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Zhonghai LuView profile

Professor

• Computer Architecture
• Embedded Systems
• Network-on-Chip (NoC)
• +7 more

Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence

Kai GongView profile

Assistant Professor

• Sustainable Infrastructure Materials
• Environmental Sustainability
• Materials Science
• +4 more

Dr. Kai Gong is an Assistant Professor of Civil and Environmental Engineering at Rice University, with affiliations at the Rice Advanced Materials Institute and Ken Kennedy Institute. His research focuses on sustainable infrastructure materials, environmental sustainability, and materials science. He holds a Ph.D. in Civil & Environmental Engineering and Materials Science from Princeton University, an MEngSci from Monash University (Australia), and dual B.S. degrees from Monash University and Central South University (China). Research Interests: Development of durable, sustainable infrastructure materials Waste encapsulation and conversion to value-added products Carbon mineralization and utilization Advanced characterization techniques (synchrotron/neutron scattering) Data-driven modeling and atomistic simulations Notable Awards: 2023 Le Chatelier Medal (Cement and Concrete Research) 2024 Giatec Award for Best Paper in Sustainability Walbridge Fund Graduate Award (2019) His work integrates computational methods (e.g., molecular dynamics) with experimental techniques to address decarbonization challenges in infrastructure. The Gong Research Group actively seeks motivated researchers for opportunities in sustainable materials innovation.

Max V. BirkView profile

Associate Professor

• Digital Health
• Game Design
• Human-Computer Interaction
• +3 more

Max V. Birk is an Associate Professor at Eindhoven University of Technology (TU/e), affiliated with the Human-Technology Interaction group in the School of Industrial Engineering and Innovation Sciences. He holds a secondary appointment as EAISI Health Associate Professor. His interdisciplinary research focuses on game-based design strategies, digital health, and motivational interface design, with contributions to games user research, eSports, and interactive experience tools. Birk earned a Psychology degree from the University of Trier (2012) and a Computer Science PhD from the University of Saskatchewan (2018), focusing on avatar customization for health engagement. Research Contributions: Birk’s work spans player experience, behavioral data analysis, and game-based digital biomarkers for stress. He leads the NWO Veni-funded project on game-based biomarkers for acute/chronic stress. His research has been published in top HCI venues like CHI and ACM Transactions on CHI. Organized global workshops and co-chaired CHI PLAY 2020/2021 Collaborations with Tencent, AlienTrap, and global game developers Editorial roles at JMIR Mental Health Awards & Honors: 2023 TU/e Young Researcher Award 2022 Best MSc Teacher Industrial Design NWO Veni Grant (2020) Multiple ACM awards and recognitions Academic Leadership: Vice-chair of Eindhoven Young Academy of Engineering Board member of EWUU Centre for Unusual Collaboration Member of De Jonge Akademie (KNAW) Teaching: Courses include 'Digital Tools & Human Wellbeing', 'Human Factors', and 'Social Media & Online Life'.

Phuong H. NguyenView profile

Associate Professor

• Smart Energy Systems
• Power System Control
• Distributed Energy Resources
• +3 more

Phuong H. Nguyen is an Associate Professor at the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He leads the digital Power & Energy Systems (digi-PES) lab, focusing on smart energy systems, distributed control, and IoT integration for future energy grids. His research addresses energy transition challenges, including microgrid optimization, flexibility markets, and data-driven grid management. Nguyen holds a PhD from TU/e (2010) and has held visiting researcher positions at Clemson University. His work contributes to UN SDG goals related to affordable and clean energy. Education: Bachelor's in Electrical Engineering, Hanoi University of Science and Technology (2002) Master's in Electrical Engineering, Asian Institute of Technology (2004) PhD in Electrical Engineering, TU/e (2010) Key Research Areas: Smart Grids and Cyber-Physical Systems Distributed Energy Resource Management Local Energy Markets and Flexibility Provision IoT and Big Data in Energy Systems Awards & Recognition: Best Paper Award SEST 2020 Projects & Grants: REACT-D: Reactive Power Management (Project Manager) Al-driven Applications to Smarten Power System Operation (Project Lead) UNIversity Campus as a Self-Regulated Network (Co-Manager) Labs & Teams: The digi-PES lab develops cyber-physical tools for energy transition, including microgrid simulations, flexibility markets, and grid resilience strategies.

