Dr. Iftikhar Ahmed is a Professor of Software Engineering at the University of Europe for Applied Sciences (UE Innovation Hub) in Germany. He holds a Ph.D. in Engineering Sciences from Saarland University and a Master's in Computer Science from Albert Ludwigs University of Freiburg. As an active editorial board member for PeerJ Computer Science, Dr. Ahmed contributes to academic publishing and peer review. Education: Ph.D. in Engineering Sciences, Saarland University, Germany Master's in Computer Science, Albert Ludwigs University of Freiburg, Germany Dr. Ahmed's research spans Responsible AI , Explainable AI , and Social Network Analysis , with interdisciplinary applications in healthcare, finance, and digital governance. His work integrates machine learning and computational modeling to address real-world challenges. Recent publications highlight trends in AI ethics (healthcare equity in South Asia), natural language processing (citation context analysis), deep learning (stock market prediction), and scientific computing (emulsion stability modeling). These reflect his focus on explainability and cross-domain adaptability of AI systems.
Karolin Winter is an Assistant Professor at the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). Her research focuses on business process compliance, process mining, and natural language processing (NLP) techniques. PhD in Computer Science from University of Vienna (2021) Research Interests: She specializes in digitalizing compliance management by bridging regulatory documents and process execution logs. Key areas include constraint discovery, predictive compliance monitoring, and exception handling in business processes. Recent Article Trends: Her work integrates NLP and process mining for automated compliance verification, deviation detection between external/internal regulations, and predictive monitoring of process-aware systems. 2021: Runner-up Best Dissertation Award (BPM 2021) 2020: Best Student Paper Award (ER 2020) Labs/Teams: Collaborates with the Information Systems group (IE&IS) at TU/e and international conference teams for CAiSE, BPM, and CBI.
Muhammed Kotan is an Assistant Professor at the Department of Information Systems Engineering , Sakarya University , where he has served since 2022. His academic career includes roles as a Research Assistant (2011–2022) at Sakarya University and Afyon Kocatepe University. He holds a PhD in Computer and Information Engineering (2020), an MSc in Computer and Information Engineering (2014), and a BSc in Computer Engineering (2011) from Sakarya University. Research Interests : Artificial Intelligence, Computer Software, Image Processing, Machine Learning, Computer Vision, Medical Imaging, Energy Efficiency Optimization, Natural Language Processing, Sentiment Analysis, Feature Selection, 3D Reconstruction, Industrial Machine Detection, Optimization Algorithms, Real-Time Systems, User Reviews Analysis, E-Commerce Analytics. Key Contributions : Developed hybrid methods for 3D reconstruction and industrial machine defect detection, optimized tow train routing in manufacturing, and applied Marine Predators Algorithm for mental health screening. Scientific Recognition : Awarded the Eğitim Öğretimde Üstün Başarı Ödülü by Sakarya University (2024). Served as editor for the Sakarya University Journal of Computer and Information Sciences (2023–2024) and peer reviewer for journals like Signal, Image and Video Processing and Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi . Advising : Co-advisor for TÜBİTAK projects (2022–2024) and supervised student research on topics like cosmetic product recognition, natural language processing, and sentiment analysis. Designed courses in Digital Image Processing, Text Mining, and Advanced Information Systems.
Tessa Cook is an Assistant Professor of Radiology whose work bridges artificial intelligence, clinical systems, and medical education. She focuses on clinical decision support, data integration, and human-computer interaction within radiology workflows. Research Interests: Clinical decision support, data mining, imaging informatics, and generative AI applications in radiology Education: Not explicitly mentioned in text Awards: None listed Recent publications highlight trends in AI governance, LLM implementation, and patient-centered radiology. Her work emphasizes ethical AI development, workflow optimization, and democratizing access to radiology education tools via generative models. No formal advising or grant details were specified in the provided text.
Paul Sheridan is an Assistant Professor at the School of Mathematical and Computational Sciences, University of Prince Edward Island, specializing in text analysis and ontologies for computational literary studies. He develops novel term weighting schemes through statistical significance testing to improve document retrieval, classification, and summarization methods. His Literary Theme Ontology (LTO) provides the first controlled vocabulary of literary themes for media annotation and information retrieval. Research Grants: AI/machine learning for engine maintenance (2024), keyword extraction efficacy analysis (2023–2024), GPT-2 unnatural language generation (2022–2025) Academic Leadership: Statistics and Analytics Program Lead (2024–present), ACENET Research Directorate member His recent publications focus on lexical diversity analysis, term dispersion quantification, and statistical foundations of TF-IDF. He supervises students in projects spanning energy-efficient NLP, causal inference in finance, and low-resource language embeddings. Paul actively contributes to open-source projects like stoRy and PAFit packages.
