Find Academic Supervisors

Tijana GajićView profile

Researcher

• Language Education
• Digital Learning Tools
• Foreign Language Acquisition
• +4 more

Tijana Gajić is a researcher at Singidunum University in Belgrade, Serbia. She holds a Ph.D. in Philology (2020), an MA in English Language and Literature (2009), and a BA in the same field (2007) from the University of Belgrade. Her work focuses on the intersection of language education, digital tools, and critical pedagogy. Doctoral Studies: Philology , University of Belgrade (2017-2020) Master Studies: English Language and Literature , University of Belgrade (2008-2009) Bachelor Studies: English Language and Literature , University of Belgrade (2003-2007) Dr. Gajić’s research explores the impact of digital technologies on language acquisition, critical thinking development, and the sociopolitical dimensions of English language learning. She has published extensively on mobile applications like Duolingo, HelloTalk, and WordUp, analyzing their efficacy in modern pedagogy. Her recent publications (2024-2025) examine digital tools’ influence on language education, critical pedagogy in Serbian classrooms, and AI-driven language learning innovations. Earlier works (2020-2016) established her expertise in tandem learning, vocabulary enhancement via apps, and affective factors in language acquisition. Notable collaborations include partnerships with researchers like N. Maenza , A. Gagić , and J. Nikolić . While no specific grants or awards are mentioned, her involvement in conferences such as Sinteza and ELTA Serbia underscores her active role in advancing language education. Her 2022 book, Nastava stranih jezika i razvoj kritičkog mišljenja u eri neoliberalizma , synthesizes her critical pedagogy framework. Current projects likely continue her focus on digital transformation in language teaching.

Maja J MatarićView profile

Professor

• Socially Assistive Robotics
• Human-Robot Interaction
• Assistive Technology
• +7 more

Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.

April Yi WangView profile

Assistant Professor

• Human-Computer Interaction
• Educational Technology
• Programming Support
• +4 more

April Yi Wang is a tenure-track Assistant Professor in the Department of Computer Science at ETH Zürich, where she directs the Programming, Education, and Computer-Human Interaction Lab (PEACH Lab). She is a core faculty member at the Institute for Intelligent Interactive Systems and associated with the ETH AI Center. Wang is also an active member of ETH HCI and Swiss CHI communities, contributing significantly to human-computer interaction and educational technology research. Dr. Wang's educational background includes: Ph.D. in Information Science from University of Michigan (2023), advised by Steve Oney and Christopher Brooks M.Sc. in Computer Science from Simon Fraser University (2018), advised by Parmit Chilana B.Eng in Computer Science from Zhejiang University (2016) Dr. Wang's research focuses on human-centered approaches to programming and data science. Her work reimagines programming as a form of literature that communicates with both machines and people, exploring creative representations like text, shapes, animations, and everyday objects. She investigates how to make programming more natural and intuitive through literate programming environments, with applications in professional and educational contexts. Her research spans human-computer interaction, educational technology, and AI-assisted programming tools. Analysis of Dr. Wang's recent publications reveals a strong focus on AI-enhanced educational tools, particularly for programming and data literacy. Her work increasingly integrates large language models to scaffold learning while maintaining user agency. There's a clear trajectory toward developing situated learning approaches that connect abstract concepts to real-world contexts through augmented reality and tangible interfaces. Her research bridges HCI, education, and AI to create more accessible and engaging technical learning experiences. Dr. Wang has received numerous prestigious awards including: 2023 Gary M. Olson Award and Honourable Mention Award at ACM CHI 2022 Rising Stars in EECS and Heidelberg Laureate Forum Young Researcher 2020 Best Short Paper Award at IEEE VL/HCC and Honourable Mention at ACM CHI 2019 Best Paper Award at ACM CSCW Dr. Wang actively mentors students through thesis projects at ETH Zürich, supervising numerous bachelor's and master's students on topics ranging from AI-assisted programming to data literacy tools. Her lab, PEACH Lab, has secured funding including the recent innovedum funding for the Coducate project. She serves on program committees for major conferences including CHI and UIST, and regularly reviews for top HCI and education journals. The PEACH Lab, directed by Dr. Wang, focuses on creating expressive, intelligent, and human-centered systems that make technical topics more accessible. The lab explores textual, visual, and embodied representations for programming, with emphasis on enhancing communication, collaboration, and learning. Current research directions include balancing automation with user agency, supporting diverse learning needs, and developing tools for interdisciplinary technical communication.