Chen LiView profile

Professor

• Data Management
• Database Systems
• Machine Learning
• +8 more

Chen Li is a Professor in the Department of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences (ICS). He holds a Ph.D. from Stanford University and bachelor's and master's degrees from Tsinghua University. His research focuses on data management, including databases, query optimization, machine learning systems, and open-source tools like AsterixDB and Texera. He has received prestigious awards such as the NSF CAREER Award and IEEE Fellow recognition. Li is also a board member of the VLDB Endowment and the former Faculty Director of UCI's ICS Master of Computer Science Program. Education: Ph.D., Computer Science, Stanford University M.S. and B.S., Computer Science, Tsinghua University Research interests span next-generation databases, approximate query processing, and AI-driven data analytics. Notable contributions include the Texera system for collaborative data science workflows and the Apache AsterixDB project. He has led NIH-funded initiatives in diabetes research and pandemic prediction, emphasizing real-world applications of data science. Professional roles include PC co-chair of VLDB 2015, General Co-chair of SIGMOD 2027, and a visiting research scientist at Google. His awards highlight his impact in both academia and industry. Advising and mentoring are central to his career, with a focus on graduate education. He has pioneered outreach programs like DS4ALL to teach high-school students data science using Texera.

Christopher MorrisView profile

Assistant Professor

• Machine Learning
• Graph Neural Networks
• Computer Science Theory
• +3 more

Christopher Morris is a tenure-track Assistant Professor at RWTH Aachen University and a DFG Emmy Noether fellow. He leads the Learning on Graphs (LoG) research group, focusing on machine learning methods for structured data, particularly graph neural networks. His work bridges machine learning, computer science theory, and discrete mathematics, addressing challenges in generalization, expressivity, and algorithmic efficiency. Education: PhD in Computer Science from TU Dortmund University (advised by Petra Mutzel and Kristian Kersting), postdoctoral research at Mila - Quebec AI Institute (Siamak Ravanbakhsh) and McGill University, and Polytechnique Montréal (Andrea Lodi). Research interests emphasize graph machine learning, including generalization theory, combinatorial optimization, and scalable graph embeddings. Notable contributions include analyzing Weisfeiler-Leman algorithms' impact on GNNs' expressivity and generalization (VC dimension connections). Awards: DFG Emmy Noether Fellowship. Supervises six PhD students in Aachen. Active in teaching, offering courses on graph-based machine learning foundations and applications since 2022. Labs/Teams: LoG group at RWTH Aachen, collaborating with institutions like Mila and Polytechnique Montréal. Erdős number 3 through Petra Mutzel’s collaboration network.

Rashmi VinayakView profile

Associate Professor

• Computer Systems
• Networked Systems
• Information Theory
• +5 more

Rashmi Vinayak is an Associate Professor in the Computer Science Department at Carnegie Mellon University, with a courtesy appointment in the Electrical and Computer Engineering Department. She is a member of both the Systems group and Theory group at CMU and leads TheSys research group. She is also affiliated with the Parallel Data Lab (PDL). Her educational background includes a Ph.D. from UC Berkeley in 2016, followed by postdoctoral studies at the same institution. Rashmi's research spans the intersection of computer/networked systems and information/coding theory. Her current focus is on robustness and resource efficiency in data systems across storage, communication, and computation. Key thrusts include storage systems, caching systems, and systems for machine learning. Her work on SIEVE, a cache eviction algorithm, has been widely adopted by industry including VMware, Google, Redpanda, and numerous open source libraries. Her recent publications demonstrate a strong trend toward practical systems research with theoretical foundations, particularly in caching algorithms, storage systems, and machine learning infrastructure. Many of her papers have received best paper awards and industry adoption. Notable awards include: Sloan Research Fellowship (2023) IEEE Information Theory Society Goldsmith Lecturer (2023) NSF CAREER Award (2020) Multiple USENIX NSDI Community (Best Paper) Awards VMware Systems Research Award (2021) Facebook and Google Research Awards Rashmi has supervised numerous PhD, Master's, and undergraduate students, many of whom have gone on to prestigious positions at Harvard, Google, Meta, and other leading institutions. Her research has been generously funded by NSF, Sloan Foundation, Open Compute Project, Google, Facebook/Meta, VMware, and Amazon Web Services. She actively collaborates with industry partners including Google, Microsoft, NetApp, Facebook, Cisco, Intel and Cloudera. She leads TheSys research group which focuses on designing next-generation data systems that are robust, efficient, and performant. The group takes a multi-disciplinary approach spanning computer systems, information theory, and machine learning.

