جست‌وجوی استاد و پژوهشگر

• International Trade
• Agricultural Trade Policy
• Agricultural and Food Policy
• +۳ مورد دیگر

Wusheng Yu is a full-time Professor in the Department of Food and Resource Economics at the Faculty of Science, University of Copenhagen, holding this position since March 2018 after serving as Associate Professor from 2005-2018. His academic foundation includes a PhD in Agricultural Economics from Purdue University (2000) and an MSc in Economics from Renmin University of China (1995). Educational background: PhD in Agricultural Economics, Purdue University, West Lafayette, IN, USA (2000) MSc in Economics, Renmin University of China, Beijing, China (1995) Professor Yu's research centers on international trade, agricultural policy, computable general equilibrium modeling, and climate-food security intersections. He employs advanced quantitative methods to analyze global trade dynamics and environmental policy impacts, with recent work emphasizing geopolitical dimensions of food systems under climate stress. His methodological approach combines empirical analysis with scenario-based modeling to address real-world policy challenges. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Geostrategic assessments of food security amid global tensions, (2) Climate-agriculture policy integration through emission modeling, and (3) Trade mechanism innovations like carbon border adjustments. His work bridges theoretical economics with actionable policy insights, particularly for EU and developing economies. Professor Yu coordinates graduate courses in Advanced International Trade and Applied Trade and Climate Policy Models. His research is funded by the European Union, Danish governmental bodies, and international organizations, operating under an advisory committee with private sector representatives as specified by departmental Terms of Reference. He maintains strict institutional alignment as a full-time permanent employee without external remuneration. His collaborative network spans seven countries with significant engagement in Danish animal food sectors and public authorities. The Section for Production, Markets and Policy at IFRO serves as his primary research hub, facilitating cross-border studies on trade-displacement effects and food demand elasticity modeling under socioeconomic pathways.

• Signal Processing
• Wireless Communications
• Networking
• +۳ مورد دیگر

Philippe Ciblat is a Professor at TELECOM Paris Tech, affiliated with the Department of Signal Processing and Communications. His research spans signal processing, wireless communications, and machine learning applications in networking. He has collaborated extensively with institutions like the University of Paris-Saclay and international researchers in areas such as cooperative communication protocols, resource allocation, and coding theory. Research Interests: Machine learning for signal processing, wireless channel modeling (Rician fading), lattice decoding, caching strategies, and distributed optimization. Notable Work: Pioneered transformer-based packet scheduling, neural network approaches to lattice decoding, and effective capacity analysis in fading channels. His contributions include over 170 publications in top venues (IEEE Trans. Signal Process., IEEE Trans. Wireless Commun.) and collaborations with industry partners on practical implementations like cache-aided polar coding. He has advised multiple researchers in distributed systems and wireless resource management.

Eric C.K. Cheungمشاهده پروفایل

دانشیار

• Insurance Risk Theory
• Ruin Theory
• Aggregate Claims Analysis
• +۶ مورد دیگر

Eric C.K. Cheung is an Associate Professor in the Department of Statistics and Actuarial Science at the University of New South Wales (UNSW), where he has worked since July 2017. Previously, he held positions at the University of Hong Kong (HKU) from 2010 to 2017, including Assistant Professor (2010–2016) and Associate Professor (2016–2017). He earned his BSc (Actuarial Science) from the University of Hong Kong and MMath and PhD in Actuarial Science from the University of Waterloo. His research focuses on insurance risk theory, ruin theory, stochastic processes, and financial mathematics. He has secured multiple grants, including from the Australian Research Council and the Society of Actuaries. Currently, he supervises PhD and Honours students in areas like risk analysis and financial modeling. Cheung teaches actuarial science courses at UNSW, HKU, and the University of Waterloo. His work appears in top journals like Insurance: Mathematics and Economics , and he is an Associate Editor of this journal. He has advised over 10 students at HKU and UNSW.

