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Benjamin AroldView profile

Assistant Professor

• Labor Economics
• Education Economics
• Public Economics
• +7 more

Benjamin Arold is a University Assistant Professor at the Faculty of Economics, University of Cambridge. His academic career spans theoretical and applied research in labor economics, education economics, and AI-driven policy analysis. Previously, he held postdoctoral positions at ETH Zurich and visiting roles at Harvard University and Princeton University. Education: Ph.D. in Economics, LMU Munich His research bridges economic theory with empirical applications, focusing on: Long-term impacts of school curricula on student outcomes AI and NLP applications in labor economics Economics of religion and its societal implications Recent Article Trends: His work highlights intersections between education policy and technological innovation, including meta-analyses of private schooling effects, charter school performance evaluations, and computational methods for analyzing educational interventions. Scientific Awards: Prize for Best Dissertation (2021/2022) - German Economic Association Fürther Ludwig Erhard Prize (2023) Teaching: He teaches advanced courses on NLP in economics at Princeton University and Basel University, with a focus on computational methods for non-standard data.

Gavin McNicolView profile

Assistant Professor

• Soil Biogeochemistry
• Biosphere-Atmosphere Exchange
• Wetland Ecology
• +3 more

Gavin McNicol is an Assistant Professor in the Department of Earth and Environmental Sciences at the University of Illinois at Chicago (UIC). His research focuses on soil biogeochemistry and its connection to Earth’s climate system, particularly methane emissions from wetlands, temperate rainforests, and waste systems. He employs field measurements, laboratory experiments, and data science to study greenhouse gas dynamics across scales. McNicol teaches courses on data science (EAES 420) and climate-ecosystem interactions (EAES 579). His work integrates machine learning with satellite remote sensing to monitor global wetland methane emissions. Key collaborations include FLUXNET-CH4, CRMRCN, and the SOIL NGO in Haiti. McNicol holds a BS from the University of Stirling (2010) and a PhD from UC Berkeley (2016). Research interests span wetland methane budgets, carbon cycling in coastal ecosystems, and climate impacts of sanitation systems. He has secured grants from NASA, DOE, and NSF, totaling over $87,000. His team, the McNicol Lab, emphasizes interdisciplinary approaches combining fieldwork, lab analysis, and computational modeling. Recent publications (2023–2025) address global methane upscaling, tropical wetland monitoring gaps, and boreal-Arctic methane feedbacks. McNicol’s work bridges local measurements to global climate models, with applications in climate change mitigation and sustainable development.

Richard E. TurnerView profile

Professor

• Probabilistic Machine Learning
• Environmental Prediction
• Spatio-temporal Modelling
• +4 more

Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.

Mohamed NoureldinView profile

Assistant Professor

• Structural Engineering
• Artificial Intelligence in Civil Engineering
• Structural Health Monitoring
• +5 more

Mohamed Noureldin is an Assistant Professor at the Department of Civil Engineering, Aalto University , Finland, with prior academic roles at Sungkyunkwan University, South Korea (2015–2022). His expertise lies in integrating Artificial Intelligence (AI) with Structural Health Monitoring (SHM) , Structural Digital Twin , Predictive Maintenance , and Seismic Retrofitting . Research Focus : AI-powered sustainable structural design, smart retrofitting, predictive maintenance, structural material innovation, and next-generation performance-based seismic/wind design. Industrial Experience : 20+ years in offshore/onshore structural engineering (Hyundai Heavy Industries, Samsung Engineering, Arab-Swiss Engineering Company, Zuhair Fayez Partnership). Teaching : Courses in structural analysis, seismic design, dynamics, and reinforced concrete at Aalto and Sungkyunkwan Universities. Laboratory : Leads the Structural Design AI Lab (SDAI), focusing on AI-driven resilient infrastructure. Contact : mohamed.noureldin@aalto.fi , +358504544861. His publications explore cutting-edge applications of AI, ML, and DL in seismic retrofitting, structural durability, soil stabilization, and hybrid damping systems. Collaborative work emphasizes life-cycle cost assessment and augmented reality for predictive maintenance.

