جست‌وجوی استاد و پژوهشگر

• Sedimentary Geology
• Stratigraphy
• Subsurface Fluid Flow
• +۳ مورد دیگر

Professor Gary Hampson is a Professor of Sedimentary Geology at the Department of Earth Science & Engineering, Faculty of Engineering, Imperial College London. His roles include Director of Undergraduate Studies (2023–present) and former Director of the Petroleum Geoscience MSc course. He holds affiliations with the Energy Futures Lab, Grantham Institute, and NORMS research groups. Hampson's research focuses on depositional systems, stratigraphy, and subsurface reservoir characterization, with applications to CO2 storage and fluid flow dynamics. Education: PhD in Sedimentology and Sequence Stratigraphy, University of Liverpool (1991–1995) BA in Natural Sciences (Geology), University of Cambridge (1988–1991) Research Interests: His work integrates sedimentology, stratigraphy, and reservoir modeling to understand subsurface fluid dynamics. Key areas include sediment dispersal patterns, stratigraphic architecture, and the impact of heterogeneity on reservoir performance. Recent studies emphasize CO2 storage potential in sedimentary systems and the application of advanced modeling techniques. Articles Trends: Recent publications highlight CO2 storage mechanisms, sedimentological heterogeneity in reservoirs, and the use of machine learning in geological analysis. His work bridges theoretical stratigraphy with applied reservoir engineering, particularly in carbonates and fluvio-deltaic systems. Awards: 2020 IAMG Best Paper Award 2016 AAPG Bennison Lecturer 2010 SEPM Excellence Awards Advising & Grants: Hampson has advised numerous students and led projects on reservoir characterization. His editorial roles include co-chief editor of the Journal of Sedimentary Research and guest editorships in Petroleum Geoscience. Labs/Teams: Active in the Energy Futures Lab and NORMS initiative, focusing on novel reservoir modeling and simulation for subsurface energy systems.

Bharanidharan Shanmugamمشاهده پروفایل

دانشیار

• IoT Security
• Cyber Risk Management
• Network Security
• +۳ مورد دیگر

Dr. Bharanidharan Shanmugam is an Associate Professor in Information Technology at Charles Darwin University's Faculty of Science and Technology. He specializes in cybersecurity, IoT security, and cyber risk management in microgrids. His research focuses on addressing real-world challenges in IoT, smart grids, and medical devices to enhance community impact. **Research Interests:** - IoT Security - Cyber Risk Assessment in Microgrids - Network and Information Security - Applied Cybersecurity Solutions - Blockchain and Privacy-Preserving Technologies **Key Projects (2019–2025):** - Cyber Territory Skills Hub (2023–2025) - Renewable Energy Microgrid Hub (2021–2024) - Blockchain-Based Digital Identity (2019–2020) **Publications:** Focuses on IoT security frameworks, intrusion detection systems, and AI-driven cybersecurity solutions. Recent works include studies on smart grid load forecasting, medical IoT threat detection, and water leakage detection using machine learning. **Grants & Supervision:** Principal Investigator on multiple ARC-funded projects. Supervises PhD students in DevSecOps and IoT security. Active in organizing workshops on digital awareness for indigenous communities.

Liqiang Wangمشاهده پروفایل

استاد

• Big Data Analytics
• High-Performance Computing (HPC)
• Parallel Computing
• +۳ مورد دیگر

Liqiang Wang is a Professor in the Department of Computer Science at the University of Central Florida (UCF), where he directs the Big Data Lab. Previously, he served as faculty at the University of Wyoming (2006-2015). He holds a Ph.D. in Computer Science from Stony Brook University (2006) and spent a visiting research period at IBM T.J. Watson Research Center (2012-2013). His research focuses on big data analytics, high-performance computing, parallel systems optimization, and applying deep learning to detect programming errors and enhance model robustness. Education: Ph.D., Computer Science, Stony Brook University (2006); Visiting Researcher, IBM Watson (2012-2013). Research Interests: Improving accuracy and security of big data models, optimizing parallel computing systems (HPC, Cloud, GPUs), program analysis for concurrency errors, and deep learning applications in anomaly detection and adversarial robustness. Notable projects include scalable LSQR algorithms for seismic tomography and the OpenMP Analysis Toolkit (OAT) for concurrency error detection. Key Awards: NSF CAREER Award (2011), Castagne Faculty Fellowship (2013-2015), UCF Mid-Career Refresh Award (2020), and grants including a $50K NSF CIVIC-PG grant (2022) and Google/Meta donations. Advising and Grants: Supervises over 20 Ph.D./M.S. students and has secured grants totaling over $100K. Notable collaborations include seismic tomography with NCAR and cloud computing optimization. Labs/Teams: Director of UCF’s Big Data Lab, collaborating on projects like Parallel LSQR and Anti-Neuron Watermarking.