• Reactive flow and transport modeling
• Geochemical processes
• Groundwater dynamics
• +۵ مورد دیگر

Martin Andersen is an Associate Professor with the Water Research Laboratory and School of Civil and Environmental Engineering at the University of New South Wales (UNSW). His research focuses on hydrogeology, groundwater dynamics, and surface water-groundwater interactions, with particular emphasis on coastal zone processes and reactive flow and transport modeling. Dr. Andersen's research interests span several critical areas in hydrogeology and environmental science. His work on reactive flow and transport modeling examines how chemical reactions affect water movement through geological formations. He investigates geochemical processes and groundwater dynamics in coastal zones, which is crucial for understanding saltwater intrusion and managing coastal aquifers. His research on surface water-groundwater interactions helps inform sustainable water resource management, particularly in Australia's diverse hydrological environments. Andersen has made significant contributions to understanding how climate variability affects groundwater recharge processes and the implications for water security in arid and semi-arid regions. Analysis of Dr. Andersen's recent publications reveals a strong focus on understanding complex hydrological systems through innovative methodologies. His work spans multiple disciplines including hydrogeology, climate science, biogeochemistry, and computational modeling. A key trend in his research is the investigation of groundwater dynamics in various environmental contexts - from arid zone aquifers to coastal wetlands and fractured rock systems. He employs diverse approaches including field monitoring networks, laboratory experiments, statistical modeling, and paleoclimate reconstructions to address pressing water resource challenges. His research has significant implications for sustainable groundwater management, climate adaptation strategies, and ecosystem conservation. Dr. Andersen is actively involved in several research teams and laboratories. As an Associate Director in the School of Civil and Environmental Engineering at UNSW, he contributes to the Water Research Laboratory's mission of advancing water science and engineering. His work often involves interdisciplinary collaborations across hydrology, geology, environmental science, and climate research. Through his leadership in establishing monitoring networks and conducting field experiments, he has helped build valuable infrastructure for ongoing water research in Australia.

Konrad Kowalczykمشاهده پروفایل

دانشیار

• Speech and Audio Signal Processing
• Machine Learning
• Deep Neural Networks
• +۶ مورد دیگر

Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.

Bletsas Aggelosمشاهده پروفایل

استاد

• Wireless Transmission
• Wireless Relay Networks
• Software-Defined Radio
• +۳ مورد دیگر

Aggelos Bletsas is a Professor at the School of Electrical and Computer Engineering, Technical University of Crete. He holds a PhD from MIT (2005) and has expertise in wireless communication, backscatter networks, and RFID systems. His research focuses on scalable wireless networks, ultra-low-cost sensor technologies, and signal processing. Education: PhD, MIT Media Lab (2005) MSc, MIT Media Lab (2001) Diploma in Electrical & Computer Engineering, Aristotle University of Thessaloniki (1998) Research Interests: His work spans wireless transmission techniques, backscatter sensor networks, and RFID systems. Key areas include: Ultra-low-cost sensor deployment RFID localization and multi-static systems Energy-efficient hardware implementations Probabilistic inference in distributed networks Awards: IEEE Marconi Prize Paper Award (2008) Technical University of Crete Research Excellence Award (2012-2013) Multiple best paper awards at RFID-TA, ISWCS, and SENSORS Academic Contributions: He advises students who have won IEEE best thesis awards and leads projects funded by ERC grants. His laboratory focuses on practical implementations of wireless sensor networks and backscatter systems. Labs & Affiliations: Director of the Telecommunications Laboratory and affiliated with the Telecommunication Systems Institute (TSI).

Lazaros Symeonidisمشاهده پروفایل

مدرس ارشد

• Financial Econometrics
• Asset Pricing
• Commodities
• +۷ مورد دیگر

Dr. Lazaros Symeonidis is a Senior Lecturer in Finance at Essex Business School (University of Essex), specializing in financial econometrics, asset pricing, and commodities. He holds a PhD in Finance from the ICMA Centre, University of Reading, and has taught at the University of East Anglia and the University of Stirling. His research focuses on volatility modeling, commodity risk premia, and the economic drivers of market uncertainty. Education qualifications include a PhD in Finance (University of Reading), MSc in Mathematics for Finance (Athens University of Economics and Business), and BSc in Management Science and Technology (Athens University of Economics and Business). Research interests span financial econometrics, forecasting, empirical asset pricing, and commodities, with a focus on volatility dynamics, jumps, and market microstructure. His work has been published in journals like Journal of Financial Markets and Journal of Banking and Finance . Recent articles analyze convenience yield risk, factor models, and commodity market volatility, reflecting his expertise in energy and financial markets. He has secured grants from the British Academy and ESCP for research on commodity storage premiums. Academic supervision includes doctoral student Shengqiu Ma. Lazaros serves as an associate editor for the Journal of Commodity Markets and contributes to the Commodity Insights Digest.

