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Lan WangView profile

Professor

• Internet Architecture
• Network Security
• Wireless Sensor Networks
• +7 more

Lan Wang is a Professor in the Department of Computer Science at the University of Memphis, temporarily assigned as a Program Director at the NSF. She holds a PhD in Computer Science from UCLA (2004). Her research focuses on Internet architecture, network security, and wireless sensor networks, with major grants from NSF, NIST, and DoD, including a $15M NSF-funded 'Named Data Networking' project. She has served as Department Chair (2016-2023) and is an IEEE Senior Member. Dr. Wang's research emphasizes scalable, reliable, and secure Internet infrastructure. She has pioneered advancements in named-data networking (NDN), including protocols for adaptive forwarding, secure access control, and efficient traffic management. Her work extends to applications in smart cities, public safety, and healthcare data sharing. She has received prestigious awards including the Willard R. Sparks Eminent Faculty Award (2022) and Dunavant Professorship (2021). Her teaching spans courses like Networking and Information Assurance, Advanced Computer Networks, and Wireless and Mobile Computing. She actively promotes gender diversity in CS, co-organizing events like the 'Networking Networking Women' panel at SIGCOMM. Her grants total over $15M, including NSF’s NDN project and university-funded initiatives. She advises the Women in Computing student chapter and has served on over 50 conference committees.

• Energy-Efficient Computing
• Hardware Acceleration
• Machine Learning
• +3 more

Dr. David Boland is a Senior Lecturer at the School of Electrical and Computer Engineering, University of Sydney. He holds an MEng and PhD from Imperial College London. His research focuses on energy-efficient hardware acceleration, particularly using FPGAs and application-specific integrated circuits (ASICs), to optimize computational efficiency in domains like machine learning and optical communications. He has contributed to projects involving custom hardware accelerators, federated learning for edge computing, and real-time signal processing. Education: MEng, Imperial College London, 2007 PhD, Imperial College London, 2012 Research Interests: Dr. Boland’s work emphasizes reducing computational overhead through customized hardware solutions. He explores techniques for minimizing unnecessary computations while maintaining accuracy, leveraging FPGA-based designs for parallelism and energy efficiency. Key areas include: Hardware acceleration for machine learning FPGA optimization for neural networks Energy-efficient algorithms for edge computing Online arithmetic and latency-accuracy trade-offs Grants & Collaborations: 2022: On-Board Federated Learning in Orbital Edge Computing (NSW Department of Industry) 2017: Fast Automated Anomaly Detection in Communication Networks (Defence Science & Technology Group) Affiliations: Member of the Net Zero Institute, collaborating on sustainable computing solutions.

Amlan ChatterjeeView profile

Associate Professor

• High-Performance Computing
• Big Data
• Graph Compression
• +5 more

Dr. Amlan Chatterjee is an Associate Professor in the Department of Computer Science at California State University, Dominguez Hills. His work focuses on high-performance computing, big data analytics, and GPU-based graph compression techniques. He has held academic roles since 2015, including Assistant Professor (2015-2021) and currently serves as Associate Professor. His research explores efficient computation on large datasets using multi-core architectures and GPUs, alongside cloud computing optimization and IoT applications in aviation and health monitoring. Education: Ph.D., Computer Science, University of Oklahoma, 2014 M.S., Computer Science, State University of New York, 2009 B.Tech., Computer Science & Engineering, West Bengal University of Technology, 2007 Research Interests: Dr. Chatterjee's research spans graph compression, social network analysis, cloud resource optimization, and IoT-driven solutions for aviation safety and health monitoring. He has mentored students in projects like GPU-based big data processing and cloud computing efficiency. Awards: Graduate Computer Science Scholarship (University of Oklahoma, 2012-13) Computer Science Advisory Board Scholarship (2012) Phillips Petroleum Scholarship (2010-11) Top Undergraduate Rank (1st/68 students) Academic Contributions: He has served on numerous committees, including the Research Chair for Untenured Faculty (2016-17), IEEE conference session chairs, and accreditation boards. His teaching includes courses on data structures, operating systems, and introductory computer science.

