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Judith MöllerView profile

Professor

• Political Communication
• Social Media Effects
• Algorithmic Filtering
• +4 more

Prof. Dr. Judith Möller is the Scientific Director of the Leibniz Institute for Media Research | Hans-Bredow Institute (HBI) and holds a joint professorship at the University of Hamburg's Institute for Journalism and Communication Studies. Her research focuses on political communication effects, media's role in public opinion formation, and algorithmic impacts on society. She previously served as Associate Professor at the University of Amsterdam and Adjunct Professor at the University of Trondheim. Key awards include the 2019 VENI Talent Track grant from NWO for her work on AI-driven societal effects. Her collaborative projects address disinformation, media regulation, and AI ethics. She advises institutions like ERGA, EPRA, and German media authorities, emphasizing practical applications of her research. Notable contributions include the Reuters Digital News Report series and studies on generative AI's societal implications. Education: Previous academic roles at University of Amsterdam & Trondheim. Affiliated with ASCoR, CPC, and ICDS research initiatives.

• Information Systems
• Healthcare IT
• Knowledge Management
• +5 more

Atreyi Kankanhalli is a Professor at the National University of Singapore, specializing in Information Systems with a focus on knowledge management, healthcare IT, and digital innovation. Their research spans over three decades, with prolific contributions in top journals like MIS Quarterly, Journal of AIS, and Information & Management. They have co-authored over 150 papers addressing topics such as crowdsourcing, online communities, and the impact of AI on scholarly practices. Notable work includes studies on user adherence to health apps, innovation in public sector data utilization, and the ethical challenges of generative AI in peer review. Kankanhalli has also led research on global virtual teams and digital technologies' role in social justice, reflecting a commitment to both technical and societal dimensions of information systems. Education & Background: While specific degree details are not provided, their extensive publication history and academic roles imply advanced qualifications in Information Systems or related fields. They have collaborated with global researchers across institutions like NUS, University of Illinois, and Singapore Management University. Research Themes: Core areas include digital health interventions (e.g., fitness app adherence), organizational innovation via open data and crowdsourcing, and the socio-technical challenges of AI in academia. Their work often bridges theoretical frameworks with practical applications, such as healthcare decision support systems and policy-driven technology adoption. Impact & Influence: As an editorial board member and frequent conference contributor (e.g., ICIS, PACIS), Kankanhalli shapes the field's research agenda. Their recent focus on generative AI's implications highlights proactive engagement with emerging technologies' ethical and methodological challenges.

Michael GröschelView profile

Professor

• Business Process Management
• Digital Transformation
• Business Models
• +3 more

Prof. Dr. Michael Gröschel is a Professor of Business Informatics at the Faculty of Computer Science, Mannheim University of Technology. His expertise spans Business Process Management (BPMN, Process Mining), Digital Transformation, and innovative Business Models. He teaches courses on BPM, project management, and e-business, often collaborating with industry clients through student projects. As a consultant, he specializes in BPMN training and IT-driven business strategy. His work emphasizes practical applications of IT tools like RPA and low-code platforms. Recent publications focus on RPA bot performance evaluation, AI in automotive trade, and business model-IT alignment. Prof. Gröschel’s research bridges academic insights with real-world challenges, particularly in leveraging technology for business innovation. He has authored books and articles on business intelligence tools, mobile business strategies, and digital customer care. His consulting services include executive coaching for academic career advancement and enterprise IT management best practices. Office: Building A, Room 007c | Phone: +49 621 292-6764 | Professional website available for further engagement opportunities.

Bilal FarooqView profile

Associate Professor

• Disruptive transportation technologies and services
• Cyber-physical transportation systems
• Artificial intelligence and machine learning
• +4 more

