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Moinuddin QureshiView profile

Professor

• Computer Architecture
• Memory Systems
• Hardware Security
• +1 more

Moinuddin Qureshi is a Professor of Computer Science at Georgia Institute of Technology, affiliated with the School of Computer Science and involved in the Online Master of Science in Computer Science (OMSCS) program. He holds a Ph.D. and M.S. from the University of Texas at Austin. His research focuses on computer architecture, memory systems, hardware security, and quantum computing, with notable contributions to mitigating rowhammer vulnerabilities and advancing quantum error correction. Previously, he was a Research Staff Member at IBM T.J. Watson Research Center (2007–2011), where he contributed to caching algorithms for Power-7 processors. He has held leadership roles, including Program Chair of MICRO 2015 and Selection Committee Co-Chair of Top Picks 2017. His work has been recognized with prestigious awards, including the 2019 Persistent Impact Prize and multiple best paper awards. Key research areas include secure memory design (e.g., rowhammer mitigation techniques like MINT and Moat), quantum computing (e.g., Flag-Proxy Networks and Élivágar), and hardware vulnerability analysis (e.g., Roguerfm attacks and COAXIAL memory systems). His publications span 2009–2025, addressing topics like error correction, secure tracking, and quantum annealing optimization. Awards include membership in ISCA, MICRO, and HPCA Hall of Fame, alongside contributions to conferences like HiPC and IEEE MICRO. His work bridges theoretical advancements with practical implementations in both classical and quantum domains.

Hossein JahanshahlooView profile

Associate Professor

• Blockchain Technology
• Cryptocurrencies
• Market Microstructure
• +3 more

Dr. Hossein Jahanshahloo is an Associate Professor of Financial Technology at Alliance Manchester Business School, The University of Manchester. Previously, he served as an Assistant Professor of Finance at Cardiff Business School (University of Cardiff). He holds a PhD in Finance from Leeds University Business School, alongside MSc and BSc degrees in Finance and Computer Software Engineering. His research focuses on Blockchain Technology, Cryptocurrencies, Market Microstructure, and Algorithmic Trading. He created the Cardiff University Bitcoin Database (CUBiD), which provides accessible cryptocurrency network data to academics and practitioners. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. He currently serves as an associate editor at the Journal of Research in International Business and Finance and holds a visiting researcher position at Technical University of Munich. His recent publications explore Bitcoin arbitrage dynamics, cryptocurrency market efficiency, and banking acquisition failures. Jahanshahloo advises PhD students and collaborates internationally on projects involving blockchain applications and financial technology innovations.

Benjamin SafdiView profile

Associate Professor

• Dark Matter Physics
• Astrophysics
• Particle Physics
• +3 more

Benjamin Safdi is an Associate Professor at the University of California, Berkeley, affiliated with the Berkeley Center for Theoretical Physics. He holds a PhD from Princeton University (2014), a Master of Advanced Study from Cambridge University (as a Churchill Scholar), and an undergraduate degree from the University of Colorado at Boulder. Prior to UC Berkeley, he was a Pappalardo Fellow at MIT (2014–2017), an Assistant Professor at the University of Michigan (2017–2020), and part of LBNL before joining Berkeley in 2021. Research Focus: Dr. Safdi specializes in dark matter physics, particularly axion dark matter detection via novel laboratory experiments and astrophysical probes. His work integrates high-performance computing simulations, data analysis techniques for astrophysical datasets, and collaborations with experimental facilities like the Fermi gamma-ray telescope and the Green Bank radio telescope. He co-founded the ABRACADABRA axion dark matter experiment. Awards: Department of Energy Early Career Award (2018) IUPAP C11 Young Scientist Prize (2020) His research bridges theoretical particle physics and observational astrophysics, emphasizing interdisciplinary approaches to uncovering dark matter's microscopic nature.