• Multimodal Human Behavior Modeling
• Health Informatics
• Affective Computing
• +7 more

Zakia Hammal is an Assistant Research Professor with dual appointments at Carnegie Mellon University, holding positions in the Robotics Institute within the School of Computer Science and the Department of Biomedical Engineering in the College of Engineering. Her work bridges computer science, machine learning, artificial intelligence, and social/behavioral psychology to advance computational models for human behavior analysis. Dr. Hammal's educational background includes a PhD in Computer Science, a Master of Artificial Intelligence and Algorithmic with specialization in Image Processing, and an Engineer's degree in Computer Science with specialization in Computer Systems. Her academic journey has positioned her at the intersection of technical expertise and healthcare applications. Her research focuses on multimodal human behavior modeling in social interaction, with particular emphasis on health informatics and affective computing (Emotion AI). Dr. Hammal's work has pioneered computational models for multimodal assessment of psychiatric disorders, including depression severity evaluation, automatic pain intensity measurement, assessment of expressiveness in children with facial abnormalities, analysis of non-verbal communication in mother-infant interaction, and identification of behavioral markers in autism spectrum disorder. Her approach integrates computer vision, machine learning, and behavioral psychology to create systems that can objectively measure human behaviors that are often subjective in clinical settings. Analysis of her recent publications reveals a consistent trajectory toward more sophisticated multimodal approaches to healthcare challenges, particularly in pain assessment and mental health diagnostics. Her work increasingly emphasizes interpretable AI models that can translate complex behavioral patterns into clinically meaningful insights, with growing attention to applications for vulnerable populations including infants, elderly patients, and those with craniofacial abnormalities or autism spectrum disorder. Women in AI Awards North America 2023 – AI Researcher of the Year Award Outstanding Reviewer Award at FG 2015 Best Paper award at ACII 2015 Outstanding Paper award at ICMI 2012 Dr. Hammal has secured significant research funding, primarily from the U.S. National Institutes of Health, including an R01 grant for developing a Multimodal Behavioral AI platform for pain assessment and management, and additional grants for automatic pain assessment in older adults with dementia. Her leadership extends to mentoring through her involvement in organizing workshops and conferences that train the next generation of researchers in affective computing and health informatics. As an active leader in her field, Dr. Hammal serves as ACM ICMI Steering Board Committee Member, Associate Editor for IEEE Transactions on Affective Computing and IEEE Transactions on Multimedia, and has organized numerous influential workshops including the International Workshop on Automated Assessment of Pain and Face and Gesture Analysis for Health Informatics. She is set to serve as Program Chair for FG 2025, ACII 2025, and ICMI 2026, demonstrating her growing influence in shaping the future direction of research in multimodal interaction and affective computing.

Emily WallView profile

Assistant Professor

• Data Visualization
• Visual Analytics
• Cognitive Psychology
• +3 more

Dr. Emily Wall is an Assistant Professor in the Department of Computer Science at Emory University, leading the CAV Lab (Cognition and Visualization). She earned her Ph.D. in Computer Science from Georgia Tech (2020) and completed a postdoctoral fellowship at Northwestern University before joining Emory. Ph.D., Computer Science, Georgia Tech (2020) Postdoctoral Researcher, Northwestern University Current: Assistant Professor, Emory University Her research focuses on enhancing human decision-making through data visualization and visual analytics by addressing cognitive limitations and biases. Key areas include metacognition promotion, computational strategies for bias mitigation, and human-AI collaboration in data analysis. She combines cognitive psychology principles with technical implementation using tools like D3.js and Tableau. Recent publications highlight her work on LLM applications in visual analytics, cognitive bias interventions, and trust calibration in AI systems. Current students like Mengyu Chen, Shiyao Li, and Zhongzheng Xu are presenting at top conferences such as CHI and IEEE VIS. She also explores educational applications through interactive visualization pedagogy and innovative assessment methods. Dr. Wall actively engages in academic service through graduate mentorship, conference judging, and diversity initiatives. Her lab emphasizes both technical proficiency and social justice implications in visualization design, reflected in course projects that merge artistic expression with data-driven storytelling.

