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Stephen SchneiderView profile

Professor

• Crime Prevention
• Organized Crime
• Community Development
• +3 more

Stephen Schneider is a Professor and Department Chair in the Department of Criminology at Saint Mary's University, Halifax, Nova Scotia. His roles include teaching courses such as Crime Prevention, Organized Crime, and Policing, while leading research projects on community safety, transnational organized crime, and crime prevention strategies. He has held academic positions since 1997, with a Ph.D. in Community and Regional Planning from the University of British Columbia (1997). Education: Ph.D., Community and Regional Planning, University of British Columbia (1997) M.Sc., International Development, University of Pennsylvania (1993) B.A., Political Science, Carleton University (1990) Research Interests: Dr. Schneider focuses on crime prevention strategies, organized crime dynamics, and policing practices. His work includes analyzing transnational criminal networks like the 'Ndrangheta, community safety initiatives (e.g., Wolfville Community Safety Study), and programs like PALS/SMART for at-risk youth. He emphasizes social development approaches to reduce recidivism and promote academic resilience. Key Projects: Wolfville Community Safety Study (2024): Designing a Community Safety Office model 'Ndrangheta in Canada (2022): Examining transnational power shifts Money Laundering in British Columbia (2020): Submitted to the Cullen Commission PALS/SMART Programs: Social developmental initiatives for youth Advising and Grants: Led $400,000 National Crime Prevention Centre-funded PALS program. Co-developed the SMART project, combining tutoring, mentoring, and psychological support for marginalized youth. Involved in federal grants analyzing port vulnerabilities and securities fraud. Labs/Teams: Oversees the Saint Mary’s Academic Resilience Team (SMART), a pilot program integrating academic support and recidivism prevention strategies for disadvantaged youth.

Mauricio AlvarezView profile

Senior Lecturer

• Machine Learning
• Gaussian Processes
• Bayesian Methods
• +5 more

Mauricio Alvarez is a Senior Lecturer in Machine Learning at the Department of Information Management, University of Manchester. His research focuses on Gaussian processes, Bayesian methods, and their applications in healthcare and robotics. He contributes to UN Sustainable Development Goals, particularly in advancing health innovations and AI fundamentals. He is part of the Digital Futures Institute and Christabel Pankhurst Institute. Research Interests: Gaussian Processes, Medical Imaging, Drug Discovery, Pediatric Health Analysis Collaborations: IEEE, npj Precision Oncology, and international institutions His work bridges machine learning with real-world challenges, such as cancer diagnosis and drug repositioning. He leads projects like the MCAIF Centre for AI Fundamentals and collaborates with the RAI Centre for Robotics and Artificial Intelligence. His recent articles emphasize multi-task learning and probabilistic models in healthcare.

• Employee Gentleness
• Emotion Regulation at Work
• Job Design
• +3 more

David Holman is a Professor in the Organisational Psychology Group at Manchester Business School, The University of Manchester. His research focuses on employee gentleness, emotion regulation at work, job design, and job quality. He leads a project funded by the Lord Alliance Research Grant exploring employee gentleness in social care settings through ethnographic studies. Holman serves on the editorial boards of Human Relations and the Journal of Business and Psychology , and has received a Best Paper award from the Journal of Occupational Health Psychology (2015-16). His work examines institutional factors shaping job quality across Europe, emphasizing divergent trends in job design. He collaborates internationally, contributing to risk regulation studies and safety culture development in high-risk industries. Research Highlights: Employee gentleness in care settings: Understanding organizational influences on caregiving practices. Emotion regulation dynamics: Investigating interpersonal emotion regulation in healthcare and supervisory relationships. Job quality variation: Analyzing cross-national institutional effects on European job design trends. Awards & Grants: Lord Alliance Research Grant (2023-2025) for studying employee gentleness in social care. Best Paper Prize, Journal of Occupational Health Psychology (2015-2016). Professional Roles: Editorial Board Member: Human Relations , Journal of Business and Psychology . Speaker: Delivered keynote on job redesign interventions at EAWOP conferences.

