جستجوی استاد راهنمای دانشگاهی

• AI & Innovation
• Digital Transformation
• Fintech
• +۵ مورد دیگر

David Shrier is a Professor at Imperial College Business School, specializing in AI & Innovation. He co-directs the Trusted AI Alliance and serves as interim Academic Director of the Centre for Digital Transformation. His work bridges academia and industry, focusing on digital transformation, fintech, and ethical AI. He holds visiting roles at MIT School of Engineering and has pioneered educational innovations, including creating highly successful online courses that generated over $1 billion in academic revenue. Founder of Trusted AI Alliance and co-developer of the Trust::Data Initiative. Interim Academic Director, Imperial's Centre for Digital Transformation. Visiting Scholar at MIT School of Engineering, collaborating on commercialization models. His research interests include AI governance, fintech innovation, cybersecurity, and data privacy. He has advised governments and organizations globally, including the European Parliament and Commonwealth Secretariat. His books, such as Global Fintech (2022) and Basic AI (2024), explore technology's societal impact. He chairs startups like Orbu.AI and serves on NASDAQ-listed GRIID's board. Recipient of the 2022 Choice Award for Global Fintech . Launched initiatives such as the Oxford Cyber Security for Business Leaders program and the UN Data Academy.

Alfred Heroمشاهده پروفایل

استاد

• Data Science
• Statistical Machine Learning
• Distributed Optimization
• +۱۰ مورد دیگر

Alfred Hero is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS) with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is affiliated with multiple research centers including the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS). Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization using statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His research group has produced numerous PhD students who have gone on to prominent academic and industry positions. His recent publications show a strong focus on high-dimensional statistical methods, machine learning theory, network analysis, and applications in biomedical domains. The research trends indicate increasing emphasis on multimodal data fusion, robust learning algorithms, and applications to complex systems in biology and security domains. His work bridges theoretical foundations with practical implementations across diverse application areas. Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Fourier Award in Signal Processing from the IEEE Hero has advised numerous PhD, MS, and undergraduate students who have gone on to successful careers in academia and industry. His research has been supported by various grants, though specific grant details are not provided in the source material. His lab collaborates extensively across disciplines with researchers in statistics, biomedical engineering, and computational medicine. The Hero Research Group maintains active collaborations with institutions worldwide and participates in major conferences in machine learning, signal processing, and data science.

James P McNamaraمشاهده پروفایل

استاد

• Hydrology
• Geomorphology
• Hydrogeology
• +۵ مورد دیگر

James P McNamara is a Professor in the Department of Geosciences at Boise State University , where he leads the Watershed Research Group (WPRG) . His academic focus is on hydrology, geomorphology, and hydrogeology, with extensive research in watershed processes, snow hydrology, and critical zone dynamics. He teaches courses in hydrology, hydrogeology, hydrologic analysis, and watershed hydrology. Education: Ph.D. in Hydrology, University of Alaska Fairbanks (1997) M.S. in Hydrogeology, Syracuse University (1991) B.S. in Geology, Western Michigan University (1987) Research Interests: McNamara's research centers on understanding how water moves through watersheds and the resulting impacts on landscapes and stream environments. His work integrates field experiments and modeling to explore hydrologic partitioning, snowmelt dynamics, karst systems, and the role of the critical zone in controlling water fluxes. He is particularly interested in the rain-snow transition zone and how climate change affects water availability and ecosystem function in semiarid and mountainous regions. Research Themes from Recent Publications: His recent publications highlight a strong focus on integrating advanced modeling techniques—including deep learning and geophysics-informed approaches—with field observations to study snow-dominated and karst-influenced watersheds. Themes include streamflow response to warming, post-wildfire hydrology, sediment transport, and the influence of subsurface structure on hydrologic partitioning. Students and Mentorship: McNamara mentors a number of graduate students, including Maggi Kraft , Josh Morell , Liz Crowther , and Nate Ashead , who are actively involved in field and modeling research within the WPRG. Contact: Email: jmcnamar@boisestate.edu Office: ERB 4165, Boise State University

Chunming Qiaoمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Optical Networks
• Wireless Networks
• +۶ مورد دیگر

