جست‌وجوی استاد و پژوهشگر

Shayan Ali Khanمشاهده پروفایل

پژوهشگر ارشد

• Corporate Sustainability
• ESG disclosures
• Innovation and Strategy
• +۳ مورد دیگر

Dr. Shayan Ali Khan serves as a Research Fellow at the Business Ethics & Management Accounting chair within the Faculty of Law, Economics and Business at Martin Luther University Halle-Wittenberg. He concurrently pursues doctoral research at the Wittenberg Center for Global Ethics (WCGE) under Prof. Philipp Schreck's supervision in the "Ethics and Responsible Leadership in Business" program. His educational background includes: Bachelor's (Hons) in Engineering (summa cum laude) Master's (Research) in Management Sciences with majors in Big Data Analytics (summa cum laude) As an empirical data-driven researcher, his primary focus spans corporate sustainability frameworks, ESG disclosure mechanisms, innovation strategy in R&D contexts, and labor market digitization impacts. His methodological approach integrates quantitative analytics with ethical governance models, particularly examining emerging market dynamics. His publication record demonstrates specialized expertise in sustainability reporting within transitional economies, with particular emphasis on Russian corporate practices. The 2019 conference paper reveals cross-disciplinary engagement with environmental governance metrics and stakeholder accountability systems. Through the Wittenberg Center for Global Ethics, he contributes to research initiatives examining responsible leadership paradigms in multinational corporations, focusing on empirical validation of ethical decision-making frameworks within complex organizational structures. His current research trajectory investigates digitization's disruptive effects on traditional job markets while developing analytical models for sustainable innovation management in R&D-intensive sectors.

Yarema Okhrinمشاهده پروفایل

استاد

• Statistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Prof. Dr. Yarema Okhrin serves as Full Professor at the Department of Statistics and Data Science within the Faculty of Economics at the University of Augsburg. He leads the Chair of Statistics and Data Science, which is part of the Business Analytics & Operations and Finance, Accounting, Controlling & Taxation clusters. His research group includes Dr. Sebastian Heiden, Dr. Rui Ren, and several research assistants working on advanced statistical methods and their applications in business and economics. Professor Okhrin's research spans mathematical and statistical models for data-driven problems in business and economics, with particular expertise in statistics, data science, and machine learning. His work focuses on developing methodologies for time series analysis, portfolio optimization, risk management, and statistical process control. Recent research has expanded into high-dimensional statistics, image analysis, and network monitoring applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary applications of statistical methods, particularly in finance and process monitoring. His work demonstrates increasing integration of machine learning techniques with traditional statistical approaches, especially in portfolio optimization and risk assessment. A notable pattern is the development of specialized statistical methods for high-dimensional data analysis across multiple domains. Professor Okhrin supervises bachelor's and master's theses at the Department of Statistics, with research topics including data mining, time series modeling, forecasting methods, regression analysis, and statistical data analysis. His department offers thesis opportunities in specialized areas such as asymmetric dependencies in financial markets, forecasting, investor sentiment analysis, medical statistics, volatility modeling, multivariate distributions, sustainable investing, and portfolio optimization. The Chair of Statistics maintains active research collaborations and offers academic consulting on statistical issues and data analysis. The department hosts regular research seminars to facilitate exchange of ongoing research projects and maintains connections with industry partners for practical applications of statistical methods.

Laura Zapfeمشاهده پروفایل

پژوهشگر

• Educational Inequalities
• Social Science Sustainability Research
• Quantitative Empirical Social Research
• +۱ مورد دیگر

Dr. Laura Zapfe is a Researcher at the Institute of Political Science and Sociology within the Faculty of Human Sciences at Julius-Maximilians-University Würzburg. She serves as a member of the Chair of Methods of Quantitative Empirical Social Research and coordinates internships at the institute. Academic Affiliation: University of Würzburg Research Focus: Social inequalities, educational equity, and sustainability research Teaching: Supervision of quantitative-empirical theses and organization of international summer modules Committee Roles: Member of the Gender Working Group and IPS leadership team Research Interests center on educational inequalities, particularly in sustainability contexts. Her work examines how systemic barriers affect educational outcomes and integrates quantitative methods with social science sustainability frameworks. She analyzes structural factors in educational systems and their intersection with environmental justice. Article Trends reveal a focus on sustainability science (40%), educational sociology (30%), and quantitative research methodologies (30%). Recent publications emphasize UN sustainability goals, digital divides in education, and pandemic impacts on learning systems. Advisory Roles include thesis supervision for bachelor's and master's students, with a preference for quantitative-empirical approaches. She has organized international virtual teaching modules and participates in interdisciplinary sustainability networks.

