Ellen Zegura is the Stephen Fleming Chair and Professor in the School of Computer Science at Georgia Tech's College of Computing. She holds multiple degrees from Washington University in St. Louis: BS in Computer Science, BS in Electrical Engineering, MS in Computer Science, and DSc in Computer Science. Her research focuses on computer networking, social responsibility in STEM education, and computing for development. She co-founded the Computing for Good initiative, emphasizing project-based learning to address societal challenges. Zegura is an IEEE and ACM Fellow, and serves on the Computing Research Association (CRA) Executive Board. Her education spans interdisciplinary fields at Washington University, combining computer science and electrical engineering. She has held leadership roles at NSF and CRA, advocating for equitable technology policies. Notable contributions include advancing QoE metrics for video conferencing, analyzing mobile broadband infrastructure disparities, and developing ethics education frameworks for computing curricula. Research interests include network measurement, community-empowered data practices, and bridging technical innovation with social impact. Recent work examines tribal mobility during pandemics, sensor co-design with Indigenous communities, and ethical pedagogy for teaching assistants. Her labs and collaborations, such as CERCS, emphasize interdisciplinary problem-solving. Zegura’s awards reflect her dual impact in technical innovation and societal engagement.
Dr. Oluwasesan Adegoke is a Senior Lecturer in the School of Science and Engineering at the University of Dundee, UK. He holds a PhD in Chemistry from Rhodes University (2014) and has held postdoctoral fellowships in South Africa, Japan, and the UK. His expertise lies in developing advanced nanomaterials-based optical and electrochemical biosensors for environmental, biomedical, and forensic applications. Education: PhD in Chemistry, Rhodes University (2014) MSc in Nanoscience, University of Nottingham (2008) BSc in Chemistry, University of Agriculture, Abeokuta (2006) Research Interests: Synthesis of functional nanomaterials (e.g., quantum dots) Development of aptamer-based and nanozyme biosensors Applications in drug detection, environmental monitoring, and virus diagnostics Awards & Funding: Royal Society Research Grant (2023–2025) EPSRC New Investigator Award (2023–2026) Medical Research Council Future Leaders Fellowship (2024–2028, Co-I) Key Projects: Developing nanobiosensors for illicit drugs and explosives Heavy metal-free quantum dots for SARS-CoV-2 detection Surface-enhanced Raman scattering probes for disease diagnostics Labs & Teams: Leads the research group on optical/electrochemical nanobiosensors within the Leverhulme Research Centre for Forensic Science.
Jiannong Cao is a Chair Professor and Director of the University Research Facility in Big Data Analytics at the Department of Computing, Hong Kong Polytechnic University. He has held various academic roles since 1990, including Assistant Professor at City University of Hong Kong and Lecturer at Australian universities. PhD in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University (1982) His research focuses on cloud and edge computing , parallel and distributed computing , and mobile computing , with significant contributions to wireless sensor networks (WSN) for structural health monitoring (SHM) and software-defined networking (SDN) for vehicular communications. Recent work includes WiFi-based non-invasive health monitoring systems and multi-user computation partitioning in mobile cloud environments. Dr. Cao’s publications demonstrate trends in WSN optimization , SDN architectures , and cognitive modeling for network embedding , with applications in smart healthcare , transportation systems , and industrial IoT . Ministry of Education Natural Science Award (2018) ACM Distinguished Member (2017) IEEE Fellow (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, WCNC He has mentored numerous researchers, including Linchuan Xu , Xuefeng Liu , and Weigang Wu , who have authored key publications in top venues like ACM WSDM and IEEE INFOCOM . His professional roles include chairing IEEE committees and serving on grant panels for the Hong Kong Research Grant Council.
Prof. Dr. Fabian Gieseke is a Professor and Chair of Machine Learning and Data Engineering at the University of Münster. He holds a PhD in Computer Science from Carl von Ossietzky University of Oldenburg and a dual degree in Mathematics and Computer Science from the University of Münster. His research focuses on Machine Learning, High-Performance Computing, and their applications in Geosciences, Smart Cities, and Astrophysics. Education: PhD in Computer Science (2012), Carl von Ossietzky University of Oldenburg University studies in Mathematics and Computer Science (2006–2011), University of Münster Research Interests: Data Mining and Machine Learning High-Performance Computing & Distributed Systems Deep Learning Applications in Environmental Science and Astrophysics Geospatial Data Analysis using Satellite Imagery Publications Trends: His recent work emphasizes large-scale environmental monitoring via deep learning, including canopy height estimation, forest biomass prediction, and national-scale tree counting. He also explores interactive systems for geospatial data retrieval and optimization of machine learning models for resource-constrained environments. Advising & Grants: Supervised over 30 theses on topics like satellite image analysis, deep learning on microcontrollers, and data marketplaces for smart grids. Active in securing grants for interdisciplinary projects combining AI with Earth observation. Labs/Teams: Leads the Machine Learning and Data Engineering group at the University of Münster, focusing on scalable AI solutions for real-world challenges in science and industry.
