Dr. Ceray Aldemir is an Associate Professor at the Faculty of Economics and Administrative Sciences, Mugla Sitki Kocman University, Turkey. Holding a PhD in Business and Management from the University of Manchester (2014), she specializes in digital governance, artificial intelligence applications in public administration, and circular economy policies. Education: BA in Political Science and Public Administration (Gazi University, 2006), MA in Public Administration (University of Manchester, 2010), PhD in Business and Management (University of Manchester, 2014) Research Interests focus on digital transformation in public sectors, including AI competencies for auditors, cybersecurity policy, and blockchain applications in financial systems. Her work on nudge theory examines behavioral economics in government decision-making during crises like the Covid-19 pandemic . She also explores multi-level governance and stakeholder engagement in sustainable development. Scientific Contributions include 15 recent publications analyzing intersections between technology and governance, such as digital accountability frameworks, AI policy challenges, and financial sustainability in local governments. Her work spans journal articles, book chapters, and international projects. Project Leadership includes roles in EU-funded initiatives like CirThink (circular economy integration) and RedCyberSG (cybersecurity capacity building). She has trained SMEs and higher education institutions across Europe on digital competencies.
Emilien Arnaud is a Lecturer-Researcher at Amiens-Picardie University , specializing in the intersection of Artificial Intelligence and Health Informatics . His clinical role as a Hospital Practitioner in Emergency Medicine informs his research focus on care pathway prediction and missing data completion in healthcare . Primary institution: Amiens-Picardie University Research domains: AI in emergency medicine, predictive modeling, NLP applications His recent work explores explainable AI models for patient pathway prediction, large language model ethics in medical contexts, and NLP-driven admission predictions using triage notes. A systematic review of emergency AI missing data strategies (2023) highlights his methodological rigor. Key article trends include: Emergency department workflow optimization Explainable AI for clinical transparency Missing data handling in health datasets NLP applications for triage automation Human-AI collaboration frameworks Ethical AI implementation in critical care While no formal awards are documented in the provided materials, his collaborations span AI development, healthcare operations, and pandemic response systems.
Dr. Jie Meng is a Senior Lecturer in Digital Marketing and Analytics at the Institute for Digital Technologies , Loughborough University London. She serves as Chief Programme Director for the MSc Digital Marketing program and oversees curriculum development in strategic marketing management and digital analytics. Her research bridges techno-psychological and neuroscience approaches to study AI-user interaction , eye-tracking in consumer behavior , and multi-agent simulation for social media dynamics. A recurring theme is digital wellbeing and mitigating AI hallucination risks through empirical experiments and machine learning analysis. Recent publications focus on large language models , neurodiverse learning frameworks , and para-social relationships in immersive fitness technologies. She holds funding for projects like "Leverages and Concerns of Gen-AI's Application in Advertising" and "Automated Verification of Smart Factories" . Scientific Awards : Senior Fellow of the Higher Education Academy (SFHEA) Supervision : Mentors 10 PhD students exploring topics from virtual influencers to NFTs in commerce
Dr.-Ing. Urbain Nzotcha is a Postdoc at the Jülich Research Centre , affiliated with the Institute of Energy Technologies (IET) and the Fundamentals of Electrochemistry (IET-1) department. His research focuses on Power-to-X , Techno-economic analyses , and CO2 electroreduction value chains , with an emphasis on sustainable energy systems and industrial carbon management. Dr. Nzotcha’s work explores the intersection of renewable energy systems , CO2 electroreduction , and techno-economic optimization , particularly in Sub-Saharan Africa and European contexts. His recent publications address challenges in Power-to-X scalability , pumped hydropower storage , and adaptive control for photovoltaic systems , reflecting interdisciplinary expertise in energy engineering and environmental economics. His research trends highlight the integration of electrochemical processes with industrial CO2 utilization , hybrid renewable systems , and sustainable development in African regions . Technological innovations and cross-sectoral analyses are central to his contributions.