André Niemannمشاهده پروفایل

استاد

• Hydraulic Engineering
• Water Management
• Flood Protection
• +۶ مورد دیگر

Professor André Niemann at the University of Duisburg-Essen's Institute of Hydraulic Engineering and Water Management is a leading expert in water resources management, focusing on flood protection, dam control systems, and AI-driven hydrological forecasting. His work bridges practical engineering challenges with advanced data science applications. Academic Leadership: Coordinated projects like interSim (interactive simulation for vocational training) and PROWAVE (forecast-based dam control) Research Impact: Pioneered ensemble optimization methods for reservoirs and LSTM models for inflow forecasting Technological Innovation: Developed AI frameworks for sensor data quality control in water management His research addresses critical intersections between hydraulic engineering and climate resilience, with recent projects analyzing flood forecasting systems ( HÜProS ), urban drainage optimization, and sustainable hydropower solutions using legacy mining infrastructure. Collaborations span institutions like Harz Waterworks, Deltares, and international conferences (IAHR, ICOLD, EGU). Publications since 2012 cover topics from underground pumped storage feasibility to real-time control of urban reservoirs, with a growing emphasis on machine learning applications since 2023. He actively engages in fieldwork, including excursions to dams and control centers, and teaches modules ranging from hydromechanics to environmental monitoring.

Annalena Danielsمشاهده پروفایل

پژوهشگر

• Control Engineering
• Vertical Farming
• Industrial Robotics
• +۳ مورد دیگر

M.Sc. Annalena Daniels is a Researcher at the Chair of Control Engineering , Technical University of Munich (TUM), since May 2021. Her work focuses on control systems applied to agricultural and industrial robotics. Education: 2018–2021: Master of Science in Mechanical Engineering, University of Stuttgart (Specializations: Systems Theory and Control Engineering, Modeling and Simulation) 2014–2018: Bachelor of Engineering in Mechanical Engineering, East Bavarian Technical University of Regensburg (Specializations: Modeling and Simulation, Energy Engineering) Research interests include: Vertical farming systems using closed-loop control for crop optimization. Robotic recycling with haptic-enhanced precision in uncertain environments. Advanced fault diagnosis and detection in dynamic systems. Recent publications highlight her expertise in adaptive control frameworks, with applications to greenhouse climate regulation, vertical farm irrigation, and robotic recycling. Her work often integrates machine learning and dynamic modeling for agricultural and industrial automation. Contact: a.daniels@tum.de Labs & Projects: She contributes to the Chair of Control Engineering at TUM, including the HR Recycler project for hybrid human-robot electronic recycling systems.

Mohammadhossein Malmirمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Robotic Manipulation
• Sim2Real Transfer
• +۳ مورد دیگر

Mohammadhossein Malmir is a Researcher at the Chair of Robotics, Artificial Intelligence and Real-time Systems at Technische Universität München (TUM). He joined the chair in November 2019 and currently contributes to the A-IQ Ready project (successor of AI4DI ), focusing on learning algorithms for industrial manipulators and mobile robots in manufacturing and intralogistics tasks. Education B.Sc. in Electrical Engineering (Control Systems) from Amirkabir University of Technology (Tehran Polytechnic) (2014) M.Sc. in Automation and Control Engineering from Politecnico di Milano (2018) His research addresses Sim2Real Transfer , Reinforcement Learning , Robust Control , and Model Predictive Control for autonomous robotic systems. He has published extensively on sim2real policy transfer, domain randomization, and control algorithms for manipulation and navigation tasks. Recent publications highlight his work in dexterous grasping , continual domain adaptation , and deep reinforcement learning for trajectory optimization . His advising roles include supervising 13 master's students and interdisciplinary projects, with co-advisorship on topics like vision-based robotic grasping and socially compliant path planning . He teaches courses on Cognitive Systems and Cloud-Based/Simulation-Based Machine Learning in Robotics , emphasizing reinforcement learning and sim2real methodologies.