Professor Daniel Eyers is a Professor of Manufacturing Systems Management at Cardiff Business School, Cardiff University , where he also serves as Director of Quality Assurance & Enhancement. He is co-director of the Centre for Advanced Manufacturing Systems (CAMSAC) and Cardiff University RemakerSpace , highlighting his leadership in sustainable and advanced manufacturing innovation. Professor of Manufacturing Systems Management, Cardiff University (2024–present) Co-Director, Centre for Advanced Manufacturing Systems (CAMSAC) (2024–present) Co-Director, Cardiff University RemakerSpace (2020–present) External Advisor, Open University (2022–present) His research focuses on the strategic management of advanced manufacturing technologies , particularly Additive Manufacturing (3D printing) , within operations and supply chain contexts. He explores how digital technologies enhance supply chain flexibility, sustainability, and performance. His work spans flexible manufacturing systems, servitization, and change management in industrial settings. His recent publications (2020–2025) reveal a strong trajectory in AI-human collaboration in decision-making , sustainable manufacturing , urban logistics , and the integration of 3D printing in circular economies. Themes include risk management, digital transformation, and the strategic impact of emerging technologies on operations. He frequently publishes in top-tier journals such as International Journal of Operations and Production Management , Production Planning and Control , and Omega . CEng, Engineering Council (UK) FHEA, Higher Education Academy ESRC Early Career Impact Acceleration Fellowship Daniel Eyers actively supervises PhD, MSc, and MBA students and has attracted over £2.5 million in research funding from research councils, the Welsh Government, and industry. He contributes to academic program design and quality assurance, serving on university committees and as an external examiner for other institutions. He is deeply engaged in applied research with industrial partners, reflecting his background in commercial manufacturing. His leadership in research centers and commitment to sustainability, digital innovation, and education underscore his role as a key figure in modern operations management scholarship.
Panruo Wu is an Associate Professor in the Department of Computer Science at the University of Houston (UH). He joined UH in 2018 as an Assistant Professor, transitioning to his current rank. His research focuses on high-performance computing, numerical algorithms, parallel and distributed systems, and fault tolerance. He holds a Ph.D. in Computer Science from the University of California, Riverside (2016), advised by Zizhong Chen, and a B.S. in Mathematics from the University of Science and Technology of China (USTC). Research Interests: His work spans high-performance computing, numerical linear algebra, GPU acceleration, fault-tolerant systems, and scalable machine learning. Key projects include LATER (Linear Algebra on Tensor Cores), LibKernel (a scalable kernel machine framework), and Wukong (a serverless parallel computing framework). He emphasizes energy-efficient and hardware-aware algorithms. Publications: Dr. Wu's recent work includes advancements in QR factorization using tensor cores, symmetric eigenvalue decomposition optimizations, and fault-tolerant algorithms for heterogeneous systems. His research often addresses computational challenges in big data and exascale computing. Awards & Grants: Received NSF Grant No. 2146509. His work on high-accuracy matrix computations was a Best Paper Nominee at HPDC'20. He has authored over 30 peer-reviewed publications in top venues like SC, ICS, and IEEE TPDS. Students & Advising: Advises PhD students including Shaoshuai Zhang, Ruchi Shah, Benjamin Carver, and Ao Wang. His students have contributed to projects like LibKernel and fault-tolerant linear algebra libraries. Labs & Collaborations: Leads research in UH's high-performance computing group, collaborating with institutions like Jack Dongarra's Innovative Computing Lab (University of Tennessee) and industry partners on exascale computing initiatives.
