Professor Benjamin C.M. Kao is a faculty member in the Department of Computer Science at The University of Hong Kong (HKU), affiliated with the School of Computing and Data Science. He holds a BSc from HKU (1989) and a PhD from Princeton University (1995). His career includes roles as a teaching/research assistant at Princeton (1989-1991) and a research fellow at Stanford University (1992-1995). His research focuses on Database Management Systems, Data Mining, Real-time Systems, and Information Retrieval Systems. Notable contributions include S-OLAP for sequence data analysis, collaborative resource discovery in social tagging systems, and algorithms for mining periodic patterns in sequences. He has led research grants such as the GRF-funded 'Online Analytical Processing on Sequence Data' (2008) and computational studies in uncertain data mining (2006). Professor Kao has served on program committees for major computer science conferences and reviewed for leading journals. His work bridges theoretical foundations with practical applications in data systems and information retrieval.
Dr. Abdallah Chehade is an Associate Professor in the Department of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn , where he leads the Informatics, Reliability, and Data Analytics (IRDA) lab . He holds a Ph.D. in Industrial Engineering from the University of Wisconsin-Madison (2017), with minors in Computer Sciences and Statistics, alongside an M.S. in Mechanical Engineering and a B.E. in Mechanical Engineering from the American University of Beirut. Research Interests span safe and robust deep learning solutions , explainable AI , data fusion for degradation modeling , and Bayesian statistical modeling . His work integrates AI/ML with prognostics and Internet of Things (IoT) to address challenges in reliability analytics and industrial data science . Publications highlight advancements in deep autoencoders , LSTM networks , and hybrid models for warranty forecasting , with applications in battery cells , sheet metal stamping , and rail transportation . His grants from Ford, Honda, and the U.S. Army focus on smart manufacturing , AI for sensor modeling , and digital twins . Lab Members include Ph.D. students working on topics like physics-based AI , computer vision , and deep learning for prognosis . He serves on the INFORMS Quality, Statistics, and Reliability (QSR) Council and maintains affiliations with IEEE , INFORMS , and IISE .
Nelly V. Litvak is a Full Professor in Algorithms for Complex Networks at Eindhoven University of Technology (Mathematics and Computer Science). She works on mathematical methods and algorithms for complex networks (social networks, WWW) using random graph models. She joined TU/e as a part-time professor in 2017 after being an Associate Professor at the University of Twente since 2012. Affiliations: 4TU Applied Mathematics Institute, Data Science Center Eindhoven, CTIT Industry Partners: ABN-AMRO Bank, Philips Lighting, Thales Editorial Role: Managing Editor of Internet Mathematics Her research focuses on extracting value from network data across three areas: (1) Information extraction and prediction, (2) Mathematical analysis of network characteristics, and (3) Efficient algorithms for incomplete network data. Key topics include PageRank, HITS algorithm, random graphs, homophilic networks, and network epidemiology. Recent work (2022-2025) spans network growth mechanisms, fairness in ranking algorithms, educational pedagogy, and pandemic forecasting dashboards. She contributes to SDGs through data-driven approaches to societal challenges. Teaching activities include course development at TU/e and earlier institutions, with innovative methods for computer engineering students' statistical understanding.
Professor Vania Sena is a Chair in Entrepreneurship and Enterprise at the Management School of the University of Sheffield. She is a leading scholar in innovation, entrepreneurship, big data analytics, and institutional economics, with a strong focus on productivity, SMEs, and collaborative innovation systems. Her work spans finance, public policy, and technology management, often employing advanced econometric and network analysis methods. Her research interests include big data and performance , open and collaborative innovation , institutional impacts on innovation , entrepreneurship and SMEs , circular economy , and peer-to-peer lending . She has extensively studied the role of human capital, governance, and intellectual property in firm performance and innovation outcomes. The 15 most recent articles reflect a consistent trajectory in data-driven innovation research, with increasing emphasis on AI, machine learning, resilience in supply chains (notably hydrogen), and the circular economy. Her publications appear in top journals such as Technological Forecasting and Social Change , British Journal of Management , Journal of Banking & Finance , and Journal of Economic Literature , showcasing interdisciplinary reach and methodological rigor. Her scientific contributions include influential reviews on appropriability mechanisms and innovation, empirical studies on R&D spillovers, and frameworks for evaluating resilience in emerging energy systems. While specific awards are not listed, her publication record indicates significant recognition in the field. She has supervised doctoral researchers, including recent completions on immigrant entrepreneurship and institutional effects on business survival. Her work is supported by extensive collaborations across Europe and beyond. She is actively involved in PhD supervision and research leadership within the Entrepreneurship, Strategy and International Business group. Professor Sena has contributed to major research themes such as the impact of big data on SMEs, stakeholder diversity in innovation, and the role of policy in enabling circular economy business models. She is also engaged in policy-relevant research on financial inclusion, data intelligence in local government, and the effects of labor market restructuring.
