جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Statistical Learning Theory
• Information Theory
• +۶ مورد دیگر

Alexander Jung serves as Associate Professor in the Department of Computer Science at Aalto University's School of Science. His academic appointment spans multiple research areas including Artificial Intelligence and Machine Learning (AIML) and Large-scale Computing and Data Analysis (LSCA). Jung maintains an active research profile with 85 publications to date and significant external collaborations across Europe. His research focuses on foundational questions in machine learning, particularly addressing when and how machine learning is possible for specific applications. Key interest areas include statistical learning theory, information theory, convex optimization, distributed algorithms, and network science with emphasis on explainable AI systems. Jung's work directly contributes to UN Sustainable Development Goals through technological innovation. Analysis of his recent publications reveals a strong trend toward trustworthy AI systems, federated learning frameworks, and practical applications of machine learning in domains ranging from educational data analysis to urban walkability studies. His research bridges theoretical foundations with real-world implementation challenges. Scientific Awards: Amazon Machine Learning Research Award (2018) Best Student Paper Award (2011) Promotio Sub Auspiciis Res Publicae (2012) Jung actively supervises doctoral candidates and serves on examination committees internationally, including engagements at KTH Stockholm and EPFL. His grant activity includes participation in Swedish Research Council review panels for cyber and information security funding. He maintains external positions as advisor for Miils.com since 2018 and previously taught at Integrify (2019-2020). His research network spans multiple institutions with significant connections to European academic centers. Jung contributes to academic discourse through editorial board memberships, conference committee work, and as a frequent reviewer for scientific journals.

• Clinical Psychology
• Psychotherapy
• Placebo Effects
• +۴ مورد دیگر

Jens Gaab serves as Professor of Clinical Psychology and Psychotherapy at the Faculty of Psychology, University of Basel, Switzerland. His academic position places him at the forefront of clinical psychological research with significant contributions to understanding placebo mechanisms, psychotherapeutic interventions, and the intersection of psychology with medical practice. Based in Basel at Missionsstrasse 60/62, Professor Gaab maintains an active research program while contributing to academic training within one of Switzerland's leading psychology departments. Professor Gaab's research interests center on placebo effects, particularly open-label placebo applications across diverse clinical contexts. His work examines how psychological mechanisms influence physical health outcomes, with implications spanning pain management, women's health, emergency medicine, and chronic conditions. Recent publications demonstrate growing interest in the ethical dimensions of placebo research and the emerging role of artificial intelligence in healthcare delivery. His methodological approach combines rigorous quantitative techniques with qualitative insights, often employing systematic reviews, randomized controlled trials, and mixed-methods designs to advance evidence-based practice. Analysis of Professor Gaab's recent publications reveals a consistent research trajectory focused on translating psychological principles into clinical applications. His work on open-label placebos challenges traditional assumptions about deception in treatment while exploring practical implementation pathways. The increasing presence of AI-related publications indicates strategic adaptation to emerging healthcare technologies, examining both opportunities and ethical challenges. His research maintains strong clinical relevance while contributing to theoretical understanding of mind-body interactions in health and disease. As a faculty member at the University of Basel, Professor Gaab contributes to the academic community through research leadership, potential supervision of graduate students, and participation in departmental activities. His work bridges psychological theory with practical healthcare applications, influencing both clinical practice guidelines and academic discourse in clinical psychology and related fields.

Christoph Helmمشاهده پروفایل

استاد

• Educational Research
• Educational Standards Assessment
• Digitalization in Education
• +۷ مورد دیگر