• Structural Health Monitoring
• Computer Vision
• Artificial Intelligence
• +3 more

Roya Nasimi, Ph.D., is an Assistant Professor in the Department of Engineering at California State University, East Bay, where she joined in Fall 2023. Her expertise spans structural engineering, computer vision, and artificial intelligence, with a focus on developing innovative solutions for infrastructure monitoring and safety. Dr. Nasimi's educational background includes: Ph.D. with distinction in Structural Engineering from the University of New Mexico Master’s degree in Structural Engineering from the University of Tabriz Bachelor’s degree in Civil Engineering from the University of Tabriz Her research focuses on structural health monitoring using advanced technologies. She integrates computer vision , artificial intelligence , and machine learning to develop systems for monitoring aging infrastructure, particularly bridges. Her work includes designing low-cost and high-end sensor systems, conducting full-scale bridge experiments, and collaborating on interdisciplinary projects to enhance infrastructure safety and resilience. Her recent publications (2021-2025) demonstrate a strong emphasis on non-contact monitoring techniques using drones, lasers, and computer vision. Key trends include the application of deep learning for displacement measurement, digital twinning for infrastructure, and rockfall prevention through machine learning. Her work bridges civil engineering with cutting-edge technology to address critical infrastructure challenges. Dr. Nasimi's research is supported by multiple grants: U.S. Army Corps of Engineers Transportation Research Board (TRB) Transportation Consortium of South-Central States (Tran-SET) New Mexico Consortium She serves on two TRB standing committees and mentors students in structural health monitoring and infrastructure technology. Dr. Nasimi leads interdisciplinary research teams focused on infrastructure monitoring, utilizing drones, lasers, and computer vision systems. Her work involves field experiments on bridges and rail systems, often in collaboration with government agencies and research consortia.

• Virtual Reality
• Human-Computer Interaction
• Computer Vision
• +4 more

Robert S. Allison is a Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. His research focuses on human perceptual responses in virtual environments, stereoscopic vision, and eye movement analysis. He is affiliated with the York Centre for Vision Research, Sensorium (Digital Arts & Technology), and the Centre for Innovation in Computing at Lassonde. His research interests include depth perception in natural and virtual environments, human-computer interface design for VR, machine vision applications, and the measurement of human motion. He has supervised multiple graduate students and contributed to over 260 publications. His work spans topics like cybersickness mitigation, display lag effects, and perceptual adaptation in VR. Key grants include NSERC-funded projects on perception in virtual environments and collaborations with institutions like the Australian Research Council. His teaching includes courses on human perception in human-computer interaction and digital logic design. Recent articles highlight advancements in understanding motion perception, VR-induced sickness, and multisensory integration. He collaborates widely, with affiliations including the VISTA program and York's Connected Minds initiative.

Christian KrupitzerView profile

Professor

• Food Informatics
• Self-adaptive Software Systems
• Machine Learning
• +4 more

Jun.-Prof. Dr. Christian Krupitzer is a Tenure Track Professor in Food Informatics at the University of Hohenheim's Institute of Food Science and Biotechnology, part of the Faculty of Natural Sciences. He leads the Department of Food Informatics and is a member of the Computational Science Hub (CSH). His research focuses on self-adaptive software systems, machine learning (especially edge computing), IoT technologies, and software engineering applied to food processing and agricultural systems. Education: PhD in Business Information Systems (Dr. rer. pol.), University of Mannheim (2018) M.Sc. and B.Sc. in Business Information Systems, University of Mannheim (2010–2012) High School Diploma (Abitur) from Wilhelmi-Gymnasium Sinsheim (2007) Research Interests: Krupitzer’s work integrates computational methods with food science, emphasizing adaptive systems for food quality monitoring, IoT in agriculture, and machine learning for predictive analytics. He explores edge computing’s role in real-time decision-making and secure group communication schemes for IoT networks. Publications: His recent work spans predictive maintenance in Industry 4.0, digital twins in food systems, and blockchain applications in supply chain authentication. The articles highlight trends in interdisciplinary approaches combining AI, IoT, and domain-specific challenges in food production and logistics. Awards: No scientific awards explicitly listed in the provided materials. Grants & Advising: While specific grants are unmentioned, his roles as department head and tenure-track professor suggest involvement in research funding. No formal advisee list provided, though his team includes postgraduate researchers like Dana Jox, Daniel Einsiedel, and others. Labs & Teams: Leads the Food Informatics department and collaborates with the Computational Science Hub. His team focuses on developing innovative solutions for food systems through computational methods.