Peter A. FlachView profile

Professor

• Machine Learning
• Artificial Intelligence
• Explainable AI (XAI)
• +3 more

Peter A. Flach is Professor of Artificial Intelligence at the Intelligent Systems Laboratory, School of Computer Science, University of Bristol, where he has been faculty since 1997 and was promoted to Professor in 2003. He currently directs the UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence and serves as Vice-President of the European Association for Data Science (EuADS), having previously served as its President. His research centers on rigorous evaluation and improvement of machine learning systems, with seminal contributions to classifier calibration (including Beta Calibration and Precision-Recall-Gain curves), explainable AI frameworks, and data science methodology. He pioneered the extension of CRISP-DM to data science trajectories and developed Explainability Fact Sheets for systematic assessment of XAI approaches. His work bridges theoretical foundations with practical deployment in real-world systems. Analysis of his recent publications reveals a dominant focus on interpretable and reliable machine learning, with strong emphasis on performance evaluation metrics, model calibration techniques, and human-centered explainability. His research increasingly addresses healthcare applications through projects like SPHERE and clinical decision support systems, while maintaining core contributions to fundamental ML theory. His scientific recognition includes prestigious fellowships: Fellow of the European Lab for Learning and Intelligent Systems (ELLIS) Fellow of the European Association for Artificial Intelligence (EurAI) Professor Flach has secured major research funding including UKRI Centre for Doctoral Training grants and EU network funding (TAILOR). He leads extensive collaborations across Engineering, Population Health Science, and international institutions (Monash University, Polytechnic University of Valencia), plus over 20 industry partners in the Practice-Oriented AI CDT including LV= and QinetiQ. His work with the SPHERE project demonstrates successful translation of AI research into residential healthcare settings. He leads the Intelligent Systems Laboratory at Bristol, which develops influential open-source tools including the FAT Forensics Python toolbox for algorithmic fairness and the Explainability Fact Sheets framework. His lab maintains strong connections with the European AI community through ELLIS and EurAI, positioning Bristol as a hub for human-centered and methodologically rigorous AI research.

Douglas D. ReadyView profile

Associate Professor

• Education Policy
• Social Policy
• Educational Equity
• +3 more

Douglas D. Ready is an Associate Professor of Education and Public Policy at Teachers College, Columbia University , where he serves as Director of the Consortium for Policy Research in Education (CPRE-TC). His research focuses on the intersections of education policy, social policy, and educational equity, particularly how policies influence socio-demographic disparities in cognitive development. Research Interests: Ready’s work examines the structural determinants of educational equity, including school choice architecture, personalized learning, early childhood education, and the impacts of technology on instruction. He has published extensively in journals such as Educational Evaluation and Policy Analysis and Sociology of Education . Editorial Roles: Ready contributes to the academic community as a member of the editorial boards of Educational Evaluation and Policy Analysis and American Educational Research Journal . Key Projects: He has led analyses of programs like Teach to One: Math and Pre-K for All , exploring their effects on student outcomes and equity.