Angela D KentView profile

Professor

• Microbial Ecology
• Nitrogen Cycling
• Agroecosystems
• +3 more

Angela D. Kent is a Professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois Urbana-Champaign, where she also serves as Director of the Program in Ecology, Evolution, and Conservation Biology within the School of Integrative Biology. She is affiliated with the Carl R. Woese Institute for Genomic Biology. Her research focuses on microbial communities in agroecosystems and natural environments, particularly their roles in nitrogen cycling, soil health, and sustainable bioenergy production. Key research interests include microbial interactions in plant-microbe systems, the impact of genetic variation in crops on microbial processes, and the ecological and biogeochemical implications of soil microbiomes. She has authored over 99 publications and supervised datasets on topics such as denitrification dynamics, rhizosphere microbiome assembly, and microbial contributions to nitrogen retention. Dr. Kent has received the NACTA Educator Award (2012) and has contributed to high-impact studies on topics like microbial community responses to environmental stressors and the application of stable isotopes in bioenergy research. Her work bridges microbial ecology, agronomy, and environmental science, emphasizing practical solutions for sustainable agriculture and ecosystem management.

Hossam HassaneinView profile

Professor

• Wireless Sensor Networks
• Mobile Ad hoc Networks
• Edge Computing
• +5 more

Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.

Daniel J. JacobView profile

Professor

• Atmospheric Chemistry
• Environmental Engineering
• Climate Modeling
• +5 more

Roles & Affiliations: Daniel J. Jacob is the Vasco McCoy Family Professor of Atmospheric Chemistry and Environmental Engineering at Harvard University. He leads the Atmospheric Chemistry Modeling Group at the Harvard John A. Paulson School of Engineering and Applied Sciences, part of the Department of Earth and Planetary Sciences. He has held academic ranks including Gordon McKay Professor (1994-2004) and Associate/Assistant Professor (1991-1994), and was a postdoc at Harvard (1985-1987). Education: Ph.D. in Environmental Engineering Science from Caltech (1985), Ingenieur Chimiste from École Supérieure de Physique et Chimie Industrielles (1981), and earlier studies at Lycee du Parc (Lyon). Research Interests: Jacob's work focuses on atmospheric chemistry, climate modeling, and air quality. Key areas include methane emissions, tropospheric ozone, satellite remote sensing, and global biogeochemical cycles. His research integrates field campaigns (e.g., SEAC4RS, ARCTAS) and chemical transport models (GEOS-Chem) to address environmental challenges like pollution and climate change. Publications & Awards: Jacob has authored over 550 peer-reviewed publications, with an H-index of 145 (ISI) and 184 (Google Scholar). Notable awards include the Haagen-Smit Prize (2010), Macelwane Medal (1994), and recognition for teaching and climate science leadership. His work on methane emissions and air quality has been featured in Nature , Science , and AGU journals. Grants & Advising: Jacob advises numerous PhD students and postdocs, many of whom hold academic or research roles globally. He leads international initiatives like the Carbon Mapper project and chairs committees for NASA and the National Academies. His grants support studies on methane detection, aerosol chemistry, and climate policy. Labs & Collaborations: The Atmospheric Chemistry Modeling Group collaborates with NASA, NOAA, and institutions worldwide. Key projects include the GEOS-Chem model, TROPOMI satellite inversion analyses, and the Integrated Methane Inversion (IMI) tool for emissions monitoring.

Itsik Pe'erView profile

Professor

• Human Genetics
• Computational Biology
• Genomics
• +3 more

Itsik Pe'er is a Full Professor and Vice-Chair in the Department of Computer Science at Columbia University's Fu Foundation School of Engineering & Applied Science, and holds a joint appointment as Professor of Systems Biology at the Vagelos College of Physicians and Surgeons. His research focuses on computational methods in human genetics, including genetic variation analysis, disease association studies, and algorithm development for genomic data. He leads the Itsik Pe'er Lab of Computational Genomics, which develops tools like Xplorigin, Germline, and SEACells to address challenges in genomics and medical research. His work spans machine learning applications in healthcare, microbiome analysis, and cancer genomics. Notable contributions include studies on hypertensive disorders in pregnancy, bias correction in predictive models, and the development of non-Euclidean learning libraries like Manify. Pe'er has advised students including Vladimir Vacic, Anat Kreimer, and Arthi Ramachandran, and collaborates on grants addressing genetic epidemiology and computational biology. His lab's location is in the Computer Science Building at Columbia's Morningside Campus.

