentation platforms. Publications: His recent articles highlight advancements in IoT data reduction techniques, trust sustainability in infrastructure, and the application of Markov chains for multivariate event correlation. He has also contributed to the design of experiment description languages for mobile IoT and emergency response platforms for radiological incidents. Awards & Grants: No scientific awards or grants were explicitly mentioned in the provided texts. Labs & Teams: His research involves collaborations on projects like EO4EU-AI (Earth observation with extended reality interfaces) and TRACE (logistics integration systems). He is affiliated with the university's advanced systems research groups focusing on edge computing and secure IoT ecosystems.

Dar-Jen ChangView profile

Associate Professor

• Computer Science
• Parallel Computing
• GPU Programming
• +7 more

Dar-Jen Chang is an Associate Professor in the Department of Computer Science and Engineering at the University of Louisville. He holds a B.S. in Mathematics from National Tsing Hua University (1970), an M.S. in Computer Information/Control Engineering from the University of Michigan (1982), and a Ph.D. in Mathematics from the same institution (1982). His research focuses on parallel computing, GPU programming, bioinformatics, 3D modeling, and algorithm optimization. His work spans disciplines including computer science, data mining, and biomedical applications. Chang’s research interests emphasize leveraging GPU acceleration for computational tasks such as RNA folding algorithms, hierarchical clustering, and Euclidean distance calculations. He has explored applications in 3D anatomical modeling, robotics simulation using Unity, and gaming frameworks with CUDA integration. His contributions include developing frameworks for algorithm visualization and database systems for automated process planning. His publications highlight trends in GPU-optimized algorithms, graph database analysis (e.g., Yelp and IMDb datasets), and interdisciplinary applications like medical imaging and genetic sequence prediction. He has also contributed to neural networks for fuzzy logic systems and biological sequence mining.

Longfei ShangguanView profile

Assistant Professor

• Mobile and wireless systems
• IoT systems
• Edge computing
• +5 more

Longfei Shangguan is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. He leads the Pittsburgh Experimental Research Group (PERG), focusing on hardware/software systems to advance computing, sensing, and communication in sensor/wireless technologies. Previously, he was a postdoc at Princeton and a senior researcher at Microsoft. He holds a PhD from HKUST (2015) under Professors Yunhao Liu and Ke Yi. Research Interests: Mobile/wireless systems, IoT, edge computing, and hardware/software co-design for sensor networks. Key areas include novel IoT applications, low-power hardware, and wireless protocol optimization. Key Projects Sensor-in-the-loop AI agents leveraging multimodal sensors Human-centric sensing using acoustic/vision modalities Wireless Digital Twin via neural ray tracing Professional Contributions Editor: IEEE Pervasive Computing, ACM GetMobile PC Member for top conferences including MobiCom, NSDI, SenSys Organized Dagstuhl Seminar on IoT Wireless Scenarios Awards NSF CAREER Award (2024) Google Research Scholar Award (2023) Best Paper Awards at MobiCom 2024 and MobiCom 2021 Advising & Grants Supervises 5 PhD students, 3 MS students, and undergraduate researchers Recipient of MassAITC Pilot Award (2025), NSF CAREER (2024) Labs/Teams: PERG lab focuses on experimental systems research with hands-on prototyping.

D. Brian LarkinsView profile

Associate Professor

• Parallel Computing
• High-Performance Computing (HPC)
• Distributed Systems
• +5 more

Dr. D. Brian Larkins is an Associate Professor and Chair of the Computer Science department at Rhodes College in Memphis, Tennessee. He specializes in parallel computing, high-performance computing (HPC), and distributed systems. His research focuses on optimizing runtime systems, work stealing algorithms, and leveraging network offload hardware to enhance parallel application performance. He has been recognized with the Clarence Day Award for Outstanding Teaching in 2021, reflecting his dedication to pedagogical innovation in computer science education. Before joining Rhodes College in 2015, Larkins worked at Bell Laboratories and several startups, focusing on network security, machine learning, and video streaming technologies. He holds a B.S., M.S., and Ph.D. in Computer Science and Engineering from The Ohio State University, with a focus on Parallel and Distributed Computing. As the NSF-funded XSEDE Campus Champion at Rhodes, Larkins supports faculty and students in accessing large-scale HPC resources nationwide. He also leads the installation of a 2,112-node HPC cluster in Briggs Hall for institutional research use. His teaching philosophy emphasizes blending theory with practical problem-solving to cultivate analytical thinkers. Key research contributions include frameworks for distributed data structures (e.g., Global Trees), scalable work stealing mechanisms, and GPU acceleration of scientific systems like the Advanced Regional Prediction System (ARPS). His work has been published at top venues such as the International Conference on Parallel Processing (ICPP) and the ACM/IEEE Supercomputing Conference (SC). Larkins has pioneered FPGA integration into computer science curricula and explored cognitive apprenticeship frameworks in robotics education. His current projects aim to repurpose network hardware for efficient distributed computing and dynamic load balancing.