Christoph Helm serves as Professor and Head of the Department of Educational Research at Johannes Kepler University Linz within the Linz School of Education (LSEd), which operates under the Faculty of Social and Economic Sciences. His institutional affiliations include the Competence Center for University Didactics, AI-Competent University Teaching, and the Institute of Psychology. Helm leads multiple research projects funded by federal, state, and EU sources, focusing on contemporary educational challenges particularly in Austria's school system. His research spans educational standards assessment, digital transformation in education, AI integration in teaching, and pandemic-related learning loss analysis. Helm employs diverse methodologies including meta-analyses, large-scale educational barometers, and classroom observation studies. His work bridges theoretical educational research with practical policy implications, frequently addressing teacher training, classroom quality, and educational resilience. The 15 most recent publications reveal strong thematic coherence around educational measurement systems, teacher development in digital contexts, and crisis-responsive schooling. His research demonstrates consistent focus on practical applications through projects like the JKU Education Barometer and AI implementation studies in vocational schools, showing progression from pandemic impact analysis toward future-oriented educational technology integration. Helm actively contributes to academic discourse through extensive invited lectures across Europe and serves as reviewer for publications including the European Journal of Education. His leadership in major projects like the B3 cooperative doctoral program and Extended Education facilitating Key competences through co-operative learning (EKCO) demonstrates significant research management capacity. Based in Science Park 5 at JKU, Helm maintains active scholarly presence through ORCID (0000-0001-5854-4500), Google Scholar, and ResearchGate, with 148 publications and 83 supervised works reflecting substantial academic mentorship. His research profile shows consistent productivity with multiple 2025 publications indicating current active research status.

• Animal Reproduction
• Semen Preservation
• Artificial Insemination
• +۵ مورد دیگر

Martin Schulze is a researcher at the Institute for Reproduction of Farm Animals Schönow in Bernau, Germany, specializing in animal reproduction with a focus on boar, bull, and stallion semen preservation and quality assessment. His work contributes significantly to the field of artificial insemination technology and animal breeding, with extensive publication records across multiple high-impact veterinary and reproduction journals. Dr. Schulze's research spans multiple critical areas in reproductive science. He has pioneered work in low-temperature storage protocols for boar semen that eliminate the need for antibiotics, addressing growing concerns about antimicrobial resistance in animal agriculture. His investigations into transport-related stressors (vibration, temperature fluctuations) on preserved semen have informed new management practices in AI centers worldwide. He has also developed advanced assessment techniques including multi-parameter flow cytometry and machine learning applications for sperm morphology analysis, significantly improving fertility prediction accuracy. His research on microbial contamination risks in artificial insemination doses has led to improved hygiene protocols across the industry. Analysis of Dr. Schulze's publication trends reveals a strategic evolution in his research focus. Early work concentrated on fundamental sperm quality parameters and preservation techniques, while recent publications increasingly address practical implementation challenges for commercial AI centers. His work consistently bridges species-specific differences (boars, bulls, stallions) to develop universal principles of semen preservation while accounting for critical physiological variations. The growing emphasis on antibiotic-free preservation methods reflects industry trends and regulatory changes, demonstrating his ability to anticipate and address emerging challenges in animal reproduction. Dr. Schulze maintains extensive collaborative networks, as evidenced by his 137 co-authors across multiple institutions. His research has significant economic impact on global livestock breeding operations, with practical applications that improve genetic dissemination efficiency and reproductive success rates. His work on vibration effects during transport has directly influenced shipping protocols, while his investigations into alternative antimicrobial approaches provide viable solutions to the antibiotic resistance crisis in animal agriculture.

• Transportation Safety
• Traffic Engineering
• Crash Injury Severity Modeling
• +۷ مورد دیگر

Professor Fred Mannering is a faculty member in the Department of Economics at the University of South Florida (USF), affiliated with the College of Arts and Sciences. His research focuses on transportation safety engineering, crash injury severity analysis, and econometric modeling of transportation systems. He has authored numerous studies on topics such as autonomous vehicle safety, pedestrian and cyclist safety, and the impacts of policy changes on traffic patterns. His work integrates advanced statistical methods with real-world data to address challenges in highway safety, urban mobility, and infrastructure sustainability. Notable areas of exploration include the effects of speed limits, work-from-home policies, and emerging technologies like automated vehicles on traffic-related injuries and fatalities. Mannering’s contributions span both theoretical advancements in modeling techniques and applied policy recommendations. Research interests include temporal analysis of crash data, extreme value modeling for risk estimation, and interdisciplinary approaches to transportation infrastructure. He has also contributed to textbooks such as Statistical and Econometric Methods for Transportation Data Analysis and Principles of Highway Engineering and Traffic Analysis , reflecting his expertise in methodological frameworks.

