Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.
Mirco Musolesi is a Full Professor of Computer Science at both University College London (UCL) and the University of Bologna. He leads the Machine Intelligence Lab at UCL, part of the UCL Centre for Artificial Intelligence. His research focuses on Machine Learning, Generative AI, and computational models of human behavior, with applications in ubiquitous systems and societal impacts of AI. Education: PhD in Computer Science from UCL (2007) and Laurea in Electronic Engineering from the University of Bologna (2002). Previous roles include positions at the University of Birmingham, Dartmouth College, and the Alan Turing Institute. Research spans multi-agent systems, reinforcement learning, and AI ethics. Notable awards include ACM UbiComp 10-Year Impact Award (2020/2024) and the NetExplorateur/UNESCO Top 100 Innovations (2011). His work on EmotionSense and CenceMe applications has been recognized with Test-of-Time awards. Recent publications (2024-2025) address moral alignment in AI agents, multi-agent environmental policy simulations, and creativity in LLMs. His labs explore AI-driven solutions for urban systems and ethical decision-making frameworks.
Michael O. Wood is an Associate Professor in the School of Environment, Enterprise and Development (SEED) at the University of Waterloo and currently serves as the Associate Dean of Work-Integrated Learning in the Faculty of Environment. He holds a Ph.D. in Strategy and Sustainability from the Ivey Business School at Western University, an M.A. in Environmental Studies in Sustainability Management from Western University, and a B.A. in Science from the University of Guelph. Ph.D. in Strategy and Sustainability, Ivey Business School, Western University M.A. in Environmental Studies in Sustainability Management, Western University B.A. in Science, University of Guelph Dr. Wood's research focuses on organizational responses to sustainability challenges through the lenses of space, time, scale, and social license to operate. His work spans industries like insurance, mining, carbon management, waste management, and global security, with a particular emphasis on the Blue Economy and climate change. He has contributed to frameworks for water risk assessment, sustainable packaging practices, and the role of governance in sustainability-financial performance linkages. His recent publications address transdisciplinary tools for water risk management, workforce practices' impact on sustainability, ETS policy effects, and disaster response patterns in multinational corporations. While no scientific awards are explicitly mentioned, his work has been published in journals such as the International Journal of Disaster Risk Reduction and Economic Systems Research . Dr. Wood teaches courses like Introduction to Environment and Business and Environment and Business Project , and he collaborates with researchers like Sandhu, Weber, and Bansal on sustainability-related projects. His studies often incorporate mixed-methods analysis and policy reviews for sustainable resource management in Ontario and Canada.
Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.
Necmiye Ozay is an Associate Professor in Robotics and Electrical and Computer Engineering at the University of Michigan. Her research focuses on control systems, formal methods, and cyber-physical systems, with applications in autonomy, system identification, and verification. She leads a diverse research group encompassing PhD, MS, and undergraduate students, as well as postdoctoral researchers. Her work bridges theory and practice, addressing challenges in safety-critical systems design and data-driven control. Her educational background includes a PhD in Electrical and Computer Engineering from Northeastern University and a Master’s from Penn State. She has received significant funding from NSF, ONR, and industry partners, supporting projects like the CLEVR-AI initiative and Scenic ecosystem development. Her research has been recognized through awards and collaborations at institutions like MIT, Berkeley, and Johns Hopkins. Key research areas include model-based control synthesis, robust system identification, and anomaly detection in cyber-physical systems. Notable contributions include methods for correct-by-construction control, hybrid system analysis, and learning-based approaches for autonomous systems. She actively contributes to conferences such as HSCC and CDC, and her lab’s work impacts automotive safety, energy systems, and robotics. Ozay’s advising spans over 50 students and postdocs, many of whom hold academic or industry roles. Her lab maintains active collaborations across disciplines, emphasizing interdisciplinary solutions to real-world control challenges.
