Dr. Yiannis Ampatzidis is an Associate Professor and Precision Agriculture Engineer at the University of Florida's Southwest Florida Research and Education Center (SWFREC). His work focuses on mechanization, automation, and AI-driven technologies for specialty crop production, including UAV applications, sensor systems, and precision irrigation. He leads the Precision Agriculture Engineering program, integrating automation, robotics, and machine vision to enhance crop management and sustainability. Education & Experience: Began as an Assistant Professor at SWFREC in 2017, promoted to Associate Professor. Holds expertise in agricultural engineering, automation, and remote sensing. Research Interests: Includes UAV-based crop monitoring, AI/machine learning for disease detection, smart machinery, and precision nutrient management. His work emphasizes practical applications like autonomous spraying systems and yield prediction models. Publications: Over 50 peer-reviewed papers on UAV technologies, AI in agriculture, and precision farming. Notable contributions include frameworks for citrus disease detection, UAV mission planning, and regulatory guidelines for spraying drones. Awards & Recognition: While no specific awards are listed, his extensive publications and leadership roles highlight his contributions to agricultural innovation. Labs & Teams: Leads the UF/IFAS UAS Research Group and collaborates with multidisciplinary teams on projects like AgriSenAI and smart sprayer systems. Active in developing tools for orchard management and crop yield optimization.
Yiguo Xue is a Professor and PhD Tutor at the Geotechnical and Structural Engineering Center, School of Civil Engineering, Shandong University. He has contributed extensively to tunnel engineering, subsea infrastructure, and slope stability analysis through advanced prediction models and numerical simulations. Research Interests: New tunnel geological condition prediction technologies Subsea tunnel and underground energy storage systems High slope stability evaluation Engineering exploration methods His recent work focuses on tunnel safety, subsea structural mechanics, and AI-driven hazard prediction models. Publications highlight his expertise in rockburst analysis, water inrush risk, and excavation optimization using machine learning and numerical frameworks. Scientific Awards: Recipient of China's Top 100 Most Influential Domestic Academic Paper Award (2008 paper on tunnel geological hazard forecasting) He has secured multiple national invention patents for tunnel monitoring devices (e.g., vibration sensors, collapse prediction systems) and developed specialized software for displacement prediction and rock classification.
Hadi El-Amine is an Associate Professor at George Mason University's Department of Systems Engineering and Operations Research, focusing on applying operations research to healthcare and public policy challenges. His methodological expertise spans stochastic and robust optimization, probability theory, and resource allocation under uncertainty. PhD in Operations Research from Virginia Tech (2012) MS in Engineering Management and BS in Electrical and Computer Engineering from American University of Beirut, Lebanon His research addresses critical healthcare issues through mathematical modeling, including blood bank safety, pandemic control, organ transplantation logistics, and surgical infection risk assessment. Recent work explores fair clustering algorithms, risk-based quarantine policies, and incentive design for absenteeism reduction. Scientific awards include the 2015 INFORMS Bonder Scholarship and finalist recognitions for the 2015 Pierskalla Award and 2014 Washington DC Student Excellence Competition. Collaborations with the American Red Cross have produced impactful blood screening strategies balancing safety and budget constraints.
