Emine Ayaz is a Professor at Istanbul Technical University's Department of Electrical Engineering. Her research spans fault detection in electric motors, signal processing, and nuclear power plant monitoring, with recent work integrating deep learning (e.g., dual RNN architectures) and medical applications (e.g., parasitology, plant-based wound healing). Key Collaborations : International partnerships in motor diagnostics and nuclear engineering. Projects : Led grants on high-voltage training and predictive maintenance for TEİAŞ and industrial processes. Research Trends : Recent publications emphasize neural networks for motor fault classification, coherence analysis for insulation diagnostics, and interdisciplinary work in plant biotechnology and parasitology. Labs & Teams : Involved in projects analyzing vibration signals, wavelet transforms, and sensor fusion for industrial and nuclear systems.
Olli Varis is an Aalto Distinguished Professor at Aalto University's Department of Built Environment, specializing in Water and Environmental Engineering. He holds adjunct professorships at Asian Institute of Technology (Thailand) and has served as Vice Dean for Research & Innovation at Aalto School of Engineering (2013–2018). His expertise spans water resources management in developing economies, climate change impacts, and transboundary water governance. Education: Doctoral degree in Engineering (Helsinki University of Technology, 1991), Licentiate (1988), and dual Master's degrees in Agriculture (University of Helsinki, 1986) and Engineering (Helsinki University of Technology, 1984). Research focuses on sustainable development goals (SDGs), particularly water-energy-food nexus dynamics, urbanization impacts, and global river basin vulnerabilities. Key interests include flood early warning systems, migration-environment linkages, and Asian transboundary water challenges. Notable awards include Aalto Excellence Awards (2018, 2021), Best Research Paper 2023, and Environmental Research Letters' Best Paper 2010. Over 350 publications include works on China's water risks, Nepal's flood resilience, and global migration drivers. Active in UNESCO, WMO, and UNU/WIDER. Recent projects address urbanization's environmental pressures, interdisciplinary education challenges, and东南亚数字经济的崛起与挑战 (Vietnam's digital economy challenges). Leads the Water and Environmental Engineering research group, supervising 12 theses. Involved in public outreach,如芬兰赫尔辛基大学的媒体参与案例.
Weiwei Lin is an Associate Professor in the Department of Civil Engineering at Aalto University, specializing in structural engineering with a focus on bridge systems, composite materials, and structural health monitoring. His research explores fatigue behavior of steel structures, seismic performance of composite systems, and innovative repair techniques. He holds a PhD from Waseda University (2012), MSc from Southeast University (2009), and BEng from Southwest Jiaotong University (2006). Key research areas include: steel-concrete composites, bridge redundancy evaluation, replaceable energy dissipaters, and AI-driven infrastructure diagnostics. Lin leads projects like CCU Structure (EU Horizon Europe) and RCF Mobility initiatives, focusing on sustainable construction and material recyclability. He has published 120+ peer-reviewed articles and secured 6 major grants. Lin has received prestigious awards including the IABMAS Young Award (2014) and Outstanding Reviewing Award (2017). His lab collaborates globally, hosting researchers from institutions like Israel Institute of Technology and Tsinghua University. Current work emphasizes crowdsourcing-based bridge monitoring and physics-guided AI frameworks for infrastructure diagnostics.
Dr. Cai Ladd is a Lecturer in Geography at Swansea University's School of Biosciences, Geography and Physics. His research focuses on coastal wetlands, integrating biogeomorphology, socio-ecological resilience, and ecosystem service sustainability. He leads the 'Coastal Wetlands and Ecosystem Services' theme at the Climate Action Research Institute. Dr. Ladd teaches modules such as Coastal Processes, GIS Applications, and Sustainable Development Goals, emphasizing practical fieldwork and spatial statistics. Current projects include the Community-Led Enhancement and Restoration of Coastal Ecosystems in the Cumbrian Solway Firth (2022–2025), funded at £175,977. He supervises PhD student Rhian Hedd Meara on cross-border coastal conservation. His research employs spatial statistics, hydrological monitoring, and citizen science to develop management tools for coastal conservation. Notable contributions include studies on saltmarsh carbon stocks, mangrove restoration in tropical deltas, and innovative Mini Buoy sensor technology. Dr. Ladd collaborates globally, with publications in Frontiers in Marine Science , Nature Communications , and Environmental Pollution . His work bridges natural and social sciences, addressing climate adaptation and community-led conservation strategies.