Mohammed Aledhari is an Assistant Professor at the University of North Texas, specializing in cybersecurity, machine learning, and data science. His research focuses on applications in computational medicine, bioinformatics, and autonomous systems. He holds a Ph.D. from Western Michigan University and degrees from the University of Basrah and the University of Anbar. His research interests include social cybersecurity techniques, federated learning in IoT, and AI-driven solutions for healthcare and transportation. Recent work explores blockchain-enabled digital twins, DDoS attack detection, and equitable ASD diagnostics using machine learning. His publications span cybersecurity frameworks, autonomous vehicle communication protocols, and biomedical IoT innovations. Notable contributions include optimizing intrusion detection in IoMT networks and developing interpretable machine learning models for healthcare. While no formal awards or grants are listed, his work emphasizes interdisciplinary applications of AI in healthcare, transportation, and energy markets. His email is Mohammed.Aledhari@unt.edu .
James Malazita is Associate Professor of Science & Technology Studies and Associate Director of the Games and Simulation Arts and Sciences (GSAS) program at Rensselaer Polytechnic Institute (RPI). He is affiliated with the Department of Science and Technology Studies in the School of Humanities, Arts, and Social Sciences, and contributes to the GEM (Games and Experiential Media) research group. Education: Ph.D. in Communication, Culture, and Media, Drexel University (2014) BS/MS in Digital Media, Drexel University (2009) Malazita's research lies at the intersection of game studies, platform studies, feminist technoscience, and social justice in computing. He critically investigates how game engines like Unreal shape technological and cultural infrastructures, particularly through the lenses of gender, race, and power. His forthcoming book, Enacting Platforms: Feminist Technoscience and the Unreal Engine (MIT Press, July 2024), offers a groundbreaking feminist and critical analysis of one of the most influential game development platforms. His work also extends to digital humanities, critical design, and the politics of computer science education. His recent publications reveal a strong thematic focus on the co-production of technology and society, with recurring interests in epistemic cultures, disciplinary formation, localization, and critical pedagogy. These works span journals in game studies, design, digital humanities, and STS, reflecting an interdisciplinary approach that bridges technical systems and humanistic critique. Scientific Support and Affiliations: Supported by NEH Office of Digital Humanities Supported by NEH Division of Educational Programs Supported by Popular Culture Association Supported by New Jersey Historical Commission Supported by Red Hat Inc. Supported by Rensselaer’s Teaching and Learning Collaboratory Member of GEM (Games and Experiential Media) ORCID: 0000-0003-3206-3682 Malazita advises graduate students in STS and Games, with former advisees placed at Yale, NYU, and UT Austin. He teaches courses in game studies, human-centered design, interpretive methods, and critical technology studies. His current course includes STSO 2610: DSIS Studio II (Spring 2024). His research raises fundamental questions about the material and cultural limits of platforms, the analyst-platform relationship, and the future of the web as shaped by game technologies.
Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.
Prof. Dr. Mirko Meboldt serves as a Full Professor at ETH Zurich's Department of Mechanical and Process Engineering, where he holds dual leadership roles as Head of Lecturers' Conference and Deputy Head of the Institute of Machine Tools and Manufacturing. His office is located at Leonhardstrasse 21 in Zürich, Switzerland. Professor Meboldt's research spans multiple engineering domains with particular emphasis on: User-oriented product innovations New production technologies Mechanical engineering applications Biomedical device development CAD/PDM systems standardization Engineering education methodologies His recent publication portfolio reveals a distinctive interdisciplinary approach that bridges traditional mechanical engineering with cutting-edge medical applications. Key research trends include human-robot collaboration systems, intelligent medical devices for neurosurgery, augmented reality training platforms for medical procedures, advanced manufacturing processes, and AI-assisted healthcare communication analysis. This diverse research portfolio demonstrates his commitment to solving complex real-world engineering challenges through cross-disciplinary innovation. Professor Meboldt places significant emphasis on the educational impact of his work, explicitly stating that he 'regards the impact on the education of young engineers and its relevance for industry as a key motivation and benchmark for his research.' His industrial background at Hilti AG informs his practical approach to academic research, ensuring strong industry relevance across all his projects.