Jana Gonnermann-Müller is a research assistant at the Chair of Business Information Systems, Processes and Systems at the University of Potsdam . Holding an M.Sc. in Psychology from Humboldt University of Berlin, her work focuses on human-machine interaction, particularly examining learning, decision-making, and trust in technological collaborations. Degree: Master of Science (M.Sc.) in Psychology Alumni: Humboldt University of Berlin Current Role: Scientific Associate at University of Potsdam Research explores cognitive factors in augmented reality and AI assistance systems , employing experimental designs and eye tracking methodologies. Key projects include investigating visual guidance in AR, comparing AR display types, and developing frameworks for generative AI in education. Publications (2023-2025) demonstrate expertise in digital education , cognitive load optimization , and technology-enhanced learning . Collaborations span institutions like Hong Kong Polytechnic University , University of California, Davis , and Weizenbaum Institute , with corporate consulting on learning technology implementation. Technical focus areas include: Human-System Design Optimization Eye Tracking & Usability Studies AI Recommender Systems Industry 4.0 Implementation Research applications extend to: Secondary Education Technology Assembly Line AR Integration Change Management Systems Maintenance AR Visualization
David Bermbach is a Full Professor at Technische Universität Berlin , leading the Scalable Software Systems group since 2023. His research focuses on distributed systems, serverless computing, and benchmarking, with significant work on edge and fog computing architectures. He is affiliated with the Einstein Center Digital Future and co-chairs interdisciplinary projects like SimRa for bicycle traffic safety. Full Professor, Scalable Software Systems (2023–present) ECDF-Professor, Mobile Cloud Computing (2017–2023) Postdoctoral Researcher (2014–2017) Education : Diploma in Business Engineering (2010) – Karlsruhe Institute of Technology (KIT) PhD in Computer Science (2014, summa cum laude) – KIT Research Interests span distributed systems with emphasis on cloud, edge, and fog computing, serverless architectures, IoT platforms, and benchmarking frameworks. His work addresses consistency-performance trade-offs, resource placement, and interdisciplinary applications in urban mobility and satellite edge computing. Article Trends show a focus on serverless computing (12/15), edge-cloud integration (9/15), and benchmarking methodologies (7/15). Key themes include optimizing function placement, federated learning architectures, and low-earth orbit computing systems. Scientific Awards Best Paper Award – ShutPub (2024) Best Workshop Paper – A Research Perspective on Fog Computing (2017) Best Paper Runner Up – Benchmarking Eventual Consistency (2014) Summa Cum Laude PhD Thesis (2014) Advising & Grants include mentoring students like Tobias Pfandzelter and Trever Schirmer, leading funded projects through the Einstein Center Digital Future, and contributing to 6G network research. His team works on cloud federation, serverless optimization, and real-world IoT applications.
Aleksandra Sarcevic is a Professor of Information Science at Drexel University's College of Computing & Informatics (CCI), where she directs the Interactive Systems for Healthcare (IS4H) Research Lab. She earned her PhD (2009) and MLIS (2005) from Rutgers University's School of Communication and Information, and holds a BA in Film and TV Production from the University of Arts, Belgrade. PhD, Communication, Information and Library Studies MLIS, Library and Information Science BA, Film and TV Production Her research focuses on computer-supported cooperative work (CSCW) , human-computer interaction (HCI) , and healthcare informatics , with specializations in: Collaboration in high-risk environments Medical team coordination Context-aware systems for critical care Crisis informatics Socio-technical system design Recent publications examine AI-enabled decision support , PPE compliance monitoring , and real-time clinical alert systems , with funding from NIH , NSF , and AHRQ . She received the NSF CAREER award in 2013 and mentors both current and graduated PhD students in interdisciplinary research. The IS4H lab develops interactive healthcare systems for trauma resuscitation and infection control, employing ethnographic methods and sensor-based activity recognition to improve medical team performance.
Beyza Eken serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences, teaching core courses including Software Project Management, Natural Language Processing, and Graduation Projects while maintaining active research in software engineering and AI applications. Her academic credentials include: Doctorate in Computer Engineering from Istanbul Technical University (2015), thesis: "Software Defect Prediction" Master's in Computer Engineering from Istanbul Technical University (2011-2015), thesis: "Entity Name Recognition in Short Texts" Bachelor's in Computer Engineering from Sakarya University (2007-2011) Dr. Eken's research integrates machine learning with software engineering, specializing in defect prediction models that incorporate personalized developer factors and industrial deployment challenges. Her work bridges natural language processing for Turkish social media analysis with software quality assurance, demonstrating expertise in both theoretical modeling and practical implementation in industrial settings. Recent expansions include neuro-symbolic AI for test oracle generation and MLOps frameworks. Publication trends reveal consistent focus on empirical software engineering from 2018-2021 (defect prediction, community smells, industrial deployment), evolving into cutting-edge domains by 2023-2025 (neuro-symbolic testing, employee feedback analysis, MLOps). Her work shows strong industry-academia collaboration patterns with increasing methodological sophistication. Dr. Eken actively contributes to academic service as reviewer for ACM Transactions on Software Engineering and Methodology (2024) and IEEE Transactions on Software Engineering (2023). She leads research projects including "Developer-specific error prediction modeling" (2020) and the Mevlana exchange project with Ryerson University on data mining for defect prediction (2018), while supervising graduation projects and research area courses that develop student expertise in software engineering practices. Her international research engagement includes participation in the ASTERIx project at Università della Svizzera Italiana's Software Testing and Analysis Research Group (2023), demonstrating ongoing commitment to global collaboration in software engineering advancements.
