Pasqualina Santaguida is a Part-Time Assistant Professor in the Department of Health Research Methods, Evidence, and Impact at McMaster University , focusing on evidence-based medicine and clinical research methodology. Key roles in systematic reviews and clinical guideline development Research emphasizes outcome measures in primary healthcare and diagnostic accuracy Her research interests span systematic reviews, evidence appraisal, and health policy translation. Recent work explores artificial intelligence in disease prediction , neck pain interventions , and biomarker reliability in heart failure. Notable article trends include applications of GRADE methodology , Delphi consensus in clinical decision-making, and BNP/NT-proBNP diagnostics. She contributes to clinical practice guidelines and harms reporting standards in trials. In teaching , she instructs graduate courses on Evaluating Sources of Evidence and Health Solutions Design , emphasizing methodological rigor and patient-centered outcomes.
PARK SOMIN is an Assistant Professor and NUS Presidential Young Professorship holder at the Department of Chemistry, Faculty of Science, National University of Singapore. She holds a Ph.D. from the University of Kentucky (2020) and completed postdoctoral training at Northwestern University (2024) and the University of Toronto (2023). Her research focuses on developing strategies to control surfaces of organic and hybrid semiconductor materials, leveraging advanced synthesis and characterization techniques to enhance optoelectronic device performance. Key areas include flexible electronics, artificial intelligence, efficient lighting, and sensing applications. Education: Ph.D., University of Kentucky, 2020 M.S. and B.S., Gyeongsang National University, 2013 Postdoctoral Research: Northwestern University (2024), University of Toronto (2023) Research Interests: Combining organic material synthesis and surface characterization to investigate carrier dynamics, interface energetics, and holistic energetic landscapes. Developing multi-layered photonic devices and surface-modified materials for applications in flexible electronics, AI, and energy-efficient systems. Recent Trends in Publications: Her work emphasizes stability enhancement in perovskite solar cells, quantum dot LED optimization, and ligand engineering for surface passivation. Notable contributions include studies on low-loss contacts, thermotropic liquid crystals, and bifunctional surface passivation strategies. Awards: NUS Presidential Young Professorship (2024) American Chemical Society Physical Chemistry Young Investigator Award (2023) Rising Stars in Materials Science (2023) Advising & Team: Supervises graduate students (e.g., Seongbeom Lee, Yoomi Ahn) and postdoctoral researchers. The Park Group collaborates globally on interdisciplinary projects in optoelectronics and sustainable materials. Current openings include postdocs and PhD candidates in organic synthesis, photoelectron spectroscopy, and optoelectronic applications. Labs & Facilities: Based in the Department of Chemistry at NUS, with access to advanced characterization tools and fabrication facilities for next-generation optoelectronic devices.
Professor Patrick Naylor is a faculty member in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. His research focuses on acoustic signal processing, speech processing, and audio technology, with a particular emphasis on spatial audio, robot audition, and applications in human-robot interaction. His work spans theoretical advancements like polynomial eigenvalue decomposition (PEVD) and practical innovations in audio security, data augmentation, and assistive technologies for hearing aids. He leads the Speech and Audio Processing research group, affiliated with Imperial's Robotics Forum, Artificial Intelligence Network, and Natural and Machine Hearing initiatives. His research addresses challenges in multi-channel speech compression, secure audio transmission, and adaptive systems for dynamic acoustic environments. Notable projects include developing neural network-based methods for sound source localization and enhancing speech recognition for post-stroke aphasia patients. Prof. Naylor's work intersects electrical engineering, machine learning, and cognitive sciences. He has pioneered techniques for binaural speech enhancement using deep learning and contributed to standards in acoustic environment profiling. His lab's research is showcased at www.ee.ic.ac.uk/sap . His publications emphasize audio security (e.g., watermarking, privacy-preserving processing) and ambient cooperative intelligence systems. Current trends focus on explainable AI for acoustic neural embeddings and scalable microphone array processing for smart environments. His research frequently addresses real-world applications in healthcare, robotics, and telecommunications. Prof. Naylor collaborates across disciplines, integrating signal processing with robotics and clinical needs. His lab explores both foundational algorithms and deployable systems, reflecting Imperial's engineering ethos of bridging theory and practice.
Professor Tom Bäckström is a faculty member at Aalto University, focusing on advanced speech and audio processing technologies with a strong emphasis on privacy and security in voice communication systems. His work addresses challenges posed by smart devices and speech interfaces, particularly in ensuring privacy in public spaces and open environments. Research interests include speech privacy preservation, distributed speech/audio coding, federated learning for on-device speaker recognition, and the ethical design of conversational AI systems. His contributions span technical innovations like anonymization techniques for speaker attributes, real-time noise suppression, and privacy-aware acoustic systems. Recent studies highlight user perceptions of anonymity in voice assistants across cultures, optimization of vector quantization methods, and the integration of machine learning into speech enhancement frameworks. While no specific awards or grants are explicitly listed in the provided texts, his prolific publication record reflects sustained impact in signal processing and privacy research. He leads a research group exploring the intersection of audio engineering and privacy, emphasizing practical solutions for emerging technologies like IoT audio networks and smart office acoustics. His work bridges theoretical advancements with real-world applications, addressing both technical and socio-ethical dimensions of speech technology.
