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JIANG LingxiaoView profile

Professor

• Software Engineering
• Artificial Intelligence
• Data Science
• +7 more

Professor Lingxiao Jiang is a full-time faculty member at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU) , where he serves as Director of the Centre for Research on Intelligent Software Engineering (RISE) . His research and teaching focus on software engineering , program analysis , code search & reuse , and deep learning of code to enhance software quality, development productivity, and security. PhD, University of California, Davis (2009) Professor Jiang's research explores context-aware software deep learning , code clone detection , and security & privacy through tools like DECKARD (code clone detection), EqMiner (functionally equivalent code), and SmartEmbed (smart contract security). His work leverages distributed computing , symbolic execution , and neural program models for large-scale code analysis. Recent publications highlight trends in deep learning-based code analysis , smart contract vulnerabilities , and automated program transformation . Key themes include code clone detection , semantic patches , and cross-language API mappings . Scientific Awards ACM SIGSOFT Impact Paper Award (2018) for scalable code clone detection As advisor, Professor Jiang has mentored 14 graduate students, including Lucia (Ph.D. 2014) , Shaowei Wang (Ph.D. 2015) , and current advisees in intelligent software engineering. He leads multiple grants on code mining , security of digital platforms , and AI systems governance . RISE Lab at SMU develops tools like MANDO (smart contract analysis), iTiger (issue title generation), and TreeCaps (code processing with capsule networks). The lab actively recruits researchers in software engineering and AI.

Jianbing (Jason) LiView profile

Professor

• Environmental pollution control
• Petroleum waste management
• Soil and groundwater remediation
• +3 more

Dr. Jianbing Li is a Professor and Professional Engineer (P.Eng.) in the Environmental Engineering Program at the University of Northern British Columbia (UNBC), holding prestigious fellowships from CSCE, CSSE, EIC, and Engineers Canada. His research program addresses critical environmental challenges with significant real-world impact, particularly in northern and remote communities of British Columbia. Education: PhD in Environmental Systems Engineering, University of Regina Research Focus: Dr. Li's work centers on environmental pollution control , petroleum waste management , soil and groundwater remediation , environmental modeling , risk assessment , and oil spill response . His innovative approaches integrate machine learning, advanced materials, and sustainable engineering principles to develop practical solutions for complex environmental problems, with particular emphasis on resource recovery from waste streams. Publication Trends: Analysis of his 15 most recent publications (2023-2025) reveals a strategic focus on oil spill response technologies, wastewater treatment innovations, and waste valorization. Key advancements include nano/micro bubble flotation systems, chitosan-based adsorbents, and machine learning models for pyrolysis optimization, demonstrating his leadership in translating laboratory research to field applications. Scientific Recognition: 2024 Fellow of Engineers Canada and Engineering Institute of Canada 2023 CSCE Dr. Albert E. Berry Medal (Canada's top environmental engineering award) Multiple UNBC Research Excellence Awards (2010, 2014, 2019, 2023) 2013 Northern BC Business and Technology Award with Husky Energy Best paper awards from International Academy of Science and Environmental Geotechnology Society Research Leadership: Dr. Li has secured over $800,000 in 2023 and $1.9 million in 2020 for oil spill response research through NSERC, DFO, and NRCan. His current portfolio includes groundwater protection for Indigenous communities, next-generation decanting technologies, and water security for remote regions. He actively mentors PhD, MSc, and MASc students while serving on NSERC evaluation committees and co-directing the UNBC/UBC environmental engineering program (2013-2017). Collaborative Networks: Dr. Li leads multi-institutional partnerships with UBC, government agencies, industry (including Husky Energy), and Indigenous communities like Lheidli T'enneh First Nation. His work through the Multi-Partner Research Initiative addresses practical challenges in rural British Columbia while advancing fundamental knowledge in environmental systems engineering.