Lin CaiView profile

Associate Professor

• Wireless Communication
• Networking
• Green Radio Communication Networks
• +5 more

Lin Cai is an Associate Professor in the Department of Electrical and Computer Engineering at Illinois Institute of Technology, serving as Director of Graduate Affairs. She holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada (2010). Her research focuses on sustainable wireless communication and networking, particularly in energy harvesting, IoT, and deep reinforcement learning for resource management. Key research projects include energy sustainability in renewable-energy-powered networks, energy-efficient 5G protocols, and radio resource management over unlicensed bands. She has received the NSF CAREER Award (2016-2020), Best Paper Awards, and NSERC Postdoctoral Fellowship. Her work spans theoretical frameworks and practical implementations in HetNets, MIMO, and UAV-based systems. Education: Ph.D., E&CE, University of Waterloo, Canada (2010) Affiliations: Executive Editorial Committee member, IEEE Transactions on Wireless Communications (2021–present); Associate Editor for multiple IEEE journals Teaching: Courses include ECE 407/408 (Computer Networks) and ECE 517 (Modern Wireless Network Protocols) Her research emphasizes cross-layer protocol design, sustainable capacity planning, and topology control. Notable contributions include frameworks for energy sustainable radio resource management and optimization in dense HetNets. She actively contributes to conference organization and technical program committees for IEEE INFOCOM, GLOBECOM, and ICC.

Yiting XiaView profile

Assistant Professor

• Computer Networks
• Data Center Networking
• Optical Communications
• +3 more

Dr. Yiting Xia is a tenure-track faculty member at the Max Planck Institute for Informatics (MPI-INF), leading the Network and Cloud Systems research group. She previously worked as a research scientist at Facebook and holds a PhD in Computer Science from Rice University (2018) and a B.S. in Telecommunications Engineering from Beijing University of Posts and Telecommunications and Queen Mary University of London (2011). Her research focuses on high-performance and energy-efficient networking for cloud computing, including reconfigurable data center networks, optical communications, and network protocols. Notable contributions include innovations in transport protocols, time synchronization for optical networks, and failure-resilient network design. Education: PhD in Computer Science, Rice University, 2018 M.S. in Computer Science, Rice University, 2014 B.S. in Telecommunications Engineering, BUPT & QMUL, 2011 Research Interests: Data center networking, optical communications, cloud systems, network protocols, distributed systems, and network security. Her work bridges theoretical contributions with practical implementations, addressing challenges in latency-sensitive flows, traffic engineering, and system reliability. Awards include the Ken Kennedy-Cray Fellowship and the N2Women Rising Star Award (2021). She has co-lectured courses on distributed systems and data networks at Saarland University and previously contributed to teaching at Rice University. Key projects include Aurora (for MoE inference optimization), Lighthouse (an open research framework for optical networks), and Occam (a reliable network management system). Grants & Projects: Focus on deployable optical network architectures and resilient backbone management during pandemic-driven traffic shifts. Labs/Teams: Leads the Network and Cloud Systems group at MPI-INF, collaborating with academia and industry on cutting-edge networking solutions.

Feng YanView profile

Associate Professor

• Big Data
• Machine Learning
• Systems
• +6 more

Dr. Feng Yan is an Associate Professor at the University of Houston's Computer Science Department, leading the Intelligent Data and Systems Lab (IDS Lab). He previously held an Associate Professor position at the University of Nevada, Reno. His research focuses on bridging Big Data, Machine Learning, and Systems, with interdisciplinary applications in wildfire science, materials engineering, and civil infrastructure. He has received prestigious awards such as the NSF CAREER Award and the Regents' Rising Researcher Award. Education: Ph.D. (2016) and M.S. (2011) in Computer Science from College of William and Mary; B.S. (2008) in Computer Science from Northeastern University. Research experience includes roles at Microsoft Research and HP Labs. Research Interests: Large Language Models (LLM), Distributed Deep Learning, AutoML, Serverless Computing, Federated Learning, and AI-driven domain sciences. His work emphasizes real-world impact through collaborations with industry and national labs. Publications: Over 60+ papers in top-tier venues like NeurIPS, ICLR, KDD, AAAI, SOSP, SC, and VLDB. Key contributions include ZeRO++ (collective communication optimization), Gradient Compression techniques, and Federated Learning frameworks like TiFL and HDFL. Awards: NSF EPSCoR Award ($20M), NSF CAREER Award, FAA BAKFAA Grant, and multiple best paper awards (IEEE CLOUD 2018, CLOUD 2019). Active in program committees for HPDC, ICAC, ICPE, and AAAI. Advising: Supervised over 30+ graduate/undergraduate students, with placements at Microsoft Research, IBM, Oak Ridge National Lab, Facebook, and MathWorks. Runs a vibrant lab with a focus on interdisciplinary AI/Systems research.

