جستجوی استاد راهنمای دانشگاهی

Senad Bušatlićمشاهده پروفایل

استاد

• Management
• Strategy
• Leadership
• +۵ مورد دیگر

Dr. Senad Bušatlić serves as a Full Professor of Management, Organization, and Strategy at the International University of Sarajevo (IUS), where he holds key administrative roles including Head of the Department of Economics and Management, Coordinator of the Leadership and Entrepreneurship Center, and active IUS Senate member. His extensive leadership extends to former positions as Vice Rector, Vice Dean, and Acting Dean, significantly shaping university strategy and policy implementation since 2010. His research spans Management, Strategy, Leadership, and Innovation with regional focus on Bosnia and Herzegovina. Core interests include organizational performance, tourism innovation, quality assurance, and human resource management, addressing practical business challenges in post-conflict economies and multinational contexts. His work bridges theoretical frameworks with real-world applications in public and private sectors. Recent publications reveal evolving research trajectories toward technology integration (IoT, blockchain in smart cities), value-based leadership models, and cross-cultural management studies. His 50+ scientific papers demonstrate consistent focus on leadership dynamics, employee engagement, and strategic management applications across hospitality, banking, and healthcare sectors in the Balkans. Dr. Bušatlić has mentored 30 graduate students while leading four major multi-stakeholder research projects involving hundreds of participants. His industry background includes executive roles at Coca-Cola, Procter & Gamble, and Henkel, providing practical insights that inform his academic work and grant-funded initiatives on organizational development. Through the Leadership and Entrepreneurship Center, he cultivates academic-industry partnerships that drive leadership development programs, entrepreneurial training, and innovation ecosystems connecting IUS students with regional business communities.

Venkatesh N. Murthyمشاهده پروفایل

استاد

• Neuroscience
• Olfaction
• Neural Circuits
• +۳ مورد دیگر

Venkatesh N. Murthy is a neuroscience faculty member specializing in olfactory processing and neural circuit dynamics. His research focuses on deciphering how neural systems encode, process, and respond to sensory stimuli, particularly odors. Key areas include odor mixture perception, neural algorithms for compressed sensing, and the role of feedback loops in olfactory bulb-cortex communication. Murthy employs rodent models and computational approaches to study adaptive behavior, learning mechanisms, and sensory navigation in dynamic environments. Research Focus: Murthy's work bridges experimental neurobiology and theoretical modeling, with emphasis on: Neural representation of odorants and mixtures Cortical feedback mechanisms in sensory processing Biomimetic applications for electronic nose design Dopamine signaling in associative learning His recent publications demonstrate consistent focus on olfactory coding, neural plasticity, and adaptive algorithms, with emerging themes in social behavior circuitry and AI-driven neural signal analysis.

• Machine Learning
• Computer Vision
• Data Compression
• +۴ مورد دیگر

Donald Adjeroh is a Professor and Associate Chair of Computer Science at the Lane Department of Computer Science and Electrical Engineering, within the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University. He also serves as the Program Director for Computer Science Graduate Studies and is the Project Lead PI for the WVAR-CRESH (West Virginia Arkansas Research Collaboration in Engineering, Science, and Health) initiative. Dr. Adjeroh received his Ph.D. from the Chinese University of Hong Kong in 1997. His academic journey has led him to become a prominent researcher in multiple interdisciplinary fields, with a focus on developing computational methods for biomedical applications. Dr. Adjeroh's research spans several interconnected domains: Machine Learning approaches for biomedical data analysis Computer Vision techniques for medical image processing Data Compression algorithms with applications in bioinformatics Search Algorithms & Data Structures for large-scale biological data Bioinformatics and Computational Biology methods for genomic analysis Digital Health applications for healthcare cost reduction and improved outcomes His publication record demonstrates consistent contributions across these fields, with recent work focusing on parameterized string algorithms, probabilistic suffix arrays for protein family prediction, and robust texture analysis techniques. His research shows a clear trajectory toward increasingly sophisticated applications of computational methods to biomedical problems. Dr. Adjeroh has received significant research funding from various sources including the National Science Foundation (NSF), Department of Energy (DOE), NASA, Department of Defense (DOD), Department of Homeland Security (DHS), and others. His current projects include "NSF NRT: Bridges in Digital Health" and "RII Track 2 FEC: Multi-Scale Integrative Approach to Digital Health," among others. He has mentored numerous students through research projects and serves as an advisor for graduate students interested in pursuing PhD research in areas related to search data structures, computational biology, bioinformatics, biometrics, machine learning, and digital health. Dr. Adjeroh is actively involved in organizing academic events, including the Summer School on AI & Smart Health, and serves as a key collaborator in multi-institutional research efforts focused on leveraging artificial intelligence to improve healthcare outcomes.

