Ko, Jonghyeon is a researcher affiliated with the Ulsan National Institute of Science and Technology (UNIST) , specifically the Department of Materials Science and Engineering within the College of Natural Science and Engineering. His work spans multiple disciplines including process mining, anomaly detection, blockchain technology, AI computing, and environmental engineering. His research interests include: Anomaly detection in business process event logs Blockchain-based systems for nuclear/radioactive waste management AI computing using neuromorphic devices Statistical leverage and information-theoretic approaches to process mining Optimization of autonomous vehicle safety systems Recent publications demonstrate expertise in developing formal languages for data quality simulation, probabilistic trace alignment methods, and practical tools for anomaly detection like AIR-BAGEL. While no explicit scientific awards are mentioned in the text, his work has been published in venues such as Information Systems , npj Unconventional Computing , and Expert Systems with Applications .
Peyman Mashhadi is a Senior Lecturer at the School of Information Technology , Halmstad University. His research focuses on machine learning applications in predictive maintenance, automotive systems, and computational optimization. Position: Senior Lecturer University: Halmstad University Email: peyman.mashhadi@hh.se His work spans machine learning , deep learning , and predictive maintenance , with a particular emphasis on feature selection , optimization algorithms , and automotive diagnostics . He has contributed to multitask learning, domain adaptation, and industrial applications of neural networks. Recent publications highlight trends in automotive engineering (battery health estimation, turbocharger diagnostics), computational methods (genetic algorithms, metaheuristics), and machine learning (stochastic optimization, transfer learning). Key themes include robustness in predictive models and cross-domain adaptability. Contact details: peyman.mashhadi@hh.se
Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.
Nikos Giatrakos is an Assistant Professor at the School of Electronic & Computer Engineering, Technical University of Crete, and a core member of the Software Technology and Network Applications Lab (SoftNet) . His work bridges Big Data systems, IoT, and advanced analytics, with a focus on real-time processing and scalable architectures. Previously, he served as a postdoctoral researcher at the same laboratory. Education PhD in Computer Science, University of Piraeus (2012) Postgraduate Diploma in Information Systems, Athens University of Economics and Business (2008) BSc in Computer Science, University of Piraeus (2006) Research Focus : Nikos specializes in software architectures for Big Data streaming, including Distributed Big Data Processing , Federated Machine Learning , Cloud-to-Edge Data Management , and Approximate Query Processing . His work has also advanced Complex Event Processing and Outlier Detection in decentralized environments. Scientific Contributions : His research has led to the DAG* workflow optimizer for IoT, the SuBiTO framework for real-time neural learning, and the INFORE approach for cross-platform analytics. He received the Best System Demonstration Award at ACM CIKM 2020 for INforE. Academic Leadership : Nikos teaches Object-Oriented Programming, Data Science, and Distributed Systems. He has supervised numerous European and national grants as Principal Investigator and served on program committees for top-tier conferences like SIGMOD, VLDB, and DEBS.
Dr. André Artelt is a researcher at the University of Bielefeld within the Faculty of Engineering and affiliated with the Machine Learning Group at the Center for Cognitive Interaction Technology (CITEC). His work focuses on Explainable AI (XAI), particularly counterfactual explanations, and their applications in critical infrastructure like water distribution networks. Current Research: Explainable AI Counterfactual explanations Water network monitoring Physics-informed graph neural networks Scientific Contributions: His recent publications explore reinforcement learning for water pump scheduling, scalable graph neural networks for water systems, and benchmark frameworks like EPyT-Flow. He investigates how training data affects explanation quality and develops tools for robust counterfactual reasoning. Awards: Project Lamarr Fellowship
Tevfik Aktekin is a Professor in the Department of Decision Sciences at the Paul College of Business and Economics, University of New Hampshire. His research focuses on Bayesian inference and stochastic modeling in service systems and business analytics. Education : Ph.D. in Decision Sciences (George Washington University), M.B.A. in Management Decision Making (George Washington University), B.S. in Mechanical Engineering (Yildiz Technical University). His work applies Bayesian state-space models to multivariate count data, with applications in call center staffing, product modification cycles, and public health analysis. He has published in journals like Annals of Applied Statistics , Bayesian Analysis , and European Journal of Operational Research . The 15 most recent articles highlight trends in sequential Bayesian learning for dynamic systems (e.g., bike-sharing, web traffic), time series of non-Gaussian data, and stochastic optimization under uncertainty. These works bridge statistical theory with practical applications in business and health.
