جست‌وجوی استاد و پژوهشگر

• Bioinformatics
• Computational Biology
• Protein Structure Prediction
• +۳ مورد دیگر

Tobias Olenyi is a researcher at the Department of Informatics, Technical University of Munich (TUM), working under the Chair for Bioinformatics led by Prof. Dr. Burkhard Rost. Holding an M.Sc. degree, he contributes to computational biology research with focus on protein structure analysis and machine learning applications. His office is located at Boltzmannstr. 3, 85748 Garching bei München (Room 5609.01.055) with office hours Tuesday and Wednesday 10:00-17:00. Education: M.Sc. in relevant computational/biological field Research Interests: Olenyi's work centers on bioinformatics and computational biology , specifically protein structure prediction, visualization, and functional analysis. He leverages deep learning (protein language models, embeddings) and structural bioinformatics to develop tools for protein space visualization (FlatProt, ProtSpace), transmembrane protein analysis (TMVisDB), and phenotype prediction (LambdaPP). His research bridges computational methods with biological applications, emphasizing accessibility through interactive platforms and crowdsourced data collection. Publication Trends: From 2018-2025, Olenyi's work shows a clear trajectory toward protein-centric machine learning . Early projects focused on crowdsourced data platforms (VoiLA, 2018; gamified collection, 2019), while 2021-2022 publications established his expertise in protein embeddings for variant effect prediction and Gene Ontology annotation. His 2025 output demonstrates maturation into specialized visualization tools (FlatProt, TMVisDB), reflecting a cohesive research program that integrates AlphaFold2 structures and deep learning to solve protein analysis challenges. Laboratory and Team Affiliation: Olenyi operates within the Rost Lab at TUM—a globally recognized bioinformatics group specializing in protein structure prediction. The lab maintains strong industry/academic collaborations and emphasizes open-source tool development (e.g., PredictProtein server). His work directly supports the chair's mission to translate computational advances into biological insights, particularly in protein function and disease mechanisms.

Marten Borchersمشاهده پروفایل

پژوهشگر

• Smart City
• Smart Mobility
• Urban Planning
• +۷ مورد دیگر

Marten Borchers is a Researcher at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, University of Hamburg. His work focuses on Business Information Systems and Socio-Technical Systems Design (WISTS) . Education : Dual Master's Degree from University of Bremen (AI, ML, NLP, IT Management), semester at University of Tartu Professional Experience : 4+ years in IT Consulting (Public Service Consultant) Research Interests include: Smart City and Climate Change Mitigation AI/ML Applications in Urban Mobility Collaboration Engineering for Citizen Participation E-Government and Digital Transformation His publications since 2023 focus on: ML-Based Energy Prediction for Electric Buses Bloom's Taxonomy in LLM-Assisted Learning Mobile Apps for Storm Flood Emergency Management AI Support for Urban Participation Platforms Scientific Awards : 2025 Hamburg Teaching Award (team award) for cross-border teaching innovations Advising & Grants : Co-author with Eva Bittner on 6 publications Collaboration with Springer, ACM, and IEEE journals Labs & Teams : Active member of WISTS (Socio-Technical Systems Design) research group Participant in D²S²C Hamburg and DDLitLab projects Collaboration with Hochbahn AG and STEAM ecosystem analysis

Ombretta Gaggiمشاهده پروفایل

دانشیار

• Mobile Computing and Networking
• Web Technologies
• Social Networks
• +۷ مورد دیگر

Ombretta Gaggi is an Associate Professor at the Department of Mathematics, University of Padua, where she has been conducting research and teaching since April 2006. Her academic journey began with a Laurea degree cum laude in Computer Science from Ca' Foscari University of Venice in 1998, followed by a PhD in Computer Science from the Consortium of Universities of Bologna, Padova and Venice in 2003. Her research spans mobile computing and networking, web technologies, social networks, and semantic web, with a strong emphasis on accessibility technologies and serious games for educational and medical applications. She has made significant contributions to developing solutions for visually impaired users, early identification of developmental dyslexia, and safety mechanisms for mobile communications. Professor Gaggi's recent publications demonstrate a consistent track record of high-impact research in top venues including ACM Transactions on the Web, IEEE Transactions on Mobile Computing, and IEEE Access. Her work increasingly focuses on leveraging technology for social good, addressing challenges in accessibility, education, and online safety. Best Paper Award at IEEE International Conference on Multimedia and Expo (ICME 2013) As an educator, Professor Gaggi teaches Web Technologies and Mobile Programming and Multimedia courses at the University of Padua. Her research group offers opportunities for students to work on cutting-edge projects at the intersection of mobile computing, web technologies, and accessibility solutions. She has established international collaborations and regularly publishes in top-tier conferences and journals, demonstrating sustained research productivity across multiple domains. Her laboratory work focuses on practical implementations of theoretical frameworks, particularly in accessibility technologies and serious games applications, with strong connections to real-world challenges in education and healthcare.