John DeNeroView profile

Teaching Professor

• Artificial Intelligence (AI)
• Computer Science Education
• Data Science
• +3 more

John DeNero is the Giancarlo Teaching Fellow and Associate Teaching Professor in UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department. He joined UC Berkeley in 2014 to focus on undergraduate education in computer science and data science. He teaches and co-develops introductory courses like CS 61A (Computer Science) and Data 8 (Data Science), which serve thousands of students annually. His research spans natural language processing and education innovation, with notable contributions to textbooks like Composing Programs and Computational and Inferential Thinking . Education: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley (2010) M.A. in Philosophy, Stanford University (2002) B.S. in Mathematical & Computational Science and Symbolic Systems, Stanford University (2001) Research interests focus on advancing AI education and curriculum design. His work emphasizes scalability, equity, and student success in large courses. Notable contributions include the Pac-Man projects for AI education and the Berkeley Data Science Education Program. Recent trends in publications highlight AI-assisted education tools, machine translation improvements, and large-scale student feedback systems. Awards include the UC Berkeley Distinguished Teaching Award (2018) and multiple accolades for teaching excellence. Advising and grants: While currently not taking new students, his team supports massive course infrastructures. Labs include the Berkeley Artificial Intelligence Research (BAIR) Lab and contributions to Data Science undergraduate studies.

Soheil SalehiView profile

Assistant Professor

• Hardware Security
• AI-Enabled Security
• Internet of Things (IoT) Security
• +15 more

Soheil Salehi is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, with a joint appointment in Systems and Industrial Engineering. He is the Director of the Privacy-preserving, Intelligent, and Secure Computing (PRISM) Lab, established in August 2022. Prior to this, he was an NSF-Sponsored Computing Innovation Fellow and Postdoctoral Research Fellow at the University of California, Davis. Ph.D., Electrical and Computer Engineering, University of Central Florida, 2020 M.S., Electrical and Computer Engineering, University of Central Florida, 2016 B.S., Isfahan University of Technology, Iran, 2014 Dr. Salehi's research focuses on the intersection of hardware, AI, and security. His work spans hardware and AI-enabled security in IoT , Generative AI for hardware design and security , neuromorphic and biologically-inspired AI hardware , emerging spin-based devices , reconfigurable architectures , low-power VLSI circuits , and digital twins and mixed reality for semiconductor workforce development . He also explores the application of Generative AI in personalized education . His recent publications, spanning 2023–2025, reveal a strong trend toward integrating AI and machine learning into hardware security and design. Key themes include automated secure IC design flows , AI-driven hardware obfuscation , firmware and side-channel attack analysis , security in neuromorphic and spiking neural networks , and educational frameworks using digital twins and generative models . His work appears in top venues like DAC, ICCAD, USENIX Security, IEEE TCAS-I, and ISCAS. Outstanding Reviewer Award, IEEE/ACM Design Automation Conference (DAC), 2023 Best Presentation of the Symposium Award, UC Davis Postdoctoral Research Symposium, 2021 UCF Excellence by a Graduate Teaching Assistant (University-Level), 2016 Nominated for 30-under-30 Award, UCF, 2020 Nominated for Postdoctoral Research Excellence Award, UC Davis, 2022 Dr. Salehi has secured significant research funding as PI and Co-PI, including a $300K NSF SaTC EAGER grant on Generative AI-based Personalized Cybersecurity Tutor, a $174,000 University of Arizona PIF Award, and multiple RII grants totaling over $198K. He has also received industry funding from CHEST. He actively mentors students and leads the PRISM Lab, which focuses on privacy-preserving and intelligent secure computing. His service includes roles as Technical Program Committee (TPC) Member and Session Chair at premier conferences such as DAC, ICCAD, CCS, NDSS, and GLSVLSI. The PRISM Lab, under his direction, conducts cutting-edge research in secure and intelligent hardware systems, with applications in IoT, edge computing, and workforce development. The lab emphasizes interdisciplinary collaboration and innovation in both research and education.