Johannes SoedingView profile

Researcher

• Computational Biology
• Bioinformatics
• Protein Structure Prediction
• +9 more

Johannes Soeding is a Research Group Leader in the Computational Biology department at the Max Planck Institute for Multidisciplinary Sciences in Göttingen, Germany. His work bridges physics, bioinformatics, and molecular biology, focusing on computational methods for biological data analysis. His research interests include computational biology, protein structure and function prediction, metagenomics, transcriptional regulation, and statistical genomics. He develops widely used software tools such as HH-suite, HHpred, MMseqs2, and Foldseek for protein sequence and structure analysis. The recent publications demonstrate a strong focus on high-throughput biological data, particularly in protein structure search (e.g., Foldseek), metagenomic gene discovery (e.g., MetaEuk), and regulatory genomics. His work combines algorithm development with deep biological insights, often published in top-tier journals like Nature Biotechnology , Science , and Nature Methods . He has been involved in significant methodological advances in sequence clustering, contact prediction, and eQTL analysis, showing a consistent trend toward scalable, data-driven approaches in genomics and proteomics. Soeding has contributed to major projects in gene regulatory networks and RNA biology, often in collaboration with experimental groups. His leadership in developing open, efficient bioinformatics tools has had a broad impact on the scientific community. He is affiliated with several graduate programs including IMPRS Physics of Biological and Complex Systems, Biomolecules: Structure - Function - Dynamics, and Genome Science, indicating active participation in training the next generation of scientists.

• Wireless Networks
• Time-Sensitive Networking (TSN)
• WiFi and UWB Integration
• +7 more

Jetmir Haxhibeqiri is a Postdoctoral Researcher at Ghent University 's Faculty of Engineering and Architecture , Department of Information Technology. His work focuses on Time-Sensitive Networking (TSN) over wireless systems, WiFi optimization, and Industrial IoT solutions. Key projects: IMEC Postdoctoral Fellowship Collaborations: Jeroen Hoebeke (UGent), Ingrid Moerman (UGent), Xianjun Jiao (UGent) Research Interests : Wireless network coordination, SDN integration for heterogeneous networks, low-latency communication, and machine learning applications in network optimization. Specialized in WiFi-LPWAN coexistence , In-Band Network Telemetry , and Cross-Technology Synchronization . Recent Publications : 2025 work on Wi-Fi-UWB synchronization, 2024 studies on coordinated spatial reuse in WiFi 7, and 2022 research on hardware-efficient PTP clock synchronization. Contributions span from theoretical models to practical implementations in industrial environments. Technical Expertise : Network densification strategies, interference management, and performance evaluation of large-scale wireless deployments. Developed simulation frameworks for LoRaWAN and WiFi TSN, with a focus on ns-3 validation. Education : PhD in Industrial Wireless Communication (2019, Ghent University).

Murphy Yuezhen NiuView profile

Assistant Professor

• Quantum Computing
• Quantum Error Correction
• Machine Learning
• +3 more

Murphy Yuezhen Niu is an Assistant Professor and Stansbury Chair in Computer Science at the University of California, Santa Barbara (UCSB), since 2024. She holds an adjunct role as Adjunct Assistant Professor at the University of Maryland, College Park, and the University of Maryland Institute for Advanced Computer Studies. Niu earned her Ph.D. in theoretical and mathematical physics from MIT (2018) and a B.A. in Physics from Peking University. Her research focuses on quantum computing paradigms, including quantum control optimization, quantum error correction, quantum machine learning, and scalable quantum architectures. Her work applies deep reinforcement learning and generative models to quantum systems, with contributions to superconducting qubit-based processors, ion traps, photonic systems, and neutral atom qubits. Niu's research emphasizes reducing the computational cost of quantum digitization while achieving real-world impacts. Notable achievements include the Claude E. Shannon Research Assistantship for her work in photonic quantum computation and quantum cryptography. Niu’s publications (2021–2025) span topics like quantum error correction thresholds, hybrid analog-digital quantum simulators, and machine learning-driven quantum decoding. Her research bridges theoretical physics and applied quantum computing, with a focus on fault-tolerant architectures and scalable quantum control protocols. Education: Ph.D., Physics, MIT (2018) B.A., Physics, Peking University Awards: Claude E. Shannon Research Assistantship Labs/Teams: Google Quantum AI Team (former role) UCSB Quantum Computing Research Group