• Software Engineering
• Industry 4.0/IIoT Platforms
• Model-Based Software Development
• +7 more

Dr. Holger Eichelberger is part of the Academic Staff in the Software Systems Engineering (SSE) department at the University of Hildesheim's Institute of Computer Science. He is affiliated with Faculty 4: Mathematics, Natural Sciences, Economics and Computer Science. His roles include membership in the Managing Committee of the Institute of Computer Science and the Committee for Student Scholarships. He has extensive experience in model-based software development, Industry 4.0 platforms, and performance engineering. Research Interests: Software Engineering for adaptive systems, Asset Administration Shells (AAS), IIoT platforms, MLOps, container orchestration, and open-source tools like EASy-Producer and SPASS-meter. His work focuses on bridging research and industrial needs, particularly in smart manufacturing and edge computing. Publications highlight contributions to IIoT platform analysis, AI integration in Industry 4.0, and performance benchmarking of communication protocols. He has organized conferences like ICPE and SSP and reviewed for top journals such as IEEE Transactions on Software Engineering. Key projects include the IIP-Ecosphere platform and contributions to standards like AAS. Collaborations involve institutions like the University of the West Indies and industry partners through funded projects like BMBF AI-Lab HAISEM. His research emphasizes reproducibility, interoperability, and scalable solutions for industrial challenges.

Nesime TatbulView profile

Researcher

• Large-scale Data Management Systems
• Time Series Analytics
• Observability Data Management
• +4 more

Nesime Tatbul is a Senior Research Scientist at Intel Labs and MIT's Computer Science and Artificial Intelligence Lab (CSAIL). She leads Intel's Data Systems and AI Lab (DSAIL) and previously held a faculty position at ETH Zurich. She holds a PhD and MS from Brown University and BS/MS from Middle East Technical University (METU). Her research focuses on large-scale data management systems, learned systems, time series analytics, and observability. Key contributions include the Aurora/Borealis and S-Store systems. She has served on program committees for SIGMOD, VLDB, and CIDR, and holds roles as an ACM Distinguished Member and IEEE Senior Member. Her work spans over 70 publications, including influential contributions to stream processing and query optimization. Awards include the PVLDB Distinguished Editor Award (2023), CIDR Test of Time (2025), and ACM SIGMOD Best Paper (2021). Current projects include DSAIL, Exathlon, and Mach, advancing observability and AI-driven data systems. She also contributes to editorial roles at VLDB and PVLDB.

• Complex Event Processing
• Healthcare Informatics
• Semantic Computing
• +5 more

Fabio Persia is a Professor at the University of Naples Federico II with an extensive publication record spanning 15 years (2009-2025), demonstrating continuous academic engagement. His research portfolio spans multiple institutions through collaborations with over 70 co-authors, most notably Daniela D'Auria (43 publications), Mouzhi Ge (17), and Giovanni Pilato (14). Dr. Persia's research interests focus on the intersection of semantic computing, event processing, and practical applications. His work evolved from foundational contributions to multimedia recommender systems (2013) to developing the ISEQL interval-based surveillance event query language (2016), and most recently to healthcare AI applications. He has pioneered complex event processing frameworks for video surveillance, created multi-agent systems for epilepsy detection (PredictMed-epilepsy), and explored social sensing for personalized routing during the pandemic. His recent work increasingly integrates large language models with healthcare monitoring systems, reflecting current AI trends. Analysis of his publication trends since 2020 reveals a strong healthcare focus (65% of recent work), particularly in patient monitoring architectures, clinical decision support, and medical AI integration. His publications demonstrate consistent methodological rigor across domains, often combining semantic computing with real-time event processing to address practical challenges in healthcare and social computing contexts. Dr. Persia has served as guest editor for six special issues in the International Journal of Semantic Computing (2023-2025) covering Robotic Computing, Transdisciplinary AI, and Multimedia Computing, confirming his leadership in these research communities. His editorial roles complement his extensive publication record, which includes 25 journal articles and 76 conference papers. His collaborative research includes significant projects with Stefania Costantini on patient monitoring systems, with Mouzhi Ge on multimedia recommenders, and with Sven Helmer on interval joins and event detection. While specific grant information isn't detailed in publications, his sustained output suggests successful funding from multiple sources. His current research trajectory indicates growing emphasis on LLM integration in medical contexts and context-aware systems for public health applications.