Seb FalkView profile

Research Fellow

• History of Astronomy
• Late Medieval Sciences
• Scientific Instruments
• +3 more

Seb Falk is a College Fellow and Research Fellow at Girton College, University of Cambridge, and a Senior Admissions Tutor. He holds academic roles including Praelector and Director of Studies for the Cambridge Foundation Year. His research focuses on medieval mathematical sciences, particularly astronomy, navigation, and the history of scientific instruments and texts. Falk earned a BA in History and Spanish from Oxford University, followed by an MPhil and PhD in History and Philosophy of Science from Cambridge. His research explores the interplay between religion, literature, and science in the Middle Ages, challenging the 'Dark Ages' narrative. Key projects include studies on late medieval astronomical tables and instruments, and a global comparative analysis of natural sciences around the year 1000. Falk has published widely, including the acclaimed book The Light Ages (2020), which was shortlisted for the Hughes Prize and awarded the Osterbrock Prize in 2025. His articles span topics like medieval astronomical practices, manuscript studies, and the cultural history of science. Falk is also a sought-after media contributor, appearing on BBC programs and podcasts, and has engaged in public outreach through lectures and museum collaborations.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +5 more

Gordon Geoffrey J. is a Professor at Carnegie Mellon University, specializing in Machine Learning , Artificial Intelligence , and Computer Science . His research spans Reinforcement Learning , Domain Adaptation , Graph-based Planning , and Relational Learning , with significant contributions to Adversarial Learning , Deep Learning Dynamics , and Mobile Sensor Networks . He has pioneered algorithms like ARA* and Anytime Dynamic A* for efficient planning under constraints. Key research areas: Transfer Learning , Bayesian Knowledge Tracing , Multi-agent Systems , Database Optimization His work has been cited thousands of times across foundational topics in AI and robotics, demonstrating sustained impact in algorithm design and applications to complex systems.

Niels PinkwartView profile

Professor

• Educational Technologies
• Artificial Intelligence in Education
• Learning Analytics
• +5 more

Prof. Dr. Niels Pinkwart is a Professor of Computer Science at Humboldt University Berlin and Scientific Director of the Educational Technology Lab at the DFKI Berlin Project Office. His work focuses on AI-driven educational technologies, including roles as spokesperson for the interdisciplinary ProMINT program and leadership positions in the German Society for Informatics' Learning Analytics and Educational Technologies working groups. Education: Computer Science and Mathematics at University of Duisburg Doctorate: Collaborative learning technologies (2005) Postdoctoral: Carnegie Mellon University's Human-Computer Interaction Institute Research interests span educational technologies , human-AI collaboration , and digital learning systems , with applications in serious games for ASD , creativity assessment , and healthcare informatics . His publications (over 200) demonstrate expertise in scaling educational mentoring through AI. Current affiliations include the Einstein Center Digital Future and Weizenbaum Institute for the Networked Society. He has led projects like AZUKIT (AI tutors for performance assessment), tech4comp (scalable mentoring processes), and AI.EDU Lab (AI in higher education).

Ian DavidsonView profile

Professor

• Machine Learning
• Data Mining
• Constraint Programming
• +6 more

Professor Ian Davidson is a faculty member in the Department of Computer Science at the University of California Davis, College of Engineering. His research focuses on machine learning, data mining, and constraint programming, with applications in neuroscience, healthcare, and social networks. He emphasizes rigorous algorithm design and human-in-the-loop learning paradigms. Editorial Board Member: ACM TKDD, IEEE TKDE, Springer DMKD Conference Leadership: PC Chair (SDM 2012), Vice/Area Chair (IEEE ICDM, ACM KDD, SIAM DM, ECML/PKDD 2013-2015) Research Interests: Human-in-the-loop learning (active, transfer, and transductive frameworks) Constraint programming and spectral methods for clustering and classification Applications in neuroimaging analysis, intelligent tutoring systems, and social impact domains Fairness in machine learning and clustering algorithms Tensor decomposition and matrix factorization techniques Interdisciplinary collaborations in neuroscience and healthcare Recent publications highlight his work on fairness-aware clustering with constraint programming, advanced spectral methods for brain connectivity analysis, and explainable AI frameworks. His research often combines theoretical rigor with practical applications in clinical domains. Scientific Awards: Best Paper Award, SIAM Data Mining Conference 2005 Best Paper Award, ECML/PKDD 2006 Best Paper Award, ICDM 2006 Students & Collaborators: Former students: Xiang Wang (IBM Watson), Buyue Qian (Xi'an Jiaotong University), Tom Kuo (Google), Sean Gilpin (Google) Current advisees: Aubrey Guess, Zilong Bai, Erin McGinnis, Zheng Fang, Hongjing Zhang