Jakob SchoefferView profile

Assistant Professor

• Responsible AI
• Explainable AI
• Human-Centered AI
• +6 more

Jakob Schoeffer is a tenure-track Assistant Professor in the Artificial Intelligence department at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen (Netherlands). His work focuses on the intersection of human decision-making and artificial intelligence, particularly in high-stakes contexts where fairness, transparency, and appropriate human-AI collaboration are critical. Dr. Schoeffer's research interests center on responsible and explainable AI, with specific focus areas including: Human-AI collaboration dynamics in decision-making processes Fairness perceptions and interventions in AI systems Appropriate reliance on AI recommendations Explainable AI techniques for high-stakes domains Transparency mechanisms that improve human-AI team performance Label indeterminacy issues in medical AI applications His recent publications (2023-2025) reveal a strong trend toward applying AI research in critical domains like healthcare (particularly neurological recovery prediction), while maintaining a rigorous focus on the human aspects of AI deployment. His work spans both theoretical foundations of human-AI interaction and practical implementations, often employing mixed-methods approaches that combine technical AI development with behavioral studies. Dr. Schoeffer actively collaborates with researchers across institutions including the University of Texas at Austin and has made significant contributions to top conferences in AI ethics, fairness, and human-computer interaction. His research has been featured in multiple news outlets and policy discussions, indicating real-world impact of his work on responsible AI development. Prior to his current appointment, Dr. Schoeffer was a Postdoctoral Research Fellow at the University of Texas at Austin. He received his PhD from the Karlsruhe Institute of Technology (KIT) in Germany with a dissertation titled "On the Interplay of Transparency and Fairness in AI-Informed Decision-Making." He also holds a master's degree in Operations Research from Georgia Tech and industry experience as a Senior Data Scientist at IBM.

John P. O'DohertyView profile

Professor

• Decision Neuroscience
• Reward-related Learning
• Computational Modeling
• +3 more

John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.

Na DuView profile

Assistant Professor

• Human-Computer Interaction
• Human Factors in Smart City Technologies
• Human-Centered Computing
• +4 more

Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.

Frank L. Hammond IIIView profile

Assistant Professor

• Adaptive Robotic Manipulation
• Soft Robotics
• Wearable Robotics
• +5 more

Frank L. Hammond III serves as Assistant Professor at Georgia Tech's Woodruff School of Mechanical Engineering since April 2015, directing the Adaptation Robotic Manipulation (ARM) Laboratory. A Carnegie Mellon PhD graduate, he previously held postdoctoral positions at MIT and Harvard as a Ford Fellow. His interdisciplinary work bridges mechanical engineering, biomedical applications, and computational design. Education Ph.D. in Mechanical Engineering, Carnegie Mellon University M.S. in Mechanical Engineering, University of Pennsylvania M.S. in Electrical Engineering, University of Pennsylvania B.S. in Electrical Engineering & Biomedical Engineering, Drexel University Hammond's research pioneers adaptive robotic manipulation (ARM) systems that operate in unstructured human environments through bioinspired computational design. His lab develops xenomorphic (non-biomorphic) robots using soft pneumatic actuation, flexible electronics, and machine learning to achieve biological-level versatility. Key application domains include wearable human augmentation devices , haptic-enabled surgical teleoperation , and autonomous soft platforms for medical and industrial use. The ARM methodology integrates empirical biomechanics characterization with simulation-driven optimization and rapid prototyping. Analysis of his 15 most recent publications (2023-2025) reveals three dominant trends: (1) Medical rehabilitation breakthroughs through intention-driven exoskeletons with soft bioelectronics, (2) Novel locomotion strategies for soft robots in complex environments (sand, water, cluttered spaces), and (3) Advanced haptic feedback systems leveraging multimodal sensory substitution for proprioceptive restoration. These works consistently bridge biomechanics, control theory, and human factors. Awards Ford Postdoctoral Research Fellowship at Harvard School of Engineering Hammond actively mentors graduate researchers including PhD candidates Lucas Tiziani (soft actuators) and Bangyuan Liu (earthworm robotics), and Master's student Alex Hart (pediatric haptics). His lab secures research funding for projects like tunable mechanical interfaces for neuropathy treatment and cognition-focused wearable devices, with strong industry and clinical partnerships evident in co-authored medical device publications. The ARM Lab maintains robust collaborations across Georgia Tech's robotics, neuroscience, and biomedical engineering communities. The Adaptation Robotic Manipulation Laboratory operates from Whitaker Building Room 4102, housing specialized facilities for soft robot fabrication (3D printing, shape deposition manufacturing) and biomechanics testing. Current projects include pediatric haptic feedback displays, biomimetic swimming robots, and kirigami-skinned earthworm robots for subsurface locomotion. The lab emphasizes translational research with multiple pending medical device patents and active participation in K-12 STEM outreach programs.