Dr. Chunming Qiao is a SUNY Distinguished Professor and Chair of the Department of Computer Science and Engineering at the University at Buffalo (SUNY) , leading the Lab for Advanced Network Design, Evaluation and Research (LANDR) since 1993. His work spans cyber-physical systems , optical networks , and Internet of Things (IoT) , with a focus on safety, reliability, and protocol design. Education: PhD in Computer Science from the University of Pittsburgh (1993) BS in Computer Science and Engineering from the University of Science and Technology of China (1985) Dr. Qiao’s research interests combine theoretical and applied network design, including autonomous vehicles , quantum computing , and cloud services . He pioneered optical burst switching (OBS) and iCAR systems for wireless convergence, cited in BusinessWeek and Wireless Europe . His recent publications emphasize quantum networking , federated learning , and autonomous driving security , with projects on entanglement routing , edge inference optimization , and LiDAR adversarial attacks . Articles span IEEE and ACM venues , and include best paper awards . Scientific Awards: TC-CSR Distinguished Technical Achievement Award (2015) SUNY Chancellor's Award for Excellence (2013) IEEE Fellow (2009) UB Exceptional Scholar-Sustained Achievement Award (2005) Dr. Qiao has secured over two dozen NSF grants and collaborations with Google , Cisco , and NEC Labs . His 7 US patents and consulting experience highlight his industry impact, while his editorial roles and conference leadership underscore academic influence. He actively contributes to multi-disciplinary research through the New York State Center of Excellence in Bioinformatics and Life Sciences and CEDAR , advancing high-performance computing and document analysis .

José PICHERALمشاهده پروفایل

استاد

• Signal Processing
• Acoustic Imaging
• Array Processing
• +۵ مورد دیگر

José Picheral is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). He holds a PhD (2003) and HDR (2017) in high-resolution signal processing methods and inverse problems. His research focuses on array processing, source localization, acoustic imaging, and vibration analysis, with applications in aeroacoustics, automotive systems, and industrial monitoring. He has supervised multiple PhD students and contributed to projects like Valeo’s smartphone-based car key replacement system. Education: Engineering Degree: Supélec (1999) and Politecnico di Milano (1999, Erasmus-TIME) PhD: Paris Sud University (2003) Habilitation (HDR): Université Paris Sud (2017) Research Interests: High-resolution methods for distributed sources, sparse signal processing, acoustic imaging, asynchronous measurements, and sensor array design. Current projects include spatial source covariance estimation, EEG spectrum analysis, and automotive applications using smartphone localization. Key Contributions: Over 50 publications in top journals/conferences (e.g., IEEE Transactions, ICASSP). Notable work on MUSIC algorithm robustness, DAMAS optimization, and sparse approaches for tip-timing signals. Advising & Collaboration: Supervised 7 PhD students. Collaborations with SAFRAN, Valeo, and academic teams in Bayesian inference and inverse problems. Active in L2S’s Inverse Problems Group and SYCOMORE team. Labs/Teams: Member of L2S’s Signal Processing and Statistics group, leading research in systems and control, telecommunications, and energy systems.

Maria Lilla' Montagnaniمشاهده پروفایل

استاد

• Intellectual Property Law
• Competition Law
• Digital Copyright
• +۲ مورد دیگر

Maria Lilla' Montagnani is a Full Professor of Commercial Law at Bocconi University and Director of the LL.M in European Business and Social Law (EBSL). She holds a Law Degree from Università Cattolica del Sacro Cuore (Milan), an LL.M from Queen’s University of London, and a PhD in Competition Law from Università degli Studi di Perugia. Her research focuses on AI governance, intellectual property law, digital copyright, competition law, and public art law. Educational Background: Law Degree: Università Cattolica del Sacro Cuore (Milan) LL.M: Queen’s University of London PhD in Competition Law: Università degli Studi di Perugia Her research interests emphasize the intersection of technology, law, and commerce. Notable areas include regulatory frameworks for AI, digital copyright challenges in the music and art sectors, and antitrust implications of IP misuse. Recent publications explore open patenting models, legal protection for art exhibitions, and the role of internet intermediaries in fostering creativity. No scientific awards have been explicitly mentioned in the provided texts. She teaches advanced courses such as "European and International Advanced IP Law," "Cyber Risk and Data Protection Law," and "Artificial Intelligence and the Law." No advising/grants information or lab affiliations were found in the text.

• Vector Biology
• Malaria Control
• Infectious Disease Ecology
• +۵ مورد دیگر

Professor Fredros Okumu is a leading researcher in vector biology and infectious disease ecology at the University of Glasgow's School of Biodiversity, One Health & Veterinary Medicine. He holds a PhD in Infectious Diseases from the London School of Hygiene and Tropical Medicine and an MBA in International Health Management from the University of Basel. His work focuses on malaria vector control, insecticide resistance, and ecological management of vector-borne diseases. Okumu leads research programs at the Ifakara Health Institute in Tanzania, collaborating internationally to advance surveillance and intervention strategies. Education: Public Health (Moi University), Applied Parasitology (University of Nairobi), Geo-Information Sciences (Lund University), PhD (London School of Hygiene & Tropical Medicine), MBA (University of Basel) Research: Mosquito biology, malaria transmission dynamics, genomics of Anopheles species, insecticide resistance mechanisms, and innovative control tools like mid-infrared spectroscopy and spatial repellents Awards: Howard Hughes Medical Institute–Gates Award, Wellcome Trust Fellowship, and WHO advisory roles His recent work includes genomic studies of Anopheles funestus resistance patterns, AI-driven mosquito age grading, and spatial modeling of vector habitats. Okumu's research bridges basic science with public health applications, emphasizing African-led R&D ecosystems.