• Distributed Computing
• Theoretical Computer Science
• Parallel Algorithms
• +4 more

Hsin-Hao Su is an Assistant Professor in the Department of Computer Science at Boston College. His research focuses on distributed computing through the lens of theoretical computer science, emphasizing parallelism, locality, communication, combinatorial optimization, symmetry breaking problems, and gossip-based algorithms. He completed his Ph.D. at the University of Michigan (2010–2015), advised by Seth Pettie, followed by a postdoctoral fellowship in Nancy Lynch's group at MIT (2015–2017). **Education:** Ph.D. in Computer Science, University of Michigan (2010–2015) Postdoctoral Associate, MIT (2015–2017) His work addresses foundational challenges in distributed systems, including efficient algorithms for shortest paths, graph decomposition, and task allocation. Notable contributions include advancing parallel and distributed approaches to clustering, matching, and quantile computations. Su has served on program committees for major conferences like ISAAC, ESA, and DISC, and held roles such as Workshop Chair (PODC 2021) and Student Travel Awards Organizer (PODC 2018). **Research Trends:** His articles emphasize algorithm design under distributed and parallel constraints, with recent focus on optimizing communication, reducing computational rounds, and bridging theoretical guarantees with practical efficiency. Topics span graph algorithms, approximation techniques, and bio-inspired methods.

Fengjing LiuView profile

Associate Professor

• Ecohydrology
• Watershed Hydrology
• Biogeochemistry
• +4 more

Fengjing Liu is an Associate Professor at the Great Lakes Research Center (GLRC) of Michigan Technological University. His research focuses on ecohydrology, watershed hydrology, and biogeochemistry, with an emphasis on understanding the impacts of human perturbation and climate change on water resources. He leads studies in snowmelt-dominated watersheds, black ash wetlands, and the interactions between landscape and climate in the Great Lakes region. Education: PhD, Hydrology and Biogeochemistry, University of Colorado at Boulder MS, Hydrology and Water Resources, Lanzhou Institute of Glaciology & Geocryology, Chinese Academy of Sciences BS, Hydrogeology and Engineering Geology, Lanzhou University, China Research Interests: Dr. Liu’s work addresses three core themes: (1) the effects of forest management and invasive species on black ash wetlands, (2) snowmelt dynamics and climate change impacts in headwater catchments, and (3) landscape-climate interactions influencing water and greenhouse gas fluxes. His methods include isotope hydrology, end-member mixing analysis, and advanced hydrochemical tracing. Publications: Recent work explores snowmelt-regulated stream chemistry, climate-driven glacier mass loss, and agroforestry’s role in nitrogen mitigation. His studies span diverse regions from the Tibetan Plateau to the Great Lakes Basin, emphasizing interdisciplinary approaches to water sustainability. Grants & Advising: His research has been supported by grants focusing on ecohydrology and climate adaptation. While specific grants are not listed, his publications reflect collaborative projects with institutions like the USDA Forest Service and the National Science Foundation. Labs/Teams: Maintains active research programs within the GLRC, focusing on field experiments, hydrological modeling, and environmental monitoring in cold and temperate regions.