• Artificial Intelligence
• Natural Language Processing
• Multimodal Dialogue Processing
• +6 more

Cecilia O. Alm is a Professor in the Department of Psychology within the College of Liberal Arts at Rochester Institute of Technology (RIT), where she serves as the Artificial Intelligence Program Director. She holds multiple leadership roles including Director of the Center for Human-aware AI and Director of the Computational Linguistics and Speech Processing Lab (CLaSP). Her institutional affiliations span the School of Information, Ph.D. Programs in Cognitive Science and Computing and Information Sciences, Department of Computer Science, and MS in Data Science program. Dr. Alm earned her Ph.D. from the University of Illinois at Urbana-Champaign. Her research focuses on human-centered artificial intelligence with particular emphasis on linguistic and multimodal sensing, affective computing, and natural language processing. She investigates how AI systems can better understand and respond to human communication through multimodal dialogue processing, with applications in accessibility, education, and healthcare. Her recent publications demonstrate a strong trend toward developing inclusive AI systems, particularly through projects addressing Deaf community needs (MULTICOLLAB-ASL), subtle emotion recognition (FUSE corpus), and bias mitigation in NLP. The work consistently integrates multimodal data streams (speech, gaze, gesture) to create more responsive human-AI interaction frameworks. Current research directions emphasize diversity in AI education, visual prosody in sign languages, and human-in-the-loop AI development. Dr. Alm leads several significant NSF-funded initiatives including the AWARE-AI program, IRES AI-PROWIL international research experience, and collaborative projects with Gallaudet University focused on Deaf scientist-centered AI research. She has secured over $2.5 million in external funding for her work on human-aware AI systems. She directs the CLaSP lab which provides research opportunities for PhD, MS, and undergraduate students, with graduates employed at major technology companies including Amazon, Apple, Microsoft, and Facebook. The lab focuses on real-world AI applications in accessibility, human-robot interaction, and multimodal communication systems.

Paul TaeleView profile

Academic

• Sketch Recognition
• Human-Computer Interaction (HCI)
• Artificial Intelligence
• +5 more

Paul Taele is an Instructional Assistant Professor and Deputy Lab Director in the Sketch Recognition Lab at Texas A&M University's Department of Computer Science & Engineering. He holds a Ph.D. (2019), M.S. (2010), and dual B.S. degrees in Computer Science and Mathematics from the University of Texas at Austin (2006). His research focuses on sketch recognition, haptics, and intelligent interfaces for education and accessibility, with notable work in mid-air gesture recognition, educational sketching tools, and assistive technologies for disabilities. He has contributed to projects like Kanji Workbook , Hashigo , and HaptiMoto , and has published over 50 peer-reviewed articles across venues like CHI, IUI, and AAAI. Taele has received awards including the NSF Student Travel Grant (2014) and Ford Foundation Honorable Mention (2015). He teaches courses in capstone design, programming, and sketch recognition, and mentors students across all academic levels through strict eligibility criteria for research collaborations. Education : Ph.D. Computer Science, Texas A&M University (2019) M.S. Computer Science, Texas A&M University (2010) B.S. Computer Science & Mathematics, University of Texas at Austin (2006) Concentration in Mandarin Chinese, National Chengchi University (2007) Research Interests : Taele's work bridges HCI and AI to create accessible educational interfaces. His projects emphasize: Sketch Recognition : Developing algorithms for mid-air gestures, children's developmental assessments, and language learning Accessibility : Haptic systems for visually impaired learners and algebra education Educational Tech : Intelligent tutoring systems for music, math, and East Asian languages Awards & Grants : EAAI-20 Travel Grant (2020) Ford Foundation Dissertation Honorable Mention (2015) NSF East Asia-Pacific Summer Institutes (2013, 2012) Royce E. Wisenbaker Fellowship (2009) Lab & Teams : Director of the Sketch Recognition Lab (SRL) and collaborator with global institutions like Singapore Management University and National Taiwan University. Active in organizing workshops like SketchRec at IUI conferences.