Carol Hon is an Associate Professor at the School of Architecture & Built Environment, Faculty of Engineering, Queensland University of Technology (QUT). Her research focuses on construction safety, mental health, and Building Information Modelling (BIM). Prior to academia, she worked as an Assistant Quantity Surveyor in Hong Kong. Education: PhD, Hong Kong Polytechnic University MPhil, Hong Kong Polytechnic University Bachelor of Science in Surveying, University of Hong Kong Graduate Certificate in Academic Practice, QUT Research Interests: Construction safety and health, BIM adoption, mental health in construction, gender diversity, and professional ethics. Key Publications: Over 50 publications in journals like Safety Science, ASCE Journals, and Engineering, Construction and Architectural Management. Recent works include studies on mental health scales for construction workers and safety communication barriers. Awards: Including two Hong Kong doctoral dissertation awards, an ARC DECRA, and best paper awards. Recognized for contributions to construction safety and mental health initiatives. Grants/Projects: Co-investigator on projects funded by the National Natural Science Foundation of China, Hong Kong Research Grants Council, and industry partners. Topics include organizational learning from accidents, electrical safety in RMAA works, and ethnic minority safety. Roles: Safety Theme Coordinator for Building 4.0 CRC, BrisBIM Committee member, and reviewer for top-tier construction journals.
Dr Erin Chao Ling serves as a Senior Lecturer in Artificial Intelligence and the Future of Work at the University of Surrey, holding a joint appointment between the Surrey Institute for People-Centred Artificial Intelligence (PAI) and Surrey Hospitality and Tourism Management within the Faculty of Arts, Business and Social Sciences. Her work bridges AI technology with human-centered applications across hospitality, tourism, and workforce development sectors. Education: PhD in Tourism and Hospitality Management (2018-2021), University of Surrey MSc in Management (2016-2017), University of Bristol Dr Ling's research focuses on the societal implications of AI, particularly examining human-AI collaboration in digital marketing, AI skills development, service robot adoption, and generative AI's impact on work behavior. Her work emphasizes ethical considerations and responsible AI implementation to enhance human wellbeing. She has pioneered studies on AI assistants for travel, developing validated scales for measuring perceived intelligence in conversational agents. Her publication portfolio demonstrates consistent output in high-impact journals covering AI adoption drivers, human-robot interaction, and workforce transformation. Key trends include interdisciplinary work connecting computer vision with hospitality applications, ethical AI frameworks for international contexts, and consumer behavior analysis in AI-assisted service environments. Scientific Recognition: Best Paper Award in Hospitality Management at CHME 2022 Conference IFITT Journal Paper of the Year Award As an active supervisor, Dr Ling leads multiple PhD projects examining AI applications in aging populations, hotel revenue management, and tourism marketing. She provides consultancy to government bodies including the UK Foreign Commonwealth and Development Office on AI policy and ethical regulation. Her media presence includes features in The Guardian, FORTUNE, and Yahoo Finance, where she discusses AI's societal impacts. Dr Ling also contributes to policy discussions through All-Party Parliamentary Group meetings on Youth Affairs and AI. She directs research initiatives including the ESRC-funded 'GenAI for International College Students Mental Health & Wellbeing' project and leads the 'Advancing AI Agenda in Thailand' consultancy for the British Embassy Bangkok, establishing herself as a key bridge between academic research, industry implementation, and policy development in responsible AI adoption.
Dr. Christina Silver is an Associate Professor (Teaching) in Sociology at the University of Surrey and Director of the CAQDAS Networking Project. She holds a PhD from the University of Surrey (2002), an MSc in Social Research Methods (Surrey, 2000), and a BA (Hons) in Politics and Sociology (Essex, 1997). Her work focuses on qualitative methodology, particularly the integration of technology like CAQDAS software into research and pedagogy. As Director of the CAQDAS Networking Project since 1998, she oversees training programs, advisory services, and course development for researchers globally. Her research explores how software tools like NVivo, ATLAS.ti, and MAXQDA enhance qualitative analysis, with a focus on pedagogical frameworks such as the Five-Level QDA method. She has pioneered online teaching strategies for CAQDAS during the pandemic, emphasizing synchronous learning and digital adaptability. Key contributions include co-developing the Five-Level QDA method, which structures learning through objectives, plans, and software tool utilization. Her recent work addresses AI’s role in qualitative analysis, balancing innovation with critical reflection. She has collaborated with institutions like Southampton and Birmingham Universities on projects such as the Mass Observation longitudinal study. Publications span over two decades, covering CAQDAS software comparisons, methodological debates, and educational innovations. Her work bridges technical proficiency with methodological rigor, influencing both researchers and educators worldwide.
