Find Academic Supervisors

Thomas SakschewskiView profile

Professor

• Event Management
• Event Technology
• Sustainability in Events
• +5 more

Prof. Thomas Sakschewski is a Professor at Berlin University of Technology's Department of Mechanical Engineering, specializing in Event Technology and Process Engineering. His research focuses on event safety, sustainability, and risk management. He leads the Hygieia project on infection control protocols and advises on cultural venue sustainability. His recent work includes developing the Hygieia-Modell for pandemic preparedness and analyzing crowd management at large events like Berlin's CSD parade. He supervises numerous theses on event logistics, safety planning, and circular economy strategies. Collaborations include the IFAF-funded Hygieia project with partners like Clubcommission e.V. and ASH Berlin. Education: Not explicitly stated in provided texts. Research Interests: Event technology, sustainability in cultural venues, risk analysis, security concepts, and public health protocols. He authored works on technical management roles in events, hygienic safety measures, and ISO 20121 certification for theaters. His teaching focuses include project management, approval planning, and event logistics. Recent Publications: Key papers on infection control models, awareness concepts at festivals, and sustainable practices in event technology. His 2023 Journal of Public Health article introduced the Hygieia model for standardized infection protocols.

Christian RieckView profile

Professor

• Market Theory
• Behavioral Finance
• Game Theory
• +3 more

Prof. Dr. Christian Rieck is a Professor of Finance and Economic Theory at Frankfurt University of Applied Sciences, affiliated with Faculty 3: Business and Law. He leads the International Finance (B.Sc.) degree program and focuses his research on behavioral finance, game theory, and the future of the financial industry. His work explores digitalization trends, fintech innovations, and the impact of emerging technologies like robo-advisors and robotics on traditional banking systems. Research Interests: Prof. Rieck’s studies span experimental economics, such as analyzing TED Talk success factors, gender/ethnicity biases in hiring, and auction/negotiation dynamics. He also investigates how robotics influences human financial decision-making through trust games. His writings address topics like network effects in Tesla’s strategy and Coase conjecture applications. Publications: Over 40 academic works, including books on game theory, behavioral finance, and financial markets. Notable works include 'Spieltheorie – eine Einführung' (2015), analyses of insider trading in experimental markets (1999), and studies on risk measurement (1997). Recent columns discuss digitalization in finance and Fintech challenges for traditional banks. Grants/Advising: Leads multiple research projects with teams of students and collaborators, including studies on TED Talks, robot-mediated trust games, and gender bias in job selection. No explicit grant details provided in texts. Labs/Teams: Active in experimental finance labs, collaborating with institutions like the Zukunftsinstitut. Research often involves student teams analyzing real-world data (e.g., TED Talk databases, experimental auction setups).

Mahed MaddahView profile

Assistant Professor

• Data Quality
• User-Generated Content
• Social Media Analytics
• +3 more

Mahed Maddah is an Assistant Professor in the Department of Information Systems and Operations Management at Suffolk University. His research focuses on data quality, user-generated content, social media analytics, health information exchange, design science research, and cognitive psychology. He holds a PhD from Florida International University, an MBA from the University of Tehran, and a BSc from Khaje Nasir Toosi University of Technology. Dr. Maddah’s work has been recognized with the 2019 MISQ Paper of the Year and the Best Information Systems (IS) Publications Award from the AIS College of Senior Scholars. His recent research explores topics such as cryptocurrency adoption dynamics, AI in healthcare, and robotic companions for aging populations. He teaches courses including Management Information Systems and Data Analytics. His intellectual contributions span peer-reviewed journals like MIS Quarterly and conferences such as the Hawaii International Conference on System Sciences. Key themes in his articles include the impact of data structures on user perceptions, cognitive trust in AI systems, and the behavioral implications of health information technology adoption. Awards: 2019 MISQ Paper of the Year, 2019 AIS Best IS Publications Award Professional Links: LinkedIn | Google Scholar Office: Sargent Hall, Room 5104

Virginie HACHARDView profile

Associate Professor

• Finance
• Entrepreneurship
• Telework
• +4 more

Virginie Hachard is an Associate Professor of Finance at EM Normandie since 1996, serving as Deputy Dean of the Faculty and Head of the Law, Finance & Control Department. She earned her PhD in Management Sciences from the University of Caen-Normandie in 2009, focusing on neo-entrepreneur creation processes. Her research spans managerial relationships, telework dynamics, and new forms of entrepreneurship, with a focus on digital strategies, gender equality in remote work, and social enterprise support systems. Her academic leadership includes editorial roles at journals such as Journal of Enterprising Communities and International Journal of Technology Transfer and Commercialisation . Research interests are reflected in over 40 publications since 2011, addressing topics like telework’s impact on gender inequality, cognitive strategies in resource recognition, and ESG rankings influenced by financial innovation. Her contributions extend to pedagogical innovation, including crowd-learning methodologies and AI integration in education. Notable works include analyses of telework’s effects on managerial relationships and psychosocial risks, as well as studies on hybrid organizations’ business models in open-source contexts. She actively participates in international conferences and collaborates on policy-oriented research, such as the RSE et risques d’entreprise conference in 2023.