Min ChiView profile

Professor

• Artificial Intelligence
• Deep Learning
• Reinforcement Learning
• +5 more

Dr. Min Chi is a Professor in the Department of Computer Science at North Carolina State University, where she joined in 2013 as a Chancellor's Faculty Excellence Program cluster hire in the Digital Transformation of Education. Her academic journey includes a Ph.D. and M.S. in Intelligent Systems from the University of Pittsburgh and a B.E. in Information Science and Technology from Xi'an Jiaotong University, China. She completed postdoctoral fellowships at Carnegie Mellon University's Machine Learning Department and Stanford University's Human Sciences and Technologies Advanced Research Institute. Dr. Chi's research focuses on the development and empirical evaluation of cutting-edge Artificial Intelligence, Deep Learning, and Reinforcement Learning frameworks tailored for addressing human-centric challenges. Her work spans multiple domains including advanced learning technologies, AI and intelligent agents, data sciences and analytics, and human-computer interaction. She has made significant contributions to intelligent tutoring systems, healthcare applications, nuclear power systems, and humanitarian efforts such as food distribution and disaster relief. Her publication record demonstrates a strong focus on applying AI techniques to real-world educational challenges, with recent work examining metacognitive knowledge transfer, reinforcement learning for pedagogical policy induction, and deep learning approaches for proactive help in educational settings. Her research also extends to healthcare applications, food distribution systems, and other socially impactful domains. 10 Best Paper, Best Student Paper, and Outstanding Paper Awards Prestigious Alcoa Foundation Engineering Research Achievement Award NSF CAREER Award Dr. Chi leads multiple significant research projects funded by the National Science Foundation, National Institutes of Health, and the Department of Energy, with a total funding exceeding $7 million. Her work bridges theoretical advances in AI with practical applications that address critical societal challenges in education, healthcare, and humanitarian operations.

• Women Philosophers of Education
• Collaborative Learning
• Religious Education
• +3 more

Dr. Siobhán Dowling is a Senior Lecturer in Education at the School of Education, University College Cork (UCC), with expertise in Religious Education, Music Education, and the intersection of The Arts in Education. She earned her PhD from the University of Wales, Trinity St David, Lampeter, UK, in 2011 and has served in both teaching and administrative roles at UCC since 1998, including Director of the Structured PhD in Education. Education: PhD (2011) in Biblical and Musical Reception History, University of Wales, Trinity St David. Current Roles: Senior Lecturer, Tutor, Placement Supervisor, Director of Structured PhD in Education. Her research focuses on the reception history of biblical narratives in music , particularly themes like the Sacrifice of Isaac, Eve, and the Book of Samuel. She explores collaborative learning in teacher education and integrates creative pedagogy in Religious Education. Her work spans interdisciplinary analysis of the Bible’s influence on music, art, and cultural identity. The 15 most recent publications highlight her pioneering work on biblical stories in musical compositions (2025), visual and musical reception of Dives (2024), and peer-reviewed studies on collaborative learning (2023). Her contributions to the Encyclopedia of the Bible and its Reception (2025–2016) cover topics like patriarchs, psalms, and gendered biblical figures. Scientific Awards: Recipient of two book awards in the USA for co-authored work 'The Bible in Music: A Dictionary of Songs, Works and More' (2015). Research Grants and Supervision: Awarded multiple Erasmus+ Staff Mobility Grants (2023, 2014) for international training and a Kurt Weill Foundation Travel Award (2014). Supervised PhD, MEd, and PME students while serving as Internal/External Examiner. Leadership: Coordinated UCC’s Cohort PhD in Education (2001–2014) and led the MAAP in Guidance and Counseling program. Delivered invited seminars at KU Leuven, University of Tuebingen, and Trinity College Dublin.