Professor Maia Angelova is a leading academic in data science and mathematical physics at Aston University's Aston Digital Futures Institute (ADFI) and College of Engineering and Physical Sciences. Her research focuses on interdisciplinary AI applications in healthcare, including precision medicine, chronic disease modeling, and athlete performance analytics. She previously held roles as Professor of Data Analytics at Deakin University (2017–2023) and Professor of Mathematical Physics at Northumbria University (1997–2016), with early experience as a College Lecturer at Oxford University (1991–1996). Education: PhD, MSc, and BSc in Physics from Sofia University 'St. Kliment Ohridski'. Research interests span AI-driven healthcare solutions, dynamical systems modeling, and sports performance analysis. Her work addresses sleep disorders, diabetes management, chronic pain, and athlete performance using advanced machine learning and data analytics. She has secured over £5M in research funding and supervised over 30 PhD students and postdoctoral researchers. Awards include Fellowship of The Institute of Physics. Professional memberships include The London Mathematical Society, Australian Mathematical Society, and Complex Systems Society. Key achievements include founding the Data to Intelligence research centre (2018–2020) and leading large-scale interdisciplinary projects. Current initiatives focus on precision healthcare through AI integration in clinical decision-making systems.
Suining He is an Assistant Professor at the University of Connecticut (UConn)'s School of Computing, leading the Ubiquitous and Urban Computing Lab since 2019. Previously, he was a postdoctoral research fellow at the University of Michigan's Real-Time Computing Lab (2016–2019). He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology (2016) and a B.Eng. in Mechanical Design from Huazhong University of Science and Technology (2012). His research focuses on Cyber-Physical Systems (CPS), Smart & Connected Communities, Human-Centered Computing, and Urban Computing Cyberinfrastructure, with emphasis on mobility, equity, and AI-driven solutions. He has received prestigious awards including the NSF CAREER Award (2023), Google Research Scholar Program Award (2021), and recognition as a Stanford Top 2% Scientist (2020–2024). His work spans interdisciplinary grants from NSF, USDA, Google, NVIDIA, and industry partners. Recent publications explore autonomous driving simulation, equity-aware mobility prediction, and urban crowd activity modeling. Teaching excellence is reflected in his 2020 UConn Provost Award. He advises on reinforcement learning, CPS, and mobile computing, with openings for 2025/2026 PhD students. His lab collaborates on socially-conscious AI, privacy-preserving learning, and location-based services with industrial impact.
Smita Ghosh is an Assistant Professor in the Department of Mathematics and Computer Science at Santa Clara University, part of the College of Arts and Sciences. Her research focuses on social network analysis, algorithms for information diffusion, and applications in cybersecurity, disaster management, and machine learning. She holds a B.Tech. from the West Bengal University of Technology, India, and an M.S. and Ph.D. from the University of Texas, Dallas. Her work addresses challenges in rumor containment, clickbait detection, and optimizing network models for social media content analysis. Recent publications include studies on hypergraph-based solutions for rumor blocking and stochastic models for emergency response in social networks. She also explores cross-modal topic modeling for enhancing content detection algorithms. Notable contributions include developing data-driven strategies for identifying hate speech spreaders and improving wildfire severity predictions using environmental features. Her research bridges theoretical computer science with real-world applications in public health, education, and disaster management. Her academic contributions include organizing conference proceedings like the 18th International Conference on Algorithmic Aspects in Information and Management (AAIM 2024). She actively contributes to educational initiatives such as the Classroute project, creating multilingual educational content for Punjabi and Urdu speakers.
Mathias Lécuyer is an Assistant Professor in the Computer Science Department at the University of British Columbia (UBC) , part of the Faculty of Science . His research focuses on trustworthy AI systems, with emphasis on privacy (differential privacy), adversarial robustness, and causal machine learning. He leads the Systopia Lab and collaborates with groups such as UBC S&P, TrustML, and CAIDA. Education: PhD in Computer Science from Columbia University (2019), MSc from Columbia University (2013) and École Polytechnique (2011). Research Interests: Ensuring rigorous guarantees for AI systems via differential privacy, robustness against adversarial attacks, and causal inference. His work spans theoretical foundations and practical implementations in areas like privacy-preserving systems, federated learning, and transparent machine learning. Awards: SOSP Distinguished Artifact Honorable Mention (2024), Google Research Award (2022), Bourse Carnot Fellowship (2011), and UBC’s top teaching evaluations (2021). Advising & Service: Supervised over 20 students (PhD, MSc, undergrad) across privacy and machine learning topics. Serves on PCs for top conferences (OSDI, S&P, NeurIPS) and organizes workshops like TrustML @ UBC. Actively mentors high school students in ML research through outreach programs. Labs/Teams: Leads the Systopia Lab at UBC, focusing on AI safety and privacy. Collaborates with MSR, Google, and industry partners on practical system implementations.
Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.
Dr. David Burton is a Professor in the Department of Plant, Food, and Environmental Sciences at Dalhousie University's Faculty of Agriculture. He serves as Director of the Centre for Sustainable Soil Management and leads initiatives in soil health, greenhouse gas emissions, and sustainable agricultural practices. His teaching spans undergraduate and graduate courses in soil science, nutrient management, and climate change. Research focuses on microbial metabolism in soil, nitrogen cycling, and the environmental impacts of agricultural practices. He co-founded the Atlantic Soil Health Lab and manages the Greenhouse Gas Analysis Lab. Key affiliations include the Canadian Society of Soil Science (Fellow), Soil Conservation Council of Canada, and Fertilizer Canada's 4R Research Network. Recent work emphasizes soil's role in climate resilience, including presentations on regenerative farming and soil carbon sequestration. He collaborates with government and industry to develop climate-smart soil management policies and tools for nitrogen management optimization. Awards: Fellow of the Canadian Society of Soil Science Labs: Centre for Sustainable Soil Management, Greenhouse Gas Analysis Lab, Atlantic Soil Health Lab Grants: NSERC CREATE Climate Smart Soils
Adam Jatowt is a Professor and Head of the Data Science group at the Department of Computer Science, University of Innsbruck. He also serves as Deputy Head of the Digital Science Center and Research Center for Digital Humanities. His academic career spans roles at Kyoto University (2010-2020), National Institute of Advanced Industrial Science and Technology (AIST), and visiting positions at Karlsruhe Institute of Technology, University of La Rochelle, and University of California Berkeley. Research interests focus on temporal aspects of NLP/IR, computational history, large language models, and future forecasting. He leads projects combining digital humanities with advanced AI techniques, including temporal validity assessment and hint generation systems. Recent publications (2025) emphasize LLM applications in temporal analysis, QA systems, and energy sector digitalization. His work has been recognized through awards like the Friedrich Wilhelm Bessel Research Award (2024) and top-cited paper distinction in Information Sciences. He actively organizes conferences like ECIR 2026 and Text2Story workshops. Key contributions include developing WikiHint dataset, PlausibleQA framework, and tools like Rankify. His research also addresses societal challenges through ESG rating prediction and medical LLM applications.
Professor Reynold Cheng is a faculty member at the University of Hong Kong (HKU), specifically within the Department of Computer Science in the School of Computing and Data Science (CDS). He currently serves as the Division Head of the AI & Data Science Division at CDS and is part of the Steering Committee of the Musketers Foundation Institute of Data Science. His academic journey includes a BEng and MPhil from HKU (1998–2000) and an MSc and PhD from Purdue University (2003–2005). Prior to HKU, he was an Assistant Professor at the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. Cheng’s research focuses on data science, big graph analytics, and uncertain data management. He has received numerous awards, including the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. His work has been recognized through grants such as the HKU-TCL Joint Research Centre for AI-funded project (HKD 1M, 2020–2022) and a CRF-funded project for real-time monitoring of infectious diseases (HKD 6.5M, 2021–2022). Cheng actively contributes to academic service, including serving as PC co-chair for IEEE ICDE 2021 and editorial roles in journals like IS and DAPD. His publications span top venues like SIGMOD, VLDB, and KDD, emphasizing algorithm design for large graphs and probabilistic data systems.
Nur Zincir-Heywood is a Distinguished Research Professor and Associate Dean (Research) in the Faculty of Computer Science at Dalhousie University, Halifax, Nova Scotia, Canada. She has been a Full Professor since 2010, following progressive academic appointments from Assistant to Associate Professor. Her research centers on developing intelligent and secure systems for modeling and analyzing behaviors across networks and services. Key areas include: Cyber Security and Resilience Autonomous Cyber Operations Threat Analysis and Detection Machine Learning and Big Data Analytics Computer Communications and Networks Her recent recognition as a Distinguished Research Professor reflects her significant contributions to research and scholarship. She leads the NIMS Lab and is actively involved in research clusters focused on Systems, Big Data Analytics, AI, and Machine Learning. She also co-organized the 'Dal FCS Hands on Security Day' with industry partners like Cisco and 2Keys. A special issue she is involved with in IEEE TNSM on AI for network and service management highlights her leadership in cutting-edge domains. Scientific awards include: Distinguished Research Professor (2021–present) She advises students and leads research projects, with fellowship opportunities currently available in her lab. Her work bridges academic innovation with real-world applications in security and intelligent systems. She is also engaged with public outreach, occasionally appearing on CBC Information Morning.
Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University (CMU), where he has made significant contributions to cybersecurity, privacy, and AI research. He has co-founded and co-directed several groundbreaking graduate programs at CMU, including the Privacy Engineering Program (2012-present), the Ph.D. Program in Societal Computing (2003-2013), and the MBA track in Technology Strategy and Product Management (2005-2017). Carnegie Mellon University, School of Computer Science Software and Societal Systems Department CyLab Security and Privacy Institute Manufacturing Futures Institute Dr. Sadeh received his Ph.D. in Computer Science at CMU with a major in Artificial Intelligence and a minor in Operations Research. He holds an M.Sc. in computer science from the University of Southern California and a BS/MS degree in electrical engineering and applied physics from the Free University of Brussels (Belgium) as 'Ingénieur Civil Physicien.' Professor Sadeh's research spans cybersecurity, online privacy, Human-AI Interaction, AI governance, mobile computing, the Internet of Things, user-oriented machine learning, and language technologies. He is particularly known for his pioneering work on AI-based privacy enhancing technologies, including privacy assistants, automated privacy compliance tools, and NLP-based privacy solutions. His work has influenced the design of privacy features at major technology companies including Apple, Google, and Facebook/Meta, as well as privacy policies at regulatory agencies like the Federal Trade Commission and the California Office of the Attorney General. Analysis of his recent publications shows a strong focus on practical privacy solutions, particularly in mobile and IoT contexts, with an emphasis on making privacy more usable and understandable for end users. His work bridges technical innovation with policy implications, addressing both the technological and human aspects of privacy protection. 2018 Outstanding Entrepreneur of the Year award from the Pittsburgh Venture Capital Association Test of time award by the AAAI Conference on Web and Social Media (ICWSM) Gartner Group's Magic Quadrant leader in Security Awareness Computer-Based Training for 4 consecutive years Deloitte's Technology Fast 500 recognition for 3 consecutive years Professor Sadeh has advised numerous students, including PhD candidates like Aerin (Shikhun) Zhang, whose dissertation focused on understanding diverse privacy attitudes. His research has been funded through various grants, including NSF SaTC projects, and has resulted in technologies that protect tens of millions of users worldwide. He also founded Wombat Security Technologies, which was acquired by Proofpoint in 2018 and whose technologies are used by over 75% of Fortune 100 companies. Professor Sadeh leads several research initiatives including the Privacy Engineering Program, the Usable Privacy Policy Project, the Personalized Privacy Assistant Project, and CMU's Privacy Infrastructure for the Internet of Things. His Mobile Commerce Lab and E-Supply Chain Management Lab have produced influential research that has been commercialized by major organizations including IBM, Raytheon, Boeing, and the U.S. Army.
M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.
Professor Sara Dolnicar is an ARC Australian Laureate Fellow at the School of Business, Faculty of Business, Economics and Law, University of Queensland. With degrees in psychology and business administration from Wirtschaftsuniversität Wien, she specializes in market segmentation, sustainable tourism, social marketing, and environmental behavior. Born in Slovenia, raised in Austria, now based in Australia Over 300 refereed publications and 16 ARC grants Recipient of TTRA Distinguished Researcher Award (2017) and Slovenian Ambassador of Science (2016) Her research improves market segmentation methodology, addresses environmental sustainability in tourism, and develops behavioral interventions for eco-friendly tourist practices. She pioneered perceptions-based market segmentation and introduced bi-clustering techniques for improved data analysis. Recent publications focus on towel reuse interventions, buffet waste reduction, and smart sensor systems for hotel sustainability. She supervises PhD students in environmental behavior, tourism carbon emissions, and IoT applications for sustainability. Current grants include 'Mechanisms of Behaviour Change Theory' (2025-2028) and 'Reducing plate waste in hotels' (2021-2025). Key research areas: Sustainable tourism, market segmentation, social marketing Supervised over 15 PhD students in environmental behavior and disability employment Collaborates with industry partners like Energy Queensland and disability organizations
Mohamed F. Mokbel is a Distinguished McKnight University Professor in the Department of Computer Science and Engineering at the University of Minnesota - Twin Cities , where he also serves as the Director of Graduate Studies. He is recognized as an IEEE Fellow and ACM Distinguished Member for his contributions to spatially- and privacy-aware systems. His research focuses on database systems , spatial data management , and GIS (Geographic Information Systems) , with significant work in spatiotemporal data, location-based services, and machine learning for spatial applications. His most recent publications address scalable BERT-based trajectory imputation , spatial logistic regression frameworks , and spatiotemporal big data decay techniques . Key Awards: Distinguished McKnight University Professor (2023) ACM SIGSPATIAL 10-Year Impact Award (2022) IEEE Fellow (2020) ACM Distinguished Member (2017) NSF CAREER Award (2010) Selected Conference Papers: Recathon (2015, IEEE MDM Best Paper) ST-Hadoop (2017, SSTD Best Paper) KAMEL (2023, ACM SIGMOD Demo) Academic Service: Editor-in-Chief, ACM Transactions on Spatial Algorithms and Systems (2024–) General Co-Chair, ACM SIGSPATIAL 2025 Past Chair, ACM SIGSPATIAL (2014–2017)