GÖKHAN OCAKOĞLUمشاهده پروفایل

استاد

• Biostatistics
• Medical Statistics
• Statistical Analysis in Health
• +۷ مورد دیگر

GÖKHAN OCAKOĞLU is a full Professor in the Faculty of Medicine at Bursa Uludag University , where he leads the Department of Biostatistics . His academic career spans over two decades, with postgraduate training completed at Uludağ University's Health Sciences Institute (MSc 2006, PhD 2011). As a methodological expert in medical statistics, he contributes to interdisciplinary research across oncology, nephrology, and public health domains. Graduate of Anadolu University's Statistics Department (2003) Doctorate in Biostatistics (2011) Full Professor status (2022) His research focuses on biostatistical modeling in clinical contexts including: Inflammation and nutrition-based prognostic scoring systems AI applications in medical diagnostics Statistical shape analysis of anatomical structures Health literacy and patient decision-making Survival analysis in chronic diseases Recent publications demonstrate methodological expertise in: Retrospective cohort studies Comparative outcome analysis Medical imaging statistics Machine learning prediction models Translational medical research

Matthias Fahnمشاهده پروفایل

دانشیار

• Organizational Economics
• Relational Contracts
• Industrial Organization
• +۷ مورد دیگر

Matthias Fahn is an Associate Professor at the University of Hong Kong Business School where he serves as Associate Director of the Center for AI, Management and Organization (CAMO). He co-directs the CAMO Lab for Organizational Transformation with colleague Jie Gong. Previously affiliated with Johannes Kepler University Linz (JKU) where he held a position in the Department of Economics within the Kurt Rothschild School of Economics and Statistics (RoSES), though his JKU profile indicates he is currently on leave. Current Institution: University of Hong Kong Business School Previous Institution: Johannes Kepler University Linz Research Leadership: Co-Director of CAMO Lab for Organizational Transformation Academic Service: Co-organizer of Workshop on Relational Contracts and Asian Conference on Organizational Economics Dr. Fahn's research focuses on the intersection of organizational theory, behavioral economics, and emerging technologies. His work examines how psychological factors like present bias affect labor market outcomes, how social norms shape workplace behavior, and increasingly, how AI is transforming organizational structures. His research integrates theoretical models with empirical analysis of labor market dynamics. His publication record shows consistent output with recent papers exploring non-common priors in promotions, the impact of firing costs on present-biased employees, and informal incentives in labor markets. His work demonstrates a progression from traditional organizational economics toward the emerging field of AI's role in organizational transformation. Symposium Editor: Journal of Institutional and Theoretical Economics on Relational Contracts (11 papers) Active Research Projects: Digital transformation of journalism and Informal Incentives in Labor Markets Dr. Fahn maintains an active scholarly presence through conference organization and editorial work, positioning himself at the forefront of research on relational contracts and the organizational implications of digital transformation.

Petr Pavlovich Lukyanchenkoمشاهده پروفایل

مدرس ارشد

• Financial Mathematics
• Financial Time Series Analysis
• Structural Shifts in Financial Data
• +۳ مورد دیگر

