جست‌وجوی استاد و پژوهشگر

Susann Müllerمشاهده پروفایل

دانشیار

• Microbial Ecology
• Flow Cytometry
• Environmental Biotechnology
• +۵ مورد دیگر

Prof. Dr. Susann Müller is Senior Scientist and Group Leader of the Flow Cytometry Working Group at the Department of Applied Microbial Ecology, Helmholtz Center for Environmental Research (UFZ) in Leipzig, Germany. Since 2011, she has held an Associate Professor position for Microbiology at Leipzig University’s Faculty of Life Sciences, bridging fundamental microbial ecology with environmental biotechnology applications through single-cell analytics. Education: 1985: Diploma in Biochemistry, Martin Luther University Halle-Wittenberg 1992: PhD, University of Halle-Wittenberg (Population dynamics of S. cerevisiae) 2003: Habilitation, Technical University Dresden (Multiparametric Cytometry) Her research pioneers microbial community flow cytometry to extract single-cell high-dimensional data, applying macroecological concepts to quantify stability metrics (resistance, resilience, displacement speed, elasticity) in engineered systems. Current focus includes bio-based circular economy initiatives: developing the carboxylate platform for sustainable chemical production and biological phosphate recovery from wastewater streams for resource valorization. Recent publications (2021-2025) reveal consistent innovation in flow cytometry applications, with emphasis on stability assessment in bioreactors, predator-prey dynamics in complex communities, and real-time monitoring of wastewater systems. She integrates ecological theory with multi-omics and data science to decode microbial assembly principles across environmental, agricultural, and industrial contexts. Professional roles: President, German Society of Cytometry (DGfZ, 2008-2010) Associate Editor, Microbiology for Cytometry Part A ISAC Educational Committee (2011-2012) and Scholars Program Committee (2013-2015) Current grants: PHOM project (SMWK InfraProNet 2024-2027): €449,160 for wastewater phosphorus recovery Z-PROJECT (DFG 2022-2025): €556,550 for bacterial biofilm analysis PROMICON (EU H2020 2021-2025): €200,000 for industrial microbiome consortia Moore Foundation (2020-2024): $23,000 for archaeal evolutionary tools Chinese Scholarship Council (2022-2026): Artificial community construction The Flow Cytometry Working Group under her leadership at UFZ develops standardized mock communities (Nature Protocols 2020), automated analysis tools (flowEMMi), and cytometric barcoding methods. It collaborates with Leipzig University, Technical University Dresden, and international partners including UC Santa Barbara, driving innovations in real-time environmental monitoring and wastewater treatment optimization.

Rebecca Aschمشاهده پروفایل

دانشیار

• Fisheries Oceanography
• Climate Change Impacts
• Phenology
• +۳ مورد دیگر

Rebecca Asch is an Associate Professor in the Department of Biology at East Carolina University, where she leads the Asch Fisheries Oceanography Lab. Her research focuses on climate change impacts on marine ecosystems, particularly the phenology of fish reproduction and its synchronization with plankton blooms. She integrates long-term oceanographic data, remote sensing, and Earth System Models to address critical fisheries management challenges. Ph.D. in Biological Oceanography from University of California San Diego (Scripps Institution of Oceanography) Post-Doctoral Research at Princeton University (Atmospheric and Oceanic Science) M.S. in Biological Oceanography from University of Rhode Island B.A. in Anthropology from Smith College Her lab investigates: Historical and future shifts in marine phenology Climate-driven changes in spawning aggregation distributions Economic implications for North Carolina’s $100M/year fisheries Interactions between ocean acidification, microplastics, and fish development Recent publications analyze phenological mismatches in marine food webs, Gulf Stream impacts, and conservation strategies for tropical reef fisheries. She collaborates with NOAA to expand ichthyoplankton monitoring programs. Contact: aschr16@ecu.edu

Brett Danaherمشاهده پروفایل

دانشیار

• Industrial Organization
• Digital Piracy
• Copyright Policy
• +۶ مورد دیگر

Brett Danaher is an Associate Professor at the George L. Argyros College of Business and Economics, Chapman University , specializing in Industrial Organization, Digital Piracy, and Data Analytics . His research explores the economic implications of digital content distribution, copyright enforcement, and consumer behavior in media markets. Education : Ph.D., University of Pennsylvania His work uses empirical methods to analyze the impact of anti-piracy measures, including site blocking and price changes , on consumer behavior. He has demonstrated that multi-site blocking increases legal content consumption more effectively than targeting single platforms, while price reductions primarily deter novice pirates. His publications frequently appear in top journals like MIS Quarterly , Management Science , and Harvard Business Review . Recent publications focus on digital market dynamics , cybercrime mitigation , and policy implications of content regulation . His research has been cited in policy documents and media outlets such as BBC and Wired.