• Multimodal User Interfaces
• Affective Computing
• Social Robotics
• +۵ مورد دیگر

Prof. Elisabeth André is a Full Professor of Computer Science at Augsburg University , leading the Chair for Human-Centered Artificial Intelligence. She holds academic roles including Managing Director of the Institute of Computer Science (2004–2006) and serves on numerous national/international committees such as the Bavarian Artificial Intelligence Council and DFG panels. Her research focuses on multimodal interfaces , affective computing , and social robotics , with emphasis on human-centered design principles and ethical AI. Education: 1988: Diploma in Computer Science (Saarland University) 1995: Dr. rer. Nat. (Saarland University) Research Interests: Multimodal analysis (physiological/gaze/speech/gesture), tangible interfaces, technology-enhanced learning, and AI ethics. She has pioneered projects like CALLAS (affective multimedia systems) and CUBE-G (cultural adaptation for agents). Awards: 2021: Gottfried Wilhelm Leibniz Prize (Germany’s highest research honor) 2013: ECCAI Fellow 2010: Member of Leopoldina and Academia Europaea Grants & Leadership: Led EU projects (e.g., METABO, E-Circus), DFG initiatives, and co-chaired major conferences (ICMI, AAMAS). Supervised over 20+ PhD students and 11 ongoing doctoral candidates. Labs/Teams: Leads the Human-Centered AI team at Augsburg, collaborating on projects like FORSocialRobots and TherapAI. Active in interdisciplinary efforts for ethical AI frameworks and educational robotics.

Masatoshi Okutomiمشاهده پروفایل

استاد

• Medical Imaging
• Computer Vision
• Deep Learning
• +۵ مورد دیگر

Professor Masatoshi Okutomi is affiliated with the Department of Systems and Control Engineering at the School of Engineering, Institute of Science Tokyo. His research focuses on advanced medical imaging techniques, particularly in endoscopy and 3D reconstruction, leveraging deep learning and neural networks. Key interests include virtual chromoendoscopy for cancer detection, image restoration, and stereo matching under challenging conditions. His work bridges computer vision and healthcare, addressing real-world applications such as MRI reconstruction and foggy stereo matching. Notable contributions include developing lightweight medical segmentation networks for edge devices and advancing neural radiance fields (NeRF) for novel view synthesis. His research spans diverse domains: from improving video quality assessment to enhancing object detection in high-dynamic-range images. Collaborative projects emphasize practical solutions for medical diagnostics and robust image processing in adverse environments. Recent articles highlight advancements in temporally-consistent video restoration, few-shot view synthesis, and degraded image classification using knowledge distillation. These innovations underscore his commitment to pushing boundaries in both theoretical computer vision and applied medical technology.

Kourosh Davoudiمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.

• Deep Learning
• Medical Image Analysis
• Biomedical Engineering
• +۲ مورد دیگر

Maria Chiara Fiorentino is a Research Fellow at the Department of Information Engineering, Polytechnic University of Marche, Italy. Her work focuses on applying deep learning techniques to medical image analysis, particularly in ultrasound, MRI, and CT imaging. Education Master’s in Biomedical Engineering, Università Politecnica delle Marche (Honors) Ph.D. in Information Engineering, Università Politecnica delle Marche (Laude) Research Interests: Dr. Fiorentino specializes in deep learning for medical imaging, with applications in diagnosing neurodegenerative diseases like Parkinson’s, cardiovascular conditions, and musculoskeletal disorders. Her recent work includes federated learning for fetal ultrasound analysis, AI-driven vocal fold pose estimation, and domain adaptation in MRI segmentation. Scientific Awards: Paolo Marziali Thesis Prize for her Master’s research Gruppo Nazionale di Bioingegneria award for her Ph.D. thesis Publications: Dr. Fiorentino’s work spans fetal brain image synthesis, zero-shot learning robustness, and machine learning for catheterization management and stenosis detection.

