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Kirsten StevensView profile

Senior Lecturer

• Human Resource Management
• Flexible working practices
• Remote and online learning
• +4 more

Dr. Kirsten Stevens is a Senior Lecturer in Human Resource Management at Coventry Business School's School of Marketing and Management, part of the Faculty of Business and Law at Coventry University. She holds key academic leadership roles including Course Director for the MA in International HRM program delivered at Emirates Aviation University in Dubai and serves as Link Tutor for third-party CPD contracts. Her professional background includes extensive HR practitioner experience across public and private sectors. Qualifications: Senior Fellow of the Higher Education Academy (2016) Doctorate in Social Sciences (2009) Chartered Fellow of the Chartered Institute of Personnel and Development (2009) Her research focuses on the implications of geographically dispersed workforces, examining innovative work arrangements and their organizational impact. Primary research domains include: Remote and flexible working methodologies Virtual/multinational team dynamics Work-life integration challenges Online pedagogical approaches for adult learners Cross-cultural HR practices She also undertakes HR consultancy for non-profit organizations. Her scholarly publications demonstrate consistent focus on evolving work paradigms, particularly examining remote supervision challenges, international education models, and organizational development frameworks. Research frequently intersects HR strategy, behavioral psychology, and technology-enabled work environments. Awards and Recognition: Senior Fellow status at Higher Education Academy Chartered Fellowship in Personnel and Development Dr. Stevens supervises postgraduate researchers and teaches in the International Human Resource Management MSc and International Business MSc programs. She additionally leads multiple postgraduate modules and contributes to institutional governance through the Faculty Safety Sub Group.

Emmanuel PapadakisView profile

Senior Lecturer

• Vacuum Technology
• Planning
• Knowledge Engineering
• +3 more

Emmanuel Papadakis , Senior Lecturer in Artificial Intelligence at the University of Huddersfield 's School of Computing and Engineering , focuses on Vacuum Technology , Planning , Knowledge Engineering , and Robotics . His research aligns with UN SDGs through applications in industrial systems and clinical diagnostics. Key Affiliation : Centre for Autonomous and Intelligent Systems Research Output : 12 publications (2020-2025), including 5 articles in 2023-2025 His work explores AI applications in: Vacuum technology optimization ADHD diagnostic modeling Ontology-based transport maintenance Blockchain-enabled supply chain compliance Recent publications demonstrate interdisciplinary impact across computer science, healthcare, and engineering. Co-authorships with researchers in psychiatry and mechanical engineering highlight collaborative breadth. Leadership : Research Degree Supervision at University of Huddersfield Metrics : h-index 2 (Google Scholar), 5 citations (Scopus) as of June 2025

Pratik ShahView profile

Assistant Professor

• Artificial Intelligence in Medicine
• Computational Medicine
• Biomedical Imaging
• +13 more

Dr. Pratik Shah is an Assistant Professor in the Department of Pathology at the University of California, Irvine (UCI) School of Medicine. He leads a cutting-edge research group focused on translational artificial intelligence and biomedical technologies to advance diagnostic imaging, uncover biological mechanisms, and enable clinical deployment of digital therapeutics. Institution: University of California, Irvine School: School of Medicine Department: Department of Pathology Academic Rank: Assistant Professor Email: pratik.shah@uci.edu Dr. Shah's research lies at the intersection of computational medicine, artificial intelligence, and biomedical imaging. His work emphasizes developing interpretable and explainable AI systems that enhance model effectiveness, safety, and clinical utility. Key research areas include generative AI for nondestructive histopathology, deep learning for medical image analysis, AI-driven clinical decision support for infectious diseases, and multimodal imaging for biological insights. His lab is dedicated to creating equitable and responsible technologies to improve diagnosis and treatment of cancer, infectious diseases, and neurological disorders. The recent publications highlight a strong trend in applying deep learning to digital pathology, particularly in computational staining and tumor detection from non-stained images. There is a consistent focus on developing interpretable AI tools for medical image segmentation, with applications in cancer diagnostics, sepsis detection, and dental imaging. The research spans from foundational AI methods to clinical validation, showing a trajectory toward regulatory science and real-world deployment of AI in healthcare. MIT NEWS feature on computational staining research SPIE publication selected for Deep-Dive spotlight session SPIE publication selected for oral presentation IEEE BioInformatics and BioEngineering publication selected for oral presentation Dr. Shah has mentored a diverse group of students and postdoctoral associates, including PhD candidates in Mechanical Engineering at MIT and undergraduate researchers in Electrical Engineering and Computer Science. His trainees have contributed to high-impact publications in journals like JAMA Network and conferences like IEEE EMBC. He has fostered collaborations with institutions including Stanford, UCSF, and the FDA, indicating strong research outreach and interdisciplinary partnerships. His lab has received recognition through media features and conference selections, reflecting the significance and innovation of his work in AI for healthcare. Dr. Shah leads a multidisciplinary research team comprising postdoctoral associates, graduate students, research staff, and student researchers, primarily focused on machine learning and medicine. The lab has strong ties to MIT, where much of the prior research was conducted, and maintains collaborations with clinical and engineering experts across the US.