Aljaž Božičمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +۷ مورد دیگر

Aljaž Božič is a Research Scientist at Meta Reality Labs Research , focusing on neural rendering, 3D reconstruction, and AI-driven geometry modeling. He earned his Ph.D. in Computer Science from the Technical University of Munich (TUM) and holds a Master's in Computer Science from TUM and a Bachelor's in Mathematics from the University of Ljubljana . His research spans computer vision, graphics, and artificial intelligence , with a focus on neural rendering , generative AI , and 3D deformable object modeling , targeting applications in VR/AR and robotics. His work includes time-consistent dynamic scene reconstruction (SceNeRFlow), volumetric hair appearance modeling, and high-fidelity walkable VR spaces (VR-NeRF), alongside efficient NeRF distillation and calibration methods (Neural Lens Modeling). Key article trends include Transformer-based monocular reconstruction (TransformerFusion), neural parametric shape models (NPMs), and self-supervised non-rigid tracking (Neural Deformation Graphs). He has contributed to open-source projects like the TransformerFusion GitHub repository , emphasizing MIT-licensed tools for scene reconstruction. At TUM, he served as a Teaching Assistant for courses such as 3D Scanning and Spatial Learning and 3D Vision Seminar , bridging academic instruction with research innovation. His work integrates advanced neural networks with practical optimization techniques, advancing fields like RGB-D reconstruction (DeepDeform) and variational SLAM.

Zhongqi Liuمشاهده پروفایل

پژوهشگر

• Heat Transfer
• Machine Learning
• Subcooled Flow Boiling
• +۵ مورد دیگر

Zhongqi Liu, M.Sc., is a researcher at the Chair of Thermodynamics (Prof. Dongsheng Wen) at the Technical University of Munich. His work focuses on integrating machine learning with experimental heat transfer analysis for subcooled flow boiling applications. University: Technical University of Munich Department: Chair of Thermodynamics Academic Rank: Researcher Research interests include Heat Transfer , Machine Learning , Subcooled Flow Boiling , and Energy Systems . His projects aim to enhance experimental data processing and critical heat flux prediction using data-driven methods, as seen in the Boiling Image Dataset . Recent publications span biomedical imaging, cancer phototherapy, and nanotechnology, highlighting interdisciplinary applications of machine learning and optical imaging. Notable work includes optoacoustic dyes , photodynamic therapy , and nanocarrier development .

• Human-robot interaction
• Motion planning
• Data analysis for user-centered product development
• +۱ مورد دیگر

Dr.-Ing. Jakob Reinhardt is a postdoctoral researcher at the Chair of Ergonomics (Technical University of Munich) since May 2025, leading the Robotics for Life and Healthcare research group. His interdisciplinary work bridges robotics and medical technology. Human-robot interaction Motion planning Data analysis for user-centered product development Robotics in medical applications His research focuses on developing legible robot movements through data-driven methods, analyzing emergency service device usage, and enhancing user trust in autonomous systems. Publications span robotics, psychology, and ergonomics , addressing topics like motion identity and cooperative automation in transportation. He mentors students in human factors engineering and engages in dynamic mock-up lab projects.

Leonardo Banhمشاهده پروفایل

پژوهشگر

• Generative AI
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Leonardo Banh is a Researcher at the University of Duisburg-Essen within the Faculty of Computer Science and its Chair of Business Information Systems and Software Engineering . M.Sc. in Business Information Systems (University of Duisburg-Essen, 2022) B.Sc. in Business Information Systems (University of Duisburg-Essen, 2020) Semester abroad at Instituto Superior Técnico, Lisbon (2021) His research focuses on Generative AI and its socio-technical implications, particularly in Machine Learning and Deep Learning applications. He explores intersections with NeuroIS , Smart Tourism , and E-commerce Ecosystems , emphasizing sustainability and digital transformation. Recent publications analyze Generative AI in Software Engineering , AI in Music Sentiment Analysis , and AI-Based Sign Language Translation . His work often involves design science research and grounded theory frameworks. Best Paper in Track Award (ICIS 2024) Nominated for Best Paper Award (ICIS 2024) Outstanding Reviewer (ICIS 2024) As advisor, he supervises theses on topics including AI-Based Mental Health Chatbots , Generative AI in HR , and Smart Tourism Applications . He also contributes to the Institute of Computer Science and Information Systems and serves on appointment/habilitation committees.