Juan Duarteمشاهده پروفایل

پژوهشگر

• Media Art
• Sound Studies
• Environmental Awareness
• +۴ مورد دیگر

Juan Duarte is a Doctoral Researcher at Aalto University's School of Arts, Design and Architecture, specializing in the Department of Art and Media. His work bridges media art, sound studies, and environmental awareness through innovative technological implementations. Focuses on atmospheric processes and climate communication Recipient of multiple international art grants and residencies Developed "Augury" - a groundbreaking multichannel sound system Active in Nordic and European art networks His research explores the intersection of sound spatialization technologies with environmental consciousness, creating interactive installations that transform meteorological data into sensory experiences. Recent projects demonstrate cross-disciplinary innovation, with articles examining resonance in atmospheric contexts, embodied listening techniques, and climate data interpretation through artistic frameworks. His work has been presented at major venues including Klaipeda Culture Communication Centre and Nida Art Colony. 2024: EUR 10,000 development funding for "Augury" from Aalto Studios 2024: Culture Moves Europe mobility grant for Lithuania residency 2024: Nordic Culture Point mobility funding 2024: Frame Contemporary Art Finland travel grant 2024: Taike Finland project grant Duarte's practice combines technical innovation with ecological philosophy, creating immersive sound environments that challenge conventional understandings of atmospheric interactions and environmental stewardship.

• Analytical Chemistry
• Electrochemical Sensors
• Biosensors
• +۳ مورد دیگر

Laura PIGANI is an Associate Professor at the Department of Chemical and Geological Sciences , University of Modena and Reggio Emilia. With expertise in analytical chemistry, she specializes in electrochemical sensor development for applications in clinical, environmental, and food analysis. Research Interests : Electrochemical sensors, biosensors, chemometrics, and optoelectronic materials. Teaching : Courses in Analytical Chemistry and Chemical Sensors at both undergraduate and postgraduate levels. Her work focuses on portable systems for Cannabis sativa analysis, metal bioaccumulation studies in plants, and multisensor data fusion for agricultural monitoring. Recent publications highlight innovations in THC/CBD discrimination , caffeic acid detection , and deep eutectic solvent applications . Collaborations with forensic and environmental institutions underscore her translational research impact. Techniques : Cyclic voltammetry, screen-printed electrodes, sonogel-carbon devices, and multivariate statistical analysis. Applications : Drug testing, grape ripening monitoring, battery recycling, and sustainable pigment extraction. Dr. PIGANI's laboratory employs advanced materials like carbon black , PEDOT , and metal nanoparticles to develop cost-effective, reusable sensors. Her interdisciplinary approach bridges material science, electrochemistry, and analytical methodology.

• Artificial Intelligence
• Machine Learning
• Anomaly Detection
• +۶ مورد دیگر

Yuantao Fan is an Associate Senior Lecturer at Halmstad University's School of Information Technology, specializing in artificial intelligence and machine learning applications for industrial systems. His academic work bridges theoretical research with practical implementations in predictive maintenance and equipment monitoring. Dr. Fan's research focuses on Artificial Intelligence, Machine Learning, Anomaly Detection, and Data Mining , with particular emphasis on streaming data from real-world applications. His work spans equipment diagnosis and prognosis, predictive maintenance (PdM), and condition-based monitoring systems. His research integrates self-organizing models with expert knowledge to create robust diagnostic frameworks applicable to transportation systems and industrial equipment. Analysis of Dr. Fan's publication record from 2013-2025 reveals consistent specialization in predictive maintenance for transportation systems , particularly bus fleets and heavy vehicles. His work demonstrates progression from basic fault detection methods to sophisticated hybrid approaches combining physics-based models with deep learning. Recent publications increasingly address explainability in predictive maintenance systems and applications in healthcare analytics. Dr. Fan actively contributes to academic instruction as course responsible and examiner for Databases, Introduction to Linux and Python, and Linux administration system courses. He also teaches Artificial Intelligence, Data Mining, Perspectives on Computer Science and Engineering, Machine Learning for Predictive Maintenance, and AI Implementation courses. His technical expertise spans multiple domains including automotive systems diagnostics, battery health monitoring, acoustic signal processing, and healthcare prediction models. Dr. Fan's work demonstrates strong industry collaboration, particularly with Volvo and city bus fleet operators, translating academic research into practical industrial applications.