Yuni XiaView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Computer Science Education
• +7 more

Yuni Xia, Ph.D., is an Associate Professor of Computer Science at the Luddy School of Informatics, Computing, and Engineering at Indiana University Indianapolis. She holds a Ph.D., M.S., and B.S. in Computer Science from Purdue University (2005, 2002) and Central China University of Science and Technology (1996), respectively. Education: Ph.D. Computer Science, Purdue University (2005) M.S. Computer Science, Purdue University (2002) B.S. Computer Science and Engineering, Central China University of Science and Technology (1996) Her research focuses on machine learning, AI, computer science education (especially AI integration), data mining, data stream mining, and database systems. She is actively involved in the school’s Database, Data Mining & Machine Learning Research Group and has collaborated with students on projects like the NSF-funded SYMBIOTE system (2008–2010). She serves as a USA Computing Olympiad national team coach and emphasizes innovative teaching methods. Research Grants: Co-PI for NSF-EHCS grant “SYMBIOTE: A Reconfigurable Logic Assisted Data Stream Management System for Multimedia Sensor Networks” (2008–2010) Awarded the IU Trustees Teaching Award twice and recognized as a Digital Faculty Fellow, Xia’s contributions span both research and education. She advocates for pedagogical advancements in AI and computational systems.

• Textile-based Engineering
• Capacitive Engineering
• Actuator Engineering
• +3 more

Özgür Atalay (ORCID: 0000-0003-1050-0685) is an Associate Professor at the Department of Textile Engineering , Istanbul Technical University . His research focuses on Textile-based Engineering with emphasis on Capacitive , Actuator , and Soft Robotics Engineering . Current projects include TexSoRVA (Textile-Based Soft Robotics for VR Applications), SMARTWASTE (Agricultural/Natural Waste Textiles), and TEXWEAROTS (Self-powered Wearable Soft Robotics). Collaborations span EU-funded initiatives, Turkish Scientific and Technological Research Council (TUBITAK), and industrial partnerships. His work integrates Internet of Things (IoT) with Electronic Textiles (E-textiles) , focusing on applications in Telerehabilitation , Wearable Robotics , and Smart Textiles . Key technologies include Capacitive Sensors , Conductive Yarns , and Fog Computing Frameworks . Recent publications highlight advancements in auxetic sensor design , cloud-controlled textile gloves , and self-powered wearable systems . His research output (58 publications) demonstrates a focus on practical implementations in Therapeutic Applications and Human-Machine Interaction . Özgür leads 6 active research projects, including: TexSoRVA : 04/2025-09/2026, EU-funded SMARTWASTE : 01/2023-12/2026, TTO-funded TEXWEAROTS : 11/2022-10/2027, EU-funded With over 1623 citations (h-index 15), his work contributes to Textile Electronics , Soft Robotics , and Wearable Technology fields.

Joey PaquetView profile

Associate Professor

• Design and implementation of programming languages
• Context-driven computing
• Demand-driven computing

Dr. Joey Paquet is a Tenured Associate Professor and Department Chair in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He holds a PhD and specializes in research areas including Design and Implementation of Programming Languages, Context-Driven Computing, and Demand-Driven Computing. His work focuses on advancing programming paradigms and their applications in cybersecurity, distributed systems, and autonomic computing. Dr. Paquet is actively involved in thesis supervision across Computer Science and Software Engineering programs at both master's and doctoral levels. Research interests are centered around programming language design, particularly in demand-driven and context-aware systems. His contributions include frameworks like GIPSY and OpenISS, enabling scalable data processing and forensic computing. Recent work explores autonomic intent-driven networking, real-time gesture recognition, and IoT forensics. These efforts highlight his expertise in both theoretical constructs and practical implementations. His advising role supports students in MCompSc, MASc, and PhD programs. While specific grants are not detailed, his projects often involve interdisciplinary collaboration. Dr. Paquet is affiliated with platforms like LinkedIn and ResearchGate, reflecting an active academic presence. Key technical contributions include pioneering work on forensic computing backends, intent expression languages, and multimodal interaction systems. His research addresses challenges in cybersecurity, distributed systems, and service composition, with a focus on resilience and scalability.

Peter K. KitanidisView profile

Professor

• Groundwater Flow
• Hydrologic Modeling
• Inverse Problems
• +3 more

Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling