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Rongxing LuView profile

Adjunct Professor

• Applied Cryptography
• Privacy Enhancing Technologies
• IoT Security
• +4 more

Rongxing Lu is an Adjunct Professor at the Faculty of Computer Science, University of New Brunswick (UNB), Canada, since August 2016. Previously, he held positions at Nanyang Technological University (NTU), Singapore (2012–2016) and the University of Waterloo, Canada (PhD in 2012). His research focuses on applied cryptography, privacy enhancing technologies, and IoT-big data security. He has over 7,500 citations and received prestigious awards like the Governor General’s Gold Medal (2012) and the IEEE ComSoc Asia Pacific Outstanding Young Researcher Award (2013). He is an IEEE senior member and serves on editorial boards of journals like IEEE Network. **Education**: PhD in Electrical & Computer Engineering, University of Waterloo (2012), awarded Governor General’s Gold Medal Postdoctoral Fellow at University of Waterloo (2012–2013) **Research Interests**: Developing cryptographic protocols for IoT and big data systems Privacy-preserving techniques for distributed systems Secure communication in 5G/6G networks and vehicular systems **Awards and Recognition**: Recipient of multiple best paper awards in IEEE conferences 2016–2017 Excellence in Teaching Award at UNB **Editorial and Leadership Roles**: Symposium co-chair at IEEE Globecom’16 Secretary of IEEE ComSoc CIS-TC Organized special issues on fog computing security (Elsevier) and big data security (IEEE IoT Journal) **Key Contributions**: Pioneered privacy-aware data reporting schemes for vehicular networks Designed lightweight IoT authentication protocols Advanced secure machine learning frameworks with privacy guarantees

• Catalytic Reaction Engineering
• CO2 Hydrogenation
• Methanation Processes
• +5 more

Prof. Robert Güttel is the Director of the Institute for Chemical Engineering at the University of Ulm. He is a member of the Ulm Center for Thermal and Environmental Technology (UZWR) since 2016 and serves on its executive board. His work focuses on catalytic reaction engineering, particularly in CO2 utilization, methanation, Fischer-Tropsch synthesis, and process intensification. Research emphasizes catalyst design, reaction kinetics, and reactor modeling under dynamic conditions. Educations : Not explicitly stated in the provided text. His academic career is highlighted through his leadership roles and publications. Research Interests : Güttel’s research spans heterogeneous catalysis for CO/CO2 hydrogenation, transient kinetic analysis, polymeric reactors for extraterrestrial applications, and AI-driven reactor design. His team develops advanced catalysts (e.g., Ru/TiO2, cobalt@silica core-shell structures) and investigates novel reactor concepts like fibrous structured catalysts and sorption-enhanced processes. Recent work explores in-situ methanation on Mars and the impact of light on catalytic selectivity. Publications Trends : His 2024–2025 articles focus on low-temperature methanation beyond Earth, AI-based residence time analysis, and deactivation mechanisms. Studies highlight both experimental and computational approaches to optimize catalytic systems for sustainability and industrial relevance. Awards : No specific scientific awards mentioned in the text. Grants/Labs : Leads the Institute for Chemical Engineering at Ulm, collaborating on EU-funded projects related to power-to-X technologies and extraterrestrial chemistry. Active in developing lab-scale reactor systems and industrial partnerships for catalyst testing. Labs/Teams : Directs research groups focused on catalytic reactor design, transient kinetic methods, and sustainable process engineering. Collaborates with institutions on Mars habitat resource utilization and green hydrogen production systems.

Nicola McConkeyView profile

Lecturer

• Neutrino Physics
• Quantum Technology
• Detector Development
• +2 more

Nicola McConkey is an Ernest Rutherford Fellow and Lecturer in Particle Physics at the School of Physical and Chemical Sciences, Queen Mary University of London. She joined the Particle Physics Research Centre in 2024 and leads experimental work in neutrino interactions and detector development. Her affiliations include the Centre for Fundamental Physics and Centre for Experimental and Applied Physics. McConkey is an active member of international collaborations including SBND, DUNE, and MicroBooNE, where she contributed to the assembly of SBND and pioneered high-statistics measurements of electron-neutrino interactions using liquid argon detectors. Her research focuses on three primary domains: neutrino physics (particularly neutrino-argon scattering cross-sections), quantum technology applications for neutrino mass measurement, and liquid argon time projection chamber (LArTPC) detector development. McConkey's investigations aim to advance fundamental particle physics through precision measurements and technological innovation, with emphasis on improving detection capabilities for next-generation neutrino experiments. Publications predominantly explore neutrino interaction dynamics, cross-section measurements, and detector performance optimizations across MicroBooNE, SBND, and DUNE collaborations. Research trends demonstrate consistent focus on refining LArTPC technologies, developing machine learning applications for particle reconstruction, and probing beyond-Standard-Model physics through neutrino interactions. Scientific Awards: Ernest Rutherford Fellowship (2022) McConkey advises two PhD students (Oscar Chow, Yoshita Dabburi) and leads significant research funding including: STFC Grant: 'Piecing together the neutrino mass puzzle' (£431,666; 2024-2027) STFC Outreach Grant: 'Quantum Technologies for Neutrino Mass' (£99,999; 2024-2025) She coordinates research within the Particle Physics Research Centre laboratory and collaborates extensively within the SBND, DUNE, and MicroBooNE international teams, alongside leading the Quantum Technologies for Neutrino Mass collaboration developing novel measurement techniques.

• Fluid Dynamics
• Particle-Laden Flows
• Machine Learning
• +4 more

Dr Xinchen Zhang is a Grant-Funded Researcher (A) at the University of Adelaide's Department of Mechanical Engineering within the School of Electrical and Mechanical Engineering. His research focuses on integrating machine learning with computational fluid dynamics (CFD) to enhance predictive capabilities for multiphase flow solutions, particularly in sustainable energy applications like decarbonization technologies. He holds a PhD (2022) with a Dean's Commendation for Doctoral Thesis Excellence, emphasizing fluid and particle dynamics in particle-laden flows. His work addresses challenges in net-zero industrial processes such as limestone calcination and hydrogen production via methane pyrolysis, leveraging advanced CFD and ML-augmented methodologies. Key research areas include turbulence modeling, particle dispersion in jets, and flow regime analysis in horizontal particle-laden pipe systems. He is eligible to supervise Masters and PhD students as a co-supervisor. Dr Zhang's publications span 2018–2024, with recent trends focusing on physics-informed machine learning for turbulence modeling and multiphase flow optimization. His contributions advance computational efficiency and accuracy in predicting complex fluid-particle interactions.

Sergey TulyakovView profile

Researcher

• Computer Vision
• Machine Learning
• Generative AI
• +3 more

Sergey Tulyakov is the Director of Research at Snap Inc. , leading the Creative Vision team. His work focuses on enhancing creator capabilities through computer vision , machine learning , and generative AI , with applications in 2D/3D/4D video generation, editing, and personalization. He pioneered video generation frameworks like MoCoGAN and First Order Motion Model , and has been recognized for BEST IN SHOW AWARD at SIGGRAPH Real-Time Live! 2020. PhD (2012-2017): University of Trento, Italy MSc (2010): Belorusian State University of Informatics and Radioelectronics B.Eng (2009): Belorusian State University of Informatics and Radioelectronics His research interests span computer vision , generative models , 3D reconstruction , and personalization , with a focus on making large models efficient and mobile-compatible . Recent publications highlight advancements in 4D video generation , text-guided 3D composition , and lightweight architectures . Key scientific awards include the SIGGRAPH Real-Time Live! 2020 Best in Show for Interactive Video Stylization. He has also served on technical program committees for top-tier conferences like CVPR, ICCV, SIGGRAPH, and NeurIPS since 2022. His team organizes tutorials and keynotes, including courses on Deep Generative Models and Efficient Neural Networks . While no direct student names are listed, his collaborative work spans 60+ top-tier publications.

Guang LinView profile

Professor

• Artificial Intelligence
• Machine Learning
• Uncertainty Quantification
• +7 more

Guang Lin is the Associate Dean for Research and Innovation in the College of Science and a Full Professor in the School of Mechanical Engineering and Department of Mathematics at Purdue University. He leads the Data Science Consulting Services and has dual appointments in Statistics and Earth, Atmospheric, and Planetary Sciences. His research focuses on AI, machine learning, uncertainty quantification, and computational science, with applications in fluid mechanics, materials science, and healthcare. Lin holds a Ph.D. from Brown University (2007) and has received numerous awards, including the NSF CAREER Award and Purdue’s University Faculty Scholar distinction. He has authored over 250 publications and secured grants totaling millions, including DOE and NIH funding. His interdisciplinary work bridges academia and industry, emphasizing AI-driven solutions for complex systems. Education: Ph.D. Applied Mathematics (Brown, 2007), M.S. Applied Mathematics (Brown, 2004), M.S. Mechanics (Peking University, 2000), B.S. Mechanics (Zhejiang University, 1997). Research Grants: Includes DOE-funded projects on machine learning for plasma-wall interactions and NSF grants for multiscale modeling. Service: Editorships in SIAM MMS, ASME Journal, and leadership in Purdue’s AI initiatives. Teaching: Courses on Uncertainty Quantification, Fluid Mechanics, and Data Science.