Dr. James N. Gilmore is an Associate Professor of Media and Technology Studies and Graduate Coordinator in the Department of Communication at Clemson University's College of Behavioral, Social and Health Sciences. He joined Clemson in 2018 after completing his PhD at Indiana University and has established himself as a leading scholar in media technology studies, with expertise in wearable technologies, datafication, and media infrastructure. Dr. Gilmore's educational background includes: Ph.D. in Communication and Culture from Indiana University (2018) M.A. in Film and Television from University of California, Los Angeles (2013) B.A. in Film and Media Studies from University of South Carolina (2011) His research focuses on the cultural politics of media and communication technologies, particularly how computational technologies convert human behavior to data (datafication). Dr. Gilmore examines how everyday devices like smartwatches, fitness trackers, and body cameras reinforce systems of normalcy, surveillance, and solutionism across health, labor, accessibility, law enforcement, and other domains. His work bridges theoretical frameworks from media studies, cultural studies, and science and technology studies to analyze the social implications of emerging technologies. Dr. Gilmore's publications demonstrate consistent engagement with emerging technologies across multiple domains. His recent work spans wearable technologies, virtual reality, AI platforms like ChatGPT, streaming services, and smart home devices, revealing patterns in how technologies mediate everyday life while raising critical questions about privacy, surveillance, accessibility, and corporate power. His scholarship consistently connects technological developments to broader social, political, and cultural contexts. Dr. Gilmore has received numerous honors and awards for his research and teaching: Top Paper Award, Popular Communication Division, Southern States Communication Association (2024) Outstanding Teaching of the Year (Junior Tenure-Track), College of Behavioral, Social, and Health Sciences (2022-2023) Outstanding research publication award for 'Securing the kids' (2022) Research Faculty Spotlight (Spring 2021) Ray Camp Award for Most Outstanding Research Paper (2018) As Graduate Coordinator, Dr. Gilmore actively mentors students, with numerous co-authored publications featuring graduate and undergraduate researchers. His students have contributed to research on AI adoption, virtual reality, wearable technologies, and platform politics. Dr. Gilmore has secured internal research funding at Clemson University, including recognition through the university's research reporting system. His book projects, including the forthcoming DeGruyter Handbook of Wearable Technologies and Society, represent significant scholarly contributions that bring together international researchers. Dr. Gilmore leads research initiatives focused on wearable technologies and media infrastructure, with his recent book 'Bringers of Order' establishing him as a leading voice in wearable technology studies. He is currently editing a comprehensive handbook that will expand this research area significantly.
Amael Poulain is a hydrogeology researcher at the University of Namur specializing in karst systems and groundwater dynamics. With a PhD completed in 2017 under supervisor Hallet V., Poulain has established a strong research profile focusing on vadose zone processes, tracer testing, and hydrological monitoring in karst environments. Their work combines field measurements with computational modeling to understand complex groundwater systems in Belgian karst regions. PhD in Geology (2017), University of Namur Principal Investigator on 6 research projects (2017-2023) 16 research outputs including journal articles and conference contributions Recipient of Young Karst Researcher Prize (2015) Active contributor to international karst research conferences Poulain's research focuses on understanding groundwater recharge processes in karst systems through innovative monitoring techniques. Their work examines solute transport, breakthrough curve analysis, and the impact of flash flood events on groundwater systems. Key contributions include developing ultra-portable fluorometry for dye tracing in remote karst environments and investigating the relationship between surface water features and underground flow paths in Belgian karst regions. Analysis of Poulain's 16 research outputs reveals consistent focus on experimental hydrogeology in karst environments, particularly using tracer tests and novel monitoring technologies. The research spans vadose zone processes, breakthrough curve analysis, and the development of field instrumentation for hydrological monitoring. Recent work (2020-2023) shows increasing emphasis on technological applications including fluorimeter industrialization and gravity monitoring techniques. Young Karst Researcher Prize (2015) awarded at conference in Birmingham, UK Recognition for contributions to groundwater research and karst hydrology Active participation in international karst research community Poulain leads multiple research projects including STREAM fluorimeter industrialization and eco-village construction expertise. Their work bridges academic research and practical applications through spin-off development for hydrological monitoring equipment. As Principal Investigator on projects like 'Expertise of tracing test at the ponds of Villeneuve,' Poulain demonstrates strong grant acquisition and project management capabilities. The research involves collaboration with multiple institutions across Belgium's karst regions. Poulain's laboratory work focuses on hydrological monitoring in karst environments, particularly through the STREAM project which develops fluorometric solutions for groundwater monitoring. Their research utilizes cave percolation monitoring, gravity measurements, and electrical resistivity tomography to study water movement through vadose zones. The work is conducted primarily in Belgian karst regions including Rochefort and Furfooz, with emphasis on practical applications for water resource management.
Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.