• Artificial Intelligence
• Privacy and Data Protection
• Trustworthy AI
• +4 more

Prof. Dr. Pinar Yolum Birbil is a leading researcher in Responsible AI at the Faculty of Science , Utrecht University . Her work bridges Artificial Intelligence , Privacy and Data Protection , and Software Agents , focusing on Human-Centered AI and Trustworthy Systems . She is part of the AI & Data Science and Responsible AI research groups. PhD in Computer Science, North Carolina State University (2003) MS in Computer Science, North Carolina State University (2000) BSc in Computer Engineering, Marmara University (1998) Her research explores Privacy Preservation in collaborative systems, Computational Theory of Mind for human-agent coordination, and Norm-Based AI Systems . Recent projects include the Hybrid Intelligence Center and tools like PANOLA for privacy support. She investigates how AI can balance user autonomy , ethical norms , and societal values in applications ranging from urban planning to healthcare. Her scientific publications (2023-2025) span topics like Explainable Privacy , Trust in Hybrid Teams , and AI for Diabetes Management , appearing in venues such as JAIR , AAMAS , and ACM TOIT . She emphasizes collaborative AI , with contributions to multiagent simulations and privacy-preserving mechanisms . Scientific Awards : NC State University Alumni Hall of Fame (2017) Woman Entrepreneur of the Year (Microsoft Turkey & KAGIDER, 2017) Bogazici University Academic Encouragement Award (multiple years) Best Paper Award at ESAW 2009 She supervises a dynamic research group with PhD students working on Hybrid Intelligence , Computational Ethics , and Privacy Modeling . Her projects often involve interdisciplinary collaboration with institutions like TNO and the Transforming Cities Hub .

Kaiyu HangView profile

Assistant Professor

• Robotics
• Robotic Manipulation
• Grasping
• +8 more

Kaiyu Hang is an Assistant Professor in the Department of Computer Science at Rice University, where he directs the Robotics and Physical Interactions Lab (RobotΠ Lab). His research spans multiple domains of robotics with a focus on physical interaction systems. Before joining Rice, he completed his postdoc at Yale University, earned his Ph.D./M.Sc. at KTH Royal Institute of Technology, and received his B.Eng. from Xi'an Jiaotong University. His research interests include robotic manipulation, grasping, in-hand manipulation, optimization, planning, learning, estimation, and control systems. He develops algorithms that enable robots to physically interact with other robots, people, and the world across scales from small grasping tasks to large-scale dual-arm and multi-robot manipulation systems. His work has practical applications in factories, kitchens, hospitals, warehouses, and construction sites. His recent publications demonstrate strong trends in in-hand manipulation techniques, energy-efficient drone operations, and benchmarking frameworks for robotic grasping. The 2025 IROS papers accepted highlight his leadership in developing standardized competition frameworks for evaluating robotic manipulation capabilities across diverse hardware platforms. ASME Rising Star of Mechanical Engineering (2024) NSF CAREER Award (2023) Multiple finalist awards at IEEE-RAS Humanoids and ICRA conferences Junior Fellowship Award from Institute for Advanced Study, HKUST (2017-2018) As an educator, he has taught multiple robotics courses including COMP 462/562: Introduction to Modern Robotics and COMP 461: Senior Design in A Robotized World. He serves as Faculty Advisor for the Rice Robotics Club and participates in graduate admissions. His lab actively recruits Ph.D. students and offers research opportunities for undergraduate and master's students who have completed core robotics courses.