Garrett Johnson is an Associate Professor of Marketing and Dean's Research Scholar at the Questrom School of Business, Boston University. He is based in the Rafik B. Hariri Building at 595 Commonwealth Avenue, Boston, MA. His research centers on digital marketing, focusing on ad effectiveness and consumer privacy in online advertising environments. His research interests include digital marketing, measuring advertising effectiveness, consumer privacy, online advertising regulation, and the economic impact of data protection laws like the GDPR. He uses large-scale experiments and economic modeling to understand how digital ads work and what tradeoffs exist between personalization and privacy. The recent articles show a strong trend in evaluating privacy regulations and their market impacts, particularly focusing on the GDPR. His work bridges marketing science, economics, and public policy, with consistent publication in top journals such as Marketing Science , Management Science , and American Economic Journal: Economic Policy . The research emphasizes empirical analysis of real-world data to inform both industry practices and regulatory frameworks. Prof. Johnson has been honored with several major awards in marketing science: Paul Green Award John D. C. Little Award Weitz-Winer-O'Dell Award Finalist for the John D. C. Little Award Finalist for the Gary Lilien Marketing Science Practice Prize He has advised or collaborated with researchers including Samuel G. Goldberg and Shaoyin Du. While specific grant details are not mentioned, his research is clearly supported by institutional and possibly external funding, enabling large-scale data analysis and experimental studies. His work has been featured in prominent media outlets such as Bloomberg, The New York Times, Boston Globe, and HBR Ideacast, indicating broad impact beyond academia. Though no formal lab or research team is explicitly described, his collaborative publications suggest active engagement in a research group focused on digital marketing and privacy economics. He maintains a strong academic presence through his personal website (garjoh.com), Google Scholar, and professional networks like Twitter and LinkedIn.
Pavan Turaga is a Professor and Founding Director of The GAME School at Arizona State University (ASU), with a joint appointment in the School of Electrical, Computer and Energy Engineering (ECEE). They lead transdisciplinary research and education initiatives spanning gaming, esports, AI-enabled media creation, computer vision, and geometric modeling. Ph.D., Electrical Engineering, University of Maryland (2009) B.Tech., Electronics and Communication Engineering, IIT Guwahati (2004) Research focuses on integrating geometry and topology with machine learning , enabling advancements in: Computer vision for human activity recognition Generative AI for immersive media Health analytics and wearable rehabilitation systems AI ethics and pandemic prediction Key publications span CVPR (2023 spotlight paper PolyINR ), DLGC workshop (2023 best paper), and ICML (2019 work on GAN priors). Recent work explores LMMs , 3D human modeling , and AI for pandemic preparedness . Scientific accolades include: ASU Founders' Day Research Excellence (2025) NSF CAREER award (2015) CVPR 2023 Spotlight paper 2024 X-Prize (Rainforest Challenge) Directed research for students like Rajhans Singh and Ankita Shukla, securing grants from NSF , DARPA , and industry partners (Adobe, Google ATAP). Founded the Geometric Media Lab , emphasizing interdisciplinary collaborations with mathematicians, health scientists, and media artists.
Prof. Dr. Janick Edinger is a Professor of Distributed Operating Systems at the Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Germany. He leads a research group focused on distributed, context-aware, and adaptive computing systems, with a strong emphasis on edge computing, computation offloading, and assistive technologies. Education: PhD in Computer Science, University of Mannheim Studies at National Taiwan University Studies at University of Alberta, Canada Research stays at University of British Columbia, Hong Kong Polytechnic University, and Georgia State University, USA His research explores how edge computing and computation offloading can enable efficient, privacy-preserving processing of sensor and video data close to their sources, particularly in dynamic environments. He investigates the integration of autonomous and heterogeneous systems—such as drone fleets and mobile devices—into scalable middleware platforms for real-time monitoring and decision-making in logistics and industrial operations. His work also emphasizes societal impact, contributing to accessible routing, adaptive interfaces, and crowd-sourced mapping. The recent publications reflect a strong trend in edge computing, federated learning, privacy-preserving analytics, and assistive technologies. Topics include WebAssembly-based offloading, emotion prediction via eye tracking, real-time traffic detection, and predictive maintenance in Industry 4.0, showcasing a blend of foundational systems research and applied human-centered computing. Scientific Awards: PerCom 2021 Mark Weiser Best Paper Award Best Paper Award at IEEE PerCom 2021 for 'Voltaire: Precise Energy-Aware Code Offloading Decisions with Machine Learning' Prof. Edinger actively advises students and leads research projects involving grants and collaborations. His team includes PhD candidates and researchers working on middleware, edge systems, and context-aware applications. He has served on conference program committees, such as shadow PC member for EuroSys 2021, and publishes in top venues including IPDPS, PerCom, CHIIR, and COMPSAC. Labs and Teams: He leads the Distributed Operating Systems research group at the University of Hamburg, where he mentors students and collaborates on projects involving edge computing, IoT, and adaptive systems.