Michaela Bednarova is a Full Professor in the Department of Financial Economics and Accounting at Pablo de Olavide University, Spain. Her academic career focuses on the intersection of financial reporting, corporate communication, and digital technologies, with particular expertise in sustainability reporting and social media communication practices of corporations. Dr. Bednarova earned her doctorate from the University of Huelva with a thesis titled Corporate social responsibility reporting practices of Eurozone companies (2014), supervised by Dr. Enrique Bonsón Ponte. Her educational background provides a strong foundation in economics and business administration, which she applies to contemporary issues in corporate reporting and digital communication. Professor Bednarova's research spans several interconnected areas within business and economics. She specializes in integrated and sustainability reporting practices, particularly examining how companies communicate non-financial information to stakeholders. Her work on social media platforms (YouTube, LinkedIn, Twitter) analyzes corporate communication strategies and stakeholder engagement in the digital age. More recently, her research has expanded to include emerging technologies like blockchain and artificial intelligence, focusing on their implications for corporate reporting, governance, and transparency. Her interdisciplinary approach bridges traditional accounting and finance with digital communication and emerging technologies. Analysis of Professor Bednarova's recent publications reveals a clear evolution in her research focus. While her early work centered on traditional corporate social responsibility (CSR) reporting practices, her research has progressively incorporated digital communication channels and emerging technologies. Her publications from 2020-2025 show increasing attention to artificial intelligence, blockchain, and ESG (Environmental, Social, and Governance) reporting. The interdisciplinary nature of her work connects accounting, finance, communication studies, and information systems, reflecting the growing integration of technology in corporate reporting practices. Her research consistently examines Western European companies, particularly those in the Eurozone, providing valuable insights into regional reporting practices and their evolution. Professor Bednarova supervises doctoral students in the Information Systems and Supply Chain Management program. Her academic guidance focuses on the intersection of technology, communication, and business reporting. While specific grant information isn't detailed in the provided materials, her extensive publication record suggests successful research funding to support her investigations into corporate reporting practices across multiple digital platforms and technologies.
Sorelle Friedler is the Shibulal Family Professor of Computer Science at Haverford College and a Nonresident Senior Fellow at The Brookings Institution. Her work centers on algorithmic fairness, transparency, and policy, including co-authoring the White House AI Bill of Rights. Ph.D. in Computer Science from the University of Maryland, College Park B.A. from Swarthmore College Research interests include fairness in machine learning , accountability frameworks , and responsible AI , with applications to social networks, materials science, and civic systems. Her recent publications focus on network equity , generative AI bias , and policy-compliant algorithms . Key scientific awards include the Data and Society Research Institute Fellowship. She has secured grants from NSF, DARPA, and Mozilla for projects on algorithmic fairness and responsible computing.
Philipp Danylak is a research associate and PhD candidate at the Chair of Information Infrastructures, Technical University of Munich (TUM), Campus Heilbronn, focusing on information security and data protection certification implementation within organizations. He holds a Master of Science in Industrial Engineering and Management from Karlsruhe Institute of Technology, including an Erasmus+ semester at Linköpings Universitet in Sweden. His research centers on IS certification internalization, examining how organizations implement information security standards while addressing superficial adoption challenges. Key interests include privacy frameworks, cybersecurity compliance mechanisms, and certification process optimization, with emphasis on bridging theoretical standards and practical organizational implementation. His publication trajectory shows consistent focus on certification internalization challenges, evolving from framework development (2022) to applied criteria catalog creation (2024 DIRECTIONS project), demonstrating growing impact in security standardization practices within German research contexts. No scientific awards are documented in available sources. As a doctoral candidate, he is not currently supervising students but contributes to the DIRECTIONS project under Prof. Sunyaev's leadership, with research intersecting blockchain systems and autonomous driving safety frameworks. He actively participates in TUM's Information Infrastructures research group, collaborating on projects spanning GameUP, blockchain decentralization, and machine learning coordination systems at the Heilbronn campus.
Ruth Fong is a Teaching Professor in the Department of Computer Science at Princeton University since July 2021. Her academic journey includes a Ph.D. in Engineering Science (2020) and an M.Sc. in Neuroscience from the University of Oxford , where she was funded by the Rhodes Trust and Open Philanthropy . She completed her B.A. in Computer Science at Harvard University . Research Focus: Computer Vision, Machine Learning, Explainable AI (XAI), ML Fairness, Human-Computer Interaction (HCI) Key Techniques: Post-hoc model analysis, interpretable-by-design architectures, interactive visualization tools, concept-based explanations Her 15 most recent publications (2023-2025) span topics in interactive explainability, gender artifacts in datasets, concept-based explanation frameworks (UFO, ELUDE), and real-world AI trust dynamics. Collaborative work with Olga Russakovsky 's Visual AI Lab appears prominently. Scientific Recognition: Rhodes Scholarship (2015) Open Philanthropy AI Fellowship (2018) Princeton Engineering Council Teaching Award (2025) Keller Center Summer Course Development Grant (2025) CHI Honorable Mention (2023) As director of Princeton's Looking Glass Lab , she mentors students like Indu Panigrahi and Sunnie S.Y. Kim . Her teaching portfolio includes COS324 (Machine Learning) and COS126 (Intro CS) , where she implemented an open-ended final project gallery.