Emily S. Bernhardtمشاهده پروفایل

استاد

• Ecosystem Ecology
• Biogeochemistry
• Aquatic Ecosystems
• +۷ مورد دیگر

Emily S. Bernhardt is the James B. Duke Distinguished Professor of Biology at Duke University's Trinity College of Arts & Sciences, where she also serves as Chair of the Department of Biology. She holds a joint appointment as Professor in Marine Science and Conservation at the Nicholas School of the Environment and is Associate of the Duke Initiative for Science & Society. Her research program bridges ecosystem ecology and biogeochemistry with a focus on aquatic systems. Dr. Bernhardt's research centers on tracking the movement of elements through ecological systems, specifically documenting how aquatic ecosystems are altered by land use change (urbanization, agriculture, mining), global change (rising CO 2 , sea levels), and chemical pollution. Her work spans from headwater streams to coastal wetlands, with particular emphasis on the impacts of human activities on ecosystem structure and function. Analysis of her recent publications reveals a strong focus on contemporary environmental challenges including urban watershed contamination, mercury pollution from artisanal gold mining, salinization of freshwater systems, and carbon cycling in river networks. Her research integrates field measurements, long-term data sets, and experimental approaches to understand ecosystem responses to multiple stressors. James B. Duke Distinguished Professor (2019-Present) Chair of the Department of Biology (2020-Present) Professor of Biology (2016-Present) Professor in Marine Science and Conservation (2021-Present) Dr. Bernhardt's collaborative approach extends to her mentoring philosophy, where she emphasizes co-writing with students through read-aloud sessions to improve manuscript clarity and reduce the perceived negativity of editorial feedback. Her work has significant implications for environmental management and policy, particularly regarding watershed protection and coastal resilience in the face of climate change.

Shaileshh Bojja Venkatakrishnanمشاهده پروفایل

استادیار

• Blockchain Networking
• Cryptocurrency Algorithms
• Network Systems
• +۵ مورد دیگر

Shaileshh Bojja Venkatakrishnan is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University's College of Engineering. His research spans multiple domains at the intersection of networking, distributed systems, and machine learning with a particular focus on blockchain technologies and cryptocurrency networks. Educational Background: PhD in Computer Science from University of Illinois Urbana-Champaign (UIUC), 2017 Bachelor's degree from Indian Institute of Technology Madras, 2012 Dr. Bojja Venkatakrishnan's research interests center around fundamental algorithmic questions at the networking layer of Bitcoin and other cryptocurrency networks. He is particularly interested in applying machine learning techniques for developing algorithms over networks and graphs, with emphasis on 'first-principles' thinking to solve complex distributed systems problems. His work extends to scheduling algorithms for data center networks, peer-to-peer networks, and information theory. The breadth of his research is evident in his publications spanning blockchain networking, payment channel networks, decentralized applications, and machine learning applications for network optimization. Research Impact: Recipient of the Joan and Lalit Bahl fellowship at UIUC His publication record demonstrates significant contributions to the field, with papers appearing in top venues including SIGCOMM, NSDI, SIGMETRICS, and IEEE Transactions. His recent work focuses on enhancing blockchain scalability through peer sampling techniques, transaction reordering mitigation, and efficient peer-to-peer network design for decentralized applications. The trajectory of his research shows a consistent focus on addressing fundamental challenges in distributed systems with practical implications for real-world blockchain implementations and network architectures.