Vitaveska Lanfranchi is a Senior Research Fellow at the University of Sheffield , School of Computer Science, focusing on Human-Computer Interaction , Social Media , and Visual Analytics . Her work addresses real-time data analysis for emergency response and knowledge management, with projects funded by EPSRC, UK/SBRI, and EU Framework 6. Research Interests : Human-Computer Interaction Social Media for Emergency Response Visual Analytics Knowledge Management Mobile Interaction Recent publications (2008–2012) explore semantic user networks, knowledge dashboards, hybrid search techniques, and AI usability, particularly in aerospace engineering and emergency response. Projects like Randms and WeKnowIt emphasize collective intelligence and web-scale data analysis. Contact : v.lanfranchi@dcs.shef.ac.uk
Larry P. Heck is a Professor with a joint appointment in the School of Electrical and Computer Engineering and School of Interactive Computing at the Georgia Institute of Technology. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar . Education: BSEE, Texas Tech University (1986) MSEE, Georgia Institute of Technology (1989) PhD EE, Georgia Institute of Technology (1991) His research focuses on conversational AI , dialogue systems , and machine learning applied to natural language processing and speech recognition . He pioneered early industrial applications of deep learning in speech processing and has contributed to advancements in multimodal interaction, knowledge distillation, and real-time question answering systems. Recent publications emphasize moral reasoning in AI , multimodal dialogue , and large-scale dataset creation for conversational systems. His work bridges language modeling , sensor fusion , and ethical AI through innovations in contextual reasoning and interface masking. Scientific Distinctions: IEEE Fellow (2020) IEEE Signal Processing Society Best Paper Award Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Fellow, National Academy of Inventors (2025) He has secured significant funding from DARPA and NSA for speaker recognition systems and has led cutting-edge research at institutions including Microsoft, Google, and Samsung. His lab focuses on conversational systems and deep learning for speech and multimodal data.
Asier Perallos Ruiz is a Professor in the Faculty of Engineering at the University of Deusto, specializing in the Department of Computing, Electronics and Communication Technologies. His research focuses on RFID technology, wireless sensor networks, and computational intelligence applications with significant contributions to intelligent transport systems and antenna design. Dr. Perallos Ruiz's research interests span multiple domains with a focus on RFID technology , Wireless sensor networks , Internet of Things (IoT) , Computational intelligence , Evolutionary algorithms , and Intelligent transport systems . His work bridges theoretical advancements with practical applications, particularly in transportation systems, healthcare, and industrial automation. His research often involves interdisciplinary collaboration across engineering disciplines. His publication portfolio shows a consistent trend toward improving RFID systems, developing efficient anti-collision protocols, and applying computational intelligence to real-world problems. Recent work has focused on polarization-diversity rotation sensing, customizable RFID platforms, and the integration of RFID with IoT applications. His research demonstrates a progression from foundational RFID technology to more complex system integration and application-specific solutions. Dr. Perallos Ruiz has supervised several graduate students including Muralter Florian (2021), Arjona Aguilera Laura (2018), Cmiljanic Nikola (2018), Lopez Garcia Pedro (2016), and Moreno Emborujo Asier (2016). His research has been supported by various projects focusing on RFID technology, intelligent transportation systems, and wireless communication applications. He leads research teams focused on RFID systems development, wireless sensor networks, and computational intelligence applications. Current work appears to be advancing RFID sensing capabilities, energy-efficient protocols, and system integration for practical applications in transportation and industry.
Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.
Rebecca Yang is a Visiting Professor at RMIT University's School of Property, Construction and Project Management, specializing in building, construction, and distributed renewable energy research. She integrates theoretical knowledge with cutting-edge technologies to advance sustainable urban development. Her research focuses on solar energy applications in buildings, construction innovation, and international energy policy frameworks through her leadership roles in the International Energy Agency's Photovoltaic Power Systems Programme (PVPS) Task 15 and Solar Heating and Cooling Programme (SHC) Task 66. She established RMIT's Solar Energy Application Lab and has 8 years of BIPV expertise. Notable achievements include: Australian representative in international BIPV standardization (IEC 63092) 2019 Facilitator Prize for BIPV Tool development She supervises research projects related to: Solar building envelope optimization Machine learning for energy systems Fire safety in BIPV installations Blockchain-enabled energy trading Circular economy for PV waste
Dr. Fei Chiang is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. Her research focuses on data management , with emphasis on data quality, data privacy, information extraction , and contextual data cleaning . She has collaborated with IBM Global Services and Microsoft Research on improving data quality in enterprise systems. Key research themes include graph databases , temporal data analysis , and privacy-aware data processing Recent publications explore federated learning , SQL understanding in LLMs , and temporal graph constraints Industry collaborations with IBM Toronto Lab and Microsoft Research have led to innovations in data cleaning automation and semantic analysis. Her work bridges database theory with machine learning applications in healthcare inventory optimization and flight reliability prediction.