Christine BERNADASView profile

Associate Professor

• Information Systems Impact on Organizations
• Enterprise Resource Planning (ERP) Systems
• Cross-Cultural Research in IT
• +3 more

Christine Bernadas is an Associate Professor of Information Systems at EM Normandie since 2011. She holds a PhD in International Business Administration from Texas A&M International University (2007). Her research focuses on the impact of information systems on organizations, particularly in SMEs, cross-cultural contexts, and enterprise systems like ERP software. Education: PhD in Business Administration (2007), A.R Sanchez School of Business, Texas A&M International University. Previous academic roles include Assistant Professor at Central Washington University (2007-2011). Research interests span digital transformation, ERP implementation challenges, global technology adoption barriers, and cybersecurity strategies. Her work bridges theoretical frameworks with practical applications in SME digitization and cross-border enterprise software adoption. Publications span over 15 years, with notable contributions in Journal of Enterprise Information Management , Long Range Planning , and conference proceedings. Her 2007 paper on ERP acquisition decisions won a Best Paper Award. Awards: Best Paper Award at 2007 Applied Business Research Conference. Active in consulting reports like the ARDECO project analyzing urban e-services adoption in Caen. Collaborative work includes projects on Silver Economy opportunities in Normandie and cross-cultural tech perception studies in crowd-sourcing tools.

• Computational Mechanics
• Structural Engineering
• Offshore Wind Energy
• +3 more

Román Quevedo Reina is an Assistant Professor in the Department of Civil Engineering at Universidad de Las Palmas de Gran Canaria (ULPGC). He is affiliated with GIR SIANI: Mechanics of Continuum Media and Structures and IU Intelligent Systems and Numerical Applications. His research focuses on computational mechanics and structural engineering with particular emphasis on offshore wind energy infrastructure. Dr. Quevedo Reina's research interests center on the structural analysis and optimization of offshore wind turbine foundations, particularly jacket and monopile structures. His work integrates computational mechanics with artificial intelligence techniques, especially artificial neural networks, to develop efficient models for structural evaluation and optimization. He investigates soil-structure interaction phenomena, pile foundation behavior in non-homogeneous soils, and the seismic response of large offshore wind turbines. His research bridges civil engineering, renewable energy, and computational science to address challenges in sustainable energy infrastructure. His publication record demonstrates a consistent focus on applying artificial neural networks to solve complex structural engineering problems related to offshore wind energy. The research shows progression from fundamental structural analysis (2017) to sophisticated computational models incorporating soil-structure interaction (2021-2025). Recent work emphasizes optimization techniques like PSO and global sensitivity analysis for jacket substructures, reflecting the field's evolution toward more comprehensive and efficient design methodologies for offshore wind infrastructure. Dr. Quevedo Reina maintains strong collaborative relationships with Guillermo Manuel Álamo Meneses, Juan José Aznárez González, and Luis Alberto Padrón Hernández, among others. His work appears consistently in high-impact Q1 journals including Ocean Engineering, Engineering Structures, and Computers & Structures, demonstrating the significance and quality of his research contributions to the field of offshore wind energy infrastructure.

Hao CaiView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Image Processing
• +3 more

Hao Cai is an Assistant Professor in the Department of Computer Science at St. Francis Xavier University. His research focuses on computer vision, machine learning, and image processing, with a particular emphasis on crowd counting, generative adversarial networks (GANs), and image quality assessment. He has developed innovative methods for multi-granularity crowd counting, scale-invariant feature aggregation, and disentangled representation learning in GAN frameworks. His work addresses challenges in real-world computer vision tasks such as density estimation, perceptual quality metrics, and 3D navigation for unmanned aerial vehicles (UAVs). Dr. Cai’s research spans technical domains including deep learning architectures, stochastic aggregation networks, and adaptive convolutional neural networks. His contributions to multi-view stereopsis and visual homing algorithms have advanced robotics and autonomous systems. He has also explored sparse representation techniques for image quality evaluation, combining statistical analysis with perceptual models to enhance accuracy and efficiency. His publications reflect a strong focus on practical applications of computer vision, from crowd analysis in urban environments to robust image quality assessment without reference images. Future work directions likely include further integration of adaptive learning strategies, scalable architectures, and interdisciplinary applications of vision systems.