Veronica WebsterView profile

Associate Professor

• Flood Frequency Analysis
• Climate Change Impacts
• Uncertainty Analysis
• +4 more

Dr. Veronica L. Webster is an Associate Professor in the Department of Civil & Environmental Engineering at Michigan Technological University. She holds a BS from the University of Vermont and MS/PhD from Cornell University. Her research focuses on flood frequency analysis, climate change impacts, uncertainty analysis, and land use changes affecting flood risks. She received the NSF CAREER Award for her work integrating climate variability and human-induced hydrologic shifts into flood risk projections. Education: BS in Civil Engineering, University of Vermont MS/PhD in Civil Engineering, Cornell University Research Interests: Dr. Webster’s work examines extreme hydrologic events, climate adaptation strategies, and data-driven methodologies for flood risk assessment. Her contributions include advancing statistical techniques for regional flood analysis and evaluating human-environment interactions in watersheds. She actively engages in professional organizations like the ASCE Environmental Water Resources Institute. Grants & Awards: NSF CAREER Award (2011–2018) for flood risk projections Co-PI on international engineering programs in Tanzania Recipient of S-STEM graduate scholarships Academic Contributions: She teaches courses on stochastic hydrology, water resources engineering, and uncertainty analysis. Her funded research has supported over 30 students through scholarships and international fieldwork opportunities.

• Computational Neuroscience
• Machine Learning
• Neural Dynamics
• +5 more

Dr. Emili Balaguer-Ballester is an Associate Professor in Computational Neuroscience at Bournemouth University, UK. He co-champions the Interdisciplinary Neuroscience Research Centre and serves as Senior Fellow of the Higher Education Academy. His research spans computational neuroscience, machine learning, and virtual reality applications, with collaborations across Europe (University of Barcelona, Heidelberg University, IDIBAPS, Polyra) and North America (Indiana Purdue University, University of British Columbia). PhD in Physics (Cum Laude) from University of Valencia (2001) MSc in Neuroscience and Biology of Behaviour from University of Seville (2004) Research focuses on cortical network dynamics, mesoscopic auditory cortex modeling, top-down modulation in cognition, and affective computing in VR. His work combines nonlinear time series analysis with neurodynamic modeling to study spontaneous and task-related brain activity states. Recent publications demonstrate expertise in causal inference, neuroevolutionary optimization, and affective state detection using EMG/PPG sensors in VR environments. Collaborators include neuroscience labs (Sanchez-Vives, Durstewitz) and tech companies (Sony London, Emteq). PhD thesis award - Culture Institute 'Juan Gil-Albert', Spain (2001) Senior Fellow Higher Education Academy (2020) Secured major grants including Royal Society funding for cortical network modeling (2022), Human Brain Project ERC support for neuromorphic hardware (2022), and Santander Bank research travel grants (2016). Supervised 14 PhD students through projects involving data streams, VR immersion, and cortical dynamics. Labs include Balaguer Lab (GitHub), Interdisciplinary Neuroscience Research Centre, and collaborations with Human Brain Project. Teaching encompasses doctoral-level neuroscience, MSc data analytics, and undergraduate systems design courses.

• Distributed Systems
• Blockchain Technology
• Graph Neural Networks
• +5 more

Hans-Arno Jacobsen is a Professor at Technische Universität München (Faculty of Computer Science, Germany) and the University of Toronto (Department of Electrical and Computer Engineering, Canada). His research spans distributed systems, blockchain technology, and machine learning for energy systems. Research Interests : Blockchain consensus algorithms, federated learning, graph neural networks, quantum computing applications, and energy-efficient distributed systems. Publication Trends : Recent work focuses on decentralized consensus in blockchains, energy-aware language model inferencing, quantum chemistry simulations, and graph neural network scalability. Collaborations : Regularly works with Ruben Mayer, Shashank Motepalli, and Gengrui Zhang on blockchain and machine learning projects.

Mohsen AminiView profile

Associate Professor

• Cloud Computing
• Artificial Intelligence (AI)
• Domain-Specific Cloud Computing
• +7 more