Yan Ma serves as Professor and Chair of Biostatistics at the University of Pittsburgh, with additional appointments in Orthopaedic Surgery and Clinical and Translational Science. Previously, he was Professor and Vice Chair at George Washington University Milken Institute of Public Health (2014-2022) and Assistant Professor at Hospital for Special Surgery/Weill Cornell Medical College (2008-2014). His educational background includes: PhD in Statistics, University of Rochester (2008) MA in Statistics, University of Rochester (2004) MS in Mathematics, Syracuse University (2003) BS in Statistics, Beijing Normal University (2001) Ma's research centers on advanced statistical methodologies including missing data imputation, machine learning, meta-analysis, causal inference, and longitudinal methods, applied across orthopedics, anesthesiology, health disparities, and emergency medicine through team science and translational research frameworks. His publication trajectory demonstrates sustained innovation from methodological foundations (2008-2012) to contemporary applications in health disparities and machine learning (2016-2022), consistently addressing complex biomedical challenges through high-impact journals like JAMA and Health Services Research. His scientific recognition includes: ASA's Statistics in Epidemiology Young Investigator Award (2010) Interorganizational Team Science Award (2012) ORISE FDA Research Fellowship (2017) APHA Achievement in Academia Award Ma has secured R01 funding from NIH/AHRQ for missing data methods in health disparities research while serving as Associate Editor for ASA journals and reviewer for NIH/PCORI/VA panels, demonstrating leadership in statistical methodology development and interdisciplinary collaboration. His team-science approach bridges statistical innovation with clinical implementation across orthopedics and anesthesiology, driving evidence-based practice through methodological rigor and cross-disciplinary partnerships.
Marta Molinas is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). Her research spans multiple interdisciplinary domains with a focus on EEG technology and brain-computer interfaces. She actively supervises numerous Master's projects and maintains extensive international collaborations with institutions including Kavli Institute for Systems Neuroscience, RIKEN Center for Brain Science, University of Tsukuba, Juntendo University, and several European universities. Professor Molinas' research interests center on developing innovative EEG technologies, particularly her FlexEEG concept for reduced-channel EEG systems with brain imaging capabilities. Her work integrates signal processing, artificial intelligence, and neuroscience to create practical applications in mental health, sleep research, neurorehabilitation, and human-computer interaction. She specializes in EEG source imaging, machine learning for brain signal analysis, and the development of brain-computer interfaces for various applications including locked-in syndrome communication, ADHD treatment, and driver monitoring systems. Her publication portfolio demonstrates strong trends in interdisciplinary research combining neuroscience with electrical engineering and artificial intelligence. The work shows particular emphasis on developing practical EEG-based systems that minimize invasiveness while maintaining analytical power, with applications spanning healthcare, rehabilitation, and human augmentation. Her research bridges theoretical signal processing with real-world implementations through numerous student projects and international collaborations. Professor Molinas actively supervises a large team of Master's and PhD students across multiple projects, with each project typically requiring two students working collaboratively. Her research is supported through numerous international collaborations with institutions in Japan, India, and Europe, indicating substantial research funding and project leadership. She has developed a pipeline of student projects that build upon previous work, creating a cumulative knowledge base within her research group. She leads the EEG ITK research team at NTNU, which focuses on developing the FlexEEG headset prototype featuring flexible, wireless, dry electrodes designed to move across the scalp. This team works at the intersection of neuroscience, electrical engineering, and computer science, developing applications for sleep research, mental health monitoring, neurorehabilitation, and brain-computer interfaces. The team collaborates extensively with international partners including the Kavli Institute for Systems Neuroscience, the International Institute of Integrative Sleep Medicine at University of Tsukuba, and several engineering departments across Europe and Asia.
