Find Academic Supervisors

Florian SteinkeView profile

Professor

• Energy Management Systems
• Machine Learning Applications in Energy
• Distributed Control Systems
• +5 more

Prof. Dr. Florian Steinke is a Professor and Head of the Energy Information Networks and Systems Department at Technische Universität Darmstadt. His academic career spans roles at Siemens Corporate Technology (2009–2016) and a PhD in machine learning at the Max Planck Institute for Intelligent Systems (2006–2008). His research focuses on algorithmic energy management, distributed control systems, machine learning applications in energy grids, and resilient smart grid design. Education: PhD in Machine Learning (Max Planck Institute for Intelligent Systems, 2006–2008) Diplom in Computational Physics (University of Tübingen & University of Washington, 1999–2005) Research Interests: Development of cyber-physical systems for energy grids Optimization of thermal-electric systems using game theory and stochastic control Integration of social media data for demand forecasting Cybersecurity measures against adversarial attacks on grids Recent work emphasizes probabilistic grid modeling, resilient energy market design, and AI-driven control strategies for Fourth Generation district heating grids. His platform ecosystem research aims to support the energy transition through data-driven solutions. Labs/Teams: Leads the Energy Information Networks and Systems research group, focusing on interdisciplinary projects combining automation, data science, and energy systems engineering.

Helena van VorstView profile

Associate Professor

• Chemistry Education
• Science Education
• Context-Oriented Teaching
• +7 more

Dr. Helena van Vorst is a faculty member in the Department of Chemistry Didactics at the University of Duisburg-Essen, within the Faculty of Chemistry. She completed her habilitation in January 2024, solidifying her role as an independent academic leader. She previously served as Acting Professor at the University of Cologne and currently holds the civil service position of Academic Councillor, equivalent to Associate Professor. She is also Managing Director of the Society for Chemistry and Physics Education (GDCP) since 2022 and an elected board member of the Interdisciplinary Center for Educational Research. Doctorate (Dr. rer. nat.) in Natural Sciences, summa cum laude, University of Duisburg-Essen (2012) First State Examination in Chemistry and German for Secondary Education, University of Duisburg-Essen (2009) Habilitation in Chemistry Didactics, University of Duisburg-Essen (2024) Her research centers on improving science education through evidence-based methods. Key interests include context-oriented teaching, where real-world scenarios increase student engagement; structuring learning via 'Lernleitern' (learning ladders) to scaffold understanding; and promoting inclusion in chemistry classrooms. She emphasizes empirical evaluation of teaching innovations and the development of differentiated materials that cater to diverse student needs and interests. The recent publications reflect a strong focus on the effectiveness of structured, context-rich teaching tools. A recurring theme is the use of 'learning ladders' to organize instruction and support differentiation. Studies analyze how context characteristics influence student interest and performance, particularly across gender and cognitive dimensions. Other works explore challenges in scientific language, mathematical modeling in physical chemistry, and inquiry-based learning in inclusive settings. The body of work demonstrates a consistent commitment to bridging theory and practice in chemistry education. Fellow of the Fellowship Program MINT Didactics, Deutsche Telekom Foundation (2018–2023) University Prize for Outstanding Academic Achievements, University of Duisburg-Essen (2013) Waxmann Poster Prize, AEPF Conference (2011) First Prize in Chemistry Program, First State Examination (2010) Doctoral Scholarship, German Research Foundation (DFG) (2010–2012) Dr. van Vorst advises multiple doctoral students across funded projects, including those supported by the Mercator Foundation, Chemical Industry Fund, and RAG Foundation. Her leadership in the Graduate School on Cross-Sectional Issues in Teacher Education (GKqL) and subprojects under Teaching-Learning-Innovations at UDE highlights her active role in shaping teacher training. She develops and disseminates teaching materials, such as digital learning ladders and context-based units on acids and bases, contributing directly to classroom practice. She leads and contributes to research teams focused on inclusive science education, teacher development, and curriculum innovation. Her work with the Interdisciplinary Center for Educational Research and the Society for Chemistry and Physics Education underscores her collaborative and organizational leadership in the academic community.