Dr. Nicola Mingotti is a Research Associate in the Department of Earth Sciences at the University of Cambridge, affiliated with the Institute for Energy & Environmental Flows (IEEF). His experimental research focuses on fluid mechanics in the context of energy transition, particularly carbon sequestration and underground hydrogen storage, while also addressing oceanic microplastics dispersion and atmospheric volcanic ash transport. Research Associate, University of Cambridge IEEF Flow Laboratory His work on multiphase flows spans volcanic eruptions, oceanic microplastics separation, and contaminant dispersion in hospital environments. Recent studies include Journal of Fluid Mechanics publications on bubble plumes (2023) and sediment-laden oceanic plumes (2022), alongside Communications Earth & Environment articles on CO2 storage (2024) and volcanic dynamics (2021). Scientific awards and honors: None explicitly mentioned in the text. As an engineering researcher, he led investigations on droplet transport in ventilation systems (Royal Papworth Hospital, 2021) and historical surgical environment design (2021). His publications demonstrate interdisciplinary expertise across environmental fluid mechanics, geological engineering, and public health.
Paul Pu Liang is an Assistant Professor at the Massachusetts Institute of Technology (MIT) Media Lab and Department of Electrical Engineering and Computer Science (EECS). He directs the Multisensory Intelligence research group, focusing on building AI systems that integrate diverse sensory inputs to enhance human-AI symbiosis. His work spans theoretical foundations, large-scale resources, and neural architectures for multisensory learning. Education: PhD in Machine Learning (Carnegie Mellon University), MS in Machine Learning (Carnegie Mellon), BS with University Honors in Computer Science and Neural Computation (Carnegie Mellon) Research Interests: Multimodal machine learning, human-AI interaction, clinical AI, generative models, and responsible deployment of AI systems Key Contributions: MultiBench, HEMM evaluation framework, CLIMB clinical data foundations, and multimodal transformer architectures Recent publications emphasize multimodal foundation models , clinical applications , and socially responsible AI . His work has been recognized with multiple best paper awards and fellowships from Siebel, Facebook, and other institutions. Scientific Awards Siebel Scholars Award Waibel Presidential Fellowship Facebook PhD Fellowship Center for ML and Health Fellowship Rising Stars in Data Science Four best paper awards Paul teaches courses on machine learning and multimodal AI at MIT and CMU. He mentors students across multiple programs including Media Arts & Sciences, EECS, and IDSS, with former advisees now at institutions like OpenAI, UC Berkeley, and Princeton.
Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.
Camillo J. Taylor is the Raymond S. Markowitz President’s Distinguished Professor in the Department of Computer and Information Science at the University of Pennsylvania , where he has been a faculty member since 1997. He also serves as Associate Dean for Diversity, Equity, and Inclusion at the School of Engineering and Applied Science. His research focuses on Computer Vision and Robotics , particularly in 3D reconstruction, semantic mapping, and autonomous navigation. Education: A.B. in Electrical Computer and Systems Engineering, Harvard College (1988) M.S. and Ph.D. in Computer Science, Yale University (1990, 1994) Research Interests: Dr. Taylor’s work bridges Computer Vision and Robotics to enable autonomous systems to perceive and navigate complex environments. Key themes include semantic SLAM, event camera applications, and meta-learning for adaptive controllers. His projects often integrate vision, physics, and multi-agent collaboration, as seen in systems like EvMAPPER and OCCAM . Recent Article Trends: His 2024–2025 publications focus on semantic mapping , event-based vision , and multi-agent LLM systems , reflecting his lab’s emphasis on real-time perception, physics-informed reconstruction, and rational decision-making in robotics. These works span applications from solar eclipse imaging to wildfire analysis and natural hazard resilience. Awards: NSF CAREER Award (1998) Lindback Minority Junior Faculty Award (2001) IEEE WACV Best Paper Award (2012) Lindback Distinguished Teaching Award (2012) Advising and Service: Dr. Taylor has advised numerous PhD students, including Jason Hughes and Bowen Jiang. He has served as a Program Chair for CVPR (2006, 2017) and General Chair for ICCV (2021). His contributions to the GRASP Laboratory have advanced autonomous micro-UAVs and semantic SLAM.