• Schema discovery for Semantic Web data
• Data mining
• Recommendation systems
• +۵ مورد دیگر

Kenza Kellou-Menouer is a researcher affiliated with the ETIS Laboratory at ENSEA, France, and part of the MIDI research group . Her work focuses on schema discovery for Semantic Web data, data mining, and big data optimization. Research: Semantic schema discovery, clustering/classification algorithms, and association rules. Teaching: Semantic Web technologies, database design, algorithms, and programming languages (Java, C++, C#, C). Research Interests center on Semantic Web data integration, RDF schema inference, and hybrid machine learning approaches. She has contributed to scalable schema discovery systems and real-time profiling techniques for large datasets. Publications include work on schema inference tools (SchemaDecrypt++, HInT) and methodological frameworks presented at top-tier venues like VLDB (A*), ICDE (A*), SSDBM (A), and ISWC . Her research bridges theoretical advancements with practical implementations for RDF datasets. Community Contributions include organizing tutorials at the International Semantic Web Conference (ISWC) 2022 and developing educational materials for database and programming courses.

Alan Bovikمشاهده پروفایل

استاد

• Image and Video Processing
• Digital Television
• Digital Cinema
• +۵ مورد دیگر

Alan Bovik is a Professor at The University of Texas at Austin, holding the prestigious Cockrell Family Endowed Regents Chair in Engineering. He serves as Director of the Laboratory for Image and Video Engineering (LIVE) and maintains dual faculty appointments in the Department of Electrical and Computer Engineering and the Institute for Neuroscience. His extensive contributions have established him as a leading authority in visual information processing with global impact. Dr. Bovik's research spans multiple domains with a primary focus on image and video processing, digital television and digital cinema, computational vision, and visual perception. His work has fundamentally advanced the understanding of human visual perception, leading to practical applications in video quality assessment and image processing systems. With over 800 technical publications cited more than 75,000 times and an H-index above 100, his research has had extraordinary impact across academia and industry. He is recognized as a Highly-Cited Researcher by Clarivate Analytics, placing him among the most influential researchers globally. Analysis of Dr. Bovik's recent publications reveals a strong emphasis on video quality assessment for emerging applications like user-generated content, high-motion streaming, and adaptive video delivery. His work seamlessly integrates deep learning approaches with traditional signal processing techniques to develop perceptually accurate models. There's a clear trend toward addressing practical challenges in video streaming quality, compression artifacts, and the unique characteristics of modern video content. His research bridges theoretical foundations with real-world applications, making significant contributions to both academic understanding and industry standards. Dr. Bovik's exceptional contributions have been recognized with numerous prestigious awards: IEEE Fourier Award (2019) for seminal contributions to perception-based image and video processing Edwin H. Land Medal (2017) from The Optical Society Primetime Emmy Award for Outstanding Achievement in Engineering Development (2015) Norbert Wiener Society Award (2013) Claude Shannon / Harry Nyquist Technical Achievement Award (2005) Multiple best paper awards from IEEE, EURASIP, and Picture Coding Symposium As an educator and mentor, Dr. Bovik has guided numerous students through his leadership at LIVE. His professional service includes founding and serving as Editor-in-Chief of the IEEE Transactions on Image Processing (1996-2002) and chairing the inaugural IEEE International Conference on Image Processing in 1994. His industry impact is substantial, evidenced by his Primetime Emmy Award and frequent consultation with major institutions. Dr. Bovik is also a registered Professional Engineer in Texas, demonstrating his practical engineering expertise alongside theoretical contributions. Dr. Bovik leads the Laboratory for Image and Video Engineering (LIVE), which maintains strong affiliations with multiple research centers including the Wireless Networking and Communications Group (WNCG), Center for Perceptual Systems, Telecommunications and Signal Processing Research Center, and Institute for Computational Engineering and Sciences. These interdisciplinary connections enable research that bridges engineering, neuroscience, and computer science to advance our understanding of visual perception and processing.