• Multi-agent Systems
• Sustainability
• Intelligent Transport Systems
• +3 more

Dr. Fatemeh Golpayegani is an Assistant Professor at the School of Computer Science, University College Dublin. She leads the Multi-agent Systems and Sustainable Solutions lab (MAS3.ucd.ie) and has secured over €1.5M in research grants. Her academic roles include BSc Stage 4 Coordinator and Chair of Women@CS (2012–2023). She holds a PhD from Trinity College Dublin (2018) and professional qualifications in university teaching from UCD. Education: PhD in Computer Science, Trinity College Dublin (2018) Professional Diploma in University Teaching & Learning, University College Dublin (2024) Professional Certificate in University Teaching & Learning, University College Dublin (2023) Research Interests: Focuses on multi-agent systems, sustainability, intelligent transport systems, autonomous decision-making, and edge computing. Her work integrates reinforcement learning, ontology-based models, and adaptive systems to address challenges in smart cities, energy grids, and infrastructure monitoring. Grants & Projects: Principal Investigator for the EU-funded RE-ROUTE project (€multi-million, 2023–2026) on intelligent transport networks. Co-Principal Investigator for the Augmented CCAM project on connected/cooperative autonomous mobility. Funded investigator in SFI centres (I-Form, CONNECT, Biorbic). Awards & Recognition: Researcher of the Year Award (2022) Member of Young Academy of Ireland (2023) Teaching & Mentoring: Coordinates modules in algorithms, Java programming, and operating systems. Supervises PhD students in SFI centres and mentors postdoctoral researchers. Active in promoting EDI as Chair of d-real doctoral training centre. Labs & Collaborations: Leads the MAS3 lab, collaborating on projects like CAPTAIN CARBON (sustainable transport gamification) and ontology-enhanced traffic signal control systems.

Prashant SankaranView profile

Assistant Professor

• Artificial Intelligence
• Operations Research
• Healthcare Optimization
• +3 more

Prashant Sankaran is an Assistant Professor in the Department of Industrial and Systems Engineering at the University at Buffalo (School of Engineering and Applied Sciences). He holds a PhD in Mechanical & Industrial Engineering from Rochester Institute of Technology (2023), an MS in Industrial & Systems Engineering from RIT (2020), and a BTech in Mechanical Engineering from Sharda University (2014). His research focuses on artificial intelligence for reasoning under uncertainty, explainable AI, and applications in healthcare, energy management, transportation, and space exploration. He integrates operations research and AI techniques to address complex optimization challenges. Research interests include computational design of bioelectronic materials, kidney exchange optimization via graph machine learning, and solving NP-hard combinatorial problems with hybrid learning-optimization frameworks. His work bridges theoretical advancements (e.g., genetic algorithms) with real-world applications like autonomous logistics systems and renewable energy management. No scientific awards have been mentioned. While no advisees are listed, his publications reflect collaborations across AI, robotics, and healthcare sectors. His research spans topics from deep reinforcement learning in warehouse automation to synthetic data generation for transplant systems.

• Algorithmic Foundations of Mathematical Optimization
• Reinforcement Learning
• Data-centric Learning
• +5 more

Supriyo Ghosh is a Senior Researcher at Microsoft Research, India. Prior to this role, he held positions at IBM Research AI Lab (2019–2021) and the Institute of Infocomm Research (I2R), A*STAR. He completed his PhD in Information Systems at Singapore Management University (2017) under Prof. Pradeep Varakantham and conducted postdoctoral research at MIT's SMART and LIDS centers (2016–2017). His research focuses on data-driven decision analytics, including algorithmic optimization, reinforcement learning, urban logistics, and network resilience in cyber-physical systems. His work has addressed cloud incident management, proactive decision-making under uncertainty, and applications of large language models (LLMs) in system reliability. Notable contributions include developing automated root-cause analysis frameworks and improving incident response strategies in large-scale cloud environments. He has also explored reinforcement learning applications in healthcare treatment optimization and air traffic control systems. Award-winning research includes the Best Paper Award at ACM SoCC'22 for an empirical study on high-severity cloud service incidents. He actively serves as a PC member for top conferences like AAAI, NeurIPS, and ICML, demonstrating his leadership in advancing AI and optimization fields. His academic background includes a graduate exchange at Carnegie Mellon University (CMU) and collaborations with MIT faculty like Prof. Patrick Jaillet. His work bridges theoretical foundations with real-world applications in transportation, cybersecurity, and enterprise systems.