Arrvindh Shriraman is an Associate Professor and Program Director of Software Systems at Simon Fraser University's School of Computing Science in Surrey. His research focuses on energy-efficient software, multicore memory systems, and optimizing hardware/software interfaces for parallel programming. He holds a Ph.D. (2010) and M.S. (2006) in Computer Science from the University of Rochester, and a B.Eng. (2004) from the University of Madras. He teaches courses on parallel programming and energy-conscious software design. His research interests include synchronization mechanisms for domain-specific architectures, cache optimization, and FPGA-based acceleration. He has contributed to frameworks like Mu-grind for HLS-generated RTL instrumentation and TAPAS for parallel accelerator generation. Notable projects include RANGE-BLOCKS for synchronization in domain-specific systems and TapeFlow for gradient computation in neural networks. His work emphasizes real-time verification of autonomous systems and safety-critical trajectory planning for underwater vehicles. Shriraman collaborates closely with the Tangent Lab, exploring cutting-edge solutions in hardware-software co-design and embedded systems. His teaching and research bridge theoretical computer science with applied engineering challenges, addressing scalability, efficiency, and safety in modern computing systems.
Professor Bing Chu is an academic at the University of Southampton, actively contributing to research in control systems, robotics, and machine learning. They are a member of the Vision, Learning and Control Centre for Internet of Things and Pervasive Systems and the Centre for Robotics, focusing on interdisciplinary approaches that combine control theory with data-driven methodologies. Current research interests include: Iterative learning control Human-robot interaction Wind farm power optimization Robot behavior modeling Control system architectures Collaborative learning systems Recent publications highlight trends in data-driven control systems, human-robot interaction datasets, and optimization techniques for both continuous-time systems and wind energy applications. Professor Chu supervises multiple PhD students across robotics and electronic engineering, including Balint Gucsi, Haonan Shen, and Aleksander Wolski, while leading projects funded by Zhengzhou University and the Royal Society.
Amanda Jensen-Doss is a Professor and Director of Clinical Training in the Department of Psychology at the University of Miami's College of Arts and Sciences. Her work focuses on improving mental health care for children and adolescents through evidence-based practices, particularly in community settings. She leads the Child Implementation and Effectiveness Lab (CIELO Lab), which emphasizes translating research into clinical practice. Her research interests include measurement-based care (MBC), trauma-informed treatments, and implementation science. She has extensively studied clinician training, consultation strategies, and the role of data in optimizing youth psychotherapy outcomes. Key areas of focus include unaccompanied migrant children, adolescent treatment engagement, and therapist fidelity to evidence-based protocols. Dr. Jensen-Doss collaborates with community agencies to scale up evidence-based practices (EBPs), addressing barriers like funding and clinician self-efficacy. Her work bridges academic research with real-world clinical challenges, emphasizing pragmatic solutions to enhance mental health service delivery. She has contributed to national initiatives on MBC and EBP sustainability, and her lab provides resources for clinicians via platforms like shinyDLRs diagnostic tool. Notable grants and projects include the COMET study (Community Study of Outcome Monitoring for Emotional Disorders in Teens), which evaluates transdiagnostic treatments, and a focus on modular therapy approaches for anxiety, trauma, and conduct problems in schools. She advocates for clinician training models that balance expert consultation with cost-effectiveness. Her lab’s CIELO Lab website highlights ongoing projects on family support protocols for internalizing disorders and podcasts to improve health literacy. She is active in editorial roles, emphasizing methodological rigor and translational research in youth mental health.