Michael Harrمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Recruiting
• Digitalization and digital Transformation of Human Resource Management
• +۴ مورد دیگر

Michael Harr is a doctoral researcher and research assistant at the University of Duisburg-Essen, affiliated with the Faculty of Computer Science and the Chair of Business Informatics and Integrated Information Systems. His work focuses on artificial intelligence, digital transformation of HR management, enterprise systems, new work arrangements, and digital communication. He has published extensively on these topics in leading journals and conferences. Education: M.Sc. in Business Information Systems (2020-2023), University of Duisburg-Essen B.Sc. in Business Information Systems (2016-2020), University of Duisburg-Essen His research explores the intersection of AI and human-centric systems, including studies on retail service robots, intelligent HRIS, generative AI in MOOCs, and user annoyances in voice assistants. Recent publications analyze digital communication trends and digitalization challenges in the construction sector. Publications: Harr has contributed to key areas such as retail automation, HRIS optimization, and AI-driven e-learning, with a focus on practical applications and theoretical frameworks. Supervised Theses: Exploring ChatGPT for MOOCs AI in Knowledge Management Technostress and Quiet Quitting Taxonomy of AI Recruitment Tools Talent Recruitment on Social Networks Labs and Teams: Harr is involved in the Retail Artificial Intelligence Lab (retAIL) and SAP Innovation Lab, contributing to projects on digital transformation and enterprise systems.

• Intelligent Information Systems
• Human-Centered & Generative AI
• Internet of Things (IoT) & Smart Services
• +۴ مورد دیگر

Gero Strobel is an Assistant Professor at the Chair of Business Informatics and Software Engineering, Faculty of Computer Science, University of Duisburg-Essen. His research focuses on intelligent information systems, generative AI, and IoT-enabled smart services with applications in sustainability, healthcare, and industrial transformation. Academic Roles: Assistant Professor (2022–present), Postdoctoral Researcher (2021), Research Assistant (2017–2021) Key Research Areas: Generative AI, Smart Service Systems, E-commerce Ecosystems, Digital Health, Sustainable Business Models His work develops design principles for VR training systems, AI healthcare communication, and smart agriculture architectures. He leads the SOFTEC IoT Innovation Lab and contributes to data ecosystems research as guest editor of Electronic Markets (2024 special issue). Scientific Recognition: 2021 Science Prize of Sparkasse Essen 2021 University of Duisburg-Essen Dissertation Prize Best Paper in Track Award at ICIS 2021 Strobel co-edits the Electronic Markets special issue on data ecosystems (2024) and serves as track chair for data ecosystems at HICSS and AMCIS. His research combines design science methodology with empirical studies across 135+ e-commerce initiatives, 100+ AI startup analyses, and 183 IoT architectures.

Vahid Farrahiمشاهده پروفایل

استادیار

• Physical Activity and Health
• Wearable Technology
• Behavioral Data Science
• +۴ مورد دیگر

Vahid Farrahi is an Assistant Professor (tenure-track) at the Technical University of Dortmund, Faculty of Arts and Sports Sciences, where he serves as Head of Department 7: Data Analytics in Sport Sciences. He collaborates with fellow professors to design a new master's program in sports data science scheduled for launch in Winter 2024. Dr. Farrahi completed his MSc in Information Technology at Shiraz University of Technology (2016) and earned his PhD from the Faculty of Medicine, University of Oulu (2017-2021) with a prestigious Marie Curie grant. His doctoral work earned him the 'Best PhD Thesis Award 2021' and 'Exemplary PhD Thesis Award.' His research focuses on device-based measurement of movement behaviors, physical activity and health relationships, data-driven health interventions, and wearables for population health. He specializes in analyzing 24-hour movement and non-movement behaviors using wearable accelerometer-based activity monitors, applying machine learning and data mining techniques to understand health outcomes. Dr. Farrahi's publication record shows a strong trend toward digital phenotyping, machine learning applications in movement behavior analysis, and compositional data analysis of 24-hour movement patterns. His work bridges sports science, public health, and data analytics, with increasing emphasis on practical applications for health monitoring and intervention. His notable academic honors include: Best PhD Thesis Award 2021 from Faculty of Medicine, University of Oulu Exemplary PhD Thesis Award from University of Oulu Graduate School Marie Curie grant recipient Dr. Farrahi serves as Associate Editor for the Journal for the Measurement of Physical Behaviour and on editorial boards for the Journal of Activity, Sedentary and Sleep Behaviors and Journal of Physical Activity and Health. He has delivered numerous invited presentations including at ISBNPA 2023 and University of Oulu's Science Day 2023, and reviews for over 20 scientific journals. He actively seeks talented researchers and students to join his team, with a focus on those passionate about executing tasks with precision and accuracy in behavioral and health data science.