Marjo Kauppinen serves as Professor of Practice in Software Engineering within Aalto University's Department of Computer Science, leading research at the intersection of requirements engineering and customer value creation. Her two-decade career focuses on translating user needs into effective digital services through roadmapping and solution planning methodologies. Her research trajectory demonstrates a strategic evolution from foundational requirements engineering toward contemporary ethical AI challenges. Recent work (2020-2025) pioneers frameworks for integrating transparency and explainability requirements into AI development, addressing critical gaps between ethical guidelines and practical implementation. Concurrently, she maintains active contributions to software ecosystem research, examining planning-phase dynamics and minimum viable product impacts. Key publication trends reveal three interconnected research streams: (1) Ethical AI requirements specification, (2) Software ecosystem governance, and (3) Continuous value validation through experimentation. This triad reflects her consistent focus on bridging theoretical requirements processes with real-world business value creation. Her scholarly recognition includes: Best Paper Award at the 11th International Conference on Requirements Engineering (2007) Distinguished Paper Award at the 15th International Conference on Requirements Engineering (2011) Kauppinen actively mentors master's thesis students while contributing to academic discourse through program committee roles at major requirements engineering conferences and editorial board membership for the Requirements Engineering Journal. Her leadership extends to the Software and Service Engineering research group where she drives collaborative projects examining emerging software development paradigms. Embedded within Aalto's Department of Computer Science, her work maintains strong industry connections through practical case studies examining real-world implementation challenges across healthcare, public sector, and commercial software ecosystems.
Mirko Marras is an Assistant Professor at the Department of Mathematics and Computer Science, University of Cagliari (Italy). He holds a PhD in Computer Science (2020) and MSc (2016, summa cum laude) from the same university, along with a Computer Science Engineering certification from the University of Pisa (2016). His affiliations include academic positions at University of Cagliari, EPFL (Switzerland), and collaborations with institutions in Spain, USA, and Switzerland.
Rafik Hamza is an Associate Professor in Information Management & Cybersecurity at Tokyo International University , with prior roles at National Institute of Information and Communications Technology (NICT, Tokyo), Guangzhou University, and SONATRACH (Algeria). His work spans Cryptography , Privacy-Preserving Machine Learning , and Blockchain-Enabled IoT Security . Ph.D. (2017) in Cryptography and Security from University of Batna M.Sc. (2014) in Cryptography and Security from University of Batna B.Sc. (2011) in Applied Mathematics from University of Batna His research interests focus on securing big data ecosystems through advanced cryptographic methods, including post-quantum algorithms and homomorphic encryption. He actively explores blockchain integration for IoT authentication and privacy-preserving deep learning architectures. Recent publication trends highlight his contributions to hybrid chaotic image encryption, IP protection in distributed systems, and secure ML frameworks. Collaborations with researchers like Alzubair Hassan and Minh-Son Dao demonstrate cross-disciplinary applications. 2021-2022 : Funded by Najran University's Institutional Funding Committee (Project NU/IFC/ENT/01/013) for AI/ML in emerging technologies Associate Editor at Cureus Journal of Computer Sciences (2024–present) Conference Chair for AMLDS 2025
Alexei (Alyosha) Efros is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where he holds the Howard Friesen Professorship and is a core member of the Berkeley Artificial Intelligence Research Lab (BAIR). Prior to joining UC Berkeley in 2013, he spent a decade as faculty at Carnegie Mellon University and maintained affiliations with École Normale Supérieure/INRIA and the University of Oxford. His educational background includes: PhD in Computer Science, University of California, Berkeley (2003) BS in Computer Science, University of Utah (1997) Efros's research fundamentally explores how machines can understand and recreate the visual world using vast unlabeled data, with pioneering contributions at the intersection of computer vision and computer graphics. He champions data-driven and self-supervised learning approaches, emphasizing slow science principles while advancing applications in computational photography, visual data mining, robotics, and interdisciplinary humanities projects. His work consistently bridges theoretical innovation with practical impact, as evidenced by his prolific publication record and industry collaborations. Analysis of his 2024-2025 publications reveals three dominant trajectories: 1) Generative model interpretability (CLIP analysis, diffusion model auditing), 2) 3D scene understanding through novel representations (Gaussian splatting, persistent state modeling), and 3) Self-supervised techniques for video and multiview consistency. These threads demonstrate his lab's strategic focus on making generative systems more controllable, interpretable, and spatially coherent while maintaining strong connections to human vision principles. His exceptional contributions have been recognized with: ACM Prize in Computing (2016) Five ICCV Helmholtz Test-of-Time Prizes (1999-2017) SIGGRAPH Significant New Researcher Award (2010) NSF CAREER Awards (2006, 2010) Multiple teaching honors including the Jim and Donna Gray Award (2023) As a dedicated mentor, Efros has advised 19 PhD students to completion (including current faculty at CMU, TTIC, and Stanford) and numerous MS/BS researchers, with his trainees consistently securing prestigious fellowships and industry positions. His research has been supported by sustained NSF funding, industry partnerships with Adobe and NVIDIA, and collaborative grants through BAIR's multi-institutional initiatives. The lab maintains active international collaborations with Oxford, École Normale Supérieure, and leading AI institutes worldwide. His research group operates within BAIR's collaborative ecosystem, featuring dedicated computational resources for vision and graphics research. The lab emphasizes interdisciplinary teamwork, regularly partnering with robotics and cognitive science researchers to explore human-AI visual interaction. Current projects focus on foundational challenges in visual representation learning, with increasing emphasis on ethical AI development and societal impact through initiatives like visual data attribution frameworks.