Michael Stonebraker is a renowned computer scientist and Adjunct Professor of Computer Science at MIT's CSAIL. He is a pioneer in database technology, having developed foundational systems like INGRES and POSTGRES at UC Berkeley. His work spans database management, distributed systems, and data integration. He has founded multiple startups to commercialize his research and holds numerous awards, including the ACM Turing Award (2014) and IEEE John von Neumann Medal (2005). He earned his Ph.D. from the University of Michigan and undergraduate degrees from Princeton and Michigan. His research focuses on advancing database systems, operating systems, and big data analytics. Recent work includes contributions to video data management, cloud computing optimization, and data discovery systems. Education: Ph.D., Computer, Information and Control Engineering, University of Michigan (1971) M.S.E., Electrical Engineering, University of Michigan (1966) B.S.E., Electrical Engineering, Princeton University (1965) Research Interests: Database Technology, Distributed Systems, Data Integration, and Big Data Analytics. His work bridges theory and practice, emphasizing scalable architectures and real-world applications. Awards & Recognition: ACM Turing Award (2014) ACM SIGMOD Systems Award (2015) MIT Tech Review TR7 (2016) C&C Prize (2020) Grants & Labs: His research is supported by grants from NSF, DARPA, and industry collaborations. He leads MIT's efforts in database systems and is affiliated with CSAIL labs focused on data management and high-performance computing.
Brooks Casas, Ph.D., is a Professor at the Fralin Biomedical Research Institute at VTC, with joint appointments in the Department of Psychology (College of Science), Department of Biomedical Engineering and Mechanics (College of Engineering), and the Department of Psychiatry and Behavioral Medicine (School of Medicine) at Virginia Tech. He is also a College of Science Faculty Fellow, recognized for his contributions to decision neuroscience and computational psychiatry. Ph.D. in Psychology, Harvard University Postdoctoral Fellowship, Baylor College of Medicine Former Assistant Professor of Neuroscience and Psychiatry, Baylor College of Medicine Brooks Casas investigates the neural computations underlying social decision-making, focusing on how valuation, learning, and social preferences shape human choices. His research integrates decision neuroscience, behavioral economics, and social psychology to understand both normative and pathological decision processes. Key areas include trust, risk preferences, social influence, and impaired decision-making in psychiatric disorders such as substance abuse and borderline personality disorder. His lab employs fMRI, computational modeling, and longitudinal studies to explore these phenomena. His recent publications span topics such as machine learning applications in diagnosing borderline personality disorder, neural predictors of adolescent risk behaviors, and the role of cognitive control in substance use. His work often involves large-scale longitudinal studies, such as the decade-long investigation into early life adversity and brain development with Jungmeen Kim-Spoon. He has not received any explicitly mentioned scientific awards in the provided text. Casas leads the Casas Lab within the Center for Human Neuroscience Research and collaborates extensively with students and researchers across disciplines. His work is supported by grants from the National Institutes of Health and the Institute for Society, Culture, and Environment. He advises multiple graduate students and early-career researchers, contributing significantly to training in computational psychiatry and decision neuroscience. His lab, the Casas Lab, is part of the Fralin Biomedical Research Institute and focuses on human neuroscience research, particularly using neuroimaging and behavioral experiments to study social and economic decision-making.
Professor Dahlia Malkhi is a leading academic and researcher in distributed systems and blockchain technology. She currently holds a faculty position at the University of California, Santa Barbara (UCSB), where she heads the Foundations of Financial Technology (FfTech) research lab. Her work focuses on reliability, security, and consensus mechanisms in distributed systems, with a recent emphasis on blockchain innovations like HotStuff, which underpins Diem, Aptos, and other blockchains. She has held influential roles at industry leaders such as Chainlink Labs, Diem Association, VMware, and Microsoft Research. Education: Ph.D. in Computer Science from The Hebrew University of Jerusalem. Past roles include CTO of Diem Association (2019–2022), Principal Researcher at VMware (2014–2019), and Partner Principal Researcher at Microsoft Research (2004–2014). Research Interests: Blockchain consensus algorithms (e.g., HotStuff, Flexible Paxos), Byzantine Fault Tolerance (BFT), secure multi-party computation (FairPlay), and distributed database systems (CorfuDB). Her work bridges academic theory with industrial applications, emphasizing practical scalability and security. Awards: ACM Fellow (2011), IEEE TCDP Outstanding Technical Achievement Award (2021), IBM Faculty Award (2003/2004). She has also held leadership roles in conferences like Usenix ATC and program chairs for multiple distributed systems events. Advising & Grants: Advises projects at Space Computer, Lyquor Labs, and Chainlink Labs. Her research labs and collaborations include work on BBCA-Chain, Lumiere, and BFTBrain, advancing consensus mechanisms in decentralized systems. Labs/Teams: Leads UCSB’s FfTech lab, co-founded VMware Research, and contributed to foundational blockchain projects like DiemBFT and Espresso Systems. Her work impacts technologies such as NSX-T control planes and distributed financial infrastructure.