Professor Melissa Terras MBE FREng is Professor of Digital Cultural Heritage within Design Informatics at Edinburgh College of Art, University of Edinburgh. With over 25 years of experience producing award-winning digital projects in the Gallery, Library, Archive, and Museum (GLAM) sector, she directs impactful initiatives including Creative Informatics (an AHRC-funded creative cluster) and co-founded Transkribus, an AI platform for handwritten text recognition. Her research centers on digital cultural heritage infrastructure, where she examines AI ethics, copyright challenges in digitization (particularly orphan works), and the critical role of art schools in driving technological innovation. She investigates how handwritten text recognition transforms access to historical materials while addressing digital justice and bias in heritage data systems. Recent publications (2024-2025) reveal a cohesive focus on sustainable digital infrastructure through cooperative models like READ-COOP, ethical AI deployment in cultural contexts, and data-driven innovation in creative industries. Her work bridges technical development with critical policy analysis, emphasizing community governance and responsible technology design. Her scientific recognition includes: MBE (Member of the Order of the British Empire) FREng (Fellow of the Royal Academy of Engineering) Terras secured major funding as Director of Creative Informatics, fostering collaborations between Edinburgh's creative sector and academia. Her Transkribus platform, developed through European partnerships, exemplifies her commitment to sustainable digital infrastructure. She actively shapes national policy through contributions to UK government consultations on AI and copyright. As founding director of Transkribus and READ-COOP cooperative, she leads community-governed infrastructure development. Her work with Creative Informatics established frameworks for data-driven innovation in Scotland's creative industries, emphasizing equitable participation and ethical data practices.
Dr. Anita Juškevičienė is a Senior Researcher at the Educational Systems Group of Vilnius University's Institute of Data Science and Digital Technologies. Her work focuses on computational thinking, STEM education, and digital competence development. Research Interests : Computational thinking in primary/secondary education, STEAM integration, gender balance in STEM, educational technology adoption, and digital competence frameworks. Publications Trends : Recent work addresses pedagogical approaches for informatics education, teacher motivation, physical computing in STEM, and data modeling techniques for educational systems. Supervision : Currently supervising Snow White Bagocienė on modeling automatic assessment systems for design thinking. Projects : Active in analyzing global trends in computing education and implementing mobile learning scenarios for computer engineering training.
Sergey Vasilyevich Golub is a Professor at the Department of Software for Automated Systems at Cherkasy State Technological University. He holds a Doctor of Technical Sciences degree and has been part-time faculty since 2018. His career spans multiple roles, including dean at CHNU and head of departments in intelligent systems and software engineering. His research focuses on agent programming, intelligent monitoring, medical informatics, and machine learning. He has contributed to interdisciplinary projects linking software engineering with environmental monitoring and healthcare diagnostics. Recent publications emphasize multi-agent systems, social network analysis, and data-driven disease prediction. Key article trends include integrating satellite data for climate monitoring, authorship classification in social media, and agent-based modeling for pandemic forecasting. He collaborates with researchers in Ukraine and abroad, with works in Springer LNNS and CEUR-WS proceedings. Thanks and honorary diplomas from Cherkasy National University (2000, 2003, 2015) Honorary diplomas from Cherkasy Regional State Administration (2001, 2014) Honorary diploma from Ukraine's Ministry of Education and Science (2011) He has authored monographs on data analysis technologies and cross-platform decision support systems. His work bridges academic research and practical applications in intellectual monitoring across healthcare, ecology, and cybersecurity domains.
Roy Rosin, MBA, is Board Partner at First Round Capital and served from 2012-2024 as Chief Innovation Officer at Penn Medicine and Senior Fellow at the Leonard Davis Institute of Health Economics, University of Pennsylvania. An innovation executive rather than a tenure-line academic, he nonetheless leads large NIH- and PCORI-funded research programs, has published >70 peer-reviewed articles since 2015, and teaches Wharton Executive Education courses on business-model innovation and health-care leadership. Education: MBA, Stanford Graduate School of Business AB, cum laude, Harvard College Research & Innovation Focus: Rosen’s work sits at the intersection of behavioral economics, digital health, and rapid-cycle implementation science. His team has designed, piloted, and scaled more than 150 technology-enabled care-delivery interventions that measurably reduce readmissions, emergency-department use, medication non-adherence, and clinician burden while increasing screening rates, patient engagement, and use of high-value care sites. Key methodological themes include text-message–based coaching, conversational AI agents, financial & social-incentive design, and workflow automation inside large health systems. Publication Trends: Across 50+ recent papers he consistently pursues patient-facing digital interventions—chatbots for oncology adherence, SMS programs for maternal health, automated triage tools, and incentive trials for distracted driving—demonstrating a cross-disciplinary portfolio spanning oncology, obstetrics, ophthalmology, dermatology, emergency medicine, and population-level behavioral prevention. Honours & Recognition: Becker’s “30 Great Chief Innovation Officers to Know” (2022) Three separate CHIBE papers named among the Top-20 Clinical Research Achievement Awards by the Clinical Research Forum (2025) Advising & Grant Leadership: Although individual PhD students are not listed, Rosin directs multi-million-dollar implementation trials (e.g., NIH “iSMART”, PCORI opioid-use disorder studies) collaborating with dozens of faculty, fellows, and data scientists across Penn’s medical school, engineering, and Wharton. He mentors start-up founders nationally and serves as executive-in-residence for Wharton’s Health Care Management program. Labs & Teams: As Penn Medicine’s inaugural CIO he built the “Innovation Accelerator,” an internal consultancy that applies design-thinking, Lean Start-up, and behavioral economics to clinical operations. The group includes data scientists, UX researchers, software engineers, and quality-improvement specialists who co-design interventions with frontline clinicians and patients; many projects transition into routine operations or spin out as commercial ventures.