Stephen Paul Ellnerمشاهده پروفایل

استاد

• Community Ecology
• Population Biology
• Evolutionary Patterns and Processes
• +۵ مورد دیگر

Stephen Paul Ellner is the Horace White Professor of Ecology and Evolutionary Biology at Cornell University, affiliated with the College of Arts and Sciences. His research focuses on theoretical ecology, particularly structured population models, community ecology, and evolutionary dynamics. He holds a B.A. from the University of California, Berkeley (1975), a Ph.D. from Cornell University (1982), and completed a postdoctoral fellowship at the Weizmann Institute (1984-1985). Education: B.A. in 1975, Ph.D. in 1982 (Cornell), Postdoc at Weizmann Institute (1984-1985). Research Interests: Community ecology, population dynamics, evolutionary patterns, sustainability, and infectious disease modeling. His work emphasizes trait-based models and applications to ecological problems like species coexistence, disease transmission, and invasive species management. He is a Fellow of the Ecological Society of America (2016) and contributes to open-source software like Integral Projection Models (R packages) and LENNS for ecological analysis. His teaching includes courses on dynamic models in biology and ecological modeling. Grants & Projects: Explores eco-evolutionary dynamics, pathogen transmission in plant-pollinator networks, and the impacts of environmental variability on population outcomes. Labs/Teams: Leads the Ellner Lab, focusing on theoretical and computational ecology.

• Digital Innovation
• Crowdsourcing
• Data-Driven Decision-Making
• +۳ مورد دیگر

Michael Zaggl is a Full Professor at NEOMA Business School, specializing in Strategy & Entrepreneurship. He holds a Habilitation from TU Munich, a PhD from TU Hamburg, and degrees from the University of Koblenz. Previously, he served in tenured roles at Aarhus University and held visiting positions at Harvard, USC, and other institutions. His research focuses on digital innovation, crowdsourcing, and data-driven decision-making, with publications in top journals like Strategic Management Journal and Academy of Management Discoveries . He is an active member of professional organizations such as the Academy of Management and Association for Information Systems. Educations: Habilitation (TU Munich), PhD (TU Hamburg), Master's & Bachelor's (University of Koblenz). His research interests span digital innovation , crowdsourcing mechanisms , and open-source governance , with notable contributions to understanding distributed strategic search and organizational decision-making. Recent work examines crowdfunding dynamics , AI-human collaboration , and privacy calculus in mHealth . His articles analyze topics like crowd wisdom optimization and hierarchical biases in idea evaluation , reflecting a focus on practical applications for organizations. He has presented at leading conferences, including the Academy of Management and Hawaii International Conference on System Sciences.

Gaël Richardمشاهده پروفایل

استاد

• Machine Learning
• Audio Signal Processing
• Artificial Intelligence
• +۳ مورد دیگر

Gaël Richard is a Professor at Télécom Paris specializing in machine learning and audio signal processing. He leads the Hi! Paris center, focusing on AI and data science applications. His research emphasizes hybrid interpretable AI for sound analysis, including projects like Hi-Audio funded by a €2.5M ERC Advanced Grant (2022). Key areas include machine listening, music source separation, and speech processing. Applications span autonomous vehicle acoustics and music technology. Notable contributions include neural audio compression (QINCODEC), diffusion models for music synthesis (Diff-TONE), and source separation techniques (Inverse Drum Machine). Research & Awards Recipient of the 2022 ERC Advanced Grant for the Hi-Audio project exploring hybrid AI models that integrate domain knowledge with neural networks. This approach reduces data requirements and enhances model interpretability. Active in audio-visual scene analysis and weakly-supervised learning systems. Affiliations & Labs Executive Director of Hi! Paris, a multidisciplinary lab advancing AI and data science for societal impact. Collaborates on projects like the HI-AUDIO online platform for distributed music data collection and the MAD-EEG EEG dataset for auditory attention decoding.