Aleksandar Kelečمشاهده پروفایل

مدرس ارشد

• Android Security
• Database Systems
• Software Engineering
• +۵ مورد دیگر

Aleksandar Keleč is a Senior Lecturer at the Department of Computing and Informatics, Faculty of Electrical Engineering, University of Banja Luka. He has contributed to research in Android security, database systems, and automated database design tools. Senior Lecturer in Department of Computing and Informatics Specializes in mobile security, database optimization, and software engineering Active in international collaborations through conferences and projects Research Interests: His work focuses on enhancing security and privacy in mobile communications, particularly Android platform vulnerabilities and permissions systems. He has developed tools for database reverse engineering and visualization, and contributed to distributed computing infrastructure monitoring projects like VI-SEEM. His research spans practical applications in software testing automation, pseudorandom number generation analysis, and NoSQL database optimization. Key Article Trends: Recent publications emphasize secure communication protocols, multilingual database design systems, and Android security improvements. Technical work includes performance analysis of SQLite databases, MongoDB full-text search implementations, and testing automation frameworks.

• Information Retrieval
• Knowledge Graphs
• Natural Language Processing
• +۵ مورد دیگر

Florina Piroi is a Senior Scientist (Research Fellow) at the Center for Research Data Management (E058-06) within the Faculty of Informatics at TU Wien. Her research integrates information retrieval, knowledge graphs, and semantic technologies to advance exploratory search systems and research data infrastructure. Her core research interests include: Information Retrieval evaluation and validation methodologies Knowledge graph applications for domain-specific search Patent text mining and semantic technologies Medical concept normalization and entity linking Argumentative zoning in scientific documents Table extraction and classification systems Analysis of her recent publications reveals strong trends in applying machine learning to specialized IR tasks, particularly in patent analysis and medical text processing. Her work emphasizes robust evaluation frameworks and cross-lingual capabilities, with consistent contributions to the CLEF campaign and PatentSemTech workshops. Dr. Piroi actively collaborates with international researchers through the Network Lab at TU Wien and contributes to the university's Center for Research Data Management, focusing on semantic technologies for research data infrastructure and scholarly communication systems.

Michael Macyمشاهده پروفایل

استاد

• Computational Social Science
• Network Science
• Political Polarization
• +۶ مورد دیگر

Michael Macy is the Goldwin Smith Professor of Arts and Sciences at Cornell University , affiliated with the Department of Sociology and Department of Information Science. He directs the Social Dynamics Laboratory , focusing on computational models of social behavior. Harvard B.A. & Ph.D., Stanford M.A. Research spans political polarization, cultural dynamics, network science, and digital traces analysis. His work uses Twitter data, Spotify logs, and telecom records to study mood cycles, economic correlations with networks, and social wormholes. Recent studies examine partisan consumption patterns in science and cultural polarization mechanisms. Key publications appear in Science , Nature Human Behaviour , PNAS , and American Journal of Sociology . Collaborations include Nathan Eagle (telecom networks), Robb Willer (false enforcement norms), and Damon Centola (complex contagion).

Danijela Tešendićمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Environmental Monitoring
• +۲ مورد دیگر

Danijela Tešendić serves as an Associate Professor in the Department of Mathematics and Informatics at the Faculty of Sciences, University of Novi Sad. Her office is located in the Information Technologies and Systems office (DMI&DF), second floor, room 50, with contact details including telephone (485)-2873 and email tesendic@dmi.uns.ac.rs. She maintains a professional presence through her website at http://www.is.pmf.uns.ac.rs/daca/. Her research demonstrates dual expertise in environmental monitoring systems and library information technology. Recent work (2022-2025) focuses on machine learning applications for airborne pollen classification using CNN architectures and domain adaptation techniques, contributing to standardization frameworks in environmental science. Earlier contributions (2013-2019) established foundational work in library management systems, particularly through the BISIS software platform, business intelligence integration, and mobile accessibility solutions for visually impaired patrons. Analysis of her publication trajectory reveals a strategic shift toward interdisciplinary environmental informatics while maintaining connections to information systems. Her pollen monitoring research bridges computer vision with ecological applications, emphasizing real-time processing and cross-device compatibility. Concurrently, her library science work pioneered cloud-based service models and inclusive design principles, showcasing adaptable technical frameworks across distinct domains. Scientific Awards No awards or honors were documented in the provided materials. Advising and Grants Student mentorship details are absent from the source text. No research grants or funding sources are explicitly referenced.