Yann Renisioمشاهده پروفایل

پژوهشگر ارشد

• Social stratification
• Higher education
• Kinship networks
• +۵ مورد دیگر

Yann Renisio is a CNRS Research Fellow at the Center for Research on Social Inequalities (CRIS) at Sciences Po, a position he has held since October 2021. He is also an affiliated researcher at the Department of Sociology of Education and Culture at Uppsala University. His work focuses on understanding social structures and inequalities through rigorous sociological research. Dr. Renisio earned his PhD in sociology from EHESS in 2017. Prior to his current position, he held postdoctoral positions at Sciences Po, Uppsala University, and the Collège de France, demonstrating his international research experience and academic mobility across prestigious institutions. Yann Renisio's research spans multiple interconnected areas within sociology. His primary interests include social stratification, higher education systems, kinship networks, and the sociology of science. He investigates how social inequalities are reproduced and transformed through educational pathways and professional specialization, with particular attention to methodological innovations in social science research. His current projects examine higher education trajectories, professional specialization in medicine, practice-report gaps in survey data, and kinship networks. His methodological approach often integrates digital traces with traditional research methods to provide more comprehensive insights into social phenomena. Dr. Renisio's publication record reveals a strong focus on methodological innovation and the sociology of knowledge. His recent work explores the integration of digital trace data with survey methodologies, topic modeling of scientific disciplines, and the social organization of academic knowledge. Across his publications, he consistently examines how social structures shape individual opportunities and choices, with particular attention to educational and professional trajectories. His research bridges theoretical sociology with empirical analysis of contemporary social issues, particularly in understanding how digital practices intersect with traditional social stratification patterns. Dr. Renisio is actively involved in significant research projects including ANR RECORDS (2019-2023), which investigates music streaming practices and their relationship to social stratification, and ANR MEDSPE (2021), which examines the choice of medical specialties and locations among French doctors. These projects demonstrate his commitment to understanding how social structures influence individual choices in contemporary society through both traditional and digital methodologies. As a CNRS Research Fellow, Dr. Renisio contributes to the vibrant research community at CRIS, collaborating with scholars across disciplines and institutions. His work exemplifies the interdisciplinary approach needed to address complex social inequalities in modern societies, particularly through innovative methodological approaches that bridge digital and traditional research paradigms.

Alessandro Lameiras Koerichمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Pattern Recognition
• +۶ مورد دیگر

Alessandro Lameiras Koerich is a Professor in the Department of Software Engineering and IT at École de technologie supérieure (ÉTS), part of the Université du Québec network in Montreal, Canada. He is affiliated with the LIVIA (Imaging, Vision and Artificial Intelligence Laboratory), where he conducts research in machine learning, computer vision, and pattern recognition. His work focuses on developing AI solutions for real-world applications in healthcare, security, and multimedia analysis. His research interests span multiple domains of artificial intelligence, with particular emphasis on multimodal machine learning, affective computing, and trusted machine learning systems. Dr. Koerich's work explores how AI can interpret human emotions through facial expressions and audio analysis, develop robust models resistant to adversarial attacks, and create efficient learning systems that can adapt to changing data streams. His research bridges theoretical advances with practical applications in healthcare monitoring, security systems, and multimedia content analysis. Analysis of his recent publications reveals a strong focus on multimodal emotion recognition, with increasing emphasis on transformer architectures, knowledge distillation techniques, and addressing challenges in real-world settings (in-the-wild recognition). His work consistently combines computer vision with audio processing, demonstrating expertise in both visual and auditory modalities for affective computing applications. Recent trends show growing interest in large language models for audio classification and addressing challenges of concept drift in text stream mining. Dr. Koerich actively supervises numerous graduate students across various research projects, with a particular focus on multimodal expression recognition, music genre classification, and robust machine learning systems. His collaborations span multiple institutions and research groups, reflecting the interdisciplinary nature of his work in artificial intelligence. He is a key member of the LIVIA laboratory, which has established ÉTS as a leading institution in computer vision research in Canada (ranked 6th according to CSRankings). The laboratory focuses on solving real-life problems through AI engineering, particularly in developing complex deep learning models with massive amounts of data that have incomplete annotations.

• Process Mining
• Anomaly Detection
• Blockchain
• +۳ مورد دیگر

Ko, Jonghyeon is a researcher affiliated with the Ulsan National Institute of Science and Technology (UNIST) , specifically the Department of Materials Science and Engineering within the College of Natural Science and Engineering. His work spans multiple disciplines including process mining, anomaly detection, blockchain technology, AI computing, and environmental engineering. His research interests include: Anomaly detection in business process event logs Blockchain-based systems for nuclear/radioactive waste management AI computing using neuromorphic devices Statistical leverage and information-theoretic approaches to process mining Optimization of autonomous vehicle safety systems Recent publications demonstrate expertise in developing formal languages for data quality simulation, probabilistic trace alignment methods, and practical tools for anomaly detection like AIR-BAGEL. While no explicit scientific awards are mentioned in the text, his work has been published in venues such as Information Systems , npj Unconventional Computing , and Expert Systems with Applications .