Changjian Li is an Assistant Professor in the School of Informatics at the University of Edinburgh. He leads the GraphViX Group (Graphics, Vision and X) and is a member of the Institute of Perception, Action and Behaviour (IPAB). His research spans computer graphics, computer vision, and human-computer interaction with a focus on 3D generation and analysis. Education: Bachelor's Degree from Shandong University (2014) Ph.D. from the University of Hong Kong (2019) under Prof. Wenping Wang Postdoc at University College London (UCL) with Prof. Niloy Mitra Starting Researcher position at Inria with Dr. Adrien Bousseau Research Interests: Changjian's research focuses on sketch-based 3D modeling, CAD modeling, point cloud processing, and medical imaging applications. He develops systems that bridge intuitive sketching with precise CAD workflows, enhances 3D animation pipelines, and applies neural methods to sparse medical data reconstruction. Scientific Recognition: Best Paper Honorable Mention Award (MICCAI 2021) CADTalk selected as Highlight (CVPR 2024 top 10%) ACM SIGGRAPH Asia 2018 cover image selection ACM SIGGRAPH Asia 2015 technical paper highlight CVPR 2019 poster highlighted in 'Computer Vision News' Advising & Collaborations: He mentors postdocs and PhD students including Duolikun Danier, Haocheng Yuan, Ankan Bhunia, and Lei Zhong. Former advisees include Salvatore Esposito (now at Edinburgh), Guangshun Wei (Shandong University), and Mingjun Yang (University of Melbourne). Collaborates with Oisin Mac Aodha, Hakan Bilen, and Niloy Mitra. Professional Service: Currently serves as Associate Editor for IEEE TVCG and participates in program committees for SIGGRAPH Asia, SIGGRAPH, EuroGraphics, and Geometry Design and Computing (GDC) conferences.
Markus Vincze is an Associate Professor at the Institute of Automation and Control Engineering (ACIN) at Vienna University of Technology (TU Wien). He founded the Vision for Robotics (V4R) group in 1996 to advance robotic perception, particularly in real-world environments and homes. His work focuses on cognitive computer vision techniques for robotics. Education: Diplom in Mechanical Engineering (1988) and PhD (1993) from TU Wien; M.Sc. (1990) from Rensselaer Polytechnic Institute. V4R coordinates EU projects like ActIPret, robots@home, HOBBIT, and national initiatives like vision@home. Markus has edited a book on Robust Vision with Gregory Hager and authored 62 peer-reviewed journal articles and over 400 reviewed publications. His recent research explores zero-shot 6D pose estimation, sim-to-real transfer, and transparent object detection. Markus has served as program chair for ICRA 2013 and organized HRI 2017 in Vienna. He has advised numerous students and secured grants from the Austrian Academy of Sciences for work at HelpMate Robotics and Yale's Vision Laboratory. The V4R group leads innovations in robotic vision, including frameworks for synthetic data generation (Unrealgensyn), depth completion (CAGT), and educational robotics applications for sustainability. Their work spans household robotics (RH3), agricultural robotics (EdgeSoil), and human-robot collaboration.