Daniel B. Neill is a Professor of Computer Science, Public Service, and Urban Analytics at New York University (NYU), jointly appointed across the Courant Institute of Mathematical Sciences, Robert F. Wagner Graduate School of Public Service, and the Center for Urban Science and Progress (Tandon School of Engineering). He also serves as the Director of the Machine Learning for Good Laboratory (ML4G) and is affiliated with NYU's Center for Data Science and Tandon Department of Computer Science and Engineering. Education: Ph.D. in Computer Science, Carnegie Mellon University M.S. in Computer Science, Carnegie Mellon University M.Phil. in Computer Speech, Cambridge University Research Interests: Dr. Neill's research focuses on developing novel machine learning methods for social good, with applications in disease surveillance (e.g., early outbreak detection), healthcare (e.g., anomalous care patterns), and urban analytics (e.g., predicting citizen needs). He also explores algorithmic fairness , causal inference , and pre-syndromic surveillance using unstructured data. His work bridges theoretical machine learning with real-world policy challenges, collaborating with health departments, hospitals, and city governments to deploy data-driven tools that enhance public health, safety, and security. Scientific Awards & Honors: NSF CAREER Award NSF Graduate Research Fellowship IEEE Intelligent Systems' "Top Ten AI Researchers to Watch" Yelp Dataset Challenge Winner Hidden Signals Challenge Runner-Up (DHS) Grants & Funding: He has received significant funding from the National Science Foundation (NSF), including grants on fairness in AI (IIS-2040898), bias in urban analytics (IIS-1926470), and others. He also acknowledges support from UPMC, MacArthur Foundation, and Richard King Mellon Foundation. Laboratory & Leadership: He directs the Machine Learning for Good Laboratory (ML4G) at NYU, focusing on AI for social impact. He previously co-directed NYU's Urban Initiative (2019-2022) and led the Event and Pattern Detection Laboratory at Carnegie Mellon University.
Cesare Franchini is a full Professor at the University of Vienna's Faculty of Physics, leading the Computational Materials Physics research group. His work focuses on theoretical understanding and computational modeling of quantum materials using first principles methods, particularly VASP. He maintains an active research program with numerous postdocs, PhD students, and collaborations across multiple institutions including the University of Bologna. Professor Franchini's research centers on quantum materials with many interacting degrees of freedom (lattice, spin, and electron orbital) that enable novel electronic and magnetic phases. His specific interests include metal-insulator transitions, polaron physics (electron-phonon interactions), non-collinear spin orderings, topological Dirac/Weyl phases, multiferroism, and superconductivity. He has increasingly incorporated machine learning data-driven tools and diagrammatic Monte Carlo techniques into his computational approaches. Analysis of his recent publications (2024-2025) reveals a strong focus on polaron physics across multiple material systems, with significant work on hematite, titanium dioxide, and quantum paraelectrics like KTaO3. His research increasingly integrates machine learning with traditional first-principles methods, particularly for studying hydrogen diffusion, surface science phenomena, and electronic structure calculations. There's also substantial work on single-atom catalysis and the application of advanced computational techniques to understand fundamental charge transport mechanisms in energy materials. Professor Franchini actively supervises numerous PhD students and postdocs, including Andrea Angeletti, Viktor Birschitzky, Lorenzo Celiberti, and several others working on diverse aspects of computational materials physics. He leads or participates in major research projects including TACO (Taming Complexity in Materials Modeling), DCAFM (Doctoral College Advanced Functional Materials), and the recently launched Spin-orbit entangled anharmonic polarons project. His group maintains strong collaborations with experimentalists at Charles University, Technical University of Vienna, and other international institutions.