Xueyuan Michael Han-Vanbastelaer is an Assistant Professor in the Department of Computer Science at Wake Forest University , focusing on systems-level security and privacy research. His work integrates data provenance, machine learning, and distributed systems to develop robust security mechanisms. Ph.D. in Computer Science (2022) from Harvard University Master of Science in Computer Science (2022) from Harvard University B.Sc. in Computer Science (2015) from University of California, Los Angeles His research interests span systems security , privacy , data provenance , and graph analysis , where he develops operating system infrastructures, language-level frameworks, and algorithms to enhance system transparency and detect sophisticated attacks. His publications reveal a trend of advancing provenance-based security solutions, including intrusion detection systems like KAIROS and Unicorn, eBPF security frameworks like SafeBPF, and data deletion techniques like Splice. He has contributed to 15 major publications since 2016, with recent work (2024–2025) focusing on hardware-assisted kernel security and real-time provenance auditing. His teaching includes courses like CSC 111: Introduction to Computer Science and CSC 250: Computer Systems I , covering programming fundamentals, system architecture, and memory management.
Klaus Mueller is a Professor in the Computer Science Department at Stony Brook University , with additional appointments in Biomedical Engineering and Radiology. He serves as Director of the Visual Analytics and Imaging (VAI) Lab, Liaison for the SUNY Korea CS Program, and Interim Chair of the Department of Technology and Society . His career spans roles at Brookhaven National Lab and leadership positions at SUNY Korea. Dr. Mueller earned his PhD in Computer and Information Science (1998), MS in Computer and Information Science (1996), and MS in Biomedical Engineering (1990) from The Ohio State University , alongside a BS in Electrical Engineering (1987) from the Polytechnic University of Ulm, Germany. His research focuses on visual analytics , explainable AI , algorithmic fairness , computational imaging , and medical imaging . He has pioneered GPU-accelerated CT reconstruction techniques, bias mitigation frameworks (e.g., D-BIAS), and tools like DOMINO for causal reasoning. His work bridges data science , human-computer interaction , and medical applications , often integrating large language models for visualization tasks. Recent publications highlight advances in multivariate volume rendering , LLM-driven bias detection , and mDDPM-based medical image synthesis . His articles span IEEE Transactions , Nature Machine Intelligence , and conferences like IEEE VIS and ACM CHI . Award highlights include NSF CAREER (2000), SUNY Chancellor Award (2011), IEEE Golden Core Award (2016, 2022), induction into the National Academy of Inventors (2018), and elevation to IEEE Fellow (2024). He has chaired major conferences and served as Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022). He teaches graduate and undergraduate courses in visualization , medical imaging , and GPGPU programming , and leads the Visual Analytics Seminar (CSE 648). His lab ( VAI Lab ) fosters interdisciplinary research in GPU-accelerated analytics and ethical AI.
Nicolas Berland serves as a Professor at Paris-Dauphine University, where he has established himself as a leading scholar in management control and organizational management. His office is located in room P416, with contact numbers 01 44 05 40 04 and 06 62 45 04 36. With a prolific academic career spanning several decades, Professor Berland has developed a distinctive research approach focused on the intersection of management control with strategic processes and organizational management. Professor Berland's research spans multiple dimensions of management control systems, with particular emphasis on their historical development, contemporary applications, and future trajectories. His work critically examines traditional budgeting practices, exploring alternatives such as management without budgets and the relationship between accounting systems and organizational behavior. A significant strand of his research addresses the application of management control in public sector organizations and its relationship to sustainable development initiatives. He has also investigated the evolving role of financial directors and the impact of artificial intelligence on management control systems. Analysis of Professor Berland's recent publications reveals a consistent focus on organizational complexity, the tensions between different control mechanisms, and the adaptation of management control to contemporary challenges including environmental sustainability, digital transformation, and financialization. His work demonstrates a sophisticated understanding of how accounting practices intersect with broader organizational phenomena, often employing historical and critical perspectives to challenge conventional wisdom in the field. The methodological diversity in his research portfolio, ranging from historical analysis to contemporary case studies, reflects his comprehensive approach to understanding management control in practice. Throughout his career, Professor Berland has contributed extensively to academic discourse through editorial roles, including serving on the editorial board of Comptabilité contrôle audit. His leadership in the academic community is further evidenced by his participation in numerous conferences and his contributions to major reference works in management control. As an educator, he has shaped the field through textbooks and teaching materials that have influenced generations of management control practitioners and scholars.