Zahra Ebrahimi MamaghaniView profile

Researcher

• Approximate Computing
• Reconfigurable Accelerator Design
• In-Network Computing for 5G/6G
• +3 more

Zahra Ebrahimi Mamaghani is an academic researcher affiliated with the Embedded Systems team at the Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum. Her work focuses on approximate computing, reconfigurable accelerators, and energy-efficient embedded systems. She completed her B.Sc. and M.Sc. at Sharif University of Technology (Iran) and is a PhD student at TU Dresden. She managed the X-DNet (BMBF-funded, collaborating with Huawei) and GREEN-DNN (acatech/BMDV-funded) projects, emphasizing distributed and in-network computing for 5G/6G applications. Education: B.Sc. and M.Sc. in Electrical Engineering, Sharif University of Technology, Iran PhD Candidate at TU Dresden (Cfaed Institute, 2018–2024) Research Projects: ReAp (DFG, 2018–2021) Relearning (ESF, 2021–2023) X-ReAp (DFG, 2023–2025) X-DNet (BMBF, with Huawei) GREEN-DNN (acatech/BMDV) Research Interests: Approximate computing, energy-efficient edge-to-cloud systems, SW/HW co-design, and reconfigurable architectures for 5G/6G. Her recent publications emphasize cross-layer approximation techniques, energy-efficient CGRAs, and distributed computing frameworks for multi-kernel applications. She advises students on topics like approximation of ML models for high-throughput systems, requiring expertise in FPGA programming (Verilog/VHDL), Python, and ML frameworks like TensorFlow/PyTorch. Her lab focuses on embedded systems and collaborates with industry partners like Huawei. She holds a notable position in managing interdisciplinary projects bridging academia and industry, particularly in sustainable computing for next-generation networks.

Andreas DeckertView profile

Researcher

• Non-Communicable Diseases (NCDs)
• Epidemiology
• Global Health
• +4 more

Dr. Andreas Deckert is a Scientist at the Heidelberg Institute of Global Health (HIGH), University of Heidelberg. He holds roles including Principal Investigator (PI) and project manager in multiple international health initiatives. His work focuses on non-communicable diseases (NCDs), epidemiology, public health, and global health systems. He has led projects in Zambia, Malawi, Uzbekistan, Ethiopia, and Cambodia, emphasizing capacity building and NCD integration in healthcare systems. Education: PhD in Epidemiology (2013, Heidelberg University), Dipl. Inform. Med. (2008, Heidelberg University). Research Interests: NCD prevention, health system strengthening, migrant health, and pandemic response strategies. Key contributions include the CoNoCo project in Zambia (2023–2029), SARS-CoV-2 surveillance trials (B-FAST), and capacity development programs in Africa. He also oversees global health education initiatives, including Master’s programs in medical biometry and epidemiology. Publications span NCD epidemiology, antimicrobial resistance, cervical cancer screening, and migration health. Collaborations include institutions in Liberia, Uzbekistan, and Southeast Asia. He has secured grants from DAAD, EKFS, and the German Federal Ministry of Education and Research (BMBF).

Jürgen BechtloffView profile

Professor

• Mechatronics
• Automation Technology
• Digital Production
• +3 more

Prof. Dr.-Ing. Jürgen Bechtloff serves as Professor of Measurement, Control and Regulation Technology at the South Westphalia University of Applied Sciences since 1998. He has held significant administrative roles including Founding Dean of Mechanical and Industrial Economics (2002-2004), Dean of Engineering and Economics (2004-2012), and Head of Scientific Center for Dual Studies and Continuing Education (2012-2019). His academic career spans industrial experience at Klöckner-Moeller GmbH (1992-1998) and academic research at TU Braunschweig (1987-1992). Education: Diplom from TU Braunschweig (1987), Dr.-Ing. from TU Braunschweig (1992) Laboratory: Operates the TransferFactory Industry 4.0 demonstrator since 2013 Research Focus: Industrial automation systems, particularly in Industry 4.0 implementation, Digital Production methodologies, and Mechatronic Systems simulation. His work emphasizes practical solutions for real-world challenges in: IoT gateway and platform integration Feasibility studies and implementation support Control engineering and measurement technology Electronics cam systems for motion control Robotic programming and interpolation methods Publication Trends: 15 most recent publications (2000-2020) demonstrate consistent focus on Industry 4.0 implementation, Mechatronics , and Digital Production technologies. His work bridges theoretical concepts with practical applications through case studies and demonstrator projects like the TransferFactory system. Teaching Contributions: Coordinates courses in Mechatronic Systems and Simulation and Digital Production for both bachelor and master programs, with emphasis on project-oriented learning and practical implementation of automation concepts using modern tools like MATLAB/Simulink. Technology Transfer: Provides services in measurement technology (data processing/visualization), control engineering (simulation and implementation), and mechatronics (3D motion design, kinematic analysis). Operates with state-of-the-art equipment including Beckhoff TwinCAT, Siemens S7/TIA systems, ABB/Kuka robots, and MES platforms.