Vibhanshu AbhishekView profile

Associate Professor

• Technology-enabled markets
• Digital Advertising
• Retail
• +7 more

Vibhanshu Abhishek is an Associate Professor in the Department of Information Systems at the Paul Merage School of Business, University of California - Irvine. His research investigates the impact of emerging technologies on consumer behavior, business strategy, and market dynamics, with a focus on digital advertising, multi-channel retail, and online platforms. Education: PhD in Operations and Information Management, The Wharton School, University of Pennsylvania MA in Statistics, The Wharton School, University of Pennsylvania BTech in Computer Science, Indian Institute of Technology - Kanpur Research Interests: His work lies at the intersection of information systems, marketing, and operations. He studies how consumers interact with digital advertising, how firms can optimize multi-channel strategies, and how e-commerce marketplaces reshape traditional retail. Recently, he has explored consumer behavior in shared urban mobility systems using machine learning and causal inference methods. Scientific Awards: Google Faculty Research Award Adobe Faculty Grant Flipkart Research Grant ISA-INFORMS Best Paper Award CIST Best Student Paper Award ISS Nunamaker-Chen Dissertation Award Advising and Industry Engagement: Dr. Abhishek has collaborated with leading firms including McKinsey & Co., Sequoia Capital, LEGO, Adobe, FICO, IBM, and Omnicom. He actively advises high-tech startups and has served as a faculty advisor in PhD programs. His research has been supported by competitive grants and has influenced both academic and industry practices. Labs and Research Teams: While specific lab affiliations are not mentioned, his research is conducted within the Information Systems group at the Merage School, which emphasizes data-driven decision-making, digital transformation, and platform economics. He is likely involved in interdisciplinary research centers related to digital innovation and analytics at UC Irvine.

• Geomatics
• Spatial Data Science
• Geographic Information Systems (GIS)
• +10 more