Anqi Liu is an Assistant Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. She maintains significant affiliations with the Johns Hopkins Mathematical Institute for Data Science (MINDS) and the Johns Hopkins Institute for Assured Autonomy (IAA), while also collaborating extensively with the Center for Language and Speech Processing (CLSP) and the Laboratory for Computational Sensing and Robotics (LCSR). Her research focuses on developing principled machine learning algorithms for building reliable, trustworthy, and human-compatible AI systems in real-world applications. Key research areas include: Distributionally robust learning under covariate shift Uncertainty quantification for AI safety and fairness Safe exploration in control systems Fair machine learning under distribution shift Active learning under label shift Dr. Liu's work addresses critical challenges in high-stakes AI applications where reliability, safety, and societal impact are paramount. Her methods ensure AI systems remain robust to changing data environments, provide accurate uncertainty estimates, and incorporate human preferences in interactions. Analysis of her recent publications reveals a strong trajectory in trustworthy AI research with significant contributions to distribution shift handling, uncertainty quantification techniques, and safe decision-making frameworks. Her work bridges theoretical foundations with practical applications across healthcare, robotics, and social media analysis. Amazon Research Award Dr. Liu actively mentors eight PhD students and teaches specialized courses on Machine Learning for Trustworthy AI and standard Machine Learning at Johns Hopkins University, preparing the next generation of researchers to address critical challenges in AI safety and reliability.
Professor Chun-Hung Chen is a distinguished academic at George Mason University ’s Volgenau School of Engineering , where he holds the rank of Professor in the Department of Systems Engineering and Operations Research . He has also held professorships at National Taiwan University and visiting roles at institutions like University of Pennsylvania and Microsoft Research Asia . Education: PhD in Decision and Control, Harvard University (1994) MS in Electrical Engineering, National Taiwan University (1989) BS in Control Engineering, National Chiao-Tung University (1987) Research Interests focus on Stochastic Simulation Optimization , particularly his pioneering Optimal Computing Budget Allocation (OCBA) methodology. OCBA enhances simulation efficiency by dynamically allocating computational resources to critical design alternatives, reducing computation time by orders of magnitude. Applications span air transportation , healthcare , power grids , and semiconductor manufacturing . His 15 most recent articles (2022–2025) explore intersections of simulation optimization , artificial intelligence , reinforcement learning , and personalized medicine , emphasizing computational efficiency and stochastic systems in domains like microgrids and organ transplant logistics . Scientific Awards include: IEEE Fellow (2015) K.D. Tocher Medal (2017) Best Paper Awards at IEEE CASE (2019), LOGMS (2019), and IEEE ICC (2021) Harvard’s Eliahu I. Jury Award (1994) Advisory roles include editorial leadership in IIE Transactions , Journal of Simulation , and IEEE Transactions series. He has coordinated graduate programs at George Mason (2006–11, 2015–19) and led conferences like INFORMS International Meeting (2025) and Harvard Control Workshop (2024). His work is funded by organizations such as the National Science Foundation , National Institutes of Health , and Department of Energy , with applications in healthcare logistics and microgrid control .
Krishna Jagannathan is a full-time Professor in the Department of Electrical Engineering at the Indian Institute of Technology Madras (IIT Madras), India. He specializes in stochastic modeling, communication networks, information theory, and queuing theory. He obtained his B.Tech from IIT Madras in 2004, followed by S.M. and Ph.D. degrees from MIT in 2006 and 2010, respectively. After post-doctoral positions at Caltech and MIT, he joined IIT Madras in 2011. Education: B.Tech in Electrical Engineering, IIT Madras (2004) S.M. in Electrical Engineering and Computer Science, MIT (2006) Ph.D. in Electrical Engineering and Computer Science, MIT (2010) Research Interests: His research focuses on stochastic modeling and analysis of communication networks , information theory , and queuing theory . He has made significant contributions to understanding network performance, resource allocation, and risk-aware decision-making in complex systems. He leads the Networks and Stochastic Systems lab at IIT Madras, mentoring a large cohort of Ph.D. and M.S. students working on cutting-edge problems in networking, optimization, and stochastic systems. Scientific Awards: Best Paper Award at WiOpt 2013, Tsukuba, Japan Young Faculty Recognition Award for Excellence in Teaching and Research, IIT Madras (2014) Teaching & Mentorship: He has taught a wide range of courses including Probability Foundations , Stochastic Modeling and Queuing Theory , Convex Optimization , and Signals & Systems , consistently receiving high teaching evaluations. He has supervised over 15 Ph.D. and M.S. students to completion and continues to guide several active researchers.