Mark Bun is an Assistant Professor in the Department of Computer Science at Boston University, affiliated with the College of Arts & Sciences. He specializes in theoretical computer science with a focus on data privacy, computational complexity, cryptography, and machine learning foundations. His work bridges discrete and continuous mathematical techniques, particularly using polynomial methods to analyze Boolean functions and develop privacy-preserving algorithms. Education: PhD in Computer Science from Harvard University. Joined BU as a tenure-track faculty member in 2019 after a fellowship at UC Berkeley's Simons Institute for Theory of Computing. Research Interests: Data privacy mechanisms (especially differential privacy) Computational complexity of privacy-preserving algorithms Connections between machine learning theory and privacy Boolean function analysis via polynomial approximations Recent Work Trends: Focus on foundational limits of private learning, algorithmic frameworks for demographic fairness in data release, and memory-constrained statistical inference. Explores trade-offs between privacy guarantees, computational efficiency, and statistical accuracy across multiple domains. Advising/Grants: No specific students/grants listed; research supported by theoretical CS funding streams. Labs/Teams: Not explicitly mentioned, but collaborates widely in privacy theory and computational complexity communities.
Mark Ho is an Assistant Professor of Psychology at New York University (NYU), affiliated with the Department of Psychology in the College of Arts & Science. Previously, he served as an Assistant Professor at Stevens Institute of Technology (2023–2024) and held postdoctoral roles at Princeton University and UC Berkeley. He holds a Ph.D. and M.S. in Cognitive Science and Computer Science from Brown University, alongside a B.A. in Philosophy from Princeton University. His research focuses on cognitive, motivational, and social processes underlying human problem-solving, particularly in intentional action interpretation, theory of mind, and the emergence of social phenomena from individual intentions. Using computational modeling and behavioral experiments, his work spans higher-level cognition and social psychology, including planning, pedagogy, and coordination. He also explores applications of computational cognitive science to develop human-interpretable AI systems. Research interests include: cognitive costs in decision-making, representational alignment in teaching, hierarchical planning structures, and the interplay between reinforcement learning and cognitive constraints. His lab's work bridges cognitive science and AI, emphasizing human-AI collaboration and ethical machine learning. Professional affiliations include the NYU Center for Data Science (2022–2023) and multiple academic conferences. His articles frequently address human-AI interaction, decision-making frameworks, and the theoretical underpinnings of social cognition.
Rampi Ramprasad is Professor and Michael E. Tennenbaum Family Chair at Georgia Tech, holding joint appointments as Georgia Research Alliance Eminent Scholar in Energy Sustainability. His research develops machine learning methods for accelerated materials discovery. Education: B.Tech. in Metallurgical Engineering, Indian Institute of Technology Madras M.S. in Materials Science & Engineering, Washington State University Ph.D. in Materials Science & Engineering, University of Illinois Urbana-Champaign Research Leadership: Leads multi-institution projects including ONR MURI programs on polymeric dielectrics. Research areas: Machine learning for polymer, metal, and dielectric design High-performance materials for energy storage applications Computational frameworks for predicting material properties Data-driven discovery of sustainable materials Recognition: Fellow of multiple scientific societies including American Physical Society and recipient of Max Planck Society Fellowship. Current research demonstrates strong focus on AI-augmented materials design, particularly for energy storage polymers and recyclable materials.
Nidhal Abdulaziz is an Assistant Professor at the School of Engineering & Physical Sciences. His research focuses on autonomous systems, robotics, machine learning, and their applications in healthcare, safety, and environmental monitoring. He contributes to the UN Sustainable Development Goals (SDGs), particularly in advancing technology for sustainable solutions. Key research areas include autonomous vehicle perception, collision avoidance strategies, and AI-driven health monitoring systems. He has collaborated on projects like BeeBetter for bee health and Al-Powered cleaning robots. His work integrates machine learning, signal processing, and neural networks to address challenges in robotics, rehabilitation, and environmental safety. Abdulaziz has published extensively in conferences such as IEEE EDUCON and ICRAE. His recent articles explore topics like YOLO algorithms for vehicle perception, multi-modal beehive systems, and deep learning for medical diagnostics. He actively participates in academic collaborations to bridge engineering research with real-world applications.