Andrea Ajmar is an Associate Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Territorial Sciences, Planning and Policies (DIST). He serves as the contact person for the University Language Centre (CLA) and leads research in geomatics, spatial data science, and emergency management. His work is aligned with UN SDG 11: Sustainable Cities and Communities, and he contributes to major EU-funded projects such as LEXIS (H2020). Scientific Disciplinary Sector: CEAR-04/A - Geomatics (Area 0008 - Civil Engineering and Architecture) Research Laboratory: SDG11Lab (DIST) ERC Sectors: PE10_14, SH2_10, PE10_4, SH2_9 Andrea Ajmar's research focuses on applying Spatial Data Science techniques to transportation, urban planning, and disaster response. His work leverages GIS, remote sensing, and spatial data infrastructures to analyze urban resilience, environmental change, and emergency workflows. He has published extensively on topics including climate adaptation in cities, glacier monitoring, flood modeling, and post-disaster assessment tools. The recent publications of Andrea Ajmar demonstrate a strong trend in integrating high-performance computing, AI, and big data with geospatial technologies for urban and environmental applications. His work spans climate resilience, disaster impact modeling, and satellite-based environmental monitoring, reflecting a multidisciplinary approach to sustainable development. Scientific Awards and Recognitions: BEST POSTER PAPER AWARD, ISPRS (2008) Teaching and Advising: Andrea Ajmar is actively involved in teaching and mentoring. He supervises multiple PhD students in the Urban and Regional Development and Architectural and Landscape Heritage programs. He teaches advanced courses such as "Advanced Geospatial Data Management" and "Geomatics for Urban and Regional Planning." He has also earned teaching certifications including Learning to Teach (L2T), Mentoring Polito Project (M2P), and Learning to Teach with Innovative Teaching Project (L2T_ITP). Research Projects: He served as the Scientific Manager for the EU H2020 LEXIS project, focusing on HPC and Big Data enabled large-scale test-beds and applications in disaster management and urban planning. Professional Service: Ajmar has held leadership roles in international organizations, including Vice-President of the International Working Group on Satellite Emergency Mapping (2013–2014), Secretary of ISPRS ICWG IV/VIII (2008–2012), and member of the ASITA Scientific Committee (2008–2012). He also served as Guest Editor for the journal Remote Sensing (2019–2021) and participated in conference program committees such as GISTAM 2019 and 2020.

Anna SidorovaView profile

Professor

• Artificial Intelligence in Business
• AI Ethics and Governance
• Digital Transformation
• +7 more

Anna Sidorova is a Professor and Chair of the Department of Information Technology and Decision Sciences at the G. Brint Ryan College of Business, University of North Texas. She holds a Ph.D. in Information Systems from Washington State University and has extensive experience in both academia and industry. Ph.D., Information Systems, Washington State University MBA, Washington State University B.A., Washington State University Her research focuses on Artificial Intelligence in Business , AI Ethics and Governance , Business Intelligence and Analytics , Text Mining , and Digital Transformation . She explores how organizations adopt and benefit from advanced technologies, particularly in decision-making and process improvement. The analysis of her publications reveals a strong trajectory in AI and machine learning applications in business, with a growing emphasis on ethical and governance aspects. Her work spans technical implementation and strategic implications, often published in premier journals like MIS Quarterly and Decision Support Systems . Dr. Sidorova has held significant leadership roles, including Academic Associate Dean for Undergraduate Programs at RCOB. She previously served as an Assistant Professor at the University at Albany, SUNY, and worked as a business consultant at PricewaterhouseCoopers, advising on business strategy and IT systems. She teaches graduate courses in Artificial Intelligence in Business, Information System Development, and Information Systems Theory and Research, contributing to the development of future business and technology leaders.

Anahita KhojandiView profile

Associate Professor

• Decision Making Under Uncertainty
• Reinforcement Learning
• Markov Decision Processes
• +9 more

Anahita Khojandi is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville, where she also holds the Heath Endowed Faculty Fellow in Business & Engineering title. She is also a Joint Associate Professor at the Bredesen Center for Interdisciplinary Research and Graduate Education and served as the Director of the Reliability and Maintainability Engineering Program from 2021 to 2024. Her interdisciplinary research spans healthcare, transportation, environmental engineering, and advanced manufacturing. Education: PhD in Industrial Engineering, University of Pittsburgh MS in Industrial Engineering, University of Pittsburgh BS in Industrial Engineering, Sharif University of Technology, Tehran, Iran Her research focuses on decision-making under uncertainty, reinforcement learning, Markov decision processes, and predictive analytics. These methodologies are applied to critical domains such as sepsis prediction, Parkinson’s disease treatment using wearable sensors, intelligent transportation systems, nuclear safety, and green infrastructure resilience. She integrates machine learning with stochastic modeling to improve real-time decision-making in complex systems. The recent publications highlight a strong trend toward AI-driven solutions in healthcare and infrastructure, with emphasis on explainability, anomaly detection, and real-time adaptation. Her work frequently appears in high-impact journals such as Management Science , IEEE Transactions , and Nuclear Technology , reflecting both methodological rigor and practical relevance. Scientific Awards and Honors: AAAS Rapid Response AI Fellow (2024) NIH AIM-AHEAD Fellowship in Leadership (2023) First Place, Harvey J. Greenberg Research Award (INFORMS, 2022) Best Paper Award, OMEGA Journal (2020, 2021) INFORMS Senior Member (2024) Teaching Award, IISE DAIS Division (2024) Khojandi has advised numerous graduate students and postdoctoral researchers, many of whom are co-authors on her publications. Her research has been supported by the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Energy (DOE), and other federal and institutional grants. She actively contributes to professional service, including leadership roles in INFORMS, where she served as Chair of the Diversity, Equity, and Inclusion Committee and currently serves as Vice President of Membership and Professional Recognition. She leads research initiatives at the intersection of industrial engineering and data science, often in collaboration with the Health Innovation Technology & Simulation (HITS) Lab and the Center for Transportation Research. Her lab focuses on developing AI-enabled decision support tools for clinical and operational environments, with future work targeting autonomous systems, personalized medicine, and climate-resilient infrastructure.