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +13 more

Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama, College of Engineering. He has established himself as a prominent researcher in artificial intelligence, particularly in computer vision, deep learning, and image processing. His educational background includes: Postdoctoral Research Fellow, Computer Science, Boston College (2023) Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2023) D. Eng., Computer Science and Technology, University of Electronic Science and Technology of China (2021) M.S., Electrical and Electronic Engineering, Eastern Mediterranean University (2017) B.S., Electrical Engineering, University of Guilan (2012) Dr. Golilarz's research spans multiple domains of artificial intelligence with a particular focus on computer vision, deep learning, and image processing applications. His work addresses challenges in medical imaging, satellite imagery, and cognitive neuroscience. He has made significant contributions to image denoising techniques, control chart pattern recognition, and AI applications in healthcare. His recent work has expanded into generative AI, large language models, and secure machine learning operations. His publication portfolio demonstrates consistent productivity with over 2500 citations and an h-index of 25. His most impactful work includes applications of blockchain and federated learning for COVID-19 detection, optimized support vector machines for medical diagnosis, and innovative image denoising techniques using metaheuristic optimization algorithms. Among his professional achievements: Co-founded AI Letters journal in 2024, serving as Associate Editor-in-Chief Served as Lead Guest Editor and Topic Editor for several SCI-indexed journals Held the role of Conference Program Chair Dr. Golilarz has supervised numerous graduate students and research projects, with his work spanning theoretical advancements in AI algorithms to practical applications in healthcare, energy systems, and cybersecurity. His research group has established collaborations with institutions including Boston College and Mississippi State University.

• Computer Vision
• Deep Learning
• Reinforcement Learning
• +7 more

Dr. Chandranath Adak is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Patna (IIT Patna), and concurrently serves as a Visiting Fellow at the School of Computer Science, University of Technology Sydney (UTS), Australia. He holds a Ph.D. in Analytics from UTS (2019) and previously served as an Assistant Professor at Indian Institute of Information Technology Lucknow (IIITL) and the Centre for Data Science at JIS Institute of Advanced Studies, Kolkata. Education: Ph.D. (Analytics), University of Technology Sydney (2019) M.Tech., Computer Science and Engineering, University of Kalyani (2014) B.Tech., Computer Science and Engineering, West Bengal University of Technology (2012) Research Interests: His work spans Computer Vision, Deep Learning, Reinforcement Learning, Document Image Analysis, and AI-driven solutions for healthcare, forensics, and industrial automation. He has pioneered methods in biomarker detection using electrochemical sensors combined with ML models, handwriting analysis for educational and forensic applications, and anomaly detection in industrial systems. His research bridges theoretical advances with real-world applications, such as medical diagnostics and quality control systems. Publications: His recent work includes innovations in biosensor-based medical diagnostics, handwriting evaluation systems, and transformer networks for historical document analysis. These contributions reflect a focus on interdisciplinary applications of AI across healthcare, cultural heritage preservation, and industrial automation. Awards: Start-up Research Grant, SERB, India (2022) Dr. Kalam Doctoral Scholarship, UTS (2018) IEEE CIS Graduate Student Research Grant (2017) Senior Member, IEEE (2024) Teaching & Supervision: Taught courses at UTS including 'Introduction to Data Analytics' and supervised research in machine learning and computer vision. His mentorship emphasizes hands-on experience with AI tools and real-world problem-solving. Labs & Teams: Engaged in collaborative projects at UTS's CIBCI Centre and Griffith University's IIIS, focusing on computational intelligence and sensor-driven AI systems.

Huining LiView profile

Assistant Professor

• Mobile Computing
• Internet of Things
• Cybersecurity
• +5 more

Huining Li is an Assistant Professor in the Department of Computer Science at North Carolina State University . Her research focuses on Internet of Things (IoT) , cybersecurity , and mobile computing , with a specialized emphasis on mobile health (mHealth) technologies. Education: Ph.D. in Computer Science and Engineering from University at Buffalo (2024). Her work addresses privacy-preserving sensing mechanisms , biomarker measurement , and fairness in dynamic mobile environments , developing systems for chronic wound care, Parkinson’s disease management, and mental health therapy. Recent publications highlight innovations in mmWave biometrics , machine learning for health diagnostics , and non-contact monitoring . Scientific Awards: Best Paper Awards (SenSys 2019, BodyNet 2021, ICHI 2022) Best Paper Candidate (SenSys 2022) Harold O. Wolf Achievement Award (2024) EECS Rising Star (2023) NIH mHealth Training Institute Scholar (2025) She teaches courses in Mobile Health Systems and Applications and Computer Networks , and actively serves on NSF panels , TPC committees (ACM MobiSys, IEEE-EMBS BSN), and as Associate Editor for journals like Elsevier Smart Health.