Jens Erikssonمشاهده پروفایل

دانشیار

• Sensor Science
• 2D Materials
• Machine Learning
• +۳ مورد دیگر

Jens Eriksson is an Associate Professor at Linköping University's Department of Physics, Chemistry and Biology (IFM) within the Faculty of Science and Engineering. He serves as Head of the Applied Sensor Science Unit and actively contributes to national networks like the SIO Grafen Graduate Network for 2D-materials research. Lead researcher in sensor technology and 2D materials Collaborator in cross-disciplinary biomedical/environmental projects Active in academic-industry partnerships His research focuses on advanced sensor systems combining 2D materials like graphene and MoS 2 with machine learning for applications in cancer diagnostics (ovarian/early detection), environmental monitoring (methane/NO 2 ), and food safety. Current projects involve electronic nose technology and computational modeling of biomarker adsorption phenomena. Recent publications demonstrate expertise in both experimental and computational approaches to sensor development, with 2025 publications spanning Journal of Big Data , Applied Surface Science , and Small Structures . His work bridges materials science, biomedical engineering, and environmental monitoring. Scientific achievements include securing the first Swedish-U.S. collaboration grant under the NSF Convergence Accelerator program, Phase 1. This funding supports international research partnerships in sensor technology applications.

Andrei Bursucمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Autonomous Driving
• +۳ مورد دیگر

Andrei Bursuc is a Research Scientist and Deputy Scientific Director at Valeo.ai, where he focuses on perception for autonomous driving. Concurrently, he serves as a Research Associate at the Astra Inria project team and teaches the Deep Learning DIY course at Paris Institute of Technology (École Polytechnique) since 2019. He holds a PhD from Mines ParisTech and Telecom SudParis (2012) and a BSc from Polytechnic University of Bucharest (2009). His research spans computer vision and machine learning , with applications in autonomous driving, uncertainty quantification, self-supervised learning, and foundation models. Recent work emphasizes scalable representation learning, open-vocabulary segmentation, and generative modeling for driving scenarios. His publications (2024–2025) cluster around themes of semantic segmentation , depth estimation , and vision-language models , with innovations in unsupervised learning, cross-modal distillation, and efficiency. Trends indicate a focus on robustness, real-world generalization, and multi-sensor integration. He co-organizes workshops (e.g., Uncertainty Quantification at CVPR/ECCV) and serves as Area Chair for NeurIPS/CVPR/ECCV. He contributes to EU projects (e.g., ELLIOT) and open-source initiatives.

• Crystallography
• Coordination Chemistry
• Structural Biology
• +۷ مورد دیگر

Dr. Horst Puschmann is a Professor in Practice at Durham University, affiliated with the Department of Mathematical Sciences and the Department of Chemistry. He specializes in crystallography, computational crystallography, and structural refinement techniques, with particular expertise in X-ray structure determination, Hirshfeld atom refinement, and metal-organic frameworks. His research bridges theoretical and experimental approaches, focusing on advanced structural analysis of coordination polymers and biological interactions of metal complexes. His recent work explores the application of crystallographic software for precise structural modeling, including studies on ruthenium-based antitumor agents (2024), anomalous dispersion corrections (2022), and Cu Kβ radiation for crystalline sponges (2022). He has extensively contributed to the development of Olex2, a molecular graphics tool for small-molecule crystallography, enabling automated refinement and data visualization. Publications highlight his interdisciplinary collaborations in photocatalysis, magnetic properties, and bioactive coordination compounds. As a co-developer of Olex2, he has pioneered accessible crystallographic software through open-source frameworks and virtual teaching platforms. His projects integrate structural analysis with biological applications, such as DNA/protein binding, cytotoxicity, and luminescent properties of metal complexes. While no specific student names or scientific awards are mentioned in the provided data, his work demonstrates sustained innovation in crystallography education and computational methodologies.