• Process Systems Engineering
• Large-Scale Optimization
• High-Performance Computing
• +2 more

Carl D. Laird is the John E. Swearingen Professor and Department Head of Chemical Engineering at Carnegie Mellon University. He leads an internationally recognized research program in process systems engineering, known for high-performance computing techniques in large-scale nonlinear optimization, parallel scientific computing, and open-source software development. Education: Ph.D. in Chemical Engineering, Carnegie Mellon University (2006) B.S. in Chemical Engineering, University of Alberta (2000) Research Focus: His work solves problems in non-traditional domains including public health, homeland security, critical infrastructure, and energy systems through advanced optimization methodologies. Current research integrates machine learning with optimization for improved decision-making in complex systems. Publication Trends: Recent work focuses on mathematical optimization frameworks, decomposition methods for large-scale problems, integration of machine learning surrogates, and applications in energy systems and chemical manufacturing. Research demonstrates consistent innovation in computational methods for engineering challenges. Awards and Honors: Steven J. Fenves Award for Systems Research INFORMS Computing Society Prize CAST Division Outstanding Young Researcher Award NSF CAREER Award Montague Center Teaching Excellence Award Wilkinson Prize for Numerical Software (for IPOPT development) Leadership and Funding: As director of the Center for Advanced Process Decision-Making, he oversees collaborative research with industry partners. His research has been supported by NSF, DOE, and industrial consortia.

• Computer Engineering
• Embedded Systems
• Radiation-Hardened Electronics
• +3 more

Andrea Portaluri is a Ph.D. candidate in Computer Engineering (37th cycle, 2021-2024) at Politecnico di Torino's Department of Control and Computer Engineering (DAUIN). He also serves as an external lecturer and teaching collaborator for the course 'Operating Systems' in the 2024/25 academic year. His research focuses on radiation-hardened FPGA design, real-time embedded systems reliability, and fault tolerance in software-hardware co-design. Research interests include radiation effects on electronics, FPGA-based acceleration, real-time operating systems (RTOS), and mitigation techniques for soft errors in embedded systems. He collaborates with researchers like Luca Sterpone, Sarah Azimi, and Corrado De Sio on projects addressing reliability in space and safety-critical applications. His recent publications analyze radiation-induced errors in FPGAs, GPU-enhanced CAD tools for radiation-hardened designs, and multi-core neural network accelerators. Work trends emphasize improving embedded system reliability through hardware-software co-design, domain isolation, and fault-tolerant architectures. Portaluri actively participates in international conferences, presenting at venues like ACM Computing Frontiers, IEEE IOLTS, and IEEE Parallel and Distributed Computing. He has contributed to over 15 peer-reviewed articles and conference proceedings since 2021, focusing on radiation-hardening techniques and embedded system resilience.

Sabrina M. NeumanView profile

Assistant Professor

• Computer Architecture
• Robotics
• Domain-Specific Accelerators
• +3 more

Sabrina M. Neuman is an Assistant Professor of Computer Science at Boston University, specializing in computer architecture design informed by domain-specific insights, particularly for robotics applications. She holds a PhD in Electrical Engineering and Computer Science from MIT and was a postdoctoral NSF Computing Innovation Fellow at Harvard University. Her research focuses on closing computational gaps in robotics through domain-specific accelerators, leveraging robot morphology for hardware design. She has received honors including the 2021 EECS Rising Star and the Boston University Innovation Career Development Professorship (2023-2026). Education: PhD in Electrical Engineering and Computer Science, MIT M.Eng. in Electrical Engineering and Computer Science, MIT S.B. in Electrical Engineering and Computer Science, MIT Research Interests: Her work emphasizes domain-specific computing for robotics, including automated hardware design flows, FPGA/ASIC acceleration for motion planning, and scalable accelerator deployment across robot platforms. She develops methodologies like Robomorphic Computing, which translates robot morphology into customized hardware accelerators, achieving significant speedups in critical robotics kernels. Awards & Recognition: 2021 EECS Rising Star IEEE Micro Top Picks Honorable Mentions (2022, 2023) 2023-2026 Boston University Innovation Career Development Professorship Grants & Contributions: Developed RobotPerf, an open-source benchmarking suite for robotics computing systems Worked on Grid (GPU-accelerated rigid body dynamics) and RobotCore (ROS 2 hardware acceleration) Labs & Teams: While no specific lab name is mentioned, her work integrates closely with Boston University's robotics and computer architecture groups, emphasizing open collaboration with industry and academia.