Fabian Wunderlichمشاهده پروفایل

پژوهشگر

• Sports Analytics
• Machine Learning in Sports
• Football/Soccer Analysis
• +۵ مورد دیگر

Dr. Fabian Wunderlich is a Researcher at the German Sport University Cologne, working within the Institute for Training Science and Sports Informatics in the Department of Sports Informatics and Sports Game Research. His office is located in IG II, Room 115, and he can be contacted via email at f.wunderlich@dshs-koeln.de or by phone at +49 221 4982-4845. Previously, he completed his doctoral studies at the same institution. Wunderlich's research focuses on the intersection of sports science and data analytics, with particular emphasis on football/soccer analysis. His work spans several key areas including sports forecasting, machine learning applications in sports, artificial data generation, and the analysis of gambling markets in sports contexts. His research fingerprint shows strong concentrations in forecasting (100%), sport (90%), gambling (44%), and artificial data (44%), reflecting his methodological approach to sports analytics. Analysis of his recent publications reveals a consistent trend toward applying advanced data science techniques to sports performance analysis, particularly in football. His work frequently examines patterns in game events, player movements, and scoring opportunities using machine learning and statistical methods. A significant portion of his research investigates data sparsity issues in sports forecasting and develops methods to handle incomplete datasets through imputation and artificial data generation. Wunderlich has been actively involved in multiple research projects, including 'Datenbasierte Ansätze zur Analyse von Fußballspielen aus der e-science Perspektive' (ongoing since 2020), 'How does spectator presence affect football?' (a funded project examining home advantage during the COVID-19 pandemic), and 'Nutzung von Big Data Analysen in Vorhersagemodellen im Sport' (2017-2022). His primary collaborator appears to be Daniel Memmert, with whom he has 29 joint publications and 4 joint projects. His work has gained attention in academic circles with 12 readers on Mendeley for some publications and pickup by news outlets, particularly for research on Twitter data analysis during football matches and the effects of spectator presence on performance. Wunderlich's research demonstrates strong connections between theoretical data science approaches and practical applications in sports performance analysis.

Hans Bauerمشاهده پروفایل

پژوهشگر

• Digital Humanities
• Historical Cartography
• Open Science/Open Access
• +۳ مورد دیگر

Hans Bauer is a researcher at the Institute for East and Southeast European Studies (Regensburg), specializing in digital humanities , historical cartography , and library science . He has worked on projects like GeoPortOst and OstDok , focusing on open access, georeferencing, and digital collection management. His career spans roles in corporate history, education, and library sectors since the early 2000s. MA in Slavic studies and history of Eastern/Southeastern Europe (LMU Munich, 2003) Current role: Research associate at IOS, managing digital collections and Polonica library Pioneering work on map metadata, open science, and spatial information retrieval Bauer’s research bridges library innovation and historical scholarship , particularly through georeferenced maps and open access initiatives. His work transforms archival materials into accessible digital resources, emphasizing interdisciplinary collaboration and metadata-driven discovery. Recent publications and lectures address trends in digital humanities , cartographic digitization , and research data management . He co-edits the open-access journal e-Perimetron , focusing on cartography and digital technologies. Projects like GeoPortOst highlight his expertise in linking historical maps with modern spatial analysis tools. Bauer actively contributes to library education through workshops on Zotero, KI-tools, and open science practices. He advocates for libraries as dynamic hubs for research support, emphasizing transparency, digital preservation, and user-centric services.