Cornelia Kienleمشاهده پروفایل

پژوهشگر

• Aquatic Ecotoxicology
• In Vitro and In Vivo Test Systems
• Biomonitoring
• +۵ مورد دیگر

Cornelia Kienle is a scientist at the Ecotox Centre, affiliated with Eawag and EPFL. She has been working in aquatic ecotoxicology since 2008, focusing on in vitro and in vivo bioassays for monitoring aquatic ecosystem health. University: Eawag Expertise: Aquatic ecotoxicology, bioassay development, wastewater treatment impact assessment Her research spans aquatic toxicology , environmental risk assessment , and biomonitoring . She specializes in evaluating chemical micropollutants, endocrine disruptors, and stressor interactions in freshwater systems using advanced bioassay batteries and biomarker analysis. Recent publications highlight her work on micropollutant elimination via ozonation, biological early warning systems , and molecular biomarker applications . She contributes to Swiss and EU water quality frameworks through test system design and multi-assay evaluation. Key project areas include: Effect-based water quality monitoring Post-treatment wastewater analysis Ecotoxicity of facade runoff Integrated stream water quality management

Lars Bernardمشاهده پروفایل

استاد

• (Geo-)Information Infrastructures
• Geoinformation Usability
• Distributed Geoprocessing
• +۷ مورد دیگر

Lars Bernard is Full Professor for Geoinformatics at the Faculty of Environmental Sciences, Technische Universität Dresden since 2015. He also holds the position of Chief Digitalization and Information Management Officer (CDIO) at TU Dresden since 2020 and serves as member of the TUD Rectorate. PhD in Geoinformatics (University of Münster, 2001) Diploma in Physical Geography (University of Münster, 1995) His research focuses on (Geo-)Information Infrastructures , Distributed Geoprocessing , and Smart Environmental Monitoring . He has extensive experience in developing geospatial standards through roles in AGILE, EuroSDR, and INSPIRE initiatives. His publications demonstrate a consistent emphasis on: Metadata provision and data quality frameworks Interoperability solutions for geospatial systems Climate information service architectures User-driven provenance mechanisms As co-chair of RFII (2020-) and spokesperson for NFDI4Earth (2019-), he leads major German research data initiatives. He has advised numerous students and coordinated TU Dresden's Master Geoinformation Technologies program until 2018. Current leadership roles include serving on scientific editorial boards for Environmental Modelling & Software and International Journal of Spatial Data Infrastructures Research .

Kasper Støyمشاهده پروفایل

استاد

• Robotics
• Modular Robotics
• Bio-Inspired Robotics
• +۲ مورد دیگر

Kasper Støy is a Professor in Robotics and Embodied Artificial Intelligence at the IT University of Copenhagen, specializing in distributed control of multi-robot systems and modular robotics. He has published over 95 works and authored the MIT Press book Self-Reconfigurable Robots: An Introduction . As co-founder of Universal Robots, he bridges academic research with industrial applications. M.Sc. in Computer Science & Physics, University of Aarhus (1999) Ph.D. in Computer Systems Engineering, University of Southern Denmark (2003) His research focuses on: Modular Robotics Bio-Inspired Robotics Human-Robot Interaction Embodied Artificial Intelligence Self-Reconfigurable Systems Recent publications address anxiety-reducing pocket robots, tactile comfort systems, and origami-inspired mechanical designs. He has won awards including the 2017 GECCO Virtual Creatures Competition. 2017: GECCO Virtual Creatures Competition Active in academic service, he chaired recruitment committees for Statistics and Machine Learning positions. Collaborations span European Commission projects like MUHMI-MOZART and BIG-MAP.