Sergi Abadal CavalleView profile

Professor

• Chip-scale Wireless Communications
• Nanocommunications
• Nanonetworks
• +12 more

Sergi Abadal Cavalle is a distinguished Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). He leads a dynamic research group focused on revolutionary computing architectures through wireless chip-scale networks and quantum interconnects. His work is supported by prestigious grants including an ERC Starting Grant and an ERC Proof of Concept Grant. Education: PhD in Computer Architecture, Universitat Politècnica de Catalunya (2016) MSc in Telecommunication Engineering, UPC (2011) BSc in Telecommunication Engineering, UPC (2010) Sergi's research focuses on overcoming the limitations of traditional wired interconnects in computing systems by pioneering wireless communication at the chip and package level. His work spans terahertz communications, graphene-based antennas, reconfigurable metasurfaces, and quantum-coherent networks. He has led major EU projects such as WINC, EWiC, QUADRATURE, and WiPLASH, aiming to revolutionize classical and quantum computing architectures. His research integrates electromagnetics, materials science, and computer architecture to enable ultra-fast, scalable, and energy-efficient systems. His recent publications demonstrate a strong trend toward integrating wireless technologies within computing systems, particularly using novel materials like graphene and software-defined metasurfaces. The articles highlight advancements in on-chip wireless communication, channel modeling, and the application of these technologies in 6G and quantum computing. His work bridges theoretical modeling with experimental validation and practical emulation. Scientific Awards: ERC Starting Grant (2022) ERC Proof of Concept Grant (2024) ACM NanoCom Outstanding Milestone Award (2022) NanoComNet Young Investigator Award (2019) UPC Outstanding Thesis Award (2016) IN-NOVA Award to Best Master Thesis (2021) IBM Award to Best Academic Record (2021) Medal from Real Academia de la Ingeniería (2024) IEEE Senior Member (2025) Sergi actively mentors a large group of PhD and Master's students, many of whom have received awards for their thesis work. He leads the N3Cat research group and collaborates with institutions such as IBM Research, NEC Labs Europe, RWTH Aachen, EPFL, University of Nottingham, and Intel Labs. His lab is at the forefront of experimental validation of wireless interconnects and quantum architectures, with ongoing projects focusing on emulation and commercialization of chiplet communication technologies.

• Database Systems
• Stream Processing
• Distributed Data Management
• +5 more

Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.

Mustafa OnurView profile

Professor

• Petroleum Engineering
• Reservoir Management
• Inverse Problem Theory
• +4 more

Mustafa Onur is the McMan Professor and Chair of Petroleum Engineering at The University of Tulsa, where he directs the TU Petroleum Reservoir Exploitation Projects (TUPREP). He holds a Ph.D. and M.S. in Petroleum Engineering from The University of Tulsa and a B.S. from Middle East Technical University. Previously, he held professorships at Istanbul Technical University and Universiti Teknologi Petronas (Malaysia), including a Schlumberger Chair position. Research Focus: Dr. Onur specializes in inverse problem theory, mathematical optimization, and data science applied to reservoir management, geothermal systems, and uncertainty quantification. His work integrates machine learning with traditional reservoir engineering to solve complex problems in energy extraction and carbon sequestration. Publication Trends (2024-2025): His 15 most recent articles emphasize deep learning-based reservoir surrogates, CO₂ storage optimization, geothermal energy extraction, and constrained production optimization. Key innovations include Embed-to-Control frameworks, physics-driven interwell simulators, and stochastic optimization algorithms for uncertainty management in subsurface systems. Awards & Recognition: 2010 SPE Formation Evaluation Award 2014 SPE Distinguished Member 2018 SPE Reservoir Description and Dynamics Award Leadership: As TUPREP director, he leads advanced research in reservoir exploitation, focusing on practical applications of AI and optimization in petroleum and geothermal engineering. He serves as Associate Editor for SPE Journal and Journal of Petroleum Science and Engineering .