• Machine Learning
• Deep Learning
• Predictive Maintenance
• +۳ مورد دیگر

Peyman Mashhadi is a Senior Lecturer at the School of Information Technology , Halmstad University. His research focuses on machine learning applications in predictive maintenance, automotive systems, and computational optimization. Position: Senior Lecturer University: Halmstad University Email: peyman.mashhadi@hh.se His work spans machine learning , deep learning , and predictive maintenance , with a particular emphasis on feature selection , optimization algorithms , and automotive diagnostics . He has contributed to multitask learning, domain adaptation, and industrial applications of neural networks. Recent publications highlight trends in automotive engineering (battery health estimation, turbocharger diagnostics), computational methods (genetic algorithms, metaheuristics), and machine learning (stochastic optimization, transfer learning). Key themes include robustness in predictive models and cross-domain adaptability. Contact details: peyman.mashhadi@hh.se

Wayne Wobckeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.

Nikos Giatrakosمشاهده پروفایل

استادیار

• Big Data Management
• Internet of Things (IoT)
• Distributed Systems
• +۳ مورد دیگر

Nikos Giatrakos is an Assistant Professor at the School of Electronic & Computer Engineering, Technical University of Crete, and a core member of the Software Technology and Network Applications Lab (SoftNet) . His work bridges Big Data systems, IoT, and advanced analytics, with a focus on real-time processing and scalable architectures. Previously, he served as a postdoctoral researcher at the same laboratory. Education PhD in Computer Science, University of Piraeus (2012) Postgraduate Diploma in Information Systems, Athens University of Economics and Business (2008) BSc in Computer Science, University of Piraeus (2006) Research Focus : Nikos specializes in software architectures for Big Data streaming, including Distributed Big Data Processing , Federated Machine Learning , Cloud-to-Edge Data Management , and Approximate Query Processing . His work has also advanced Complex Event Processing and Outlier Detection in decentralized environments. Scientific Contributions : His research has led to the DAG* workflow optimizer for IoT, the SuBiTO framework for real-time neural learning, and the INFORE approach for cross-platform analytics. He received the Best System Demonstration Award at ACM CIKM 2020 for INforE. Academic Leadership : Nikos teaches Object-Oriented Programming, Data Science, and Distributed Systems. He has supervised numerous European and national grants as Principal Investigator and served on program committees for top-tier conferences like SIGMOD, VLDB, and DEBS.

• Explainable AI
• Counterfactual Explanations
• Water Distribution Networks
• +۳ مورد دیگر

Dr. André Artelt is a researcher at the University of Bielefeld within the Faculty of Engineering and affiliated with the Machine Learning Group at the Center for Cognitive Interaction Technology (CITEC). His work focuses on Explainable AI (XAI), particularly counterfactual explanations, and their applications in critical infrastructure like water distribution networks. Current Research: Explainable AI Counterfactual explanations Water network monitoring Physics-informed graph neural networks Scientific Contributions: His recent publications explore reinforcement learning for water pump scheduling, scalable graph neural networks for water systems, and benchmark frameworks like EPyT-Flow. He investigates how training data affects explanation quality and develops tools for robust counterfactual reasoning. Awards: Project Lamarr Fellowship

• Bayesian Methods
• Stochastic Processes
• Time Series Analysis
• +۳ مورد دیگر

Tevfik Aktekin is a Professor in the Department of Decision Sciences at the Paul College of Business and Economics, University of New Hampshire. His research focuses on Bayesian inference and stochastic modeling in service systems and business analytics. Education : Ph.D. in Decision Sciences (George Washington University), M.B.A. in Management Decision Making (George Washington University), B.S. in Mechanical Engineering (Yildiz Technical University). His work applies Bayesian state-space models to multivariate count data, with applications in call center staffing, product modification cycles, and public health analysis. He has published in journals like Annals of Applied Statistics , Bayesian Analysis , and European Journal of Operational Research . The 15 most recent articles highlight trends in sequential Bayesian learning for dynamic systems (e.g., bike-sharing, web traffic), time series of non-Gaussian data, and stochastic optimization under uncertainty. These works bridge statistical theory with practical applications in business and health.