Bilal Farooq is an Associate Professor and Program Director for the Master of Engineering in Interdisciplinary Engineering (MEIE) at Toronto Metropolitan University, holding the Canada Research Chair in Disruptive Transportation Technologies and Services within the Department of Civil Engineering. His educational background includes a PhD from the University of Toronto (2011), MASc from Lahore University of Management Sciences (2004), and BSc from the University of Engineering and Technology (2001). Dr. Farooq's research pioneers disruptive transportation solutions through cyber-physical systems, AI/machine learning applications, behavioral modeling, and optimization techniques. His work specifically targets on-demand multimodal systems, sustainable urban transportation, urban air mobility, automated vehicles, and extended reality applications, addressing critical urban mobility challenges with human-centered approaches. Analysis of his recent publications reveals a strong trend toward quantum-enhanced computational methods, privacy-preserving federated learning frameworks, and sustainability-focused decarbonization strategies across transportation domains, with increasing emphasis on human factors and real-world implementation. Notable scientific awards include: Ontario Early Researcher Award (2018) Canada Research Chair (2017) MassMotion Academic Pedestrian Modelling Project of the Year (2016) Québec Early Researcher Award (2014) Dr. Farooq actively supervises graduate students and secures significant research funding through his Canada Research Chair position and Early Researcher Awards. He directs the Laboratory of Innovations in Transportation (LiTrans), which develops interdisciplinary solutions integrating mathematics, engineering, computer science, and economics to address emerging transportation challenges. LiTrans focuses on disruptive transportation technologies, complete streets design, cyber-physical systems, pedestrian dynamics, resilience, and climate change impacts, collaborating with industry and government partners to translate research into practical urban mobility innovations for smart cities worldwide.

Kevin A. ShinpaughView profile

Professor

• Space Systems Engineering
• High-Performance Computing
• Spacecraft Design
• +2 more

Kevin A. Shinpaugh is Collegiate Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech’s College of Engineering. Since 2019 he has led instruction and research in spacecraft design and propulsion, leveraging decades of experience in high-performance computing and space-systems engineering. Education Ph.D., Aerospace Engineering, Virginia Tech (1994) M.S., Aerospace Engineering, Virginia Tech (1989) B.S., Aerospace Engineering, Virginia Tech (1986) Research Focus Dr. Shinpaugh’s scholarship centers on the intersection of high-performance computing (HPC) and space systems engineering . He develops and applies advanced computational techniques to spacecraft design, propulsion analysis, and mission planning. His work spans numerical simulation of complex aerospace systems, optimization of propulsion architectures, and creation of scalable HPC frameworks that enable rapid design iteration for spacecraft and launch vehicles. Publication Trends Across more than thirty refereed papers and design-competition reports, a clear trajectory emerges: early contributions in experimental fluid-mechanics instrumentation (laser-Doppler velocimetry, fiber-optic sensors) evolved into large-scale computational studies of space systems, and most recently into student-led mission-concept designs for CubeSats, lunar exploration, and interplanetary missions. Keywords consistently include spacecraft design, propulsion, deployable structures, and mission architecture. Service & Committees Chair, Virginia Tech HPC User Committee (2004–2011) Member, VT HPC Advisory Board (2007–present) NSF TeraGrid/XSEDE Campus Champion for Virginia Tech (2006–2013) IBM HPC/AI Customer Advisory Council DC (2019–present) Member, VT AOE Seminar Committee (2019–present) Laboratory & Computing Resources Dr. Shinpaugh has long stewarded Virginia Tech’s high-performance computing ecosystem. He directs students and collaborators in leveraging the university’s Advanced Research Computing (ARC) clusters, as well as national facilities through XSEDE and DoD HPCMP, to execute spacecraft-design simulations and propulsion analyses at scale.

Michael LuckView profile

Professor

• Intelligent agents
• Multi-agent systems
• Trust and reputation
• +3 more

Michael Luck is a Professor of Computer Science at University College London, specializing in intelligent agents, multi-agent systems, and trust and reputation frameworks. His work contributes to UN Sustainable Development Goals related to education and innovation. He holds a BSc and PhD from UCL (1988, 1993). Education: BSc (1988), PhD (1993) in Computer Science from UCL Research focuses on formal frameworks for agent systems, norms, trust mechanisms, and applications in genome analysis and grid computing. Recent projects include AQUAREOS (robotics collaboration), SAIS (AI assistants), and TrustATrip (reputation systems). Publications emphasize adaptive service environments, norm enforcement, and Bayesian trust models. He has been awarded Fellow of the British Computer Society (2005). Advising/Grants: Lead/co-investigator on 21 projects including EPSRC-funded initiatives. Supervised 14 students. Active in global conferences like AAMAS and contributed to standards via EPSRC Peer Review College.