Cheng HuangView profile

Assistant Professor

• Computational Fluid Dynamics
• Aerospace Propulsion
• Data-driven Modeling
• +3 more

Cheng Huang is an Assistant Professor in the Department of Aerospace Engineering at the University of Kansas. His research focuses on computational fluid dynamics, aerospace propulsion, turbulent combustion modeling, and reduced-order modeling techniques. He is affiliated with the Computational AeroPropulsion Laboratory and can be contacted at chenghuang@ku.edu. Education: B.S. from Shanghai Jiaotong University M.S. and Ph.D. from Purdue University Research Interests: LES Modeling of Turbulent Reacting Flows Data-Driven and Reduced-Order Modeling of Complex Fluid Flows Combustion Instability Analysis in Aerospace Propulsion Recent Work Trends: His publications emphasize reduced-order modeling techniques for rocket combustion dynamics, rotating detonation engines, and multiscale fluid systems. Key methodologies include projection-based models, data-driven approaches, and nonlinear approximations of latent dynamics.

Jorge BarretoView profile

Associate Professor

• Quantum Photonics
• Semiconductor Optics
• Cryogenic Light-Matter Interaction
• +4 more

Dr. Jorge Barreto is an Associate Professor in Quantum Technologies at the University of Bristol, affiliated with both the School of Physics and the School of Electrical, Electronic and Mechanical Engineering. As Director of the Centre for Doctoral Training in Quantum Engineering, he leads research at the intersection of applied physics and semiconductor technologies. Senior Lecturer, Quantum Engineering Technology Labs (QET Labs) EPSRC Quantum Engineering Centre for Doctoral Training member Bristol Quantum Information Institute participant His research focuses on integrating photonic circuits with single photon detectors, modulators, and sources to develop optical quantum information processors. Key areas include cryogenic photonic calibration , quantum-referenced tomography , and silicon photonics applications. Recent projects explore photonic integrated circuits operating at low temperatures and scalable quantum communication systems. Notable contributions include 2023 work on spontaneous emission tomography and 2022 research on zero-power calibration . He has authored publications in Nature Materials , ACS Photonics , and Quantum Science and Technology , with over 140 citations. Dr. Barreto collaborates internationally and has participated in workshops like the NSF Workshop (2015). He supervises 8 research works and leads datasets on photon pair sources and quantum tomography. Current projects address cryogenic light-matter interaction and hybrid quantum platforms like silicon-BTO integration.

Jose Costa RequenaView profile

Researcher

• 5G Mobile Communication
• Network Slicing
• Internet of Things
• +5 more

Jose Costa Requena is a Researcher and Research Manager at Aalto University, serving as a Staff Scientist in the Department of Information and Communications Engineering within the School of Electrical Engineering. His work centers on advanced networking infrastructure for next-generation wireless systems. Education: Doctoral degree in Engineering and Technology, Helsinki University of Technology (2007) Licentiate degree in Engineering and Technology, Helsinki University of Technology (2004) Research Interests: Dr. Costa Requena specializes in 5G/6G mobile communication, network slicing, and IoT systems. His research bridges theoretical networking concepts with industrial applications, focusing on low-latency solutions, deterministic networking for robotics, and scalable data generation frameworks for distributed sensor networks. He actively develops experimental testbeds for validating novel communication architectures. Recent Publication Trends: His 2024-2025 publications reveal a concentrated effort on practical 5G/6G implementation challenges, including QUIC-based name resolution, time-sensitive networking for industrial automation, and Sub-THz backhauling solutions. These works consistently address reliability, latency, and scalability constraints in mission-critical applications. Scientific Awards: No specific awards are documented in the available information. Advising and Grants: He has supervised at least one thesis. As principal investigator, he leads major EU and nationally funded projects including FUWIRI 2+ (2025-2027), 6G-EXP (2023-2024), and ZERO-SWARM (2022-2024), focusing on wireless infrastructure innovation and 6G test network development. Labs and Teams: Costa Requena operates within Aalto's Networked Systems research group and maintains strategic collaboration with VTT Technical Research Centre of Finland, contributing to Finland's national 5G/6G test network initiatives in Otaniemi.