Petr Pavlovich Lukyanchenko is a Senior Lecturer at the Faculty of Computer Science, National Research University Higher School of Economics (HSE), where he has been working since 2009. He is affiliated with the Department of Big Data and Information Retrieval and serves as Head of the Project-based Learning Laboratory "Artificial Intelligence in Mathematical Finance" under the Institute of Artificial Intelligence and Digital Sciences. His academic journey began with dual bachelor's degrees in Computer Science and Business Informatics in 2009, followed by a Master's degree in Economics in 2011. Lukyanchenko's research focuses on Financial Mathematics, particularly the analysis of financial time series and structural shifts. His work bridges computer science and finance through mathematical modeling and machine learning applications. He has supervised numerous bachelor's theses across multiple departments including the Faculty of Computer Science, Faculty of Economic Sciences, and International College of Economics and Finance. His recent publications (2022-2023) demonstrate a consistent research trajectory in applying advanced computational methods to financial problems, with particular emphasis on time series analysis, market microstructure, and financial risk modeling. The publications reveal expertise in both theoretical financial mathematics and practical implementation of machine learning solutions. Honorary Diploma from the International Institute of Economics and Finance of HSE (October 2023) Letter of gratitude from the First Vice-Rector of HSE (March 2023) Letter of thanks from the First Vice-Rector of HSE (February 2023) Best Teacher award - 2017–2023, 2011 Lukyanchenko has successfully led multiple research projects and organized seminars for the Applied Data Analysis program. His industry experience includes leadership roles at Ecom.Tech (Head of Machine Learning Department since 2023), Moscow Exchange (Head of Liquidity Analysis and Market Maker Relations, 2020-2023), and previous positions at the Russian Energy Agency and Foundation for Advanced Research. He has also contributed to significant projects including the development of a unified software framework for AI applications and forecasting systems for legal case volumes. He actively participates in academic events including the launch of the 'Artificial Intelligence in Mathematical Finance' laboratory in September 2023, conferences on 'Artificial Intelligence and Society,' and various seminars and workshops connecting academia with industry applications.

• Medical Robotics
• Orthopedic Surgery
• Arthroscopy
• +۳ مورد دیگر

Dr. Anjali Tumkur Jaiprakash is a research professor at Queensland University of Technology's Institute of Health and Biomedical Innovation, specializing in medical robotics and surgical innovation. Her work spans orthopedic surgery, robotic arthroscopy, healthcare co-design, and medical imaging technologies. With over 40 publications since 2012, she maintains active research collaborations across engineering and clinical disciplines. Her primary research explores: Robotic systems for minimally invasive surgery, particularly knee arthroscopy Computer vision applications for surgical navigation and anatomical measurement Deep learning approaches for medical image analysis Human-centered design for healthcare services and medical devices Orthopedic infection prevention and fracture healing biomechanics Publication analysis reveals strong emphasis on surgical robotics (43%), orthopedic clinical applications (36%), and healthcare design methodologies (21%). Recent work shows increasing focus on AI integration in surgical devices and epidemiological studies of orthopedic complications. Collaborative outputs demonstrate interdisciplinary networks spanning engineering, clinical medicine, and design disciplines. Dr. Jaiprakash contributes to significant research infrastructure including the Australian Centre for Robotic Vision and leads projects on robotic knee arthroscopy systems. She collaborates extensively with Princess Alexandra Hospital and international institutions on surgical technology innovation.

Norbert Siegmundمشاهده پروفایل

استاد

• Software Engineering
• Machine Learning Systems
• Performance Analysis
• +۳ مورد دیگر

Norbert Siegmund is Professor and Chair of Software Systems at Leipzig University's Institute of Computer Science in Germany. His research bridges software engineering and artificial intelligence, focusing on configurable software systems, performance analysis, and energy consumption optimization. He has established himself as a leading researcher in the field of configurable systems through extensive publications in top-tier software engineering venues. His research interests span multiple interconnected domains: Configurable, complex systems that evolve over time Non-functional properties, particularly performance and energy consumption Sampling, learning, and optimization methods from AI applied to software engineering Contemporary empirical methods in software engineering MLOps and DevOps practices Social aspects of AI-production teams His publication record reveals a consistent focus on creating accurate performance models for configurable software systems, with increasing attention to energy consumption aspects in recent years. The research trajectory shows evolution from basic performance modeling to incorporating machine learning techniques, transfer learning across environments, and addressing energy efficiency concerns. His scientific achievements have been recognized through several prestigious awards: ACM SIGSOFT Distinguished Paper Award at ICSE 2015 SPLC Best Paper Award in 2012 Multiple keynote invitations including at QAVS 2021 Open-Source Award from the state of Thuringia (2020) Best PhD award from University of Magdeburg (2013) Prof. Siegmund has secured significant research funding including DFG projects like 'Green Configuration' (€590,000) and 'Pervolution' (€290,000), as well as BMBF projects related to AI and software engineering. He has mentored numerous students through research projects and has served on program committees for major conferences including ICSE, ASE, ESEC/FSE, and SPLC. His teaching portfolio includes courses on Software Engineering for AI-Enabled Systems, Configurable Software Systems, and Automated Software Engineering.