Arda Goknil serves as an Assistant Professor in the Department of Computer Science at the Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet). His research focuses on critical challenges in Industrial Internet of Things (IIoT), sustainable computing, and data reliability within cyber-physical systems. Dr. Goknil's primary research areas include: Carbon-aware machine learning pipelines for environmental sustainability Intermittent computing solutions for batteryless IoT devices Industrial data quality assurance and repair mechanisms Edge-based AI for manufacturing applications Computer vision in retail technology His work addresses real-world industrial needs through publications in ACM conferences and journals, emphasizing practical implementations in manufacturing and resource-constrained environments. Recent publications (2023-2025) reveal a strong trend toward developing tools like 3D-DaVa for point cloud validation and REPTILE for continual learning in IIoT, demonstrating cross-cutting contributions to data reliability and energy efficiency. While specific advising relationships are unlisted, his research aligns with OsloMet's Innovation, Digital Transformation and Sustainability group. No scientific awards or grant details appear in the provided text, though his active publication record indicates ongoing research leadership in sustainable IoT systems.
Jarmo Viinikanoja serves as a Senior Lecturer at the Institute of Information Technology within the School of Technology at JAMK University of Applied Sciences. His institutional contact includes phone +358406604071 and email jarmo.viinikanoja@jamk.fi. His research spans critical domains of modern computing infrastructure: Information Technology (core systems and applications) Computer Science (theoretical and practical foundations) Software Engineering (development methodologies) Data Science (analytical techniques) Artificial Intelligence (algorithmic intelligence systems) Networking (communication protocols and architectures) No scientific awards were documented in the source material. Advisory activities and research grant involvement cannot be confirmed from available information. Research team affiliations or laboratory leadership roles were not specified in the provided text.
Richard Scherl is an Associate Professor in the Department of Computer Science and Software Engineering at Monmouth University, where he has been teaching since 2002. His office is located in James and Marlene Howard Hall 222. Education: Ph.D. in Computer Science, University of Illinois M.A. & Ph.D. in Anthropology, University of Chicago Diploma in Tamil Research, Madurai University (India) Research Interests: Professor Scherl's primary research focuses on logic-based knowledge representation and automated reasoning using formal frameworks like situation calculus. His work bridges Artificial Intelligence with Natural Language Processing , Cognitive Science , and Computational Social Sciences to model common sense knowledge, linguistic context, conversational actions, and socio-cultural phenomena. He investigates how reasoning systems integrate database structures with natural language semantics while addressing philosophical and anthropological dimensions of knowledge construction. Publications: His scholarly output demonstrates a consistent trajectory in formalizing context-aware reasoning systems. Recent works (2022) develop situation calculus models for knowledge, belief, and conversation, while earlier research (2015-2010) explores socio-cultural inference from text, semantic classifiers for big data, and epistemic planning. The publications reveal an interdisciplinary methodology connecting AI with linguistics and social sciences, yielding applications in homeland security, social computing, and narrative analysis. Awards: No scientific awards were mentioned in the provided information. Advising and Grants: The available documentation does not specify details regarding student advising or research grant funding. Labs and Teams: No information about dedicated research laboratories or collaborative teams was provided in the source materials.