Jamshid Mohammadiمشاهده پروفایل

استاد

• Structural Engineering
• Civil Engineering
• Risk Analysis
• +۵ مورد دیگر

Jamshid Mohammadi is the Interim Provost and Professor of Civil and Architectural Engineering at Illinois Institute of Technology (IIT), within the Armour College of Engineering. He holds a Ph.D. in Civil Engineering (Structural Engineering) from the University of Illinois at Urbana-Champaign. His research focuses on structural integrity, seismic damage analysis, bridge performance, and risk assessment in transportation systems. Research Projects: He leads studies on bridge fatigue, seismic vulnerability, structural health monitoring, and disaster resilience. Notable projects include investigating horizontally curved bridges, seismic damage to skewed bridges, and probabilistic models for fatigue failure in metals. His work often involves collaborations with institutions like NASA and the Illinois Department of Transportation. Publications & Books: Mohammadi has authored over 150 peer-reviewed articles and two influential books: Systems Engineering, with Economics, Probability and Statistics and NDT Methods Applied to Fatigue Reliability Assessment of Structures . His work bridges theoretical models with practical engineering solutions. Expertise: His expertise spans system reliability, highway bridge analysis, and probabilistic methodologies for infrastructure assessment. He advises on temporary structure design, post-disaster risk mitigation, and lifecycle cost optimization. Grants & Recognition: His projects are funded by federal and state agencies. While no specific awards are listed, his extensive publications and leadership roles highlight his impact in civil engineering education and practice.

Aaron Maxwellمشاهده پروفایل

دانشیار

• Geospatial Science
• Remote Sensing
• Machine Learning
• +۶ مورد دیگر

Aaron Maxwell is an Associate Professor in the Department of Geology and Geography at West Virginia University (WVU). He serves as the principal investigator of West Virginia View, a member of AmericaView, and director of the WV GIS Technical Center. His research focuses on spatial predictive modeling, geohazard mapping, machine/deep learning applications in remote sensing, and thematic map accuracy assessment. He holds degrees from Alderson Broaddus University (B.S. in Biology, Chemistry, Environmental Science) and WVU (M.S. and Ph.D. in Geology), with a GIS Professional (GISP) certification. Education: Bachelor of Science in Biology, Chemistry, and Environmental Science – Alderson Broaddus University Master of Science in Geology – West Virginia University Doctor of Philosophy in Geology – West Virginia University Research Interests: Dr. Maxwell’s work emphasizes computational methods to extract insights from geospatial data. Key areas include: Deep learning for geomorphic feature extraction (e.g., LIDAR-based semantic segmentation) Machine learning applications in forest fuel load estimation and slope failure modeling Best practices for assessing deep learning outputs in remote sensing Synthetic data generation for predictive modeling Community flood resiliency and participatory GIS Publications: His recent work highlights advancements in geospatial deep learning (e.g., the geodl R package), accuracy assessment metrics for imbalanced datasets, and modeling post-mining landscape evolution. Articles often blend theoretical frameworks with applied case studies across environmental and geotechnical domains. Grants & Funding: Supported by NSF (CAREER Award) and AmericaView, his work trains students and develops open-source geospatial tools. Current projects include synthetic forest stand modeling and flood resiliency initiatives. Labs & Teams: Leads WV View, fostering remote sensing education and data sharing. Collaborates on geospatial workforce development and open-source software initiatives (e.g., GIScience courses, ArcGIS Pro labs).

Nicola Dragoniمشاهده پروفایل

استاد

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +۷ مورد دیگر

Nicola Dragoni is a Professor in Cybersecurity Engineering at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). As Deputy Director and Head of Section, he leads research initiatives focused on securing emerging technologies. Key Research Areas : Internet of Things (IoT) security, machine learning for intrusion detection, cyber-deception techniques, fog computing, malware analysis, blockchain applications, and wireless sensor network security. Supervision : Actively supervising multiple PhD students in projects related to cyber-deception, moving target defense, and bio-inspired security mechanisms. Recent Publications : Contributions to IoT honeypots, drone identification via RF signals, passkey adoption challenges, and cyber range taxonomies.