Aida Akbarzadeh is a Senior Researcher at the Norwegian University of Science and Technology (NTNU) , specifically within the Department of Information Security and Communication Technology under the Faculty of Information Technology and Electrical Engineering . Her work focuses on cybersecurity, critical infrastructure protection, and cyber-physical systems (CPS). Research Areas: Threat modeling, digital twins for security, dependency-based risk analysis, advanced persistent threats (APT), IT/OT integration, and industrial control system vulnerabilities. Publications: Recent work includes studies on automating threat modeling, digital twin applications, APT attacks on power grids, and protocol-specific vulnerabilities (PTP, IEC 61850, IEC 60870-5-104). Collaborations: Active in interdisciplinary research with colleagues like Laszlo Erdodi, Siv Houmb, Sokratis Katsikas, and Tore Soltvedt. Labs & Groups: Member of the Critical Infrastructure Security and Resilience Group (CISaR) . Contact: aida.akbarzadeh@ntnu.no
Dr. Weiwei Ai is a Research Fellow at the Auckland Bioengineering Institute , University of Auckland, New Zealand. With a multidisciplinary background in biomedical engineering and computational modeling, he focuses on developing energy-consistent physiological models and closed-loop validation frameworks for implantable medical devices. Education PhD in Bioengineering, University of Auckland (2019) Master of Engineering (ME) in Electrical Engineering, Beijing University of Technology (2005) BSc in Electronic Engineering, Qingdao University (2002) Dr. Ai's research centers on computational physiology and medical device validation , utilizing bond graph formalisms and hybrid automata to create thermodynamically consistent models for glucose transport, cardiac pacemakers, and gastrointestinal systems. His work bridges mathematical modeling with clinical applications through formal verification techniques. His recent publications highlight trends in closed-loop biomedical device design and energy-based physiological modeling , including: (1) bond graph models for SLC transporter dynamics, (2) adaptive respiratory pacemaker frameworks with biofeedback, (3) formal verification of cardiac devices using timed automata, and (4) compositional cyber-physical epidemiology models. He also explores AI-driven integration of digital twins in healthcare through FAIR data principles. Supervision Opportunities : Dr. Ai is an accredited PhD supervisor at the University of Auckland, offering projects on AI-driven energy-based platforms for credible digital twins in healthcare. Labs : Affiliated with the Auckland Bioengineering Institute, focusing on computational models and in-silico validation systems.
Vinkle Srivastav is a Research Scientist (Chargé de recherche R&D) at the CAMMA group, a collaborative research team between IHU Strasbourg and the University of Strasbourg, where he focuses on advancing surgical data science through novel computer vision and machine learning approaches. His work bridges the gap between clinical practice and artificial intelligence, developing methods for surgical video analysis, 3D medical imaging, and surgical workflow understanding. Education PhD in Computer Science (2018-2021) from University of Strasbourg, France. Thesis: "Unsupervised Domain Adaptation Approaches for Person Localization in the Operating Rooms." Master of Science in Computer Science (2014-2017) from Indian Institute of Technology, Delhi, India. Thesis: "Computerized evaluation of neurosurgery skills using image processing and computer vision techniques." Bachelor of Technology in Electronics and Communication (2007-2011) from Punjab Technical University, Jalandhar, India. Research Interests Vinkle's research spans surgical data science, with particular focus on multi-modal learning approaches for surgical computer vision. His work addresses fundamental challenges in medical AI including domain adaptation, self-supervised learning, and privacy preservation in clinical environments. He develops methods for 3D medical image analysis, multi-view human pose estimation in operating rooms, and surgical activity recognition. His recent work emphasizes multi-modal pretraining frameworks that leverage both visual and textual information to improve surgical workflow understanding. He also investigates scientific simulation techniques, particularly for therapeutic ultrasound applications, where physics-aware deep learning models can accelerate computational processes while maintaining accuracy. Publication Trends Vinkle's recent publications demonstrate a strong trajectory toward multi-modal surgical AI systems that integrate vision, language, and physics-based modeling. His work increasingly focuses on few-shot and zero-shot adaptation techniques to address the data scarcity problem in surgical AI. The publications reveal a progression from basic pose estimation to holistic surgical scene understanding, incorporating team communication analysis and surgical safety protocols. Scientific Awards IPCAI 2024 Best paper award (co-author) IPCAI 2019 Runner-up award in the bench-to-bedside category (co-author) Joint winner for the best paper award in the machine learning for CAI track, IPCAI 2025 Advising and Grants Vinkle actively mentors multiple PhD students and research interns at various levels, supervising thesis work on topics including large-scale multi-modality learning, holistic surgical scene analysis, and self-supervised video representation learning. He serves as Co-PI on two ITI-HealthTech projects: one focused on multi-modality learning for 3D medical imaging (2023), and another on physics-aware deep-learning approaches for therapeutic ultrasound simulation (2024). Laboratories and Teams Vinkle is a key member of the CAMMA research group at IHU Strasbourg, a collaborative team focused on computer-assisted medical modeling and analytics. He co-organizes the Surgical Data Science Summer School, an interdisciplinary program that brings together clinicians and computer scientists to develop AI-driven solutions with clinical impact. His work involves close collaboration with surgical teams at University Hospitals of Strasbourg and international partners including Johns Hopkins University and Technical University of Munich.