Ibraheem Abdullah Mohammed ShayeaView profile

Assistant Professor

• Machine Learning
• 5G Networks
• Face Recognition
• +4 more

Dr. Ibraheem Abdullah Mohammed Shayea is an Assistant Professor at Istanbul Technical University's Faculty of Electrical and Electronics Engineering. His research spans machine learning, 5G/6G networks, and biometric technologies, with recent publications in IEEE Open Journal of Intelligent Transportation Systems and Alexandria Engineering Journal. His work includes optimizing face recognition systems under challenging lighting conditions, developing AI-enabled routing protocols for next-generation networks, and applying hybrid models for hydroponic system analysis. Key research themes involve edge computing, millimeter-wave communication, and intelligent networking. Recent publications (2025) demonstrate expertise in 5G-and-beyond networks, handover optimization, and environmental impact modeling. Despite 3370 Scopus citations and 183 research outputs, no specific student mentoring or scientific awards are documented in available sources.

• Computer Science
• Artificial Intelligence
• Creativity Support Tools
• +2 more

Konstantinos Zachos is a Research Fellow and Chief Technology Officer at the Centre for Creativity enabled by AI (Bayes Business School, City, University of London). His work focuses on integrating Artificial Intelligence with Human-Computer Interaction to develop Digital Creativity Tools that enhance innovation across sectors like journalism, manufacturing, social care, and elite sports training. He co-leads the JECT.AI spin-out and the QUEST research project. Expertise : AI for Creativity, Software Engineering, Requirements Engineering, Service Discovery Collaborations : Co-developed tools with Neil Maiden, James Lockerbie, and teams at City, University of London and Miele GmbH His recent publications highlight Generative AI applications in opportunity discovery, with empirical evaluations in newsrooms and manufacturing environments. Key themes include: Creative process modeling Cross-domain AI integration Interactive systems for collaborative innovation Requirements surfacing in evolving systems Konstantinos maintains IEEE membership and actively bridges technical innovation with Human-Centered Design principles.

Susan HopkinsView profile

Senior Lecturer

• Education
• Enabling education
• Gender studies
• +3 more

Dr Susan Hopkins is a Senior Lecturer in Curriculum and Pedagogy at the School of Education and Tertiary Access, University of the Sunshine Coast, Queensland, Australia. As an award-winning teacher and researcher, she contributes to education policy and practice through research projects, professional learning initiatives, and teacher training programs. PhD in Social Sciences (Queensland University of Technology, 2000) Master of Education (Queensland University of Technology) Bachelor of Education (Queensland University of Technology) Research Interests: Susan's work spans education, enabling education, gender studies, media studies, popular culture, and youth studies. Her research focuses on bridging educational equity gaps and analyzing contemporary pedagogical frameworks. Professional Roles: Full registration with Queensland College of Teachers (Registration #780052), member of National Association of Enabling Educators of Australia (NAEEA), and Queensland Department of Education staff (ID #5175099).