Katrin Wellerمشاهده پروفایل

استاد

• Computational Social Science
• Social Media Research
• Digital Behavioral Data
• +۴ مورد دیگر

Prof. Dr. Katrin Weller is a leading scholar in computational social science and research data management. She holds the Professorship for Research Data Management in the Social Sciences at Heinrich Heine University Düsseldorf (since 2024) and serves as Scientific Director of the Data Services for the Social Sciences department at GESIS - Leibniz Institute for the Social Sciences . Her career includes co-leading the Research Data and Methods team at the Center for Advanced Internet Studies (CAIS) (2021-2023) and heading GESIS's Digital Society Observatory team (2015-2024). Academic Appointments Professor for Research Data Management in Social Sciences, HHU Düsseldorf (2024-present) Scientific Director, Data Services for Social Sciences, GESIS (2024-present) Research Themes Digital behavioral data quality and reusability Ethical challenges in social media research Altmetrics and scholarly communication Platform data preservation and archiving Key Collaborations Center for Advanced Internet Studies (CAIS) Library of Congress (Digital Studies Fellow) International conferences (AoIR, WebSci, ICWSM) Scientific Contributions span 15+ years of publications addressing social media's methodological, ethical, and archival dimensions. Notable works include: Frameworks for assessing data quality in digital social research Ethical implications of decentralized platform data collection (e.g., Mastodon) Preservation strategies for Twitter and Wikipedia references Analysis of affective drivers in political information seeking Her teaching includes seminars on social media research fundamentals at Heinrich Heine University. As an organizer , she has led European symposia on computational social science challenges and co-founded the #FAIL workshop series to study social media research failures.

Vladimir Mladenovićمشاهده پروفایل

استاد

• Symbolic computation and processing
• Artificial intelligence and machine learning
• Deep learning and neural networks
• +۷ مورد دیگر

Prof. Dr. Vladimir Mladenović is a Full Professor at the Department of Information Technologies , Faculty of Technical Sciences in Čačak , University of Kragujevac , Serbia. A 1975 native of Paraćin, he earned his Dipl. Ing. from the Faculty of Electronic Engineering, University of Niš (2000), followed by an M.Sc. (2005) and Ph.D. in Technical Sciences (2009) from the University of Kragujevac. His career spans industry (IT Manager, Serbian Glass Factory, 2001–2004), secondary education (teacher, 2004–2009), and academia (Professor of Vocational Studies, 2009–2013) before joining the University of Kragujevac. Research Focus: Symbolic software & tools, higher integration in IT systems Signal & multimedia processing, computer vision & AI/ML 5G/IoT architectures, edge computing, data protection Modern communication systems, web & network technologies He directs the Digital Innovation Lab at his faculty and has authored over 100 peer-reviewed publications in leading journals and conferences, with a strong emphasis on cross-layer modeling, deep learning, and IoT applications. He also co-holds 18 Serbian utility patents covering smart IoT systems ranging from greenhouse monitoring to smartwatch data collection, fire detection, and pedestrian safety. Honors & Professional Standing: Licensed Technology-Transfer Engineer – WIPO & Intellectual Property Office of Serbia (2012) Member, Union of Engineers and Technicians of Serbia Teaching Portfolio: Undergraduate: Modern Software Architectures, Multimedia Systems, Data Protection Master: Modern Communication Systems, Web Programming Doctoral: Modern Network Technologies, Applied Computer Vision