Serge A. ShapiroView profile

Professor

• Seismology
• Geophysics
• Rock Physics
• +5 more

Prof. Serge A. Shapiro is a Full Professor of Geophysics at Freie Universität Berlin since 1999 and Director of the PHASE consortium since 2004. He holds a Diploma in Applied Geophysics from Lomonosov Moscow State University (1982), a PhD from the Moscow Research Institute of Geosystems (1987), and a Habilitation from Karlsruhe University (1995). His research focuses on seismogenic processes, induced seismicity, rock physics, and subduction zone dynamics, with applications to geothermal energy, CO2 storage, and hydraulic fracturing. Education: Diploma in Applied Geophysics, Lomonosov Moscow State University (1982) PhD in Geophysics, Moscow Research Institute of Geosystems (1987) Habilitation, Karlsruhe University (1995) Research Interests: Induced seismicity from fluid operations CO2 storage and hydraulic fracturing risks Seismic hazard assessment Rock physics under stress Key Contributions: Developed the Seismogenic Index Model for induced earthquakes Pioneered DAS-based seismic monitoring techniques Advanced understanding of fault stability and pressure diffusion effects Awards: Virgil Kauffman Gold Medal (2013) for work in microseismic monitoring and rock physics Grants & Projects: PHASE consortium leader (2004–present) Utah FORGE EGS project advisor Labs/Teams: Seismology Group, Freie Universität Berlin PHASE university consortium

• Machine Learning
• Database Systems
• Artificial Intelligence
• +3 more

Christopher Re is a Professor in the Department of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and Center for Research on Foundation Models. His research focuses on the intersection of machine learning, database systems, and scientific computing, with applications in humanitarian efforts, scientific discovery (e.g., extrasolar neutrinos, DNA foundation model Evo), and industry partnerships with companies like Apple and Google. He has been recognized with prestigious awards, including the MacArthur Foundation Fellowship and multiple test-of-time awards. His work emphasizes advancing thermal materials, phase-change memory, and ultrafast electron microscopy technologies. Re's research contributions span database theory, systems, and machine learning, with best papers at PODS 2012, SIGMOD 2014, and ICML 2016. His lab’s innovations have been incorporated into products globally, and he actively invests in technology startups. Key projects include developing thermal interface materials for 3D integrated circuits and exploring energy-efficient neuro-inspired memory systems. His awards reflect sustained excellence: NeurIPS 2020 and PODS 2022 test-of-time awards, along with recent accolades for student-led initiatives at MIDL 2022 and ICLR22. Re’s interdisciplinary approach bridges academia and industry, driving both scientific and humanitarian impact.

Raju VatsavaiView profile

Associate Professor

• Geospatial Analytics
• Big Data Management
• Data Mining
• +5 more

Raju Vatsavai is an Associate Professor in the Department of Computer Science at North Carolina State University, affiliated with the Center for Geospatial Analytics. He joined NC State in 2014 as part of the Chancellor’s Faculty Excellence Program cluster hire in Geospatial Analytics. Education: PhD and MS in Computer Science from University of Minnesota Prior Roles: Lead Data Scientist at Oak Ridge National Lab, roles at University of Minnesota, IBM Research, AT&T Labs, and C-DAC (India) His research in geospatial analytics spans big data management , spatiotemporal data mining , deep learning for remote sensing , and high-performance computing , with applications in national security, climate change, and crop monitoring. Recent work includes deep learning frameworks for cloud imputation , multi-sensor satellite data harmonization , and transfer learning applications in crop classification . He has been a leading investigator on grants from the National Geospatial-Intelligence Agency, Department of Energy, and Department of Homeland Security. Labs: Associate Director of the Center for Geospatial Analytics Expertise: Spatial computing, Earth observation, nuclear proliferation detection via remote sensing