Christian PoellabauerView profile

Professor

• Mobile Sensing
• Data Analytics
• Healthcare Technology
• +7 more

Christian Poellabauer is a Professor at Florida International University (FIU) in the Knight Foundation School of Computing and Information Sciences, serving as Interim Associate Dean for Research and Graduate Studies in the College of Engineering & Computing. He holds a Ph.D. from Georgia Institute of Technology (2004) and a Diplom-Ingenieur from TU Vienna (1998). His research focuses on mobile sensing, data analytics, and healthcare technologies, leading the MOSAIC Lab which develops solutions for healthcare, IoT, and smart cities. He previously led the Mobile Computing Lab at the University of Notre Dame and held leadership roles in data science institutes. Research Interests: His work spans digital biomarkers for neurodegenerative diseases, speech analysis for mental health, wearable device authentication, and wireless sensor networks. The MOSAIC Lab addresses challenges like real-time sensor data analysis on constrained devices and translating insights into clinical applications. Teaching: He has taught courses on Operating Systems, Mobile Computing, and Smart Health at both FIU and Notre Dame. Recent courses include COP4610 (Operating Systems Principles) and COP5614 (Graduate Operating Systems) at FIU. Service: He serves as Associate Editor for IEEE Transactions on Network Science and Engineering, and has organized conferences like ICNC 2023 and IEEE MASS 2021. His academic service includes roles on editorial boards and technical program committees for major conferences in distributed computing and networking. Advising & Labs: Advises current Ph.D. students in areas like multi-modal sensing for affective computing and mental health crowdsensing. Past students have pursued roles in academia and industry (e.g., Rose-Hulman Institute of Technology, Facebook, Microsoft). The MOSAIC Lab collaborates on projects like digital clinical outcome assessments and motor impairment detection.

Thomas E. DoyleView profile

Associate Professor

• Biomedical Engineering
• Human-Computer Interfacing (HCI)
• Machine Learning
• +6 more

Dr. Thomas E. Doyle is an Associate Professor at the McMaster School of Biomedical Engineering and the Department of Electrical & Computer Engineering at McMaster University. His research focuses on biomedical signal processing, human-computer interfacing (HCI), and machine learning applications for healthcare augmentation, rehabilitation, and enhancement. He holds a Ph.D. from Western Ontario, Canada, and teaches courses like COMPENG 2DI4 (Logic Design). His work bridges cybernetics and clinical applications, emphasizing AI-driven solutions for medical diagnostics, patient monitoring, and space exploration. Education: B.E.Sc, B.Sc, M.E.Sc, Ph.D. from Western Ontario, Canada Recent Projects: Developed AI systems for remote healthcare diagnostics (2023) Collaborated with NASA on medical emergency simulators for deep space missions (2017–2023) Led ventilator development efforts for local hospitals during the pandemic (2020) His research interests span machine learning for mental health diagnostics, trust quantification in medical AI, and extended reality (XR) for medical training. He emphasizes interdisciplinary approaches, integrating computational methods with healthcare challenges. Recent publications highlight applications in pediatric emergency care, chronic pain management, and reliable medical device design. Dr. Doyle actively engages in educational initiatives, including first-year engineering pedagogy and experiential learning programs. He has received funding for projects such as the Educating the Engineer of 2025 (EtE-25) awards and contributes to initiatives like the Digital & Smart Systems and Health & Bio-innovation research clusters at McMaster.

FOONG Pin SymView profile

Academic

• Patient decision aids
• Mobile health applications
• Lifestyle change and motivation
• +4 more