Kaare MikkelsenView profile

Associate Professor

• Biomedical Signal Processing
• Sleep Science
• Wearable EEG Systems
• +3 more

Kaare Mikkelsen is an Associate Professor in the Department of Electrical and Computer Engineering at the Faculty of Technical Sciences, Aarhus University, specializing in biomedical engineering with a focus on ear-EEG technology for sleep and auditory neuroscience applications. His research pioneers non-invasive monitoring systems using ear-centered EEG sensors, developing machine learning frameworks for automatic sleep staging and auditory attention decoding. Key contributions include personalized sleep scoring algorithms that adapt to individual physiological variations and deep learning models for decoding brain responses to natural speech, enabling applications in hearing assistance and brain-computer interfaces. Recent publications (2022-2025) demonstrate a cohesive research trajectory centered on overcoming real-world challenges in wearable EEG: improving long-term reliability through electrode configuration studies, developing standardized data pipelines, and validating ear-EEG against clinical polysomnography. His work bridges engineering innovation with clinical sleep medicine, emphasizing at-home deployment and user-specific adaptation. Dr. Mikkelsen leads significant research projects including: Event based attention detection (2021-2024) Ear-EEG sleep monitoring (2015-present) His work receives funding from Danish research councils and involves collaborations with clinical partners for validation studies in naturalistic settings. As part of Aarhus University's Biomedical Engineering group, he utilizes advanced laboratories for sensor development and signal processing, contributing to the department's strategic focus on healthcare technology innovation.

• Machine Learning
• Computer Vision
• Emotion Recognition
• +5 more

Marina Marjanovic is a faculty member at Singidunum University in Belgrade, Serbia, affiliated with the Faculty of Informatics and Computing. She maintains an active research profile with numerous publications spanning machine learning, computer vision, and signal processing applications. Her work demonstrates strong interdisciplinary connections between theoretical AI development and practical implementations in healthcare, security, and digital government services. Dr. Marjanovic earned her Doctoral degree in Signals and Systems from Universidad Politecnica de Madrid in 2007, following a Master's degree in the same field from the same institution in 2005. Her foundational education includes basic studies in Telecommunications at the Faculty of Electrical Engineering in Belgrade, and secondary education at the third Belgrade high school with a focus on natural sciences. This strong technical background has enabled her transition from traditional signal processing to contemporary AI research. Her research interests center around machine learning and computer vision, with significant contributions to emotion recognition, sign language processing, and explainable AI. She has demonstrated particular expertise in developing robust AI models that address real-world challenges such as demographic bias in age estimation systems, vulnerability to adversarial attacks in deep learning models, and accurate medical diagnostics through imaging analysis. Her recent work shows increasing focus on healthcare applications, security implications of AI systems, and making AI more transparent and interpretable. Analysis of her publication trajectory reveals a clear evolution from signal processing foundations toward contemporary AI applications. Her recent work (2022-2024) shows strong emphasis on healthcare AI, security applications, and explainability, often combining traditional machine learning approaches with novel optimization techniques. She frequently collaborates with international researchers across multiple institutions, demonstrating the global reach of her work. Dr. Marjanovic has established herself as a productive researcher with consistent publication output across reputable journals and conferences including IEEE transactions, Springer publications, and specialized AI conferences. Her collaborative approach is evident through her extensive co-authorship network spanning multiple Serbian and international institutions. She actively contributes to the academic community through conference organization (including Sinteza conferences) and participation in research projects at Singidunum University's research institutes. Her work bridges theoretical advancements with practical implementations across multiple domains, demonstrating versatility and impact across the AI research landscape.