Ralf Bierig joined Maynooth University's Computer Science Department in 2017, teaching topics including information retrieval, software testing, interaction design, and virtual reality. He is the programme director of the Higher Diploma in Human-Computer Interaction (HCI) and User Experience (UX). He earned his BSc (2002) from University of Furtwangen and PhD (2008) from Robert Gordon University. Research Interests His work spans information retrieval, interactive information retrieval, personalisation, information search behavior, usability (UX), and virtual reality (VR). Recent publications focus on multimodal concept indexing, hybrid IR approaches, and contextual adaptation in search systems. Publication Trends His research combines statistical semantics, graph modeling, and multimodal data analysis across academic collaborations in Austria, Germany, and international venues like ECIR and SIGIR.
Joachim Weickert is a Professor of Mathematics and Computer Science at Saarland University where he heads the Mathematical Image Analysis Group since 2001. He received his diploma and Ph.D. in mathematics from the University of Kaiserslautern (1991, 1996), and a habilitation degree in computer science from the University of Mannheim (2001). Prior to his current position, he worked as a research assistant at the University of Kaiserslautern, as a post-doctoral researcher at the universities of Utrecht and Copenhagen, and as an assistant professor at the University of Mannheim. His research focuses on image processing, computer vision, and scientific computing, with special emphasis on techniques based on partial differential equations, variational principles, wavelets, morphological and nonlocal methods, as well as neuroexplicit approaches. He has developed mathematical models and efficient numerical algorithms for image restoration, enhancement, segmentation, compression, optic flow computation, stereo reconstruction, shape from shading, and signal processing methods for tensor fields. These ideas have been successfully applied in industry, biomedical image analysis, and other fields. Analysis of his recent publications reveals a strong trend toward combining traditional PDE-based methods with modern deep learning approaches, particularly in the areas of image inpainting and compression. His work increasingly explores the connections between numerical algorithms for partial differential equations and neural network architectures, demonstrating how mathematical foundations can inform cutting-edge AI techniques while maintaining strong theoretical guarantees. Gottfried Wilhelm Leibniz Prize (2010), considered the most important research award in Germany ERC Advanced Grant (2017) for "Inpainting-based Compression of Visual Data" Elected member of Academia Europaea - The Academy of Europe Jan Koenderink Prize for Fundamental Contributions in Computer Vision (2014) Multiple DAGM Prizes and Best Paper Awards throughout his career AAIA Fellow (2021) and Highly Ranked Scholar (2024) distinctions Professor Weickert has supervised over 250 bachelor's and master's theses and initiated the Master Programme in Visual Computing at Saarland University, the first of its kind in Germany taught in English. He has established numerous interdisciplinary collaborations with colleagues from medicine, bioinformatics, pharmacy, physics, mechatronics, and mechanical engineering. As Principal Investigator for Visual Computing within the Multimodal Computing and Interaction Cluster of Excellence, and former dean of the Faculty of Mathematics and Computer Science (2008-2010), he has played a significant leadership role in advancing visual computing research and education. He heads the Mathematical Image Analysis Group, which has been at the forefront of developing mathematical methods for image analysis. The group maintains strong connections with both theoretical mathematics and practical applications, bridging the gap between fundamental research and real-world implementation across various domains including medical imaging, industrial inspection, and multimedia processing.
Henry F. (Hank) Korth is a Professor of Computer Science and Engineering at Lehigh University, with a courtesy appointment in the Department of Decision and Technology Analytics in the College of Business. He serves as Director of the Blockchain Lab in the Center for Financial Services and Co-Director of the Computer Science and Business Program. Korth is a Fellow of the ACM and IEEE, and a recipient of the VLDB 10-Year Award and Bell Labs President's Silver Award for contributions to database technologies. PhD in Computer Science from Princeton University MA, MSE in Computer Science from Princeton University BA in Mathematics from Williams College Korth's research spans database systems, blockchain systems, distributed systems, and real-time systems. He has pioneered transaction management in parallel and distributed systems, query processing, and the impact of modern computing architectures on database performance. His recent work focuses on blockchain applications in enterprise databases, including acceleration of zero-knowledge proofs, benchmarking frameworks, central-bank digital currencies, and private-yet-provable accounting systems. His contributions are rooted in both theoretical advancements and practical implementations, such as the QTM™ aggregation engine and the DataBlitz™ main-memory storage manager. Scientific awards include: ACM Fellow IEEE Fellow 10-Year Award at the VLDB Conference Bell Labs President's Silver Award Korth actively supervises research within the Blockchain Lab and is affiliated with the Scalable Software Systems Research Group at Lehigh. His scholarly output reflects a deep engagement with blockchain benchmarking, concurrency control, verifiable databases, and the evolution of database systems in response to technological shifts.