Thiemo Fetzerمشاهده پروفایل

استاد

• Political Economy
• Data Science
• Resource Economics
• +۳ مورد دیگر

Thiemo Fetzer is a Professor of Economics at the University of Bonn and the University of Warwick, with additional affiliations as a Visiting Fellow at the London School of Economics and a Fellow at the National Institute for Social and Economic Research (NIESR). He serves as Scientific Advisor to the Applied Economics AI group and has been a Cluster Faculty member since 2023. Professor Fetzer's research spans multiple domains with political economy underpinning much of his work. His primary research areas include Political Economy, Data Science, Resource Economics, Climate Change, and Financial Stability . His work often examines how economic policies, political decisions, and social dynamics interact to shape outcomes at both local and global levels, with a particular focus on the relationship between austerity policies and political outcomes. His publication record demonstrates methodological diversity, employing both theoretical models and empirical approaches to examine complex economic phenomena. Recent work has increasingly addressed climate change and financial stability issues, alongside continuing research in political economy. His articles frequently appear in top-tier journals including the American Economic Review, the Economic Journal, Review of Economics and Statistics, and the Journal of the European Economics Association. Professor Fetzer is actively engaged in policy discussions through his extensive blog covering topics such as climate crisis, Brexit, and resilience. He has contributed written evidence on the use of AI in government and is involved in collaborative research projects, including work on political expression of academics on social media published in Nature Human Behavior. His research has significant real-world impact, particularly his work demonstrating how housing benefit cuts increased homelessness and affected democratic participation in the UK.

• Software Anomaly Detection
• Railway Safety
• Intrusion Detection
• +۴ مورد دیگر

Tommaso Zoppi is a Fixed-term researcher (RTD-A) at the University of Florence, Department of Mathematics and Computer Science 'Ulisse Dini', specializing in computer science with a focus on anomaly detection and cybersecurity for critical systems. He is affiliated with the RCL Group and maintains an active research and teaching schedule within the Computer Science program. Current Position: Fixed-term researcher under Law 240/10 (RTD-A) Department: Mathematics and Computer Science 'Ulisse Dini' Research Group: RCL Group Contact: Room 156, Viale Morgagni 65, Florence, Italy Dr. Zoppi earned his Bachelor's degree in Computer Science from the University of Florence in October 2012, followed by his Master's degree in July 2014. He completed his PhD in Mathematics, Computer Science, Statistics (Computer Science curriculum) from November 2014 to November 2017 under the supervision of Prof. Andrea Bondavalli. His research interests span across multiple critical domains including safety in railway systems, anomaly detection in dynamic and complex cyber-physical systems, intrusion detection based on machine learning approaches, and crisis management support systems. His work bridges theoretical machine learning concepts with practical applications in safety-critical environments where system failures could have severe consequences. Recent publications demonstrate his focus on making machine learning classifiers more reliable and safe for deployment in critical infrastructure. Zoppi's publication record shows a clear trajectory of increasing impact, with numerous papers in IEEE, ACM, and other reputable venues. His recent work (2023-2025) focuses on practical implementations of anomaly detection in real-world systems like power grids, railway infrastructure, and ROS2-based cyber-physical systems. A notable trend is his exploration of how to make machine learning classifiers aware of their own limitations, particularly in safety-critical contexts where knowing when a classifier is uncertain could prevent catastrophic failures. As an educator, Zoppi has served as a tutor for undergraduate Computer Science students since March 2015, helping first-year students organize their studies and facilitating student-professor relationships. He has taught multiple courses including 'Quantitative Analysis of Systems,' 'Computer Architectures,' and 'Data Collection and Machine Learning for Critical Cyber-Physical Systems' across various academic years from 2018-2023. His research is conducted primarily through the RCL Group at the University of Florence, where he collaborates with Andrea Ceccarelli, Andrea Bondavalli, and other researchers on projects related to dependable computing and critical systems. His work often involves practical implementations and testing in real-world scenarios, particularly in railway safety and power grid security applications.