• Bioinformatics
• Computational Biology
• Genetics
• +۲ مورد دیگر

Perry Ridge serves as Associate Professor in the Department of Biology at Brigham Young University's College of Life Sciences, where he leads computational genomics research focused on Alzheimer's disease mechanisms and algorithm development. His laboratory, located in room 4102 LSB, actively contributes to neuroscience initiatives including the undergraduate Chiasm journal and departmental outreach programs. Dr. Ridge's research integrates bioinformatics, mitochondrial genomics, and population genetics to investigate Alzheimer's disease pathogenesis. Key projects include analyzing synonymous mutations in disease genes, developing haplotype-based association algorithms, and studying mitochondrial genome contributions to neurodegeneration. His lab also pioneers ramp sequence analysis tools (e.g., ExtRamp, Ramp Atlas) to interpret translational regulation in genomic data, with recent applications extending to SARS-CoV-2 research. Analysis of his publications reveals dual trajectories: advancing Alzheimer's genetics through population-specific studies (notably with Native communities via the Natives Engaged in Alzheimer’s Research grant) and marine genomics (fish genome assembly for conservation). His work on synonymous variants and ramp sequences demonstrates how codon usage patterns influence disease mechanisms, while Pacific ancestry pangenome development addresses health disparities in underrepresented populations. Scientific Awards: None documented in provided materials. As co-director of the biorepository for the Natives Engaged in Alzheimer’s Research grant—a multi-institution NIH-funded initiative—Dr. Ridge oversees specimen collection for American Indian, Alaskan Native, Native Hawaiian, and Pacific Islander cohorts. His laboratory leads genetic analyses of this cohort to identify population-specific risk factors. While student advisees aren't listed, his teaching of bioinformatics and genomics courses suggests active mentorship of undergraduate researchers. The Perry Ridge Lab operates as a computational hub within BYU's Life Sciences complex, specializing in genomic data mining and algorithm development. Collaborations span neurodegenerative disease consortia and marine genomics projects, with recent work on bonefish and trevally genomes supporting conservation efforts. Current initiatives focus on expanding the Pacific ancestry pangenome reference and refining machine learning approaches for variant interpretation.

Stefan Kuhlinsمشاهده پروفایل

استاد

• Business Informatics
• Information Technology
• E-commerce
• +۶ مورد دیگر

Prof. Dr. Stefan Kuhlins is a Professor of Business Informatics at Heilbronn University's Faculty of Economics. He specializes in Business Engineering Logistics and previously worked at the universities of Mannheim and Regensburg. His research focuses on E-commerce, XML technologies, and enterprise software integration. He also operates as a freelance business information technology specialist. Current: Professor at Heilbronn University (Faculty of Economics) Previous: University of Mannheim, University of Regensburg Specialization: Business Engineering Logistics (formerly Technical Logistics Management) Freelance: E-commerce and software projects Education : Graduate in Business Information Technology Research Interests : His work spans business informatics, information technology, and programming with particular emphasis on database systems, XML standards, and Robotic Process Automation (RPA). He has developed frameworks for enterprise application integration and explored wireless usability metrics through Java software agents. His publications reflect a strong focus on E-commerce systems, XML schema design, and software development methodologies. Publications Trends : His scholarly output from 1993-2018 covers XML technologies, Java applications in enterprise systems, simulation modeling for logistics, and web service integration. The articles demonstrate consistent engagement with software engineering challenges in E-commerce and enterprise contexts. Technical Leadership : Since 2014, he has served as Head of the Internship Office for Business Engineering Logistics (BEL) and Technical Logistics Management (TLM) programs at Heilbronn University. This administrative role complements his technical expertise in systems design. Programming Expertise : With deep knowledge of C++ standard libraries and Java technologies, he has contributed to areas like garbage collection, CORBA optimization, and multithreaded frameworks. His technical work spans from core programming challenges to enterprise-scale integration solutions.

Michal Töpferمشاهده پروفایل

مربی

• Machine Learning
• Component-based Systems
• Self-adaptive Systems
• +۴ مورد دیگر

Michal Töpfer is a PhD student and academic tutor at the Department of Distributed and Reliable Systems within the Faculty of Mathematics and Physics at Charles University in Prague. Holding the RNDr. degree (Doctor of Natural Sciences), he actively contributes to both teaching and research at the university. His academic affiliations include: Department of Distributed and Reliable Systems (D3S) Faculty of Mathematics and Physics Charles University Dr. Töpfer's research centers on integrating machine learning with software architecture, particularly for self-adaptive and component-based systems. His work develops systematic approaches to incorporate ML capabilities into software architectures, enabling systems that can self-optimize and adapt to changing conditions. He has made significant contributions to the DEECo component model through his ML-DEECo extension and has developed visualization components for the IVIS framework used in IoT applications. His publication trajectory shows increasing focus on leveraging large language models for software engineering tasks and optimizing component-based architectures. The research spans theoretical foundations of component models to practical implementations addressing real-world challenges in distributed systems, with applications in smart farming and Industry 4.0. As an educator, he teaches practical sessions for undergraduate courses including Introduction to Algorithms and Programming 1 during winter semesters and Programming 2 during summer semesters. While he supervises student work, specific advisees are not documented in available sources. He actively contributes to the academic community through organizing the Mathematical Correspondence Seminar (PraSe), the Young Mathematicians Camp, and the Kasiopea programming competition. His technical expertise spans web development, software architecture, machine learning integration, and data visualization.