• Automating Software Engineering Tasks
• Mining Software Repositories
• Program Analysis
• +9 more

Univ.-Prof. Martin Pinzger is a Professor at the Department of Informatics Systems, Alpen-Adria-Universität Klagenfurt. He serves as Head of Department and Member of the Senate, actively contributing to academic governance. Research Focus: Automating Software Engineering Tasks, Mining Software Repositories, Program Analysis, Software Evolution and Visualization Recent Work: Developing tools for API evolution analysis, cybersecurity AI (CAI), robotics benchmarking (RobotPerf), and dependency validation His research combines empirical studies with tool development for software maintenance and security. Current projects address challenges in REST API breaking changes, cloud security certifications, and robotic system performance evaluation. Publications since 2023 demonstrate continued engagement with topics spanning AI-driven code segmentation, microservice API evolution, and cybersecurity tool development. Key trends include cross-disciplinary applications of NLP to software engineering and security-focused tool creation. Contact: martin.pinzger@aau.at

Rynson W.H. LauView profile

Professor

• Computer Graphics
• Computer Vision
• Deep Learning
• +3 more

Rynson W.H. Lau is a Professor of Computer Science at City University of Hong Kong (CityU), leading research in Computer Graphics, Computer Vision, and Deep Learning. He holds an Honorary Professorship at Swansea University. Previously, he served on faculties at Durham University and The Hong Kong Polytechnic University. His work focuses on advancing graphics and vision techniques, including deep learning applications for graphics/vision problems, with publications in top venues like SIGGRAPH, CVPR, and NeurIPS. He has received the Adobe Research Gift (2023) and the Springer Nature Editorial Contribution Award (2025) for his editorial contributions to the International Journal of Computer Vision . Education: B.Sc. (First-class Honors) in Computer Systems Engineering from University of Kent Ph.D. in Computer Science from University of Cambridge Research Interests: Computer Graphics: Focused on 3D reconstruction, rendering, and real-time performance capture. Computer Vision: Specializing in saliency detection, object recognition, and low-light scene enhancement. Deep Learning: Developing generative models and diffusion-based frameworks for graphics and vision tasks. Editorial Roles: Editorial Board Member, International Journal of Computer Vision and IET Computer Vision . Guest Editor for special issues in journals like ACM Transactions on Internet Technology and IEEE Transactions on Multimedia. Teaching: 2024/25 Academic Year: CS4185: Multimedia Technologies and Applications CS4188/CS5188: Virtual Reality Technologies and Applications Research Team: Advises over 20+ students and collaborates internationally. Recent projects include AI-driven VR systems for healthcare and advanced 3D content generation using diffusion models.

Anil Ufuk BatmazView profile

Assistant Professor

• Virtual Reality
• Augmented Reality
• Human-Computer Interaction
• +3 more

Dr. Anil Ufuk Batmaz is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on Virtual Reality (VR), Augmented Reality (AR), and Human-Computer Interaction, with emphasis on interaction techniques, immersive analytics, and motor skill training systems. He holds a BSc in Electrical and Electronics Engineering (2007-2011), an MSc in the same field (2011-2013), and a PhD in Biomedical Engineering (2015-2018). His work bridges engineering and cognitive science, investigating how visual and haptic feedback impact user performance in immersive environments. Research interests include: 3D interaction techniques for mid-air tasks Effects of display technologies on motor coordination Hybrid UI design for mixed reality systems Training systems for precision tasks using VR Recent publications emphasize evaluation of AR/VR interfaces in healthcare, sports training, and collaborative environments. His work has appeared in venues like IEEE TVCG, ACM CHI, and ISMAR, addressing challenges in spatial navigation, error feedback, and system reliability.