Bernie TiddemanView profile

Professor

• Computer Vision
• Machine Learning
• Data Analysis
• +3 more

Bernie Tiddeman is a Professor in the Department of Computer Science at Aberystwyth University. His research focuses on computer vision, machine learning, and their applications in fisheries technology, anomaly detection, and data analysis. He has led projects funded by DEFRA, the European Maritime and Fisheries Fund, and the Welsh Government, addressing challenges in environmental monitoring, automated data capture, and heritage preservation. His work contributes to UN Sustainable Development Goals related to sustainable fisheries and responsible consumption. Key research interests include deep learning for object detection (e.g., crabs and lobsters), reinforcement learning in non-stationary environments, and neural architecture search. He collaborates with researchers like Dr. Seb Gregory Dal Toe and Muhammad Iftikhar on fisheries data automation and computer vision systems. Notable projects include the Automated Video-Based Capture of Crustacean Fisheries Data and the Improved Shellfish Data Collection Project. His recent publications (2023-2025) highlight advancements in anomaly detection, crustacean identification, and low-power hardware solutions. Tiddeman has supervised over a dozen PhD candidates and actively participates in educational initiatives like the Wales Collaborative for Learning Design.

Zihong HuangView profile

Assistant Professor

• Economics of Social Media
• Financial Innovations
• Game Theory Modeling
• +3 more

Zihong Huang is an Assistant Professor in the Information Systems and Quantitative Sciences (ISQS) department at the Rawls College of Business, Texas Tech University. He holds a PhD from the University of Minnesota's Carlson School of Management, specializing in Information and Decision Sciences, alongside advanced degrees in Computer Science, Economics, and Engineering. His research focuses on social media economics, financial innovations, and game theory modeling, with recent projects exploring creator economies and data-driven venture capital. Education includes a PhD in Information and Decision Sciences (University of Minnesota), MS in Computer Science (University of Minnesota), BS in Economics (Peking University), ME in Electronics Engineering (Tsinghua University), and BE in Information Engineering (Southeast University). Research interests emphasize the interplay between social media dynamics and economic systems, particularly in creator economies and crowdfunding mechanisms. His work has appeared in Management Science, Information Systems Research, and Financial Innovation. He currently teaches ISQS 3345: Analytics and Development with Python. No notable scientific awards are listed, but his contributions to data-driven venture capital and social media economics signal impactful academic engagement. Grants and advising activities are not explicitly detailed in the provided text.

Maria OttView profile

Researcher

• Polymer Physics
• Biophysics
• Protein Structure
• +3 more

Maria Ott is a tenured staff scientist at the Department of Protein Biochemistry, Martin Luther University Halle-Wittenberg (MLU). She holds a PhD in Physics from the University of Lübeck (2009) and conducted postdoctoral research at the Weizmann Institute (2008–2010). Since 2015, she leads projects in the DFG-funded Collaborative Research Center Polymers under Multiple Constraints (SFB-TRR102). Her research bridges polymer physics and biophysics, focusing on macromolecular crowding effects on protein structure and dynamics, particularly intrinsically disordered proteins (IDPs). Techniques include single-molecule fluorescence spectroscopy and X-ray scattering. Education: PhD in Physics, University of Lübeck, 2009 MSc Medical Physics at MLU and Ontario Cancer Institute, Toronto Research Interests: Impact of crowding agents on protein compaction IDP structural dynamics under crowded conditions Single-molecule spectroscopy and X-ray scattering methodologies Grants and Collaborations: DFG Collaborative Research Center SFB-TRR102 (since 2015) Labs/Teams: Collaborative Research Center SFB-TRR102, Department of Protein Biochemistry at MLU.

Ana VianaView profile

Professor

• Combinatorial Optimization
• Integer Programming
• Operations Research
• +5 more

Ana Viana serves as Coordinator Professor at the Polytechnic Institute of Porto, School of Engineering, and as Coordinator Researcher at the Centre for Industrial Engineering and Management of INESC TEC. She has maintained these dual appointments since December 9, 1997, demonstrating sustained commitment to both academic instruction and advanced research in industrial engineering. Her research centers on Combinatorial Optimization, specializing in the development of exact algorithms and meta-heuristic approaches to complex decision problems. Dr. Viana's work bridges theoretical advancements with practical applications in healthcare logistics and transportation systems, where optimization techniques solve real-world challenges in resource allocation and matching problems. Her recent publications reveal a strong trajectory in applying operations research to kidney exchange programs and last-mile delivery optimization. She has pioneered concepts like locally stable exchanges in kidney matching and developed innovative data-driven approaches to crowd-sourced delivery systems. Her work consistently appears in premier journals including European Journal of Operational Research, Mathematics of Operations Research, and Computers & Operations Research. As an academic supervisor, Dr. Viana has guided numerous graduate students through thesis research on photovoltaic systems, production planning in footwear manufacturing, logistics network analysis, and power grid optimization. She has led multiple publicly funded research projects that translate theoretical insights into practical industrial applications.