John WickersonView profile

Associate Professor

• Computer Hardware
• Electrical and Electronic Engineering
• Computer Software
• +7 more

Dr. John Wickerson is an Associate Professor in the Circuits and Systems group at the Department of Electrical and Electronic Engineering, Imperial College London. His research focuses on improving the reliability of high-performance computing through formal methods, with contributions to high-level synthesis, memory models, and concurrency verification. He holds leadership roles including Course Director for the Electrical and Information Engineering degree and Deputy Tutor for PhD students. Research Interests: Formal Verification of Hardware/Software Systems High-Level Synthesis (HLS) and FPGA Compilation Weak Memory Models and Concurrency Semantics Fuzz Testing for Hardware Tools Compiler Optimization and Correctness Digit Elision and Arbitrary-Precision Arithmetic Notable Achievements: Best Paper Award at EuroSys 2024 (database isolation validation) Pioneered formal methods for HLS tools (e.g., QuteFuzz, C4) Co-developed the C4 C compiler concurrency checker Published over 60 peer-reviewed papers across top venues (ASPLOS, PLDI, FPGA) Lab/Team: Part of the Circuits and Systems group at Imperial College, collaborating with industry partners like Kaihong Yann and ARM.

Turhan Hilmi DemirayView profile

Researcher

• Energy Systems
• Electrical Power Systems
• Power System Analysis
• +4 more

Turhan Hilmi Demiray is the Head of the Research Center for Energy Networks (FEN) at ETH Zürich, Switzerland , where he leads industrial and academic research projects in energy systems. His academic background includes a Ph.D. in Electrical and Computer Engineering from ETH Zürich (2008) and an M.Sc. in Telecommunications from TU Vienna (2003). He has held academic and industry roles at ABB Vienna AG, NEPLAN AG, and ETH Zürich’s Power Systems Laboratory.

Véronique HosteView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +7 more

Véronique Hoste is Senior Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy, where she serves as Department Head of Translation, Interpreting and Communication and Director of the LT3 language technology research team. She also holds the position of Research Director for the Faculty of Arts and Philosophy. Her educational background includes a PhD in Computational Linguistics from the University of Antwerp (2005) focused on optimization in machine learning for coreference resolution. Key research areas encompass machine learning for natural language processing, semantic and discourse modeling, including specialized work in event detection, entity/event coreference resolution, irony detection, and emotion analysis. Hoste's publication trends reveal strong interdisciplinary focus, with recent work bridging NLP with crisis communication, digital humanities, and ethical AI. Her team develops high-quality datasets (e.g., EmoTwiCS for emotion trajectories) and collaborates extensively with commercial partners on projects like SentEMO for aspect-based sentiment analysis. Current research emphasizes multimodal emotion analysis, fuzzy rough set methods for sentiment detection, and cross-document event coreference. Elected member of the Royal Flemish Academy of Belgium for Science and the Arts (KVAB) Francqui Chair appointment by Université Libre de Bruxelles (2023-2024) Co-founded LT3 spin-off AlfaSent (2024) for customer feedback analysis Authored first Dutch-language book on NLP: "Taaltechnologie ontrafeld" She actively supervises multiple PhD students on projects including Common-sense knowledge in irony detection (Common-sense), cross-document event coreference (Encore), and empathy modeling in conversational agents (FlandersAI). Her team secures funding through interdisciplinary collaborations like NewsDNA for news recommendation and METRICS for emotion trajectory analysis. Hoste also engages in public outreach through the "AI at school" initiative and advises on language technology integration in high school curricula. The LT3 laboratory under her leadership maintains strong industry partnerships and develops practical NLP tools including EmotioNL and Automatic Term Extraction systems, while advancing core research through projects like CLARIAH-VL for data curation.