Stefan SeifermannView profile

Professor

• Energy Flexibility in Manufacturing
• Renewable Energy Systems
• Lean Production
• +4 more

Stefan Seifermann is a Professor for Production and Logistics at the Faculty of Industrial Engineering, Technische Hochschule Mannheim, since March 2019. He became Prodekan (dean) of his faculty in July 2022 and previously served as Studiengangleiter (program director) for the Master in Industrial Engineering from July 2019 to March 2023. Diploma in Industrial Engineering (TU Kaiserslautern, SFU Vancouver 2004) Dr.-Ing. (2017, TU Darmstadt) for dissertation on hybrid production cell automation His research focuses on Energy Flexibility in Manufacturing , Renewable Energy Systems , Lean Production , Production Process Optimization , Production Automation , and Industry 4.0 . He leads funded projects like the Kopernikus SynErgie series and FLoW-mIT. Recent publications analyze blockchain for supply chain traceability, deglobalization trends in manufacturing, and energy-flexible factories. He holds an office at Building L, Room 254, reachable at +49 621 292 6837. 2025 : KPI standardization in shopfloor management 2024 : Cost-benefit analysis of energy flexibility options 2023 : Environmental assessment of geothermal plants 2022 : Blockchain tracking, techno-economic flexibility studies 2018 : Value stream method 4.0, CNC automation 2017 : Flexible lean systems design in logistics He contributes to the Kopernikus Projects (SynErgie2-3) and FLoW-mIT (2023-2025), addressing energy grid stabilization and adaptive production systems. His work bridges technical feasibility with economic viability.

Ralf HagemannView profile

Lecturer

• Embedded Systems
• Software Engineering
• Programming Education
• +5 more

Ralf Hagemann is a Lecturer/Research Associate at Osnabrück University of Applied Sciences, specifically within the Faculty of Engineering and Computer Science . His academic work combines Embedded Systems , Software Engineering , and innovative teaching methods like Problem-Based Learning . He actively develops educational hardware and tools for engineering education. Embedded Systems Board design (STMNucleo32-Baseboard) Problem-oriented curriculum development for computer science students Qt-based visualization frameworks for algorithm training His research spans geometric algorithms (2013 dissertation), digital signal processing (1991 thesis), and domain-specific programming languages (1997 PIPL invention). Recent publications focus on multicultural engineering education and cost-effective product development . As a part-time faculty member , he leads technical workshops and contributes to European Project Semester initiatives. His 1991 Diplomarbeit at Fachhochschule Bielefeld laid foundations for his work in hardware-software integration . He regularly demonstrates his MpC-Bildschirmstellwerk software at international model railway exhibitions (2008-2016) and has published in journals like MIBA digital Extra and Eisenbahn Kurier .

Benny SelleView profile

Professor

• Hydrology
• Water Protection
• Soil Hydrology
• +6 more

Prof. Dr. Benny Selle is a Professor of Hydrology and Water Protection at the Berlin University of Technology , affiliated with the Department of Civil Engineering and Geoinformation . His research focuses on hydrological system variables , process models , and causal interactions in water quality and management. Education: Diploma in Geography (University of Leipzig, 2001) with minors in Geology and Economics. Doctorate in Soil Physics (University of Bayreuth, 2005). Habilitation in Mathematics and Natural Sciences (University of Tübingen, 2014). Re-habilitation in Agriculture, Civil Engineering and Environment (University of Rostock, 2024). Research Interests include dissolved organic carbon mobilization , contamination processes in water bodies , and innovative water management solutions . His work often integrates bottom-up/top-down modeling approaches to address ecological and social relevance of hydrological relationships. Committee Activities: Active member of the Catchment Hydrology committee (European Geoscience Union, since 2008). Member of the Commission for the Opinion on Appointment Procedures at Berlin Tech (since 2017). Associate Editor of Hydrology and Water Management (HyWa) (since 2018). Member of the working group Deadwood in Water Management (DWA AG GB 2.20, since 2019). Member of the Academic Senate (Berlin Tech, since 2021). Teaching Philosophy emphasizes independent learning , student discussion , and supporting diverse academic potentials . He supervises theses on topics such as urban water management , cleaning urban drains , and remediation of contaminated sites . Professional Experience spans institutions like the University of Bayreuth , WESS (Tübingen) , and University of Potsdam , with a career in Australia as a Hydrologist & Systems Modeller (2005-2010).