• CO2 Electrolysis
• Heterogeneous Catalysis
• Sustainable Chemistry
• +3 more

Matthew Kanan is a Professor in the Department of Chemistry at Stanford University, teaching core courses including CHEM 31E: Chemical Foundations and 21st Century Problems (Autumn) and CHEM 121: Understanding the Natural and Unnatural World through Chemistry (Spring). He actively mentors students through year-round independent studies (CHEM 90, CHEM 190) and research programs (MATSCI 300, CHEM 301, CHEM 200). His research pioneers sustainable solutions for carbon management, focusing on electrochemical CO 2 reduction, catalyst development for reverse water-gas shift reactions, and novel carbon capture technologies. Key innovations include carbonate-promoted carboxylation processes, membrane-free electrochemical systems for acid-base production, and metamaterial reactor designs for energy-efficient thermochemical conversion. His work bridges fundamental electrochemistry with scalable engineering for carbon-neutral chemical synthesis. Recent publications (2023-2025) demonstrate a cohesive research trajectory toward industrial-scale CO 2 utilization, with emphasis on energy efficiency, catalyst durability, and impurity tolerance. Dominant themes include electrochemical engineering for concentrated product streams, computational modeling of catalyst microenvironments, and thermal processes for mineral-based carbon removal. This integrated approach targets practical implementation in sustainable fuel and chemical production. Professor Kanan supervises undergraduate research (CHEM 190), directed instruction (CHEM 90), and Ph.D. candidates across chemistry and materials science. His research group likely secures substantial funding for projects addressing critical challenges in carbon conversion, though specific grants aren't detailed in the source material. Collaborative work spans electrochemical engineering, materials design, and process optimization for decarbonization.

Nele MentensView profile

Professor

• Applied Cryptography
• Hardware Security
• Secure Coprocessor Design
• +12 more

Nele Mentens is a full professor at both KU Leuven and Leiden University, where she leads cutting-edge research in applied cryptography, hardware security, and secure embedded systems. At KU Leuven, she is affiliated with the Faculty of Engineering Technology and the Electrical Engineering Department (ESAT), leading the Emerging Technologies, Systems & Security (ES&S) research group at the Diepenbeek campus. Simultaneously, she holds a full professorship at Leiden University’s Leiden Institute of Advanced Computer Science (LIACS), focusing on applied cryptography and security. She has been instrumental in numerous national and international research initiatives, including Horizon Europe and NWO-funded projects. Full Professor, KU Leuven (since 2023) Full Professor, Leiden University (since 2020) Associate Professor, KU Leuven (2014–2023) Post-doctoral Researcher & Lecturer, KHLim / KU Leuven (2007–2014) Ph.D. in Engineering Science, KU Leuven (2007) M.Sc. in Electrical Engineering, KU Leuven (2003) Her research focuses on secure and efficient hardware design, particularly for cryptographic applications on FPGAs, reconfigurable architectures, IoT security, and neuromorphic computing. She explores physical attack resistance, side-channel analysis protection, and trusted computing architectures, with applications in healthcare, industrial monitoring, and endpoint AI. Her work bridges theoretical cryptography with practical hardware implementations, emphasizing energy efficiency and real-time performance. The 15 most recent publications reflect a strong trend toward secure, energy-efficient, and intelligent embedded systems. Topics include neuromorphic AI accelerators, trusted IoT architectures, dynamic reconfiguration for side-channel protection, and secure medical data processing. These works span disciplines such as computer architecture, cybersecurity, digital design, and embedded systems, with a focus on hardware-software co-design and real-world deployment. Nele Mentens has received recognition for her contributions, including: Best Paper Award, DATE'16 Best Paper Nomination, AsianHOST'17 Best Paper Award, CHES'19 She has supervised over 15 Ph.D. students and post-docs, both current and former, and has served as principal investigator in approximately 25 funded research projects. Her work has attracted significant grants from Horizon Europe, NWO, FWO, and national innovation programs. She actively contributes to the academic community through editorial roles in top journals and leadership in major conferences. Nele Mentens leads the ES&S research group at KU Leuven and collaborates closely with LIACS at Leiden University. Her team includes Ph.D. students, post-docs, and research experts working on projects like NimbleAI, NeuroSoC, and TrustedIoT. She has also established secure electronics labs through infrastructure grants and maintains strong international ties with institutions such as EPFL, Ruhr University Bochum, and ETH Zurich.