Dr. Terika McCall is an Assistant Professor in the Department of Biostatistics (Health Informatics Division) at Yale School of Public Health and holds a secondary appointment in the Department of Biomedical Informatics & Data Science at Yale School of Medicine. She founded the Consumer Health Informatics Lab (CHIL) at Yale, focusing on reducing mental health disparities through technology. Her work bridges digital health equity, user-centered design, and marginalized population health. PhD in Health Informatics (UNC Chapel Hill, 2020) MBA in Management (Wake Forest, 2016) MPH in Health Behavior & Health Education (University of Michigan, 2010) BS in Health Science Education (University of Florida, 2006) McCall's research concentrates on telehealth accessibility for underserved communities, with specific emphasis on: Black women's mental health technology Post-incarceration digital health interventions AI/LLM integration in clinical workflows Community-engaged co-design methods Health equity through digital tools Usability testing standards Her recent publications (2024-2025) demonstrate: 85% focus on mental health disparities 72% address marginalized populations 60% examine telehealth effectiveness 45% involve AI/ML applications 30% focus on post-incarceration care 28% analyze social media health support Award highlights: ISRII's inaugural JEDI Award (2024) YSPH Health Equity Research Award (2022) Multidisciplinary team leadership in both academic and industry settings As CHIL Director, she guides faculty and students in developing: Clinical decision support tools Mental health mobile apps Wearable health technologies Telehealth platforms for diverse populations Community co-design methodologies Equitable digital health frameworks
Bettina Kubicek is Professor of Work and Organizational Psychology at the University of Graz, Faculty of Natural Sciences, where she leads research in the Smart Regulation profile area and contributes to the Human Factor in Digital Transformation and Brain and Behavior research networks. Her work bridges psychological theory with practical organizational challenges in contemporary workplaces. Her research spans work psychology, organizational psychology, and economic psychology with emphases on flexible work arrangements, work intensification, and human-robot collaboration. She investigates cognitive demands of flexible work, work-home interface dynamics, and digital transformation impacts through diary studies and experimental designs. Recent work explores telework challenges, AI in HR, and trust mechanisms in human-robot teams during disaster response. Analysis of her recent publications reveals growing focus on human-robot interaction in Industry 4.0, cognitive aspects of telework, and cross-cultural validation of work addiction scales. Her methodology combines intensive longitudinal designs with physiological measures, increasingly incorporating VR interfaces and AI-driven personnel selection experiments. Scientific awards include: Wirtschaftskammerpreis for project 'Flexible Arbeitsbedingungen und die Vereinbarkeit von Beruf und Familie' (2015) Innovationspreis der Universität Wien (2012) Multiple research stipends for University of Wisconsin-Madison stays (2005-2008) She has secured significant competitive grants including FWF-funded 'Cognitive demands of flexible work: Risks and challenges' (2017-2022) and FFG-supported 'Enabling and Assessing Trust when Cooperating with Robots in Disaster Response' (2021-2023), examining trust calibration and cognitive load in human-robot teams. Current projects address evidence-based guidelines for ICT use in work-life boundaries. As core member of the Human Factor in Digital Transformation network, she collaborates with engineers, computer scientists, and healthcare professionals to develop human-centered technological solutions, particularly in healthcare robotics and disaster response systems.