Felix Naumannمشاهده پروفایل

استاد

• Data Profiling
• Data Integration
• Data Quality
• +۱ مورد دیگر

Prof. Dr. Felix Naumann is Professor of Information Systems at the University of Potsdam's Digital Engineering Fakultät and Hasso Plattner Institute. His research develops foundational methods for managing data quality in complex information ecosystems. Naumann's work spans data profiling algorithms, entity resolution systems, and distributed data processing frameworks. The Metanome project exemplifies his contributions to scalable dependency discovery in large datasets. Recent publications address challenges in raw data repair, schema matching, and quality-aware query processing. His group actively develops open-source tools for data cleaning and constraint discovery. Leadership: Speaker of Research School 'Data Science and Engineering' Editor-in-Chief of JDIQ VLDB Endowment Trustee (2018-2023) Honors: Distinguished ACM Member (2021) Multiple best paper and reviewer awards IBM Faculty Awards

• Computational Neuroscience
• Neural Network Simulation
• Large-Scale Simulation
• +۳ مورد دیگر

Hans Ekkehard Plesser is a Professor at the Department of Data Science, Norwegian University of Life Sciences (NMBU). His work focuses on computational neuroscience, large-scale neural network simulations, and advancing reproducibility in computational research. He is a key contributor to the NEST simulator, a widely used open-source tool for modeling spiking neural networks. His research spans theoretical and applied aspects of neuroscience, including neuron-astrocyte interactions, connectivity patterns in neural networks, and high-performance computing strategies for brain-scale simulations. He emphasizes reproducible computational practices through initiatives like the ReScience project, promoting transparency and validation in scientific software. His publications highlight advancements in simulation methodologies, algorithm optimization, and the integration of supercomputing resources for neuroscience. He has contributed to foundational work on spiking neuron dynamics, firing-rate models, and the theoretical underpinnings of neural network behavior.

• Atmospheric Science
• Aerosol Physics
• Environmental Chemistry
• +۳ مورد دیگر

Katrianne Lehtipalo is a Professor at the University of Helsinki, affiliated with the Institute for Atmospheric and Earth System Research (INAR) within the Faculty of Science. Her research focuses on atmospheric aerosol formation, new particle formation mechanisms, and their implications for climate and air quality. She holds a docent (Adjunct Professor) position at the Department of Physics and has contributed to major projects like the CERN CLOUD experiment and the EU-funded RI-URBANS initiative. Her work spans experimental, field, and computational studies, addressing topics such as aerosol chemistry, pollution dynamics, and the impacts of atmospheric particles on climate. Key research areas include the formation and growth of atmospheric nanoparticles, the role of organic vapors and ions in nucleation, and the effects of anthropogenic and biogenic emissions. Lehtipalo has led projects on aerosol measurement techniques and contributed to understanding particle behavior in diverse environments, from boreal forests to urban centers. She has been recognized with awards like the Väisälä Award (2022) and World Cultural Council Special Recognition (2023), highlighting her scientific contributions. Her professional activities include peer review for journals like Nature Geoscience and Environmental Science & Technology Letters , organizing conferences, and supervising doctoral research. Collaborations span global institutions, and her work has advanced the integration of field observations, laboratory experiments, and modeling in atmospheric science.

• Marine Biology
• Environmental Science
• Oceanography
• +۳ مورد دیگر

Inga Kirstein is a Scientist at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, specializing in the Ecology of Shelf Sea Systems. She leads the Heligoland Roads long-term ecological time series, managing teams, laboratories, and data communication. Her research focuses on microbial food webs, biofilm communities, microplastic pollution, and long-term ecological dynamics. She contributes to projects like the FRAM Ocean Observing System and collaborates on initiatives such as the MOSAiC expedition. Her work bridges marine microbiology, environmental monitoring, and climate change impacts. Key contributions include studies on microplastic distribution in sediments and drinking water, microbial interactions in plankton blooms, and ecosystem resilience under climate stressors. Her publications highlight advancements in understanding microplastic toxicity, fungal communities on plastics, and the role of microbial necromass in carbon cycles. She emphasizes methodological rigor in detecting microplastics using FTIR and Raman spectroscopy. Her interdisciplinary approach addresses both fundamental ecological processes and applied challenges in environmental management.

• Power Systems
• Renewable Energy
• Electric Vehicles
• +۲ مورد دیگر

Haitham Abu-Rub is a Professor at Texas A&M University at Qatar specializing in power systems, renewable energy integration, and power electronics. His research focuses on developing innovative solutions for grid stability, EV charging infrastructure, and intelligent control systems. He has published extensively in IEEE journals and conferences, addressing challenges in smart grids and sustainable energy systems. His work spans power converter design, fault diagnosis, and AI applications in energy management. Recent projects include decentralized PV trading systems, resilient inverter networks, and physics-informed neural networks for insulation diagnostics. Dr. Abu-Rub collaborates internationally on projects involving grid-interactive buildings, digital twins for power converters, and adaptive control techniques for electric vehicle charging.