Leon JololianView profile

Professor

• Component-based software architecture
• Mobile and cloud computing
• Internet of things
• +3 more

Leon Jololian is a Professor and Chair in the Department of Computer Science at the University of Alabama at Birmingham (UAB). He also serves as Undergraduate & Graduate Program Director. His research focuses on component-based software architecture, mobile and cloud computing, internet of things (IoT), and machine learning applications in healthcare and biomedical engineering. He holds a B.S. in Electrical Engineering from Manhattan College, M.S. degrees in Electrical Engineering (Georgia Tech) and Computer Science (Polytechnic University), and a Ph.D. in Computer Science from New Jersey Institute of Technology. His recent work includes developing frameworks for automated machine learning environments, predicting protein-ligand interactions using deep learning, and investigating convergence engineering across disciplines. He has contributed to healthcare system modeling, crowd trajectory prediction, and educational frameworks for machine learning courses. His publications span both foundational computer science topics and interdisciplinary applications in medicine and engineering. Dr. Jololian teaches advanced undergraduate and graduate courses in software engineering and machine learning. His research integrates computational methods with real-world challenges in healthcare, cybersecurity, and biomedical systems. Office hours are by appointment in Gorrie Hall 5251.

• HIV
• Health Services Research
• Outcomes Research
• +3 more

Dr. Michael Mugavero is a Professor in the School of Medicine at the University of Alabama at Birmingham (UAB), specializing in Medicine - Infectious Diseases. He holds multiple academic roles, including Director of the Center for Outcomes Effectiveness Research and Education (COERE), Co-Director of the Center for AIDS Research (CFAR), and Principal Investigator of an AHRQ T32 training grant. His research focuses on HIV care continuum, implementation science, health services research, and learning health systems. Dr. Mugavero’s academic appointments span across UAB’s School of Medicine, School of Nursing, School of Public Health, and interdisciplinary centers such as the Center for Clinical and Translational Science (CCTS) and the Nephrology Research & Training Center. He also serves as an Associate Dean in the School of Medicine. His research interests include HIV treatment, health disparities, and optimizing healthcare delivery through implementation science. Notable grants include leadership of the UAB CFAR and participation in initiatives like Data 4 Care Alabama to improve HIV care retention. Dr. Mugavero’s work bridges clinical practice, education, and policy to address systemic challenges in healthcare access and outcomes, particularly in underserved populations. His recent publications (2021–2025) focus on topics such as PrEP implementation, intermediate care clinic optimization, and the impact of historical redlining on HIV outcomes. He emphasizes translating research into actionable strategies for healthcare systems.

Chen Chang-WenView profile

Professor

• Multimedia Signal Processing
• Multimedia Systems
• Image/Video Processing
• +1 more

Chen Chang-Wen is a Chair Professor of Visual Computing at The Hong Kong Polytechnic University (2021–present). Previously, he was Empire Innovation Professor at the University at Buffalo (2008–2021) and held leadership roles including Dean of the School of Science and Engineering at CUHK Shenzhen (2017–2020). His research focuses on multimedia systems, signal processing, and communication. He is a Fellow of IEEE and SPIE, and has received numerous awards including the Alexander von Humboldt Research Award (2010) and the SUNY Chancellor's Award (2016). Chen has authored over 420 publications and holds three US patents. His work emphasizes interdisciplinary applications in visual computing, including contributions to autonomous systems, edge networks, and video quality assessment. He currently serves as Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics: Systems and leads initiatives in global university rankings and accreditation for engineering programs.

• Artificial Intelligence
• Knowledge Engineering
• Machine Learning
• +3 more

Konstantin Schekotihin is an Associate Professor at the Institute of Artificial Intelligence and Cybersecurity , part of the Faculty of Technical Sciences at Alpen-Adria-Universität Klagenfurt. His research focuses on AI-driven solutions for semiconductor manufacturing, failure analysis, and multi-agent systems. He specializes in reinforcement learning, scheduling optimization, and neuro-symbolic AI applications. His work bridges theoretical advancements with industrial challenges such as semiconductor fab automation and real-time process monitoring. He has collaborated with organizations like the Austrian Research Promotion Agency (FFG) and Infineon Technologies. Key research areas include: AI for semiconductor failure analysis workflows Reinforcement learning in manufacturing scheduling Multi-agent systems in immersive VR environments Neuro-symbolic AI for defect routing Predictive modeling for equipment maintenance Recent projects involve optimizing ion implantation processes, developing FA ChatBots, and improving social presence in VR simulations. His work emphasizes scalability, explainability, and real-world applicability of AI systems.