Mohammed Khalidمشاهده پروفایل

استاد

• FPGA-based Systems
• Network-on-Chip (NoC) Architectures
• Signal Processing Hardware
• +۳ مورد دیگر

Prof. Mohammed Khalid is a Professor in the Department of Electrical and Computer Engineering at the University of Windsor. He specializes in FPGA-based systems, network-on-chip architectures, and hardware acceleration for signal processing applications. His leadership roles include serving on the executive committee of IEEE Canada. His research focuses on optimizing cryptographic hardware, automotive embedded systems, and efficient algorithm implementations on FPGAs. Key contributions include advancements in PUF-based security mechanisms, high-speed elliptic curve processors, and FPGA-accelerated machine learning algorithms. He has led projects in automotive radar systems and AUTOSAR configuration tools. His work emphasizes practical applications of hardware-software co-design principles. Research Highlights : Development of novel FPGA architectures for real-time signal processing Innovative approaches to resource-efficient cryptographic hardware Pioneering work on hybrid NoC architectures for multi-FPGA systems Awards : IEEE Windsor Section Award (2019) for group leadership Best Student Paper Award (2024) for supervised research by Mohit Sharma Prof. Khalid's 150+ publications span FPGA design methodologies, adaptive signal processing, and embedded systems security. His research group collaborates with industry partners to advance automotive electronics and IoT applications.

Benoit Champagneمشاهده پروفایل

استاد

• Statistical Signal Processing
• Multi-antenna Processing
• Signal Detection and Estimation
• +۹ مورد دیگر

Benoit Champagne is a Full Professor in the Department of Electrical and Computer Engineering at McGill University, Montreal. His research focuses on statistical signal processing, with applications in wireless communications, multi-antenna systems, and adaptive filtering. He has held academic positions since 1990, including roles at INRS-Telecom before joining McGill in 1999. He teaches graduate and undergraduate courses such as ECSE 305 (Probability and Random Signals), ECSE 512 (Digital Signal Processing), and ECSE 617 (Array Signal Processing). Education: B.Eng. (Electrical Engineering) and M.Sc. (Physics) from Université de Montréal (1983, 1985), Ph.D. in Electrical Engineering from University of Toronto (1990). His research spans signal detection/estimation, speech enhancement, MIMO systems, and physical layer security, with over 150+ publications in top journals and conferences. He has supervised numerous graduate students and holds grants from NSERC, CFI, and industry partners like Nortel and Bell Canada. His work emphasizes practical implementations, including hybrid analog/digital beamforming for mmWave systems and energy-efficient resource allocation in D2D communications. He has contributed to IEEE standards through editorial roles (e.g., IEEE Transactions on Signal Processing) and conference organization (e.g., IEEE VTC 2016). Current research explores machine learning integration with signal processing for next-generation wireless systems. Notable contributions include advancements in subspace tracking, cognitive radar systems, and distributed adaptive filtering. His lab collaborates internationally, addressing challenges in 5G/6G networks, massive MIMO, and secure communications.

Jean-Luc Dugelayمشاهده پروفایل

استاد

• Biometrics
• Image Forensics
• Video Surveillance
• +۷ مورد دیگر

Professor Jean-Luc Dugelay is a faculty member at EURECOM's Digital Security Department, specializing in facial image processing, image forensics, and biometrics. He holds a PhD in Image Processing from the University of Rennes (1992) and is a Fellow of IEEE (2012) and IAPR (2018). His research focuses on security applications like deepfake detection, privacy protection, and multi-spectral imaging. He leads projects on UAV surveillance, cross-spectrum face recognition, and deepfake countermeasures. Education: PhD in Image Processing (1992, University of Rennes), HDR (2013, University of Nice). Notable projects include HEIMDALL (deepfake detection), CONVERGE (transport security), and ImVerif (image forensics). His work on biometric systems and digital watermarking has been recognized with awards like the SEE Blondel Medal (2010). Research interests span facial analysis, video surveillance, and thermal imaging. He has advised students on topics like age estimation via GANs and cross-spectrum face recognition. Key publications include work on deepfake detection (IPAS 2025), thermal-to-visible face conversion, and event-based vision systems.