Mehran Sahami is the James and Ellenor Chesebrough Professor in the School of Engineering and Tencent Chair of the Computer Science Department at Stanford University. He holds the academic rank of Teaching Professor of Computer Science and is also a Senior Fellow by courtesy at the Freeman Spogli Institute for International Studies. As a Bass University Fellow in Undergraduate Education, he has made significant contributions to computer science education at Stanford. Dr. Sahami earned both his undergraduate and PhD degrees from Stanford University's Computer Science Department. After completing his PhD, he worked as a Senior Engineering Manager at Epiphany before joining Google as a Senior Research Scientist from 2002-2007, while also teaching as a Lecturer at Stanford. In 2007, he joined the Stanford faculty full-time, continuing to consult part-time at Google until 2010. Professor Sahami's primary research interests focus on computer science education, machine learning, and information retrieval on the Web. His work has significantly influenced how computer science is taught globally, particularly through his leadership in the ACM/IEEE-CS Joint Task Force on Computing Curricula 2013 (CS2013). He has pioneered approaches to teaching introductory programming and probability theory for computer scientists, with a particular emphasis on analyzing student performance trends as CS enrollments have grown dramatically. His recent publications demonstrate a strong shift toward educational research while maintaining connections to technical expertise in machine learning and data analysis. Bass University Fellow in Undergraduate Education Professor Sahami serves as the ACM Steering Committee Chair for the CS2013 effort to define international curricular guidelines for undergraduate computer science programs. He is also the founder and first Chair of the Symposium on Educational Advances in Artificial Intelligence (EAAI), an annual meeting for researchers and educators to discuss pedagogical issues in teaching AI. He has received significant grant funding through these initiatives and has been instrumental in shaping national and international computer science curriculum standards. At Stanford, he teaches CS106A: Programming Methodology and CS182: Ethics, Public Policy, and Technological Change, with his educational materials widely distributed through the Stanford Engineering Everywhere initiative. Professor Sahami maintains connections to the startup ecosystem through advisory board positions and has published a book on Text Mining with Ashok Srivastava. His career trajectory from industry researcher to academic educator gives him a unique perspective on practical applications of computer science education.
Steffi Haag is a Professor of Digital Innovation and Entrepreneurship at the Institute of Computer Science , Heinrich Heine University Düsseldorf (HHU) . She bridges the Faculty of Mathematics and Natural Sciences and Faculty of Business Administration and Economics , collaborating with the Center for Entrepreneurship Düsseldorf (CEDUS) to inspire tech startups. Her research focuses on sustainable information systems, digital experiences, and business models. Steffi Haag’s research integrates Shadow IT , Usable Cybersecurity , and Digital Idea Management . Her work emphasizes theoretical , quantitative , qualitative , and mixed-methods research , including experimental and survey methodologies. Steffi Haag’s publications (2018–2024) span Information Systems , Cybersecurity , Digital Twins , and Sustainable Design . Common themes include Shadow IT dynamics , user behavior in security , and innovation management . Hermann Gutmann Award for special scientific achievements (2023) Schöller Fellow (2020) HMD Best Paper Award (2018) Research Award in Data Protection and Data Security (2017) Dissertation Prize (2017) Steffi Haag has secured grants such as the TU Darmstadt Postdoctoral Fellowship for Female Researchers (2016–2018) and Deloitte Foundation Fellowship (2009–2011). She actively moderates conferences and serves as Associate Editor of Business & Information Systems Engineering .