Mikko Kurimo is a Full Professor at Aalto University's Department of Information and Communications Engineering, School of Electrical Engineering. He earned his M.Sc., Lic.Tech., and D.Sc.(Tech.) from Helsinki University of Technology (1992, 1994, 1997) and pioneered neural networks for automatic speech recognition (ASR) in his PhD thesis. After research roles at IDIAP (Swiss AI center) and visiting positions at University of Colorado, Edinburgh, SRI, ICSI, and Nitech, he leads Aalto's ASR group since 2000. His work focuses on unsupervised subword modeling for morphologically complex languages (Finnish, Estonian, Turkish, Arabic) and large speech foundation models. PhD in Neural ASR (Helsinki University of Technology, 1997) Research Scientist at IDIAP (Switzerland) Visiting Fellow at University of Colorado, Edinburgh, SRI, ICSI, Nitech Head of Aalto ASR Group (2000-present) His research spans deep learning for ASR, spoken language modeling , and low-resource language solutions . Recent work explores continued pre-training of self-supervised models, multimodal emotion recognition, and pronunciation assessment using LLMs. He led the winning team in the 2017 Multi-Genre Broadcast challenge and secured competitive funding in Tekes Challenge Finland and EC's H2020-ICT-2017. Key article trends include: Advancements in children's speech recognition and dysarthric speech processing Integration of generative AI for language learning feedback Specialization in low-resource Uralic languages (Finnish, Northern Sámi) Development of robust ASR systems for complex phonetic environments Scientific Awards ACM Multimedia 2023 Computational Paralinguistics Challenge Prize First place in MGB3 2017 Arabic ASR Challenge ISCA Best Student Paper Award (2011) Professeur Invité at Université de Saint-Etienne (2005-2006) Royal Society International Short Visit Fellowship (2004) Professor Kurimo leads the Speech Recognition Group at Aalto, collaborating with COIN (Centre of Excellence in Computational Inference) and AIRC (Adaptive Informatics Research Centre). His projects like CaptainA mobile app demonstrate practical applications of ASR in language education. He has supervised numerous publications with co-authors in domains spanning bandwidth extension, stuttering detection, and speech sound disorder assessment.
Jukka Manner is a Full Professor (tenured) of Networking Technology at Aalto University's Department of Communications and Networking (Comnet), School of Electrical Engineering. With a career spanning over two decades in internet technologies, he leads research in networking, wireless systems, and energy-efficient ICT solutions. Dr. Manner received his MSc (1999) and PhD (2004) degrees in computer science from the University of Helsinki. His academic journey has been marked by significant contributions to internet standardization through the IETF since 1999, where he served as co-chair of the NSIS working group. Professor Manner's research focuses on networking, software and distributed systems, with particular emphasis on wireless and mobile networks, transport protocols, energy efficient ICT and cyber security. His work bridges theoretical advancements with practical applications, addressing critical challenges in modern communication systems, sustainable networking practices, and security frameworks. His research group has made significant contributions to 5G technologies, UAV communications, and energy-efficient network design. His extensive publication record shows a clear evolution toward sustainability in networking technologies, with recent work focusing on energy efficiency in 5G systems, sustainable web technologies, and the environmental impact of digital infrastructure. The research demonstrates strong interdisciplinary connections between telecommunications engineering, computer science, and environmental science, with particular emphasis on reducing the carbon footprint of digital systems while maintaining performance. Cross of Merit, Signals (2014) Medal for Military Merits for contributions in national defence and C4 (2015) Professor Manner has supervised over 200 MSc theses and more than 20 doctoral dissertations, establishing himself as a dedicated mentor in the field. He has been principal investigator and project manager for over 15 national and international research projects, including serving as Academic Coordinator for the Finnish Future Internet research programme (2008-2012). His leadership extends to conference organization, having served as local co-chair of Sigcomm 2012 in Helsinki, and active participation as a peer reviewer and member of various Technical Program Committees. As an active contributor to internet standardization through the IETF, Professor Manner's work has practical impact on global networking technologies. His research group maintains strong connections with industry partners and participates in shaping future networking standards and practices, particularly in the areas of sustainable networking, 5G evolution, and security frameworks for emerging technologies.