FOONG Pin Sym is a Senior Research Fellow at the Saw Swee Hock School of Public Health, National University of Singapore. She leads the Telehealth Core, a research center focused on developing and studying eHealth projects. Her academic background includes a Master's in Human Computer Interaction Design and a PhD in Interactive Media. Her research emphasizes patient-centered technologies, with a focus on chronic disease prevention, caregiver support, and mobile health applications. Her work addresses challenges in healthcare decision-making, end-of-life care, and lifestyle interventions. Notable projects include developing digital tools for diabetes risk communication, caregiver decision support systems, and studies on telehealth during the pandemic. She teaches courses in Human-Computer Interaction, Mobile Interaction Design, and Technology for Older Adults. Her research spans qualitative and quantitative methodologies, including randomized controlled trials and participatory action research. She actively participates in interdisciplinary collaborations across health, technology, and design sectors to improve patient and caregiver outcomes. Education: PhD in Interactive Media, Master's in Human Computer Interaction Design Labs/Teams: Leads the Telehealth Core research team Grants/Projects: SMART-GDM Study, Vita project for dementia care

Alexander PretschnerView profile

Professor

• Software Engineering
• Testing
• Information Security
• +5 more

Prof. Alexander Pretschner is a Professor of Software & Systems Engineering at the Technical University of Munich (TUM) and Founding Director of the Bavarian Research Institute for Digital Transformation (bidt). He also serves as Scientific Director of fortiss, a Bavarian research institute for software-intensive systems. His research focuses on software engineering, testing, information security, and ethical software development. Pretschner holds a PhD from TUM and has held academic positions at Karlsruhe Institute of Technology (KIT) and TU Kaiserslautern. He is a co-editor of several prestigious journals, including IEEE Transactions on Reliability and the Journal of Software Testing, Verification and Reliability. Education: PhD in Computer Science, Technical University of Munich MSc in Computer Science, University of Kansas (on Fulbright Scholarship) Diplom in Computer Science, RWTH Aachen University Research Interests: His work spans testing methodologies, secure software design, and ethical considerations in agile development. Notable contributions include frameworks for metamorphic testing, distributed data usage control, and accountability mechanisms for cyber-physical systems. Awards: IBM Faculty Award (2012, 2013) Google Focused Research Award (2011, 2012) EARTO Innovation Prize (2014) 2nd Platz Supervisory Award (2020) Advising & Grants: Pretschner has supervised numerous PhD and Master’s students, contributing to over 200 publications. He leads projects like EDAP (Ethical Deliberation in Agile Processes) and collaborates with industry partners on cybersecurity and AI ethics initiatives. Labs & Teams: His work is anchored in bidt, fortiss, and TUM’s Chair of Software & Systems Engineering, focusing on societal impacts of digitalization and trustworthy AI systems.

• Computer Vision
• Machine Learning
• Computational Photography
• +5 more

Alexei A. Efros is the Howard Friesen Professor in the EECS Department at UC Berkeley, affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at CMU's Robotics Institute and held a postdoc at the University of Oxford under Andrew Zisserman. He collaborates with INRIA/École Normale Supérieure in Paris. His research focuses on self-supervised learning, generative models, and visual data mining, with applications to robotics, computational photography, and art. Education & Academic Roles: Postdoc at Oxford (with Andrew Zisserman), faculty at CMU (2005–2015), currently at UC Berkeley. Teaches courses like CS 180/280A (Computer Vision) and CS 280 (Graduate Computer Vision). Research Interests: Self-supervised learning, generative models (e.g., diffusion models, inpainting), visual commonsense, and cross-modal reasoning. His work bridges computer vision and graphics, emphasizing data-driven approaches. Recent projects include Visual Jenga, Diffusion Models as Data Mining Tools, and Prioritized Generative Replay. Grants & Labs: Leads the Efros Research Group, advised over 40 PhD students (e.g., Jun-Yan Zhu, Tinghui Zhou). Collaborates with institutions like INRIA and NVIDIA. Active in grants related to AI, vision, and robotics. Labs/Teams: BAIR Lab (UC Berkeley), former affiliations with CMU Robotics Institute and Willow Team (INRIA/ENS Paris). Current lab focuses on generative AI, 3D perception, and visual reasoning.

Mike KirbyView profile

Professor

• Scientific Computing
• Physics-Informed Machine Learning
• Computational Science and Engineering
• +4 more

Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).