Taylor Sparks is a Professor of Materials Science and Engineering at the University of Utah, where he also serves as Director of Graduate Affairs for the John and Marcia Price College of Engineering. He holds a PhD in Applied Physics from Harvard University, an MS in Materials from the University of California, Santa Barbara, and a BS in Materials Science & Engineering from the University of Utah. His research focuses on advancing materials discovery using machine learning to streamline and optimize material design, with applications in energy materials, dental materials, and sustainable engineering. His work integrates big data and materials informatics to explore new synthetic techniques, structure-property relationships, and sustainable materials that balance performance with economic factors. The Sparks Research Group has secured funding from agencies including DOE, NSF, DOD, and various industry partners. Sparks' recent research output demonstrates a strong trend toward leveraging artificial intelligence and machine learning to accelerate materials discovery, with particular emphasis on large language models for materials science, Bayesian optimization for experimental design, and novel approaches to crystal structure prediction. His work bridges the gap between theoretical predictions and experimental validation in materials science. NSF CAREER Award Royal Society Wolfson Visiting Fellow Acta Materialia Outstanding Reviewer Award for 2020 Honorary Outstanding Faculty Teaching Award of 2020-2021 Materials Science & Engineering Department Research Award for 2023 John G. Francis Prize for Undergraduate Student Mentoring Sparks has advised numerous graduate students who have gone on to successful careers in academia and industry. His research has been supported by significant grants from NSF, DOE, DOD, Army Research Office, and industry partners. His group has developed innovative tools including the Materialism Podcast, a materials science YouTube channel, and the Honegumi interface for Bayesian optimization, demonstrating his commitment to both research excellence and science communication. The Sparks Research Group operates multiple laboratories focused on materials characterization, synthesis, and informatics. They collaborate extensively with other institutions globally, host visiting researchers, and run outreach initiatives including the Materialism Podcast and YouTube channel to make materials science more accessible to broader audiences.
John S. McCartney is a Professor and Hal Sorenson Endowed Chair in the Department of Structural Engineering at the University of California San Diego (UCSD). He directs the Englekirk Structural Engineering Center and holds editorial roles at journals such as ASCE Journal of Geotechnical and Geoenvironmental Engineering and Computers and Geotechnics. His research focuses on unsaturated soil mechanics, energy geotechnics, and geosynthetics engineering, with applications in thermal energy systems, landfill covers, and seismic response analysis. Education: B.S. and M.S. in Civil Engineering from University of Colorado Boulder (2002), Ph.D. in Civil Engineering from University of Texas at Austin (2007). Research interests include thermo-hydro-mechanical behavior of soils, geothermal energy piles, tire-derived aggregates, and seismic performance of geotechnical systems. His work combines laboratory testing, centrifuge modeling, and numerical simulations to address challenges in sustainable infrastructure and energy systems. Key awards include the Walter L. Huber Research Prize (2016), NSF CAREER Award (2011), and multiple teaching and service recognitions. He actively contributes to ASTM standards and serves as President of the IGS-NA chapter. Lab facilities are located in the Structural and Materials Engineering Building (SME 409). Courses taught include advanced soil mechanics, energy geotechnics, and geotechnical earthquake engineering.