Eugene Chanمشاهده پروفایل

دانشیار

• Consumer Psychology
• Political Ideology and Consumer Behavior
• Marketing Communication
• +۷ مورد دیگر

Eugene Chan is an Associate Professor in the Department of Marketing Management at the Ted Rogers School of Management, Toronto Metropolitan University. He holds a PhD in Marketing from the University of Toronto, a MA in Social Psychology from the University of Chicago, and an AB in Honors Psychology from the University of Michigan, along with an ARCT (Hons) in Piano Performance from the Royal Conservatory of Music, reflecting a multidisciplinary background. His research lies at the intersection of consumer psychology, political ideology, health communication, and sustainability. He employs experimental and survey methods grounded in social science theories to understand how individuals make judgments and decisions in marketplaces and society. Key themes include the influence of political ideology on consumer behavior, strategies for effective marketing and health communication, and promoting environmentally sustainable choices. His work often explores how emotional, moral, and cognitive factors shape consumer decisions. His recent publications span top journals such as Journal of Consumer Psychology , Global Environmental Change , Computers in Human Behavior , and Personality and Social Psychology Bulletin . These studies reveal consistent focus on behavioral interventions, ideological divides, health compliance, and environmental sustainability. Trends indicate a strong emphasis on moral psychology, emotional triggers, and real-world applications in public policy and marketing strategy. Scientific Awards: Emerging Researcher Award, Australian and New Zealand Marketing Academy (2018) Dean’s Award for Excellence in Research by an Early Career Researcher, Monash Business School (2018) He has secured research funding from diverse sources including the National Natural Science Foundation of China, ACR Transformative Consumer Research Grant, IHS Hayek Fund, and the Indiana Commission for Higher Education, supporting projects on fake news mitigation, brand revitalization, moral foundations, and pandemic behavior. He has taught at institutions worldwide, including the University of Toronto, Monash University, and Purdue University, demonstrating international academic engagement. Eugene Chan serves in editorial roles for several journals, including as Associate Editor for Australasian Marketing Journal and International Journal of Consumer Studies , and as Special Issue Editor for Frontiers in Psychology . He also co-authored the 2nd Asia-Pacific edition of the textbook Consumer Behavior , contributing to marketing education. He advises students in consumer behavior and marketing research, though specific advisees are not listed.

Eugene O’Brienمشاهده پروفایل

استاد

• Civil Engineering
• Structural Engineering
• Bridge Safety
• +۳ مورد دیگر

Professor Eugene O’Brien is a leading academic in UCD School of Civil Engineering, part of University College Dublin. His research focuses on enhancing bridge safety through innovative traffic loading analysis methods. He co-founded ROD-IS, a consultancy applying academic insights to real-world infrastructure projects, including assessments of the Malahide Railway Viaduct and Firth of Forth Bridge in Scotland. His work integrates Weigh-in-Motion (WIM) sensors and camera-based monitoring to address critical gaps in assessing long-span bridges under congested traffic conditions. Key projects include developing probabilistic models for bridge lifetime safety, optimizing traffic management to extend bridge lifespans, and reducing environmental impacts through carbon footprint minimization. He has secured significant EU funding, including an €890,000 grant for bridge longevity research. His research group collaborates internationally on projects like the Chacao Channel Bridge in Chile, emphasizing sustainable infrastructure solutions.