• Video Coding
• Machine Learning
• FPGA Acceleration
• +3 more

Jarno Vanne is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on video coding standards, real-time systems, and hardware acceleration, particularly in the context of FPGA implementations and open-source tools. He leads projects involving VVC (Versatile Video Coding), V-PCC (Volumetric Video Coding), and HEVC (High Efficiency Video Coding), with an emphasis on efficiency, low latency, and machine learning integration. Key research interests include point cloud compression, saliency-guided encoding, parallelization schemes, and real-time video communication protocols. His work often addresses challenges in multi-party video streaming, embedded systems, and encryption mechanisms for privacy protection. He has contributed to open-source projects like the UVG dataset, Kvazaar encoder, and CiThruS simulation frameworks. Recent publications highlight advancements in VVC intra encoding optimizations, machine learning-driven partitioning schemes, and FPGA-accelerated solutions for edge computing. His research bridges theoretical video coding algorithms with practical implementations, aiming to improve compression efficiency while maintaining real-time performance.

Jiang HongboView profile

Professor

• Distributed systems
• Mobile computing
• Smart sensing
• +5 more

Hongbo Jiang is a Distinguished Professor and Vice Dean of the College of Computer Science and Electronic Engineering at Hunan University, China. He holds concurrent roles as Director of the Trusted Systems and Networking Key Laboratory of Hunan Province and Director of the Hunan International Technical Cooperation Base for High-Performance Computing and Distributed Systems. His academic journey includes tenures as a Professor at Huazhong University of Science and Technology and a Hong Kong Scholar Research Fellow at The Chinese University of Hong Kong. Education: PhD in Computer Science (Case Western Reserve University, 2008), B.S./M.S. in Mathematics (Huazhong University of Science and Technology, 2002). Research Interests: Distributed systems, mobile computing, smart sensing, wireless networks, IoT, and edge computing. Ongoing projects include mobile/wireless applications, data science in IoT, and edge computing platforms. His work emphasizes practical implementations such as DriverSonar for driving safety and SmileAuth for biometric authentication. Key Achievements: Elected Member of Academia Europaea (2022), Fellow of AAIA, IET, and BCS. Notable awards include the Wu Wenjun Science and Technology Award (2020) and multiple best paper recognitions. Over 100+ publications in top venues like ACM MobiCom, IEEE/ACM Transactions. Professional Contributions: Editorial roles across 8+ journals including IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks. Conference leadership includes co-founding ACM TURC and EAI ICECI. Active in technical committees for INFOCOM, MOBIHOC, and ICDCS. Labs/Teams: Leads research groups focused on networking, IoT, and edge computing. Current openings for PhD/MSc students and PostDoc researchers with strong mathematical and systems backgrounds.

• Astronomy
• Data Intensive Computing
• Radio Astronomy
• +3 more

Andreas Wicenec is a Professor and Senior Principal Research Fellow at the University of Western Australia (UWA), leading the Data Intensive Astronomy Program (DIA) at the International Centre for Radio Astronomy Research (ICRAR). He specializes in data-intensive astronomy, high-performance computing, and large-scale data management systems. His work supports the Square Kilometre Array (SKA) and other major observatories. Education: PhD in Astronomy from the University of Tübingen (1994), Physics Diploma (1989). Professional roles include Archive Scientist at the European Southern Observatory (ESO) and leadership in the International Virtual Observatory Alliance (IVOA). Research focuses on petascale data flows, reproducible science workflows, and next-generation archive systems like NGAS. Current projects include the DALiuGE engine, SKA data handling, and gravitational wave detection pipelines using deep learning. Key Projects: SKA Science Data Processing (7M AUD contract), Data Activated Flow Graph Engine (DALiuGE), and NGAS archive system Awards: ACM Gordon Bell Prize 2020 finalist Grants: Includes SKA Bridging Design (2019–2021), ICRAR IV (2025–2030) Labs/Teams: Active in ICRAR's Data Intensive Astronomy group, collaborating internationally on large-scale astronomy initiatives.