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +7 more

Jing Jiang is a prominent researcher at Singapore Management University, specializing in Natural Language Processing (NLP), Computational Linguistics, and Artificial Intelligence. His work spans diverse areas including Vision-Language Models, Machine Translation, Knowledge Graph Reasoning, Sentiment Analysis, and Social Media Discourse Modeling. Key contributions include frameworks for consistent client simulation in mental health counseling and counterfactual contrastive prefix-tuning for many-class classification. He has pioneered methods in zero-shot VQA with interpretable reasoning graphs , cross-lingual understanding with universal syntax , and modularized zero-shot architectures . His research often combines theoretical insights with practical implementations, as seen in works on stereotypical bias in vision-language models (VLStereoSet), tensorized self-attention for dependency modeling, and collaborative relation-augmented attention for knowledge graph completion. Jing Jiang's collaborations span global experts in NLP and AI, with co-authors from institutions like SMU, Waseda University, and Microsoft Research.

Luca RizziView profile

Professor

• Geometric Control Theory
• Sub-Riemannian Geometry
• Optimal Control
• +1 more

Luca Rizzi is a Full Professor at International School for Advanced Studies (SISSA) in Trieste, Italy, where he serves as the Coordinator of the PhD program in Mathematical Analysis, Modeling, and Applications and Principal Investigator of the ERC Starting Grant Project GEOSUB (2022-2026). His research focuses on Geometric Control Theory and Sub-Riemannian Geometry, with significant contributions to the geometric analysis of spaces with non-holonomic constraints. His research interests include: Geometric Control Theory Sub-Riemannian Geometry Optimal Control Problems Geometric Analysis Rizzi leads the GEOSUB project developing geometric and functional interpolation inequalities for sub-Riemannian manifolds, with implications for geometric analysis on non-smooth spaces, hypoelliptic operators, and geometric measure theory. His work bridges theoretical mathematics with applications in control theory and geometric structures. His scientific recognition includes: ERC Starting Grant for Project GEOSUB (2022-2026) Rizzi mentors PhD students Dario Sterzi and Daniele Tiberio, and supervises postdocs Samuel Borza, Giorgio Stefani, and Ye Zhang. He organizes the Geometric Structures seminar at SISSA and manages a sub-Riemannian community mailing list with over 200 subscribers. He has organized numerous conferences including the Workshop on Geometric Variational Problems in Sub-Riemannian Geometry (2025), Riemann 200: Mathematics and Physics (2026), and the XXXV Convegno Nazionale di Calcolo delle Variazioni (2026). As a member of the editorial board of the Journal of Dynamical and Control Systems, Rizzi actively contributes to advancing research in his field through academic service and international collaboration.

Martin GronemannView profile

Researcher

• Graph Algorithms
• Graph Drawing
• Network Visualization
• +3 more

Martin Gronemann is a researcher at the Institute for Computer Science , University of Cologne, Germany. His work focuses on graph algorithms , graph drawing , and network visualization , with projects like the Open Graph Drawing Framework (OGDF) and GEODUAL (Geometric Duality). He has taught courses including Computer Science II and Efficient Algorithms , and led seminars on research-oriented programming in C++ . Research Interests: Graph Algorithms Graph Drawing Network Visualization Computational Geometry Book Embeddings Queue Layouts Publications highlight his expertise in structural graph theory, geometric representations, and algorithm engineering. Recent work includes advancements in map graphs of bounded treewidth , strictly-convex planar drawings , and NP-completeness of DAG page-number recognition . Projects: OGDF - Open Graph Drawing Framework GEODUAL - Geometric Duality Teaching: Lecture on Computer Science II (WS 18/19) Exercises for Efficient Algorithms (SS 17, WS 11/12, WS 10/11, WS 13/14, WS 16/17)

• Business Administration
• Finance
• Financial Services
• +3 more

Prof. Dr. rer. pol. Hermann Locarek-Junge is a full professor at the Department of Economics , University of Technology Dresden , Germany. His academic work focuses on business administration , with specializations in finance and financial services , risk management , and neural networks in finance . He has led research on electronic banking , market risk estimation , and financial technology . Department: Economics University: University of Technology Dresden Research Themes: Finance, financial technology, risk management, electronic banking His publications span financial mathematics , portfolio risk analysis , and digital transformation in banking . Key topics include value-at-risk modeling, neural networks for market risk, and direct banking trends. He has collaborated with researchers like Ralf Prinzler , Mario Straßberger , and Manfred Schwaiger on credit risk, customer retention, and nonlinear loss functions in portfolios. Recent work (1997-2002) explores crisis communication , volatility hedging , and dynamic limit setting in financial markets. He has edited volumes on information society challenges for data analysis and co-authored interactive learning programs in business administration. His research bridges quantitative finance and information systems in banking.