Mohsen Amini is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT). He previously held tenured positions at the University of Louisiana Lafayette (UL Lafayette) and completed postdoctoral research at the University of Miami and Colorado State University. His research focuses on Cloud 2.0 systems, AI-friendly serverless architectures, and domain-specific computing for Industry 4.0, multimedia, and edge-to-cloud integration. He leads the High Performance Cloud Computing (HPCC) Lab and has attracted over $2.7 million in research funding, including an NSF CAREER Award (2021). His work emphasizes democratizing cloud-native development and enhancing system resilience through heterogeneous computing frameworks. Education: Ph.D. in Computing and Software Engineering, University of Melbourne (2012) M.Sc. in Software Engineering, Ferdowsi University (2006) B.Sc. in Software Engineering, Azad University (2003) Research Interests: Cloud+AI Integration Fluid Computing across Edge-to-Cloud Continuum Domain-Specific Cloud Platforms (e.g., Industry 4.0, Multimedia) Confidential Computing Security Awards: NSF CAREER Award (2021) Best Service Award at IEEE/ACM CCGrid 2023 Francis Patrick Clark/BORSF Endowed Professorship (2021–2024) Grants & Projects: NSF IRES Track 1: Wireless Federated Fog Computing for Remote Industry 4.0 ($400k grant) NSF CAREER: Special-Purpose Cloud for Serverless Multimedia Streaming ($513k grant) SmartSight: AI-Based Platform for Visually Impaired Assistance ($499k grant) Teaching & Advising: Current advising 5 PhD students and leading a team of 25+ researchers Teaches courses on Cloud Computing, Distributed Systems, and Software Engineering Lab & Collaborations: HPCC Lab develops open-source tools like the E2C simulator and OaaS framework. Active collaborations with institutions like IMDEA Networks and industry partners like Hub Enterprise Inc.

• Database Systems
• Data Engineering
• Distributed Systems
• +4 more

Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.

Minos GarofalakisView profile

Professor

• Data Stream Management
• Complex Event Processing
• Differential Privacy
• +5 more

Minos Garofalakis is a Professor at the School of Electronic & Computer Engineering at the Technical University of Crete, specializing in data stream management, complex event processing, and privacy-preserving analytics. His work bridges theoretical and applied computer science, with a focus on scalable algorithms for high-velocity data. Best Paper Award at VLDB 2024 for 'OmniSketch' Leader in sketch-based and distributed stream processing Pioneer in differential privacy for relational data Research interests span stream analytics , probabilistic databases , and interactive query systems . Recent work includes oblivious parallel joins (2025), relational data synthesis under privacy constraints (2024), and cross-platform analytics frameworks (2020). His publications demonstrate a consistent focus on error-controlled approximations and real-time distributed processing. Scientific contributions have been recognized at premier conferences like VLDB, with awards highlighting innovations in multi-dimensional stream analysis and privacy-preserving operations . Collaborations span academia and industry, particularly in bioinformatics and distributed systems.

Ali JannesariView profile

Associate Professor

• High-Performance Computing
• Machine Learning
• Software Analytics
• +5 more

Ali Jannesari is an Associate Professor and Director of the Laboratory for Software Analytics and Pervasive Parallelism (SwAPP Lab) at the Department of Computer Science, Iowa State University. His research focuses on the intersection of High-Performance Computing (HPC) and AI, aiming to advance reliable and efficient software for modern parallel computing platforms. He has held academic and research roles including Senior Research Fellow at UC Berkeley and leadership positions in Germany's Technical University of Darmstadt and RWTH Aachen University. He holds a Habilitation in Computer Science (2016, TU Darmstadt), PhD (2010, KIT), and M.S. (2005, University of Stuttgart). Education: Habilitation in Computer Science, Technical University of Darmstadt, Germany (2016) Ph.D. in Computer Science, Karlsruhe Institute of Technology (2010) M.S. in Computer Science, University of Stuttgart (2005) Research Interests: High-Performance Computing (HPC), Machine Learning, Parallel Computing, Software Analytics, AI-driven compiler optimization, federated learning, and cross-language code analysis. His work bridges HPC and AI to enhance computational efficiency and scalability in data-driven applications. Recent Contributions: Leading research in GNN-based code parallelization (AutoParLLM), federated multimodal learning, and HPC performance optimization via graph-based methods. His lab's work has been published in top venues like NAACL, ICS, and NeurIPS. Lab & Team: The SwAPP Lab develops tools for software analytics and pervasive parallelism, with a focus on HPC-AI integration. Current projects include optimizing distributed training systems and improving code migration with LLMs.