• Plant Biochemistry
• Specialized Metabolism
• Solanaceae Family
• +4 more

Cornelius Barry is an Associate Professor in the Department of Horticulture at Michigan State University, with affiliations to the Plant Breeding, Genetics and Biotechnology program, AgBioResearch, and the Molecular Plant Sciences Graduate Program. He joined MSU in July 2007 with a 75% research and 25% teaching appointment. Education: PhD and BSc from the University of Nottingham and University College of Wales Current roles: Director of NSF REU Site: Plant Genomics @ MSU His research focuses on the evolution of biochemical diversity within the Solanaceae family , particularly specialized metabolites like terpenoids, flavonoids, and alkaloids. He investigates their roles in plant defense, pollinator attraction, and human applications through genomics, metabolite profiling, and synthetic biology. Recent publications show expertise in alkaloid biosynthesis , trichome chemistry , and metabolic pathway engineering . Key collaborations include teams at Boyce Thompson Institute, Texas A&M, and University of Nottingham. He advises graduate students in Genetics , Biochemistry & Molecular Biology , and Molecular Plant Sciences programs.

Brett J. BorghettiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +12 more

Dr. Brett J. Borghetti is a Professor of Computer Science in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), Graduate School of Engineering and Management, Wright-Patterson AFB, OH. He was promoted to Professor in July 2022, following prior appointments as Associate Professor (2017) and Assistant Professor (2008/2013). His expertise lies in artificial intelligence, machine learning, deep learning, cybersecurity, and human-machine teaming. Education: Ph.D. in Computer Science, University of Minnesota, Twin Cities (2008) M.S. in Computer Systems, Air Force Institute of Technology (1996) B.S. in Electrical Engineering, Worcester Polytechnic Institute (1992) Dr. Borghetti's research focuses on applying machine learning to physical science sensors (hyperspectral, seismic, RF), cybersecurity, and enhancing human-machine team performance. He teaches graduate courses in machine learning, AI, data security, and algorithm design, and advises numerous MS and PhD students in areas such as sensor exploitation, cognitive workload, and cyber situational awareness. His recent publications demonstrate strong trends in deep learning for multimodal sensor fusion, nuclear security, and neuroergonomics. Scientific Awards: AETC Educator of the Year (2021, Civilian) AFIT Ezra Kotcher Teaching Award (2021) AFIT Teaching Excellence Award (2019) AF STEM Outstanding Science and Educator Award (2015) Multiple Eta Kappa Nu Outstanding Instructor Awards Air Force Meritorious Service Medal and other military honors Dr. Borghetti has advised numerous graduate students and led research projects with significant funding and applications in defense and national security. He has directed research in AI-driven sensor analysis, cyber defense systems, and adaptive automation. His work often involves collaboration with national labs and DoD agencies. He has contributed to major research initiatives in human factors, cyber intruder detection, and machine learning for operational environments. Labs and Research Teams: His work is associated with AFIT's research in cyber security, sensor exploitation, and human-machine systems. He collaborates with teams working on the Cyber Intruder Alert Testbed (CIAT), neuroergonomic modeling, and machine learning for defense applications.

Robert J.K. JacobView profile

Professor

• Human-Computer Interaction (HCI)
• Brain-Computer Interfaces (BCI)
• User Interface Design
• +5 more

Robert J.K. Jacob is a Professor of Computer Science at Tufts University, affiliated with the School of Engineering's Department of Computer Science. His research focuses on Human-Computer Interaction (HCI), particularly implicit brain-computer interfaces (BCI) using fNIRS and EEG technologies. He has held visiting positions at University College London, Université Paris-Sud, and MIT Media Lab. Education: Ph.D. in Computer Science from Johns Hopkins University. Research Interests : Jacob's work explores novel interaction techniques, adaptive interfaces, and BCI applications. Current projects emphasize real-time fNIRS-based systems for effortless user input, cognitive workload assessment, and neuroadaptive technologies. His lab investigates how brain signals can enhance user interfaces in domains like music learning, gaming, and urban design. Recent Trends in Articles : Recent publications highlight advancements in BCI design, neuroadaptive systems, and interdisciplinary applications of fNIRS. Work spans theoretical frameworks (e.g., NeuroCHI ethics) to practical tools like the Tufts fNIRS dataset. Key themes include improving BCI calibration, exploring AI's role in urban environments, and integrating affective computing into artistic interfaces. Awards : ACM Fellow (2016) ACM CHI Academy Membership (2007) Best Paper Award at CHI 2016 Advising & Grants : Supervised 15+ Ph.D. alumni in HCI and BCI. Served as Vice-President of ACM SIGCHI and co-chair of UIST/CHI conferences. Active in editorial roles for Human-Computer Interaction and ACM Transactions on Computer-Human Interaction . Labs & Teams : Directs the Tufts HCI Lab in the Joyce Cummings Center. Collaborates with interdisciplinary teams on projects like the Marble Track Audio Manipulator and Reality-Based Interaction Framework.