Jeremy Edwardsمشاهده پروفایل

استاد

• Pharmaceutical Chemistry
• Quantitative Biology
• Genomics
• +۵ مورد دیگر

Jeremy Edwards serves as a Professor in the Department of Chemistry at the University of New Mexico, where he maintains an active research program at the intersection of pharmaceutical chemistry, genomics, and computational biology. His work spans multiple disciplines with a particular focus on developing innovative technologies for DNA sequencing and analysis. Professor Edwards' research interests center around Pharmaceutical Chemistry, Quantitative Biology, and Genomic Technologies. His work has significantly contributed to the fields of metabolic engineering, genome sequencing, and systems biology. He has pioneered approaches in nanopore sequencing technology and developed computational frameworks for analyzing complex biological systems. His research group has made notable contributions to understanding metabolic networks through flux balance analysis and in silico modeling, with applications ranging from bacterial metabolism to mammalian systems. Analysis of Professor Edwards' publication record reveals a strong trend toward developing cutting-edge genomic technologies and computational approaches for biological analysis. His recent work focuses on spatial transcriptomics, nanopore sequencing innovations, viral genome surveillance, and target illumination for drug discovery. The publications demonstrate a consistent trajectory from foundational metabolic modeling work toward increasingly sophisticated genomic technologies and applications in drug target identification and validation. Professor Edwards has established himself as a leader in computational genomics with an extensive publication record including highly cited papers such as "In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data" (1297 citations) and "The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities" (1295 citations). His work on metabolic modeling has been particularly influential in systems biology. As an active researcher, Professor Edwards has mentored numerous students and collaborators, though specific student names aren't documented in the available materials. His research has attracted significant funding supporting the development of genomic technologies and computational approaches. His laboratory appears to focus on the intersection of bioinformatics, molecular biology, and engineering, with particular emphasis on next-generation sequencing technologies and their applications. Professor Edwards leads a research group that integrates computational modeling with experimental approaches to tackle challenges in genomic analysis and metabolic engineering. His team has developed innovative tools like the Sentieon Genomics Tools, described as "a fast and accurate solution to variant calling from next-generation sequence data." The group's work spans from fundamental research on DNA sequencing technologies to applied projects in viral surveillance and drug target identification.

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +۶ مورد دیگر

Talal Shaikh is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences in Dubai. He serves as Director of Undergraduate Studies and Programme Director for BSc Computer Science, BSc CS (AI), and MSc Software Engineering. With a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. Research Interests: Pervasive Computing, IoT/M2M, AI/ML, WiFi Sensing for Healthcare, Financial Machine Learning, Educational Technology Awards: Teaching Excellence Awards (2017/18), Fellow of the Higher Education Academy (FHEA), multiple Learning and Teaching Oscars (2016, 2017, 2018) His work spans Ubiquitous Computing and IoT , focusing on sensor networks and WiFi-based sensing for healthcare. In Artificial Intelligence , he applies ML to robotics, financial analytics, and educational innovation. Recent articles analyze Reinforcement Learning , Emotion Recognition , and WiFi Sensing applications. His teaching emphasizes student-centric learning, with over 100 supervised dissertations achieving distinctions. Collaborations include international conferences and interdisciplinary research in smart environments and adaptive systems.

Ehsan Amjadianمشاهده پروفایل

دانشیار مدعو

• Artificial Intelligence
• Natural Language Processing
• Deep Learning
• +۶ مورد دیگر

Ehsan Amjadian serves as an Adjunct Associate Professor at the University of Waterloo while holding industry leadership positions. He currently acts as Artificial Intelligence Fellow at BMO Canada (Bank of Montreal) and previously served as Head of AI Acceleration & Innovation at RBC (Royal Bank of Canada), where he led advanced AI product development from ideation to production deployment. Education Ph.D. in Deep Learning & Natural Language Processing, Carleton University Research Interests Dr. Amjadian's work centers on applied Artificial Intelligence with emphasis on Natural Language Processing, Deep Learning, and Generative AI systems. His research extends to Computer Vision applications for satellite imagery, Data Protection frameworks, and AI solutions for financial systems and climate modeling. Industry patents reflect his focus on translating theoretical AI into production-grade systems for real-world financial and environmental challenges. Scientific Awards No scientific awards, fellowships, or medals were mentioned in the source material. Advising and Grants The provided text contains no information regarding graduate student supervision, research grants, or funded projects. Labs and Teams While leading AI innovation teams at RBC and BMO, no academic laboratory affiliations or university research teams were specified in the source material.