Inês Lynce is a Professor at the Department of Computer Engineering within the Instituto Superior Técnico (University of Lisbon) and a researcher at INESC-ID Lisboa . Her research focuses on Artificial Intelligence, Constraint Satisfaction and Optimization, Automated Reasoning, Formal Methods, and Bioinformatics. She leads multiple research projects, including RIGA (Indirect Discrimination Analysis), GOLEM (Automated Programming), and LAIfeBlood (AI for Blood Management), funded by FCT and EU programs. Education: While specific academic qualifications aren't listed, her roles and research output indicate advanced degrees in Computer Science/Engineering. Her work bridges theoretical computer science and practical applications, with a strong emphasis on Satisfiability (SAT) solving, constraint programming, and AI-driven solutions for complex systems. She has organized major conferences like SAT 2019 and ECAI 2025 , and serves on editorial boards for journals including Artificial Intelligence Journal and Journal on Satisfiability . Her awards include the INESC-ID Young Researcher Award (2009), APPIA PremiA Award (2009), and UTL/Deloitte Young Researcher Award (2008). Professional activities span program committee roles for AAAI , IJCAI , and CP conferences, reflecting her leadership in AI and constraint-based research. Teaching activities are managed through Fenix IST, and she collaborates with initiatives like CompSustNet for interdisciplinary research. Her work has been applied to diverse domains, including transportation scheduling, bioinformatics modeling, and cybersecurity protocol analysis.
Professor Ella Arensman is a Research Professor at the School of Public Health, University College Cork (UCC), and Chief Scientist at the National Suicide Research Foundation. Over 33 years, she has led multidisciplinary suicide prevention research programs with over 200 publications (h-index 53). She holds leadership roles in international organizations like the International Association for Suicide Prevention and WHO Collaborating Centre. Educational Background : While specific degree details are not listed, her roles imply advanced training in public health, epidemiology, or clinical psychology. Her career trajectory suggests doctoral-level qualifications in health sciences. Research Focus : Specializes in suicide/self-harm surveillance, intervention design, and implementation science. Key areas include: Public mental health policy and practice Epidemiological studies of risk factors Clinical decision support systems (e.g., PERMANENS project) Workplace mental health interventions (MENTUPP trial) Article Trends : Recent work emphasizes pandemic impacts on mental health, real-time suicide surveillance systems, and organizational-level interventions in workplaces. Methodological strengths include mixed-methods approaches, systematic reviews, and multi-country consortia. Awards : Recipient of the HRB Research Leaders Award (2015) for her SAMAGH clinical training program. Recognized internationally for contributions to suicide prevention frameworks. Advising & Grants : Secured funding from HRB, EU, HSE, and global bodies. Supervised numerous researchers through interdisciplinary teams. Currently leads the SAMAGH national clinical program for self-harm management in EDs. Labs/Teams : Directs research teams at UCC and collaborates globally via the WHO Collaborating Centre and Griffith University (Australia). Manages the National Self-Harm Registry Ireland database for real-time surveillance.
Jeremy Riel, PhD is a Visiting Assistant Professor in the Department of Educational Psychology at the University of Illinois Chicago (UIC), and Director of the TRAILblazer Lab (www.trailblazerlab.org). His research focuses on educational technology, AI applications, and computing skills education across all age groups. He teaches courses in Instructional Design and Training (IDT Minor) and Human Development and Learning BA programs. Education: PhD from UIC (2020), MA from Georgetown University (2012), BA from University of Oregon (2007), AA from Lane Community College (2005). He holds notable honors including the 2025 High-Impact Instructional Design Award and 2024 UIC Open Educational Resources Leadership Award. Affiliate Faculty at Institute of Government and Public Affairs Co-Leader of IGPA’s Science & Technology Working Group Leadership roles in AERA Division C (2021–2025) Recent grants include NSF-funded QUAILS project (2025–2026) and Google’s Computational Thinking Intensives (2024–2025). His work spans educational simulations, chatbots, and teacher professional development frameworks like ROPD. Courses taught include EPSY 380, 430, 440, and 450 focusing on instructional design and online learning assessment.
Hamidreza Samouei is an Assistant Professor in the Harold Vance Department of Petroleum Engineering at Texas A&M University, part of the College of Engineering. His research focuses on produced water treatment, CO2 utilization, and novel materials synthesis. He holds an office in the Joe C. Richardson Building (RICH 901H) and can be contacted at samouei@tamu.edu. Research Interests: His work spans produced water/wastewater treatment, CO2 mineralization, reaction kinetics, corrosion studies, and flow assurance in petroleum systems. He also explores enhanced oil recovery (EOR) and oil field chemistry, leveraging advanced analytical techniques like real-time spectroscopy. Recent Research Trends: His publications emphasize sustainable water management solutions, such as zero-liquid discharge systems and mineral recovery from waste. He also investigates CO2 storage impacts on cement integrity and novel AI methods for agriculture. Key themes include environmental sustainability, material innovation, and energy efficiency. Advising & Grants: No students or specific grants are listed in the provided information. His lab likely collaborates with industry partners on petroleum engineering challenges, though specific team details are omitted.