Douglas BirdView profile

Professor

• Ecological Anthropology
• Foraging Economies
• Fire Ecology
• +2 more

Douglas Bird is Professor of Anthropology at Penn State, researching interactions between social/ecological systems in foraging societies. His collaborative fieldwork with Martu Aboriginal communities examines hunting, fire ecology, and landscape evolution. Key research areas: Ethnoarchaeology of hunter-gatherers Anthropogenic fire regimes Co-evolution of humans/ecosystems Coastal archaeology Field sites include Australia's Western Desert and Torres Strait Islands.

• Information Retrieval
• Search Engines
• Human-Computer Interaction for Information Retrieval
• +3 more

Mark D. Smucker is a Professor in the Department of Management Science and Engineering at the University of Waterloo, cross-appointed with the David R. Cheriton School of Computer Science (Faculty of Mathematics). His research focuses on interactive information retrieval systems, including search engines and recommendation systems, aiming to enhance evaluation methods for better prediction of human search performance. He co-organized the TREC Health Misinformation Track (2019–2022) and currently co-leads the TREC DRAGUN Track, addressing health misinformation and trustworthiness assessment in news. Education: PhD (Computer Science, UMass Amherst, 2008), MSc (Computer Science, UW-Madison, 1996), BSc (Physics & Computer Science, Iowa State, 1994). Research interests include design/analysis of interactive IR systems, evaluation frameworks, and human-computer interaction. He has been recognized with the ACM SIGIR 2012 Best Paper Award and teaching excellence awards from the University of Waterloo. Teaching includes courses like Search Engines (MSCI/MSE 541/720) and Databases/Software Design (MSCI 245). Active in organizing TREC tracks and publishing over 50 peer-reviewed articles.

Valentijn VischView profile

Researcher

• eHealth Technology
• Gamification in Healthcare
• AI in Medical Decision Making
• +4 more

Valentijn Visch is a researcher and academic at Delft University of Technology's Faculty of Industrial Design Engineering, specializing in Human-Centered Design and Society, Culture, and Critique. His work focuses on eHealth interventions, gamification in healthcare, and reducing health-related stigmas through innovative design solutions. He teaches courses such as 'eHealth Design for a Healthy Society' and 'Understanding Humans,' emphasizing interdisciplinary approaches to healthcare challenges. His research projects include developing embodied coaches for stroke rehabilitation, AI-driven healthcare decision-making frameworks, and inclusive eHealth tools for low socioeconomic populations. He has been recognized with awards like the Rehabilitation Year Award 2024 for his work on cardiac rehabilitation interventions and a CHI 2024 Best Paper Honourable Mention for studies on patient preferences in AI autonomy. Visch collaborates on initiatives like the 'Emotion Aware Car Seat' project, exploring human-technology interaction. His work bridges academic research with real-world applications, addressing gaps in healthcare accessibility and patient empowerment through technology.