Jorg LiebeherrView profile

Professor

• Computer Networks
• Network Calculus
• Internet of Things (IoT)
• +6 more

Jorg Liebeherr is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto, holding the Nortel Chair of Network Architecture and Services. His research focuses on computer networks , particularly network calculus , self-organizing networks , protocol design , and traffic scheduling . Education: Diplom-Informatiker (with distinction), University of Erlangen (Germany), 1988 PhD, Computer Science, Georgia Institute of Technology, 1991 His recent work includes low-cost LoRa mesh networks for environmental sensing and mathematical frameworks for traffic control in 5G and IoT systems. Publications span journals like IEEE Internet of Things Journal and conferences such as IEEE Infocom and ACM Sigmetrics . Scientific Awards: IEEE Fellow (2008) Outstanding Service Award, IEEE ComSoc TC on Computer Communications (2006) ACM Sigmetrics Best Student Paper Award (2005) NSF CAREER Award (1996) Advising and Grants: Supervised 15+ theses (MASc/PhD) and secured grants from NSF, Virginia Engineering Foundation, and industry partners. Labs: Leads the Network Research Lab and HyperCast projects, an open-source platform for application-layer internetworking.

Isnaldi R. Souza FilhoView profile

Professor

• Sustainable Metallurgy
• Green Steel
• Hydrogen Plasma Reduction
• +4 more

Isnaldi R. Souza Filho is a Junior Professor (Chair of Sustainable Metallurgy) at the French National Centre for Scientific Research (CNRS) , affiliated with the Institut Jean Lamour (UMR 7198), Université de Lorraine . He also serves as Group Leader for Sustainable Synthesis of Materials at the Max Planck Institute for Sustainable Materials since 2021. Education: PhD, MSc, and BSc in Materials Engineering from the University of São Paulo (2016-2019, 2013-2015, 2008-2013). Research Interests: Focus on sustainable metallurgy , particularly hydrogen plasma-based reduction of iron ores and microstructural characterization of Mn-containing steels . His work addresses decarbonization in steel production, scrap-compatible alloy design , and waste valorization (e.g., red mud conversion to green steel). Publication Trends: Recent studies emphasize hydrogen plasma smelting reduction of iron ores, kinetic modeling of reduction processes, and microstructure engineering for enhanced mechanical properties. Collaborative work spans CO2-neutral steelmaking , elemental partitioning , and high-entropy alloy synthesis . Scientific Awards: CAPES Thesis 2020 for Best National Doctoral Thesis in Engineerings II (Brazil) Multiple undergraduate awards (CREA-SP, ABM, Engineering Institute) for top academic performance in 2013 Grants and Affiliations: Affiliated with the Dierk Raabe Lab at the Max Planck Institute and leads the Sustainable Synthesis of Materials group. Research funded by institutions supporting climate-neutral metallurgy and recycling strategies .