Thomas K. UchidaView profile

Associate Professor

• Biomechanics
• Dynamical Systems
• Musculoskeletal Modeling
• +7 more

Thomas K. Uchida is an Associate Professor in the Department of Mechanical Engineering at the University of Ottawa, a position he has held since May 2024. Prior to this promotion, he served as an Assistant Professor at the same institution from October 2018 to May 2024. Before joining the University of Ottawa, Dr. Uchida was an Engineering Research Associate (April 2015-August 2018) and Simbios Distinguished Postdoctoral Fellow (July 2012-April 2015) in the Department of Bioengineering at Stanford University. Dr. Uchida's research focuses on the modeling and simulation of dynamic systems, with particular emphasis on human movement biomechanics. His work spans multiple areas including: Simulation-guided design of assistive devices for improving mobility Modelling musculotendon dynamics and energy expenditure Parameter identification and model reduction methods Impact and contact dynamics Development of computational tools for biomechanical analysis He is a co-author of the book "Biomechanics of Movement: The Science of Sports, Robotics, and Rehabilitation" published by MIT Press, and actively contributes to the development of OpenSim, an open-source software platform for modeling musculoskeletal systems and generating simulations of human and animal movement. His work on OpenSim was featured on the cover of PLoS Computational Biology. Dr. Uchida's recent publications demonstrate strong activity in biomechanics, robotics, and computational modeling. His work bridges engineering principles with biological applications, particularly in understanding human movement mechanics. Key trends include applying machine learning to gait analysis, developing enhanced spine models, analyzing human balance stability with time delays, and advancing musculoskeletal simulation techniques. As an academic advisor, Dr. Uchida currently supervises seven graduate students: Firas Baklouti (expected completion August 2025) Shahin Sharafi Kazem Alambeigi Jiawei Gao Yuzhen Yan Manuel Lucas De Oliveira Blake Scott Miller Dr. Uchida collaborates with research teams focused on biomechanics and movement science. His work with OpenSim places him within an international community of researchers developing computational tools for biomechanical analysis, connecting mechanical engineering with biomedical applications in sports, robotics, and rehabilitation.

• Machine Learning
• Information Theory
• Deep Learning
• +6 more

Zhiyuan Wu is a Doctoral Research Fellow at the University of Oslo , affiliated with the Digital Signal Processing and Image Analysis research group under the Faculty of Mathematics and Natural Sciences . Education: Bachelor’s degree in Communication Systems and Information Technology from Lanzhou University, China Master’s degree from the Technical University of Munich, School of CIT Research Focus: Zhiyuan Wu specializes in machine learning, with particular emphasis on probabilistic graphical models, information theory, and tackling real-world challenges such as distributional shifts and privacy concerns. His work explores entropy regularization techniques to address label shift in distributed learning systems, aiming to improve model robustness and data privacy across diverse domains like medical applications. Publications & Research Trends: His recent publication at the International Conference on Learning Representations highlights a novel approach to mitigating label shift through entropy regularization. This aligns with his broader research goals of enhancing the adaptability and interpretability of machine learning models in dynamic, privacy-sensitive environments. Labs & Teams: He is actively involved with the Digital Signal Processing and Image Analysis (DSB) research group, contributing to collaborative projects that bridge theoretical advancements with practical implementations in machine learning.

• Photonics
• Laser Technology
• Machine Learning
• +5 more

Professor Robert Eason is a leading academic at the University of Southampton, specializing in photonics and laser technology. His research spans interdisciplinary areas combining Machine Learning , Medical Diagnostics , and Microfluidics . Research Interests : Eason focuses on AI-driven laser applications, including deep learning for phototherapy , autonomous laser machining , and low-cost paper-based diagnostic devices . His work bridges photonics with biomedicine and advanced manufacturing. Recent Publications : His 2025 article in Scientific Reports explores AI simulations for psoriasis treatment, while 2024-2022 works address laser-controlled microfluidics, deep learning in microscopy, and reinforcement learning for laser machining. Supervision : He supervises PhD student Georgia Mourkioti in laser-based research projects. External Roles : Eason has served as a speaker at international conferences including the International Symposium on Laser Precision Microfabrication (2018), LAISER (2019), and Deep Learning for Control of Light-Matter Interactions (2022).