Donato Romanoمشاهده پروفایل

دانشیار

• Bioinspired Robotics
• Biomimetic Robotics
• Animal-Robot Interaction
• +۸ مورد دیگر

Donato Romano serves as Associate Professor at The BioRobotics Institute of Scuola Superiore Sant'Anna, Italy, where he coordinates the Bio-Robotic Ecosystems Lab and co-founded the spin-off company HUBILIFE srl. His interdisciplinary work bridges robotics, biology, and AI to develop biohybrid systems for biodiversity preservation, sustainable environmental management, and life support in extreme scenarios including space exploration. With over 90 publications and an H-index of 27 (Scopus, March 2025), he has established significant academic leadership through editorial roles across 12+ international journals. Romano's educational foundation includes advanced degrees with honors: an M.Sc. in Agriculture Science and Technologies (2014) and a PhD in BioRobotics (2018), both from Scuola Superiore Sant'Anna. His academic journey includes visiting scholar positions at Khalifa University and substantial industry-academia collaboration through HUBILIFE srl, which commercializes bioinspired devices for human daily life improvement. His research program focuses on bioinspired and biomimetic robotics with particular emphasis on animal-robot interaction, biohybrid systems, and natural intelligence. Key projects address critical global challenges: SENSORBEES develops biohybrid environmental surveillance for ecological monitoring; REGOLIFE investigates lunar soil-terrestrial organism interactions for space agriculture; and OCEAN ROBOCTO explores marine ecosystem solutions. This work demonstrates a strategic progression from fundamental behavioral studies toward applied ecological and extraterrestrial systems. Analysis of his recent publications reveals strong trends in AI-driven behavioral analysis, with deep learning increasingly applied to entomological studies and pest management. The research spans agricultural applications (precision monitoring traps, larval detection systems), ecological conservation (biodiversity surveillance), and extreme-environment adaptation (lunar regolith studies). A distinctive feature is the consistent integration of biohybrid approaches where living organisms and robotic systems create synergistic capabilities exceeding either component alone. Romano's scientific recognition includes election as Junior Fellow of the Italian Academy of Engineering and Technology (2025), the Lucani fuori dal Comune award (2024), and multiple best-thesis prizes. His editorial leadership spans high-impact journals including IEEE Transactions on Medical Robotics and Bionics and Pest Management Science, where he serves as Associate Editor. As principal investigator, Romano coordinates major international projects totaling over €15M in funding: HORIZON-EIC's SENSORBEES (2024-2029), ASI's REGOLIFE (2024-2027), National Geographic's OCEAN ROBOCTO (2024-2026), and PRIN's COSMIC (2023-2025). His teaching portfolio includes PhD courses in Biosystems for Biorobotics and M.Sc. instruction in Bionics Engineering at Scuola Superiore Sant'Anna and University of Pisa. The Bio-Robotic Ecosystems Lab under Romano's direction pioneers biohybrid technologies where living organisms and robotic systems create integrated solutions. Current initiatives include SENSORBEES' environmental monitoring swarms, REGOLIFE's moonworm colonization systems, and HUBILIFE's commercial vector-control devices. The lab maintains active collaborations with space agencies, agricultural institutes, and conservation organizations, positioning biohybrid systems as next-generation tools for planetary-scale challenges.

• Machine Learning
• Data Science
• Social Sciences
• +۷ مورد دیگر

Prof. Dr. Markus Strohmaier holds the Chair of Data Science in Economics and Social Sciences at the University of Mannheim's Business School. His research focuses on applying machine learning and data science to understand socio-economic systems and human behavior, leveraging text, relational, and emerging data types. He teaches graduate courses and supervises theses in these areas. Research interests include algorithmic fairness, behavioral analysis via computational methods, and the societal implications of AI. Notable work addresses demographic representativeness in large language models, bias mitigation in rankings, and gender gaps in blockchain adoption. His team is located at L15, 3rd floor – Room 307 in Mannheim, adjacent to the central station. Collaborations span interdisciplinary topics like social media analysis, network dynamics, and computational social science. Key contributions include developing benchmarks for neural network similarity (Resi) and frameworks for measuring algorithmic fairness perceptions (FairCeptron). Research often bridges technical innovation with societal impact, addressing ethical challenges in AI and data-driven decision-making.