Stig Hellebustمشاهده پروفایل

مدرس

• Atmospheric Chemistry
• Environmental Chemistry
• Air Quality
• +۵ مورد دیگر

Dr. Stig Hellebust is a Lecturer in Physical Chemistry at the School of Chemistry, University College Cork (UCC), Ireland. Based in Room 206B of the Kane Building, he can be contacted at s.hellebust@ucc.ie or +353 214902680. His research focuses on atmospheric chemistry, environmental monitoring, and advanced data analysis techniques for understanding air quality and pollution sources across Ireland. Dr. Hellebust's research interests span several key areas of environmental chemistry and data science: Atmospheric observational data analysis, particularly high-dimensional datasets collected over extended time periods Application of multivariate statistical methods and machine learning for environmental data interpretation Source apportionment of atmospheric pollutants using receptor modeling techniques Development of algorithms for processing large environmental datasets Application of clustering and classification techniques to identify pollution sources Fourier-transform infrared spectroscopy data analysis His extensive publication record demonstrates expertise in air quality monitoring, particularly focusing on PM2.5 sources, urban pollution dynamics, and health impacts. He frequently employs advanced statistical methods including principal component analysis (PCA), positive matrix factorization (PMF), and various machine learning approaches to extract meaningful information from complex environmental datasets. His work bridges atmospheric science, public health, and data analytics, with significant contributions to understanding Ireland's air quality challenges. Dr. Hellebust has secured substantial research funding from multiple sources including the Environmental Protection Agency (EPA), Health Research Board, Science Foundation Ireland, and European Union programs. His current major projects include "Sources of PM2.5 in the Air of Irish Towns" (2024-2027, €233,796.00) and "Impact of Agricultural Emissions on Rural and Urban Air Quality" (2022-2025, €119,700.00), demonstrating his leadership in addressing critical environmental challenges. He currently supervises doctoral student Rósín Eileen Byrne and has previously supervised Eimear Heffernan who completed her PhD in 2022 on "Spatial and temporal variation of ambient carbonaceous aerosol in Ireland and strategies for effective monitoring of source contributions." His mentorship extends to interdisciplinary research connecting chemistry, environmental science, and public health. Dr. Hellebust is an active member of UCC's Atmospheric and Environmental Chemistry research group, collaborating with colleagues across Ireland and internationally on air quality monitoring and pollution source identification projects. His work has significant policy implications for urban planning, public health interventions, and environmental regulation in Ireland and beyond.

• Signal processing
• Audio processing
• Spatial audio
• +۷ مورد دیگر

Professor Christian Ritz serves as Director of SMART and Professor in the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong. He has held his professorship since 2017 and served as Associate Dean (Research) in 2022 and Associate Dean (International) from 2016-2021. Professor Ritz's research focuses on signal processing for speech, audio, acoustics and visual information. His current projects include microphone array signal processing for enhancing sound recorded in classrooms, sound scene classification for dementia care environments, spatial audio for Virtual and Augmented Reality, and undersea surveillance. His work spans multiple technical domains including signal processing, audio engineering, computer vision, and communications engineering. His recent publication trends show a strong focus on neural network applications for audio processing, particularly for classroom monitoring and spatial audio reproduction. His work integrates deep learning with traditional signal processing techniques to address challenges in spatial audio synthesis, room impulse response generation, and directional audio reproduction. He has also contributed to applications in mining automation, public safety surveillance, and positioning systems. Professor Ritz is a member of the Centre for Signal and Information Processing (CSIP), part of the Signals, Information and Communications Research Institute (SICOM) at the University of Wollongong. His research is funded by the ARC, overseas research institutes, and defense organizations. His grant portfolio is extensive, including projects such as AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods, Multi-Modal Satellite-based Vessel Surveillance, Net Zero Industry and Innovation Program, Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane, and numerous projects related to spatial audio and underwater surveillance. He has secured funding from diverse sources including the Australian Research Council, defense organizations, and international collaborations. Professor Ritz maintains an active research laboratory environment focused on signal and information processing. His work spans multiple application domains from classroom monitoring to underwater mine detection, with strong connections to both academic and industry partners. His research group appears to focus on practical applications of signal processing techniques to solve real-world problems across various domains.

Liam MacNeilمشاهده پروفایل

پژوهشگر

• Marine Ecology
• Evolutionary Ecology
• Fisheries Science
• +۳ مورد دیگر

Liam MacNeil is a researcher at GEOMAR Helmholtz Centre for Ocean Research Kiel, working within Research Division 3: Marine Ecology, specifically in the Marine Evolutionary Ecology group. His office is located in room 1.401 at the GEOMAR facility in Kiel, Germany, and he can be contacted at +49-431 600 4521 or lmacneil@geomar.de. Dr. MacNeil's research focuses on marine ecological systems with particular emphasis on: Marine biodiversity and community assembly processes Fisheries science and management, especially in the Baltic Sea Biogeography of demersal fish populations Climate change impacts on marine organisms Advanced monitoring techniques combining imaging and molecular approaches His publication record demonstrates a strong focus on spatial and temporal patterns in marine communities, with significant contributions to understanding how environmental changes affect species distributions and abundances. Recent work highlights systemic challenges in European fisheries governance and proposes ecosystem-based solutions that address both sustainability and carbon sequestration. Dr. MacNeil has developed innovative methodologies including superpixel analysis for monitoring mudsnail growth under ocean warming and integrating metabarcoding with digital holography for plankton monitoring. His research aligns with GEOMAR's core themes of Marine Ecosystems and Biogeochemical Cycles, contributing to global understanding of ocean dynamics and sustainable management practices.