• Predictive Analytics
• Population Health
• Biomedical Informatics
• +۷ مورد دیگر

Andrew Loza, MD, PhD, is an Instructor of Biomedical Informatics and Data Science at Yale School of Medicine. He holds joint appointments in both the Department of Biomedical Informatics and Data Science and the Department of Pediatrics. Dr. Loza is also affiliated with the VA Connecticut Healthcare System where he completed his Clinical Informatics fellowship and postdoctoral training. Dr. Loza's educational background includes a PhD in Biophysics from Washington University in St. Louis (2016), where he studied mechanisms of collective cell migration using time lapse microscopy, computer vision, and simulation. He earned his MD from Yale University School of Medicine and completed his residency in Internal Medicine-Pediatrics at Yale. His training was further enhanced by a Clinical Informatics fellowship in the ACGME Yale/VA program and a postdoctoral fellowship in the VA Biomedical Informatics program. Dr. Loza's research focuses on developing statistical and deep learning methods to leverage Real-World Data to improve clinical care, with particular emphasis on multimodal medical foundation models. His work spans several key areas including predictive analytics for patient risk assessment, analysis of electronic health record patterns, and understanding the impact of healthcare interventions. The Loza Lab aims to transform data into actionable information that can be seamlessly integrated into clinical workflows to enhance patient care without disrupting physician workflow. His research demonstrates strong trends in applying advanced computational methods to solve healthcare delivery challenges, particularly in pediatric care, population health management, and clinical decision support. Dr. Loza's work bridges the gap between complex data science techniques and practical clinical applications, with a focus on creating tools that physicians can readily use in their daily practice. Hartwell Foundation Award 2024 for Pediatric Predictive Modeling Dr. Loza is actively involved in mentoring and has expressed interest in developing the next generation of physician-scientists who can bridge clinical medicine and data science. His work at the intersection of Yale and the VA Connecticut Healthcare System provides unique opportunities to develop and test predictive models across different healthcare settings. Current projects include developing transformer-based generative models for healthcare applications and refining data collection processes to better inform clinical decision-making. The Loza Lab operates at the forefront of biomedical informatics, leveraging the resources of both Yale School of Medicine and the VA Connecticut Healthcare System to develop innovative solutions for healthcare delivery challenges. The lab's work emphasizes practical implementation of predictive models that can be integrated into clinical workflows without disrupting physician-patient interactions.

Silke Meyerمشاهده پروفایل

استاد

• Business Informatics
• Information Systems
• Business Intelligence
• +۷ مورد دیگر

Prof. Dr. Silke Meyer is a faculty member at Mittweida University of Applied Sciences in the Faculty of Industrial Engineering. Her work bridges business informatics and industrial systems through innovative applications of business intelligence, data mining, and product lifecycle management. Chair of Information Systems I (Research Associate, TU Chemnitz) Mittweida University of Applied Sciences (2003-present) Research Highlights: Agile Business Intelligence lifecycle management Quantum physics-inspired decision support systems Web 2.0 analytics integration PLM-enabled cost estimation frameworks Stakeholder dashboard design Case-based reasoning with imperfect knowledge Publications Trends: Research spans 1998-2016 with a focus on business intelligence evolution, fuzzy logic applications, and quantum theory intersections. Key themes include cost estimation optimization, stakeholder analytics, and PLM system integration across industries. Teaching Focus: Offers specialized courses in: Operational Information Systems Industrial Data Processing eBusiness/Supply Chain Management Business Intelligence Customer Relationship Analytics Product Lifecycle Management Contact: meyer@hs-mittweida.de | Grunert de Jácome Bau, Room 6-330 | Am Schwanenteich 4b, Mittweida

Sidney Shapiroمشاهده پروفایل

استادیار

• Artificial Intelligence
• Business Analytics
• Data Privacy
• +۷ مورد دیگر

Sidney Shapiro is an Assistant Professor at the University of Lethbridge's Dhillon School of Business , specializing in data analytics and AI applications. His research spans Artificial Intelligence , Social Networks , Program Evaluation , and Research Methods , with a focus on ethical AI deployment in education and healthcare. PhD in Multi/Interdisciplinary Studies from Laurentian University His work explores the intersection of business intelligence , data engineering , and machine learning , particularly in digital transformation contexts. Recent publications analyze generative AI governance, data privacy challenges, and AI's impact on creative industries. Shapiro bridges technical and managerial domains through projects like database modernization for non-profits and participatory evaluation methods in community programs. He also contributes to educational strategies, including HyFlex teaching models and data-driven program development.