• Finance
• Macroeconomics
• Labor Economics
• +4 more

Lawrence D. W. Schmidt is the Victor J. Menezes Career Development Assistant Professor of Finance at the MIT Sloan School of Management. He holds a PhD and MA in Economics from the University of California, San Diego, and previously served as an Assistant Professor at the University of Chicago’s Kenneth C. Griffin Department of Economics. His research bridges finance and macro-labor economics, focusing on household risk exposures, institutional investor behavior, and labor market dynamics. Key areas include human capital valuation, asset pricing implications of labor market risks, and the design of retirement savings institutions. Education: BA, University of California, Santa Barbara; PhD and MA, University of California, San Diego. Research Interests: Human capital risk and asset pricing Income inequality and retirement savings disparities Big data analysis of labor and financial markets Policy design for financial welfare Recent Trends in Publications: Schmidt’s work increasingly emphasizes the interplay between technological change and labor market outcomes, particularly in quantifying risks faced by workers and the implications for financial markets. His studies on retirement savings inequities highlight racial and socioeconomic disparities in wealth accumulation, informed by large-scale administrative datasets. Awards: 2024 Dimensional Fund Advisors Prize 2015 AQR Top Finance Graduate Award Advising & Grants: Collaborations with institutions like NBER and MIT Sloan reflect his focus on policy-relevant research. His work often involves interdisciplinary teams, as seen in co-authored papers on employer 401(k) practices and technology-driven labor displacement. Labs/Teams: Active contributor to the MIT Sloan Consumer Finance Initiative, which explores household finance and policy solutions for economic stability.

Chris ThomasView profile

Assistant Professor

• Computer Vision
• Cross-modal Retrieval
• Multimodal Knowledge Representation
• +3 more

Chris Thomas is an Assistant Professor in the Department of Computer Science at Virginia Tech’s College of Engineering. His research focuses on computer vision, cross-modal retrieval, and multimodal knowledge representation, with applications in information extraction, fake news detection, and AI safety. He leads the Sanghani Center for Artificial Intelligence and Data Analytics and has received grants from the Commonwealth Cyber Initiative and a Google Research Scholar award. Education: Ph.D. (2020) and B.S. (2013) in Computer Science, University of Pittsburgh. Research Interests: Developing robust cross-modal systems that bridge vision and language. Key areas include fine-grained visual entailment, multimodal inconsistency detection, and defending AI models against adversarial attacks. His work emphasizes practical applications like fact-checking and cybersecurity. Recent Contributions: Led the development of JourneyBench (a vision-language benchmark) and the Semantic Shield defense framework. Recent grants include cybersecurity for embodied agents and safer multimodal web agents. Awards: Google Research Scholar Award (2025), multiple Commonwealth Cyber Initiative grants (2024–2025). Advising & Grants: Advises students like Hani Alomari (ACL 2025) and collaborates with institutions like Columbia University and UCLA. His work integrates multimodal data to address real-world challenges in AI safety and information integrity. Labs/Teams: Active in Virginia Tech’s AI initiatives, focusing on interdisciplinary research at the intersection of vision, language, and security.

Michael OttoView profile

Researcher

• Virtual Technologies for Production Planning
• Immersive Virtual Assembly Assessments
• Markerless Motion Capture Technologies
• +3 more

Dr. Michael Otto is affiliated with the University of Ulm, Department of Computer Science, within the Faculty of Engineering. His research focuses on virtual technologies for production planning, immersive virtual assembly assessments, and markerless motion capture systems. He has contributed to projects like ARVIDA (Cost-efficient motion capture systems) and INTERACT (Human-centered workplaces). His work bridges computer science, manufacturing systems, and human factors. Key contributions include developing frameworks for motion capture, virtual reality benchmarking, and augmented reality applications in industrial contexts. Notable achievements include receiving the Best Industrial Paper award (2015) for his work on ubiquitous tracking using depth cameras. His research addresses challenges in assembly planning, ergonomic assessments, and spatial interaction in manufacturing environments. Education/Background: Former External PhD Candidate at University of Ulm. Research Interests: Dr. Otto’s work emphasizes practical applications of virtual and augmented reality in manufacturing. He explores how technologies like markerless motion capture and immersive environments can optimize assembly processes, enhance worker ergonomics, and improve production verification workflows. His projects often involve interdisciplinary collaboration with industry partners. Articles Overview: His publications (2014–2025) span topics such as augmented reality visualization, motion tracking algorithms, and VR-based simulation tools. These contributions highlight advancements in scalable systems, sensor fusion, and human-motion analysis within industrial contexts.