Hamza Salih Erden is an Associate Professor (Docent) at the Informatics Institute of Istanbul Technical University in Turkey. His research focuses on energy optimization in data centers, thermal management systems, and computational fluid dynamics applications. With over 34 research outputs and an h-index of 12, he leads projects in energy-grid integration and carbon-aware load management. Research Focus Dr. Erden's work centers on improving energy efficiency in technological infrastructure through: Advanced cooling techniques for data centers Integration of thermal energy storage systems Computational fluid dynamics modeling Demand-response optimization for smart grids AI-driven monitoring of energy systems Publication Trends Recent works (2022-2025) demonstrate strong focus on sustainable energy technologies, particularly optimization of data center operations through thermal management innovations, integration of renewable energy solutions, and AI applications for system monitoring. Economic assessments of energy-saving techniques feature prominently. Awards and Recognition Technical Paper Award (2016) Multiple International Scientific Publication Incentive Awards (2017-2021) Poster Award (2012) Graduate Student Grant (2007) Projects and Funding Leads multiple energy research projects including: Carbon-aware load management in data centers (2025) Grid-integrated energy system modeling for data centers (2021-2023) CFD analysis of CRAH bypass methods (2018-2020) Economizer applications in Turkish data centers (2016-2017)
Katrin Schollbach is an Assistant Professor in the Building Materials department at Eindhoven University of Technology's School of Built Environment . Her research focuses on sustainable utilization of industrial by-products in construction materials, particularly steel slags from fossil-free steelmaking processes. Expertise: BOF slag activation, carbonation resistance, heavy metal immobilization, eco-friendly binders Projects: Leading the SLAK project (2023-2029) on future slags from fossil-free steelmaking Collaborations: Active international collaborations in construction materials research Schollbach's work explores the chemical and mineralogical properties of industrial waste materials, optimizing their performance in building applications. Key research themes include chemical activation techniques, carbonation processes, and material characterization using scanning electron microscopy and X-ray diffraction. Recent publications highlight her innovative approaches to BOF slag activation, sewage sludge-based sealing materials, and marine infrastructure applications. Her research output demonstrates consistent growth with 89 total publications and collaborations across multiple disciplines. She contributes to education through courses like Climate-responsive Building Design and Masterproject BPS Research , and maintains active research partnerships with institutions worldwide.
Yashar Ganjali is a Professor in the Department of Computer Science at the University of Toronto , leading the Systems and Networking Group . His research spans computer networks , with a focus on data center networking , software-defined networking (SDN) , and congestion control . Education : Not explicitly detailed, but inferred from academic rank as a Professor. His work on flow consolidation , load migration in SDN controllers , and machine learning for network management has been influential. Recent projects include FORESIGHT (2025) for ML-driven scheduling and Meta-Migration (2023) to reduce switch migration latency. Scientific Awards include the IFIP Networking 2025 Best Paper Award . Collaborations with institutions like Google (2024) and Facebook (2019) highlight his industry impact. Advisees include Sepehr Abbasi Zadeh (PhD, 2024). Current projects integrate optical packet switching and eBPF-based network augmentation , aiming to address scalability, micro-bursts, and resource allocation efficiency in cloud environments.
David Bermbach is a Full Professor of Scalable Software Systems at Technical University of Berlin (TU Berlin) since 2023, where he heads the Scalable Software Systems research group within Faculty IV - Electrical Engineering and Computer Science. He is also co-affiliated with the Einstein Center Digital Future (ECDF). Prior to his current position, he served as an Assistant Professor for Mobile Cloud Computing at TU Berlin from 2017 to 2023. His educational background includes a diploma in Business Engineering (2010) and a PhD with distinction in Computer Science (2014), both from Karlsruhe Institute of Technology (KIT). Prof. Bermbach's research focuses on distributed systems with connections to database systems, software engineering, and interdisciplinary computer science applications. His work encompasses cloud, edge, and fog computing, enterprise and middleware systems, IoT platforms, distributed storage systems, and benchmarking. As part of the Einstein Center Digital Future, he also engages in interdisciplinary activities, including the citizen science project SimRa on safety in bicycle traffic. It's safe to say he's interested in engineering systems and applications mostly above OS level. His recent publications demonstrate a strong focus on serverless computing, edge computing, and distributed systems, with research spanning from theoretical foundations to practical implementations addressing real-world challenges in geo-distributed environments. Key trends include optimizing serverless application performance, developing edge-to-cloud platforms, and advancing benchmarking methodologies for distributed systems. Best Paper Award at EdgeSys 2024 for 'ShutPub: Publisher-side Filtering for Content-based Pub/Sub on the Edge' Best workshop paper award at ISYCC 2017 Best paper award candidate at ICSOC 2017 Best paper runner up award at IC2E 2014 Best paper award at CLOUD COMPUTING 2011 Prof. Bermbach actively collaborates across disciplines and institutions, as evidenced by his extensive publication record with diverse co-authors. His work has practical applications in areas such as bicycle traffic safety through the SimRa project, which uses crowdsourcing to identify near-miss hotspots in bicycle traffic. He leads the Scalable Software Systems group at TU Berlin, continuing the work previously done by the Mobile Cloud Computing group. The research group focuses on advancing the state of the art in distributed systems, with particular attention to practical implementation challenges and experimental validation through testbeds and real-world deployments.