Ghassan AlRegib is the John and Marilu McCarty Chair Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. He directs the Omni Lab for Intelligent Visual Engineering and Science (OLIVES), the Center for Energy and Geo Processing (CeGP), and previously led Georgia Tech's MENA initiatives (2015-2018). His research spans machine learning, image processing, and seismic interpretation with real-world applications in autonomous vehicles, medical imaging, and subsurface analysis. His research focuses on trustworthy AI systems through three pillars: enhancing interpretability, improving robustness/generalizability, and tackling domain-specific challenges. Key interests include human-in-the-loop frameworks, uncertainty quantification, explainable AI, and physics-driven learning. The OLIVES lab pioneered modern machine learning applications in seismic interpretation and developed open-source datasets for geological fault analysis. Dr. AlRegib's scientific contributions include over 270 publications, multiple U.S. patents, and leadership roles as Technical Program co-Chair for ICIP 2020/2024. His work demonstrates significant impact through awards like the IEEE Fellow designation (2022) and multiple best paper awards at premier conferences. IEEE Fellow (2022) 2023 EURASIP Best Paper Award 2019 ICIP Best Paper Award 2017 Denning Faculty Award for Global Engagement CSIP Research & Service Awards (2003) He has advised numerous PhD students including Dr. Ashraf Alattar (now Auburn professor) and Dr. Zhiling Long (Kennesaw State faculty). His lab structure emphasizes collaborative teams comprising postdocs, senior/junior PhD students, and undergraduates working on high-impact problems from autonomous systems to medical diagnostics. Current research thrusts include trustworthy neural networks, human-in-the-loop frameworks, and deployment of machine learning in seismic interpretation and ophthalmology.
Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.
James Alexandre Goulet is a Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. His research focuses on Machine Learning Methods for Civil Engineering applications such as structural health monitoring (SHM) and infrastructure maintenance planning. He leads the Canari project for online change point detection in SHM and contributes to open-source libraries like cuTAGI for Bayesian neural networks. Affiliations : Chair in Machine Learning for Infrastructure Monitoring at Polytechnique Montréal, IVADO Institute member, and GRS (Structural Engineering Research Group) member Expertise : Building engineering, structural safety, applied probability, learning theories Recent research trends include Bayesian state-space models, LSTM neural network integration for infrastructure forecasting, and uncertainty quantification in SHM systems. His work emphasizes probabilistic methods and analytical inference over black-box approaches. Teaching includes courses on structural reliability and probabilistic data analysis for civil engineers. He supervises graduate students in topics ranging from damage detection algorithms to stochastic deterioration modeling of infrastructures.
Mark Jenkinson is a Professor of NeuroImaging at the University of Oxford's Nuffield Department of Clinical Neurosciences and also holds positions at the University of Adelaide's Australian Institute for Machine Learning and the South Australian Health and Medical Research Institute (SAHMRI). He heads the Structural Modelling and Analysis Group at the FMRIB Centre, where his research focuses on multimodal population modeling and structural brain segmentation. Education: DPhil in Robotics Research (University of Oxford, 1999) BSc (Hons I) in Mathematical Physics (University of Adelaide, 1994) BE (Hons I) in Electrical and Electronic Engineering (University of Adelaide, 1993) Professor Jenkinson's research spans two major themes: multimodal modeling of populations to describe disease processes and apply to individual patient diagnoses, and structural segmentation and analysis of brain anatomy and pathology, particularly focusing on sub-cortical structures and lesions. His work integrates advanced computational methods with neuroimaging to develop tools for understanding neurological disorders. As the developer of key components of the FMRIB Software Library (FSL), he has significantly contributed to standard neuroimaging analysis pipelines used worldwide. His recent publications demonstrate a strong focus on deep learning applications in neuroimaging, uncertainty quantification in medical AI, and advanced segmentation techniques. There's a clear trend toward developing more robust, anatomically plausible models that preserve topological structures while improving diagnostic capabilities for conditions like multiple sclerosis, Huntington's, and Parkinson's diseases. Scientific Awards: Highly Cited Researcher (Clarivate Analytics 2018-2021, Thomson Reuters 2014-2016) ISMRM Outstanding Teacher Award (2009, 2014) Teaching Excellence Award, University of Oxford (2012) David Phillips Fellowship from BBSRC (2005-2010) Professor Jenkinson has supervised over 25 doctoral students whose work spans brain segmentation, connectivity analysis, and clinical applications of neuroimaging. His research is supported by significant grants including the Medical Research Future Fund (AU$2m), Wellcome Trust Centre for Integrative Neuroimaging (£11m), and NIH Human Connectome Project (US$30m), reflecting the high impact and translational potential of his work. As head of the Structural Modelling and Analysis Group at FMRIB, Jenkinson leads a team developing the FSL (FMRIB Software Library), one of the most widely used neuroimaging analysis packages globally. His group collaborates extensively with clinical researchers on applications ranging from multiple sclerosis to traumatic brain injury, translating computational advances into clinical practice.