• Evidence Synthesis
• Systematic Review Methodology
• Health Informatics
• +3 more

Ian Shemilt is an Associate Professor at the University College London (UCL) , affiliated with the EPPI-Centre within the Institute of Education . As an Associate Director of the EPPI Centre , he contributes to advancing evidence synthesis methods and co-leads key research initiatives. Education : BA (Hons) in Philosophy from the University of East Anglia. Collaborations : Cochrane Collaboration, Campbell Collaboration, GRADE Working Group, and International Collaboration for Automation of Systematic Reviews (ICASR). Research interests include developing and applying automation tools for systematic reviews, transitioning evidence synthesis workflows from document-based to dataset-driven processes, and enabling computable, living evidence syntheses. His work leverages technologies like OpenAlex and large language models to enhance efficiency in health and social care evidence production. The articles highlight his focus on systematic review methodology , automated study identification , and health equity analysis in public health interventions. Projects span living evidence syntheses , machine learning applications , and GRADE framework extensions for policy decision-making. Key contributions include: Developing EPPI-Reviewer software tools for OpenAlex integration. Co-investigating Microsoft Academic Graph for automated study identification. Advancing living systematic reviews through dynamic datasets. Collaborating on ICASR to automate evidence synthesis workflows. His affiliations span multiple institutions, including University of East Anglia (Senior Research Associate since 2005) and University of Cambridge (2011–2015). He has held leadership roles in the Campbell & Cochrane Economics Methods Group and GRADE Working Group .

Stanley Joel GreensteinView profile

Senior Lecturer

• Law and Information Technology
• Artificial Intelligence Ethics
• Data Privacy
• +3 more

Stanley Joel Greenstein is a Senior Lecturer in Law and Information Technology at the Department of Law, Faculty of Law, Stockholm University. He serves as Course Director for the optional Cyberlaw course. PhD in Law (Stockholm University, 2017) South African legal education background with civil/common law expertise His research focuses on technology-society interactions , particularly: Artificial Intelligence ethics and regulation Data protection and privacy frameworks Cybersecurity legal implications Algorithmic decision-making transparency Human dignity in digital environments Commercial data practices and autonomy Current projects include EXTREMUM (Explainable and Ethical Machine Learning for medical data) funded by Digital Futures initiative through 2024. His 2017 dissertation Our Humanity Exposed proposed empowerment strategies for regulating predictive modeling. Key research areas span: Digital society governance Algorithmic fairness in public services Legal aspects of environmental sustainability Digital innovation frameworks AI implementation in healthcare Explainable AI for regulatory compliance

Hendrik BaierView profile

Assistant Professor

• Artificial Intelligence
• Planning
• Search Algorithms
• +5 more

Hendrik Baier is an Assistant Professor in the Information Systems group at Eindhoven University of Technology (TU/e), where he joined in 2022. His research focuses on creating agents capable of succeeding in complex decision-making tasks to help human users solve real-world problems. His work spans planning for long-term goals, learning in unknown environments, and explainability of AI systems for effective human-AI interaction. Dr. Baier's research interests center on planning and search algorithms, reinforcement learning, and explainable AI systems. His work investigates how AI can think ahead and explain its reasoning process, particularly in sequential decision-making contexts. He applies these techniques to practical domains including logistics and transportation, smart manufacturing, and sustainable energy systems. His research bridges theoretical foundations with real-world applications through collaborative projects with industry partners. Analysis of his recent publications reveals a strong focus on explainability in sequential decision-making, with increasing integration of large language models to enhance traditional planning algorithms. His work spans theoretical foundations of Monte Carlo Tree Search, programmatic policy generation, multi-agent reinforcement learning, and practical applications of these techniques. A notable trend is the growing emphasis on human-AI collaboration, where AI systems must not only perform well but also effectively communicate their reasoning to human users. Dr. Baier actively collaborates with researchers across multiple institutions, including CWI Amsterdam where he maintains an affiliation, and has participated in significant interdisciplinary efforts such as the Dagstuhl Seminar on Explainable AI for Sequential Decision Making. His research group at TU/e works closely with industry partners to translate fundamental research into practical applications. He is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute) and contributes to the Decision Making with Artificial Intelligence educational program at TU/e. His laboratory work focuses on developing benchmark environments and frameworks that enable rigorous evaluation of decision-making algorithms, with recent contributions including MOMAland for multi-objective multi-agent reinforcement learning.