Amit V. Deokar serves as Associate Dean of Undergraduate Programs & Accreditation and Professor in the Department of Operations and Information Systems at UMass Lowell's Manning School of Business. He holds a Ph.D. in Management Information Systems from the University of Arizona and brings expertise from industrial and mechanical engineering backgrounds. His educational credentials include: Ph.D. in Management Information Systems, University of Arizona (2006) M.S. in Industrial Engineering, University of Arizona (2002) B.S. in Mechanical Engineering, V.J. Technological Institute, University of Mumbai (2000) Dr. Deokar's research centers on business analytics, machine learning applications, and business process management, with significant contributions to data mining, text mining, and artificial intelligence. His work bridges theoretical frameworks with practical implementations across healthcare, e-commerce, and transportation sectors, emphasizing real-world impact through industry collaborations and consulting engagements. His publication portfolio reveals a strategic focus on extracting actionable insights from complex data sources, particularly through process mining and text analytics. Recent work demonstrates increasing integration of generative AI techniques while maintaining strong methodological rigor in empirical validation. Key recognitions include: AIS Distinguished (cum laude) Member (2020) Association for Information Systems Teaching Excellence Award (2020) UMass Lowell IBM Faculty Award (2014) IBM Merill D. Hunter Award for Excellence in Research (2010) Dakota State University recognition As Associate Vice President of Technology for the Association for Information Systems, Editor-in-Chief of e-Service Journal, and leader of major conferences like AMCIS 2024, Dr. Deokar shapes academic discourse while securing research funding such as the 2017 Internal Seed Grant for urban transportation analytics. His industry consulting and keynote presentations on generative AI adoption further demonstrate applied scholarship.
Vita Kraft is a Research Fellow at the Institute of German Philology , University of Würzburg, focusing on German linguistics and language variation. Since 2021, she has contributed to empirical studies on revising speech activity and grammatical variation, with her dissertation work titled "Revidierende Sprachtätigkeit kompetenter Sprecher. Eine empirische Untersuchung". PhD in German Linguistics (ongoing, based on dissertation theme) M.A. in German as a Foreign Language (University of Würzburg, 2020) Magister in Philology (German/English, Kyiv National Linguistic University, 2008) Master in International Financial Management (Kyiv National Hetman Vyhovsky University, 2011) Her research explores revision mechanisms in language, dialectal variation, and digital humanities applications through projects like the Würzburg Dialect Database . She teaches courses on German language structure, orthography, and variation, and contributes to the organization of academic events like the "Was prägt die deutsche Sprache" conference. Scientific Recognition Eberhard Schöck Scholarship
Ricardo Henao is an Associate Professor of Biostatistics & Bioinformatics, Associate Professor in Surgery, and Assistant Professor in the Department of Electrical and Computer Engineering at Duke University. He is also a core member of the Duke Clinical Research Institute, the Duke Center for Applied Genomics and Precision Medicine, and the Duke Center for Statistical Genetics and Genomics. Education: Ph.D., Technical University of Denmark, 2011 Research Interests: Henao’s research integrates advanced machine learning with high-impact clinical and biological questions. His work spans infectious-disease diagnostics (including rapid host-response assays for bacterial vs viral infection), cardiovascular genomics and risk prediction, precision-medicine toolkits for transplant recipients, ophthalmic AI for retinal and cardiac imaging, and fairness-aware AI to ensure equitable healthcare delivery across demographic groups. Publications Trend: Across >317 peer-reviewed publications (2013–2025), a clear trajectory emerges from foundational statistical methodology to large-scale translational implementations. Recent 2025 papers emphasize automated harmonization of electronic health records, fairness-constrained predictive models for stroke risk, computer-vision systems for point-of-care echocardiography, and deep-learning segmentation of pulmonary vasculature—demonstrating a fusion of NLP, computer vision, and survival modeling to solve real clinical problems. Scientific Awards & Honors: While the supplied text does not list specific named awards, Henao’s funding portfolio serves as a proxy for recognition: he is PI or multi-PI on >34 active grants totaling tens of millions of dollars from NIH, NHLBI, NIAID, NSF, DoD, and private foundations such as Brigham and Women’s Hospital and the Henry M. Jackson Foundation. Advising & Grants: Henao mentors trainees at the intersection of data science and medicine. Current grant titles illustrate the breadth of mentee opportunities: PREEMPT: Prospective Randomized Evaluation and Management of Premature Atherosclerosis (NIH, 2025–2032) Machine Learning Guided Precision Genetic Testing for Monogenic Cardiovascular Disorders (NHLBI, 2024–2028) Synthesizing immunoinformatics and genetic epidemiology for malaria immunity signatures (NIAID, 2023–2028) NSF CC* Integration-Large: Scaling scientific workloads on distributed commodity GPUs (NSF, 2025–2027) Improving quality of life in SLE via stratified personalized health planning (DoD, 2022–2026) Multidisciplinary study of biological disparities in NASH progression (DoD, 2020–2025) Rapid point-of-care host gene-expression test for pre-symptomatic viral infection (DoD, 2021–2024) Clinical and molecular epidemiology of high-risk coronary plaque (NIH, 2019–2024) Labs & Teams: Henao leads the Division of Translational Biomedical Informatics within the Department of Biostatistics & Bioinformatics and participates in Duke’s Bass Connections and Data+ programs, embedding graduate and undergraduate students in interdisciplinary teams that span genomics, cardiology, surgery, and global health.