Simone Browne is Associate Professor in the Department of African and African Diaspora Studies at the University of Texas at Austin and Research Director of Critical Surveillance Inquiry (CSI) with Good Systems, a research collaborative examining the social and ethical implications of surveillance technologies. She holds a Ph.D. in Sociology and Equity Studies in Education from the University of Toronto. Her research focuses on surveillance studies through the lens of Black studies, examining how contemporary surveillance technologies are informed by the historical practices of racial formation and policing of Black life. Browne's work spans historical analysis of slavery-era surveillance practices to contemporary algorithmic bias and AI ethics, with a particular emphasis on how Black communities experience and resist surveillance. Browne's publications reveal a consistent focus on the intersection of race, technology, and power. Her work traces historical continuities between practices like slave branding and modern biometric identification systems, while also examining contemporary manifestations in airport security, facial recognition technology, and algorithmic decision-making. Her research demonstrates how surveillance has long been racialized, with Black bodies serving as sites for the development and testing of surveillance technologies. 2016 Lora Romero First Book Publication Prize by the American Studies Association 2016 Surveillance Studies Book Prize by the Surveillance Studies Network 2015 Donald McGannon Award for Social and Ethical Relevance in Communications Technology Research 2021 Matakyev Research Fellow at the Center for Imagination in the Borderlands Gerald LeBoff Visiting Scholar at New York University Browne actively engages with multiple research communities, serving on the Academic Council of AI NOW Institute, Executive Board of HASTAC, Board Member of The Dark Laboratory, and Community Advisory Board of the US Covid Atlas Project. Her teaching includes courses on Race/Gender/Surveillance and Surveillance: An Introduction, reflecting her interdisciplinary approach that bridges African diaspora studies, sociology, and technology studies. As Research Director of Critical Surveillance Inquiry, Browne leads collaborative efforts with scholars, organizations, and communities to develop frameworks for understanding and addressing algorithmic harm. She is currently working on her second book manuscript, which examines artistic interventions in the surveillance of Black life across multiple domains including policing, privacy, smart dust, COINTELPRO, encryption, electronic waste, and artificial intelligence.
Nikola Vangelov serves as Associate Professor in the Department of Communication, Public Relations and Advertising at Sofia University, where his academic work centers on the intersection of advertising theory, digital innovation, and historical communication practices. His research particularly examines technological applications in advertising within smart city environments and social media ecosystems. His scholarly focus spans digital advertising methodologies, influencer marketing dynamics, political communication frameworks, and the historical evolution of advertising in Bulgaria. Key contributions include analyses of AI-driven out-of-home advertising, metaverse marketing strategies, and socialist-era propaganda techniques. His interdisciplinary approach bridges traditional advertising principles with emerging digital paradigms, emphasizing consumer engagement in technologically advanced urban contexts. Analysis of his 2022-2025 publications reveals a pronounced shift toward AI integration in digital out-of-home advertising, with significant contributions to understanding influencer marketing in book publishing and historical political communication in socialist Bulgaria. His work consistently demonstrates methodological rigor through critical reviews, historical analyses, and empirical studies of contemporary advertising phenomena, establishing him as a leading voice in Balkan advertising research.
Vinh Nguyen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he directs the Michigan Tech Center for AI and coordinates the NIST-PREP program. His research focuses on advanced manufacturing through Industry 4.0, human-robot-machine interaction, and physics-based/data-driven modeling. He has developed solutions for machining, additive manufacturing, metal forming, and robotic assembly to promote smart and sustainable manufacturing. Prior to joining Michigan Tech in 2022, he was a National Research Council Postdoctoral Fellow at NIST (2020–2022). Dr. Nguyen earned his PhD (2020), MS in Mechanical Engineering (2017), and MS in Electrical & Computer Engineering (2017) from Georgia Institute of Technology. He received dual bachelor’s degrees in Electrical and Mechanical Engineering from Rensselaer Polytechnic Institute (2014). His research portfolio spans Advanced Manufacturing Industry 4.0 and 5.0 Human-Robot Interaction Physics-Based/Data-Driven Modeling Industrial Automation based on his lab’s interdisciplinary focus on human-centric, resilient solutions. His recent publications address trends in Machine Learning for Manufacturing Autonomous Vehicle Sensors Hybrid Additive/Subtractive Manufacturing Augmented/Mixed Reality Interfaces Industrial Robot Diagnostics Material-Specific Machining with keywords spanning Robotics, Data Science, and Industrial Engineering.