• Logistics
• Supply Chain Management
• Mobile Business
• +4 more

Irena Okhrin is a Junior Professor at the European University Viadrina Frankfurt (Oder) in the Department of Economics and Business Administration. Her primary focus is on Information & Operations Management, with particular emphasis on logistics and mobile business applications. Dr. Okhrin's research interests center around several key areas: Modeling of Logistic Processes in M-Business Transport Problems with Real-Time Information Exchange Use of Mobile Technologies in Supply Chain Management Her scholarly work demonstrates a consistent focus on the intersection of logistics, mobile technologies, and supply chain management. Over the period 2005-2009, she has published numerous papers examining vehicle routing problems with real-time constraints, inventory management in mobile business environments, and various aspects of supply chain management enhanced by mobile technologies. Her research often employs optimization techniques and explores practical implementations of mobile business concepts in logistics companies. Dr. Okhrin has received recognition for her contributions to the field through publications in reputable journals and conference proceedings, though specific awards are not mentioned in the available information. She has collaborated extensively with K. Richter on many research projects, indicating a productive academic partnership. While specific information about her teaching responsibilities and graduate students is not provided in the available materials, her position as Junior Professor suggests involvement in both undergraduate and graduate education.

Emanuel KitzelmannView profile

Professor

• Artificial Intelligence
• Machine Learning
• Neurosymbolic AI
• +4 more

Prof. Dr. Emanuel Kitzelmann is a Professor of Applied Artificial Intelligence at Brandenburg University of Technology and Scientific Director of the AI Laboratory since 2023. His work bridges classical symbolic AI and modern machine learning, with a focus on integrating Large Language Models (LLMs) with structured knowledge bases like knowledge graphs and ontologies to enable reliable, explainable AI. He co-leads the SCALE-C research project on secure AI content generation for cybersecurity and directs the SmartRetrieve project on GraphRAG for campus chatbots. University: Brandenburg University of Technology Department: Computer Science and Media Rank: Professor His research spans hybrid neurosymbolic AI, inductive program synthesis, and robotics as AI application areas. Recent publications explore hallucination mitigation in LLMs, RAG techniques, and AI educational tools. He actively collaborates with industry partners like membraPure and REMINE GmbH, supervising student projects in cybersecurity, chatbots, and image-based analysis. Key initiatives include workshops on machine learning with ZF Getriebe Brandenburg and program committee roles for ECAI 2025 and IJCLR 2025.

• Organic Computing
• Evolutionary Algorithms
• Learning Classifier Systems
• +6 more

Professor Jörg Hähner holds the Chair of Organic Computing at the University of Augsburg's Faculty of Applied Computer Science within the Institute of Computer Science. He leads a research team focused on evolutionary computation, self-organizing systems, and intelligent computing approaches. His educational background includes computer science studies at TU Darmstadt. His academic career progression shows steady advancement in the field of organic and self-organizing computing systems. Prof. Hähner's research spans multiple interconnected domains in computational intelligence. His primary focus is on Organic Computing, which involves developing systems that can adapt and self-organize in complex environments. Within this framework, he has made significant contributions to Evolutionary Algorithms, particularly Cartesian Genetic Programming and Learning Classifier Systems. His work explores how these techniques can be applied to real-world problems such as predictive maintenance, energy systems optimization, and industrial automation. The research demonstrates a strong emphasis on both theoretical foundations and practical applications of self-adaptive systems. An analysis of his recent publications reveals a strong concentration on evolutionary computation techniques, particularly Cartesian Genetic Programming variants and Learning Classifier Systems. His research group has been actively developing frameworks like CRust_GP and GRAHF to advance modular construction of evolutionary algorithms. There's a clear trend toward applying these techniques to industrial problems including predictive maintenance, resource allocation in networks, and energy management systems. The publications show consistent exploration of fundamental questions about algorithm behavior while maintaining strong connections to practical applications. Prof. Hähner leads an active research group with numerous PhD students and collaborators, including Karen Poloczek, Henning Cui, Victor Gerling, Dr. Michael Heider, Marco Hüller, Neele Kemper, Helena Stegherr, Jonathan Wurth, and Roman Sraj. His team regularly publishes in top-tier conferences and journals in evolutionary computation, intelligent systems, and industrial applications. The Organic Computing research group maintains a strong presence in both theoretical and applied research, with projects spanning from foundational algorithm development to industrial applications in manufacturing, energy systems, and network optimization. The group's work demonstrates a cohesive research vision centered on creating adaptive, self-organizing computational systems that can operate effectively in complex real-world environments.