• Cataract Surgery
• Corneal Diseases
• Retinal Imaging
• +۳ مورد دیگر

Lars Morten Holm is a Clinical Associate Professor in the Department of Clinical Medicine at the University of Copenhagen, specializing in Ophthalmology. Based at Blegdamsvej 3, Copenhagen, he maintains active clinical and research roles with the Capital Region of Denmark (Region Hovedstaden), as evidenced by his institutional email address and hospital affiliations. His primary research domains include: Cataract surgery techniques and patient-reported outcomes Corneal endothelial health and disease progression Artificial intelligence applications in ophthalmic diagnostics and patient education Systematic reviews of surgical risk factors and public health impacts Retinal imaging quality assessment in comorbid conditions Dr. Holm's recent work demonstrates a strong methodological focus on randomized controlled trials and meta-analyses, addressing critical gaps in surgical sequencing protocols, AI-driven accessibility tools, and tobacco-related ocular damage. His publications in high-impact journals like Acta Ophthalmologica reveal consistent collaboration with Danish and international ophthalmology teams, particularly in cataract and corneal research. The integration of generative AI for patient education videos represents an emerging frontier in his work, highlighting adaptability to technological advancements in medical communication. His research network shows significant activity within Denmark's healthcare-academic ecosystem, with frequent co-authorship across Rigshospitalet, University of Copenhagen departments, and regional hospitals. Though specific grant details aren't provided, the volume of clinical trials and systematic reviews suggests sustained research funding for ophthalmic investigations.

Cinoo Leeمشاهده پروفایل

پژوهشگر ارشد

• Racism
• Social Media
• Algorithms
• +۴ مورد دیگر

Cinoo Lee is a Research Fellow in the Department of Psychology at Stanford University, conducting impactful research at the intersection of psychology, social media, and algorithmic systems. Their work examines how race and discrimination manifest and are addressed in digital spaces, with particular focus on the experiences of marginalized communities. Lee's primary research interests include: Racism and racial discrimination in online contexts Algorithmic bias and content moderation systems Psychological impacts of social media interactions Language model behavior and societal influence Responses to and consequences of discrimination disclosures Recent publications reveal groundbreaking findings about how disclosures of racial discrimination are disproportionately flagged for removal by both algorithms and human moderators on social media platforms, creating a chilling effect on marginalized voices. Lee's research demonstrates how witnessing this suppression negatively impacts community belonging and how response patterns to discrimination disclosures influence observers' attitudes and perceptions. As a Postdoctoral Scholar working under Professor Jennifer Eberhardt, Lee contributes to Stanford's research on race and inequality, with publications appearing in prestigious journals including the Proceedings of the National Academy of Sciences and Scientific Reports. Their methodological approach combines computational analysis with psychological experimentation to understand complex social dynamics in digital environments.

• Computer Science
• Data Security
• Internet of Things (IoT)
• +۵ مورد دیگر

Eoghan Furey is a Lecturer in Computing at the Department of Computing, Atlantic Technological University. His research focuses on data security, IoT systems, robotics, and machine learning applications in structural health monitoring and environmental science. He contributes to UN Sustainable Development Goals related to industry innovation and responsible consumption. His research explores topics such as General Data Protection Regulation (GDPR) compliance, human-robot collaboration, and real-time environmental monitoring using AI-driven systems. Recent work includes developing algorithms for pollution detection in rivers and neural network-based anomaly classification in infrastructure. Collaborations span robotics sensor integration and medical AI applications like diabetes management systems. His publications reflect a multidisciplinary approach bridging computer science with engineering and environmental challenges.