Daniel LohmannView profile

Professor

• Operating Systems
• Embedded Systems
• Real-Time Systems
• +4 more

Professor Daniel Lohmann leads the Systems Research and Architecture (SRA) group at Leibniz Universität Hannover since 2017, following his role as Associate Professor (Privatdozent) at Friedrich-Alexander-Universität Erlangen-Nürnberg (2009–2016). His work focuses on configurable system software, emphasizing embedded systems, real-time systems, and dependable architectures. Key projects include danceOS (dependability in embedded OS), Sloth (minimal-effort kernels), and AspectC++ (aspect-oriented C++ extension). His research is funded by DFG and addresses challenges in variability management, fault tolerance, and real-time scheduling. Research interests span operating systems design, software product lines, generative programming, and aspect-oriented development. Notable contributions include the CiAO OS family and the Sloth RTOS, leveraging aspect-oriented techniques for flexibility and efficiency. His work on configuration analysis (e.g., static variability in Linux) and fault mitigation (e.g., AN-Codes for soft errors) highlights his expertise in system reliability and optimization. Publications emphasize real-time systems, embedded software, and software engineering, with awards including the Best Paper Award at SPLC 2010 and an Outstanding Paper at RTAS 2017. He supervises theses on system software tailoring, fault tolerance, and industrial software ecosystems. His groups collaborate on invasive computing (SFB/TRR 89) and hardware-aware OS design.

Julius ParulekView profile

Adjunct Associate Professor

• Molecular Visualization
• Bioinformatics
• Computer Graphics
• +2 more

Julius Parulek is an Adjunct Associate Professor at the University of Bergen's Department of Informatics, specializing in molecular visualization and computational methods for biomolecular structures. His work focuses on developing tools and techniques for interactive exploration of protein dynamics, ligand transportation, and large-scale biological datasets. He collaborates with domain experts in biochemistry and structural biology to create visualization solutions for complex molecular processes. Research interests include: Interactive visualization of protein tunnels and cavities Multiscale rendering of biomolecular complexes Geological modeling and sketch-based visualization Agent-based modeling for biochemical processes Key contributions include: Development of the cellVIEW tool for large-scale biomolecular visualization Pioneering work on continuous levels-of-detail for molecular surfaces Techniques for unfolding protein tunnels to analyze their dynamics His research bridges computer graphics with structural biology, enabling scientists to better understand molecular mechanisms through advanced visualization approaches.

Gabriele MencagliView profile

Associate Professor

• Parallel Programming
• Parallel Architectures
• Self-* Systems
• +3 more

Dr. Gabriele Mencagli is an Associate Professor in the Department of Computer Science at the University of Pisa, Italy. He holds a Ph.D. in Computer Science (2012) and has served as an Assistant Professor (2014–2018) and Tenure-Track Professor (2018–2021) before his current position. His research focuses on parallel systems, including architectures, programming models, and runtime systems for data stream processing. He leads work on the WindFlow stream processing library and has contributed to projects like TEXTAROSSA, ADMIRE, and EUPEX. He co-organized major conferences like HPDC 2024 and DEBS 2025 and serves on editorial boards for journals like Future Generation Computer Systems and Cluster Computing. Education: B.Sc. (2006, summa cum laude), M.Sc. (2008, summa cum laude), Ph.D. (2012) in Computer Science, all from the University of Pisa. Research Interests: Parallel programming, self-adaptive systems, data stream processing, GPU/FPGA acceleration, and high-performance computing. Over 80 publications in top journals and conferences, including IEEE TPDS, JPDC, and Euro-Par. Awards include Italian Habilitation as Full Professor (2025). Teaching: Courses on High-Performance Computing and Computer Architecture, including CUDA programming and parallel design patterns. Active in curriculum development for both bachelor’s and master’s programs. Grants & Projects: Principal investigator in EU-funded projects (e.g., TEXTAROSSA, NOUS) and collaborations with industry (e.g., List-group S.p.A., Autodesk). Focus on exascale computing, digital twins, and edge computing.