Sharib Aliمشاهده پروفایل

استادیار

• Biomedical Image Analysis
• Computer Vision
• Machine Learning
• +۱۲ مورد دیگر

Dr. Sharib Ali is a Lecturer (Assistant Professor) in the School of Computer Science at the University of Leeds, Faculty of Engineering and Physical Sciences. He is affiliated with the Leeds Cancer Research Centre and actively contributes to research in biomedical image analysis and computer vision. His work bridges cutting-edge AI with clinical applications, particularly in endoscopy and surgical technologies. PhD in Medical Image Analysis, University of Lorraine, France MSc in Computer Vision (by research), University of Burgundy, France Dr. Ali's research focuses on biomedical image analysis , computer vision , and machine learning , with applications in early cancer detection , computational endoscopy , and 3D reconstruction . He develops robust algorithms for segmentation, registration, depth estimation, and mosaicking, using both classical mathematical models and deep learning. His work emphasizes translational research and generalisability in real-world clinical settings. The recent publications highlight a strong trend in generalisability assessment , multi-modal data fusion , and AI benchmarking in endoscopy. His work spans from foundational algorithm development to clinical deployment, including federated learning , mixed reality in surgery , and multi-centre datasets , addressing key challenges like bias, data imbalance, and privacy. Dr. Ali has co-supervised multiple DPhil/PhD students and currently supervises several PhD candidates at the University of Leeds, University of Oxford, and Tec de Monterrey. He is actively involved in securing research funding and leading projects such as Leveraging multi-modality data for targeted biopsy and Federated learning in healthcare . He is a founding member of NAAMII, Nepal, where he volunteers to train students from LMICs. He also organizes international research initiatives including the EndoCV and P2ILF challenges at MICCAI, and serves on program committees and as a reviewer for journals like Nature Communications and Medical Image Analysis . His research is conducted within interdisciplinary teams, collaborating with clinicians from Oxford NHS University Hospitals, neuroscientists at Forschungszentrum Jülich, and engineers across Europe. He leads the development of open tools and datasets to advance the field of endoscopic computer vision.

Mingyi Hongمشاهده پروفایل

دانشیار

• Optimization
• Machine Learning
• Signal Processing
• +۷ مورد دیگر

Mingyi Hong is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota , where he leads the OptimAI-Lab . His work bridges optimization theory , machine learning , and signal processing , with a focus on foundation models like LLMs and diffusion models. Education : Not explicitly mentioned in the text Current Projects : NSF grants on bilevel optimization, LLM unlearning, and inverse reinforcement learning Research Themes : Bilevel Optimization : Applications in LLM alignment, unlearning, and wireless systems LLM Safety : Unlearning, alignment with human feedback, robustness Diffusion Models : Inference-time alignment, adversarial training Distributed Optimization : Privacy-preserving algorithms, federated learning Recent Publications highlight trends in LLM unlearning (BLUR, LUME), optimization theory (Barrier Functions, νSAM), and diffusion models (Direct Noise Optimization). His group has secured NSF , AWS , Cisco , and Open Philanthropy grants. Scientific Recognition : IEEE Fellow (2025) SPS Best Paper Award (2022, 2021, 2018) Doctoral Dissertation Fellowship (2024) IBM Pat Goldberg Memorial Award (2022) He mentors PhD students like Siliang Zeng and Xinwei Zhang , and collaborates with institutions including Michigan State University , Amazon , and NIH on projects spanning UHF MRI technology to climate-smart agriculture .

Christian Rohnerمشاهده پروفایل

استاد

• Wireless Sensor Networks
• Backscatter Communication
• Network Security
• +۵ مورد دیگر

Christian Rohner is a Professor at the Department of Information Technology at Uppsala University, specializing in the Division of Computer Systems. His research spans over two decades with a clear evolution from early work in opportunistic networking to current cutting-edge research in backscatter communication and physical-layer security. Professor Rohner's research interests focus on wireless communication systems , particularly backscatter communication , sensor networks , and network security . His work on analog backscatter tags has pioneered techniques for channel estimation, reliable flooding protocols, and identification systems for battery-free devices. In wireless security , he has made significant contributions to radiometric fingerprinting, physical-layer authentication, and intrusion detection for IoT systems. His research in information theory applies theoretical frameworks to practical network analysis problems, including modularity computation in probabilistic networks and information decomposition. His recent publications (2020-2025) demonstrate a strong focus on enabling low-power wireless systems, with applications ranging from medical contexts (fat intra-body communication) to temperature sensing with RFID tags. The research shows a clear trajectory toward practical implementations of battery-free sensor networks that can operate without traditional power sources while maintaining security and reliability. Professor Rohner has maintained long-term collaborations, particularly with Thiemo Voigt at Uppsala University, resulting in numerous joint publications across multiple research domains. His work bridges theoretical foundations with practical implementations, making significant contributions to both academic research and potential real-world applications in wireless networking.