Joseph A. CampbellView profile

Assistant Professor

• Explainable AI
• Robotics
• Machine Learning
• +3 more

Joseph A. Campbell is an Assistant Professor in the Department of Computer Science at Purdue University, leading the Collaborative AI for Machines and People (CAMP) Lab. He holds a Ph.D., M.S., and B.S. in Computer Science and Computer Engineering from Arizona State University. Before academia, he worked as a software engineer for five years. Prior to Purdue, he was a Postdoctoral Fellow at Carnegie Mellon University's Robotics Institute. His research focuses on explainable machine learning and robotics, particularly how agents use explanations for self-improvement and decision-making. Key areas include theory of mind in multi-agent systems, lifelong learning, and interpretable transfer learning. His work bridges robotics and AI, with applications in human-robot interaction and prosthetic control. Notable publications include advancements in reinforcement learning with language models, multi-agent collaboration frameworks, and methods for enhancing state estimation in robots. His research has been presented at top conferences like NeurIPS, EMNLP, and CoRL. Dr. Campbell maintains an active GitHub profile (joe-campbell) with repositories such as Interaction Primitives for robotics applications. His lab, CAMP, explores AI systems that collaborate effectively with humans and other machines.

Bruno Felisberto Martins RibeiroView profile

Associate Professor

• Networking and Operating Systems
• Artificial Intelligence
• Machine Learning
• +4 more

Bruno Felisberto Martins Ribeiro is an Associate Professor of Computer Science at Purdue University, joining the department in Fall 2015. His research focuses on endowing machine learning algorithms with robust invariant representations for relational and temporal data, emphasizing causal and associational tasks. Key research areas include Networking and Operating Systems, Artificial Intelligence, Machine Learning, and Natural Language Processing. He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst (2010). Education: Ph.D., Computer Science, University of Massachusetts Amherst, 2010 Research Interests: Explores invariances in mathematics and machine learning to improve model robustness. Key topics include graph and tensor invariances, causal relationships, adversarial robustness, and applications in recommendation systems, robotics, and drug discovery. His lab’s work has advanced counterfactual task frameworks and causal reasoning in machine learning. Recent Contributions: Recent publications address zero-shot generalization in graph neural networks, causal discovery methods, and defenses against adversarial attacks. His work spans conferences like ICML, NeurIPS, and SIGCOMM. Awards: Best Paper Award at ACM CODASPY 2021 Best Paper Award at SIGMETRICS 2016 Best Paper Award at IEEE NetSciCom 2014 Advising & Students: Supervises current PhD students Beatrice Bevilacqua, Jincheng Zhou, and Yucheng Zhang, along with MSc student Ipsit Mantri. Notable former students include S Chandra Mouli (Meta), Yangze Zhou (Spotify), and Jianfei Gao (Vector Institute). Labs & Teams: Leads research in invariant representations and causal ML, collaborating with institutions like Stanford during his sabbatical. His work bridges theory and practice, impacting areas like network analysis and AI-driven healthcare.

Oliver S. CossairtView profile

Adjunct Associate Professor

• Computational Imaging
• Computer Vision
• Computer Graphics
• +5 more

Oliver S. Cossairt is an Adjunct Associate Professor at Northwestern University's departments of Computer Science and Electrical and Computer Engineering. He leads the Computational Photography Lab , focusing on computational imaging, optics, and display technologies. His work bridges computer vision, graphics, and optical engineering to design novel imaging systems with applications in medical, astronomical, and scientific domains. Education: Ph.D. Computer Science, Columbia University (2011) M.S. Media Arts and Sciences, MIT Media Lab (2003) B.S. Physics, Evergreen State College (2003) Research Interests: Cossairt develops imaging systems that combine optical innovations with computational methods to enhance performance and functionality. Key areas include computational displays, depth sensing, and high-precision 3D imaging. His work emphasizes practical applications like medical imaging, holography, and non-line-of-sight sensing. Awards: NSF CAREER Award (2015–2020) Best Paper Award at ICCP 2011 NSF Graduate Research Fellowship (2008–2011) Teaching & Funding: Taught courses on computational photography and computer vision. Secured grants from NSF, NIH, and industry partners (e.g., Samsung, Omron) for projects like Coherent Computational Imaging and Snapshot 3D Holographic Microscope . Labs & Teams: Directs the Computational Photography Lab, collaborating with institutions like Argonne National Labs and museums for projects in cultural heritage imaging.