Julien Ah-Pine is a lecturer at Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS) under Université Clermont Auvergne , with affiliations at Institut national polytechnique Clermont Auvergne and École des Mines de Saint-Étienne . He also holds a Researcher position at CNRS. Research Interests His work spans machine learning , information fusion , aggregation functions , and multi-criteria decision support , with a focus on complex data types like graphs , functional data , and multi-view datasets . Recent publications emphasize anomaly detection in spectral data streams , online learning , and interpretable AI for industrial applications. Selected Publications 2025 work on OnlineBootKNN introduces a novel framework for real-time spectral anomaly detection, while 2024 research explores multiple kernel methods in functional data classification. Earlier studies cover graph-based clustering , relational data mining , and linguistic network models for NLP tasks. Laboratory & Collaborations Works within LIMOS laboratory at Université Clermont Auvergne, collaborating with institutions like Mines Saint-Étienne and CNRS. Key partnerships include Nicolas Rojas Varela and Engelbert Mephu Nguifo on data stream analysis projects.
Anshul Thakur is a Departmental Lecturer in Clinical Machine Learning at the University of Oxford's Institute of Biomedical Engineering. His research focuses on advancing data-efficient deep learning techniques, adversarial attacks, and interpretable AI frameworks for healthcare applications. He holds a PhD from IIT Mandi (2020), where his thesis explored audio signal analysis using dynamic kernels and deep learning. Education: PhD in Computing & Electrical Engineering, Indian Institute of Technology Mandi (2020) Research concentrated on bioacoustic signal pattern analysis and ML frameworks for acoustic classification. Research Interests: His work emphasizes clinical AI applications, including federated learning for medical data, multimodal diagnosis systems, and mitigating class imbalance in healthcare datasets. He develops interpretable models for medical practitioners and explores ethical AI deployment in clinical settings. Recent Trends in Publications: Recent work addresses federated learning optimization, multimodal clinical diagnosis, and early disease prediction using biomarker patterns. His studies highlight innovations in EHR analysis, privacy-preserving techniques, and cross-domain medical model adaptation. Labs & Teams: Active in the Institute of Biomedical Engineering, collaborating on projects like the RapiD_AI framework for pandemic preparedness and Continuous Patient State Attention Models for irregular EHR data analysis.
Prof. Tan Yap Peng is a Professor and Chair of the School of Electrical & Electronic Engineering at Nanyang Technological University (NTU), Singapore. He holds the President's Chair in Electrical and Electronic Engineering and serves as Associate Vice President (Lifelong Learning – Postgraduate Programmes by Coursework). His research focuses on multimedia analysis, computer vision, machine learning, and data analytics. He earned his B.S. from National Taiwan University and M.A./Ph.D. from Princeton University. He has led major initiatives including the INFINITUS Infocomm Research Centre and contributed to IEEE technical committees. His over 200 publications span image/video processing, neural network robustness, and cross-modal systems. Awards include IEEE Fellow status. Education: B.S. Electrical Engineering (NTU), M.A./Ph.D. (Princeton) Research interests emphasize interactive digital media, content-based analysis, and AI-driven solutions for visual and signal processing. His work addresses challenges in adversarial attacks, video generation, and low-light image enhancement. He has held editorial roles at IEEE Transactions and EURASIP journals. Conference leadership includes chairs for ICME and ICIP. His contributions bridge academia and industry through collaborative research networks.
Paula Branco is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa (since 2020). Previously, she was a PostDoctoral Fellow at Dalhousie University's Faculty of Computer Science (2019). She holds a PhD in Computer Science from the University of Porto, alongside a MSc in Data Mining and a Mathematics degree. She is an external researcher at INESC TEC's LIAAD laboratory. Her research focuses on Machine Learning, particularly utility-based learning and imbalanced domains, with applications in cybersecurity, healthcare, and environmental monitoring. Key areas include anomaly detection, fraud detection, rare event prediction, and spatio-temporal data analysis. Recent work emphasizes resampling techniques for imbalanced regression (e.g., SMOGN, ImbalancedLearningRegression Python package), evaluation metrics for imbalanced domains, and cybersecurity applications like intrusion detection and malware analysis. She has supervised seven graduate students and contributed to open-source projects like UBL (R package) and ImbalanceMetrics. Her teaching includes courses on databases and AI for cybersecurity. She actively publishes in top venues and collaborates on tools like SMOGN-LIDTA17 and NeighborhoodBiasResamplingRegression.
Professor Alistair Barros is Head of School and Academic Program Leader of Service Science at QUT’s School of Information Systems (Faculty of Science). He holds a PhD from the University of Queensland, with extensive ICT experience in academia and industry (e.g., Global Research Leader at SAP AG). His research focuses on enterprise systems design, optimization, and interoperability in cyber-physical environments leveraging cloud, IoT, and blockchain technologies. Key areas include service computing methods (microservices, business process management), model-based systems re-engineering, and distributed service coordination. Education: PhD in Information Systems from the University of Queensland. Research Interests: Enterprise systems modernization, service-oriented architectures, Industry 4.0 applications, and digital transformation in sectors like construction, manufacturing, and supply chains. He has published 180+ peer-reviewed articles (including 6 edited books) and holds 17 US patents. Notable contributions include the BPMN 2.0 standard co-authorship and the 'Workflow Patterns' paper (most cited in BPM). Awards: Test of Time Award (BPM 2015), Wharton Education Award (TeachConnect platform), and impactful industry collaborations (e.g., SAP NetWeaver, Federal Government WPIT project). Grants & Projects: Led ARC, EU FP6, and industry-funded projects. Current focus includes re-engineering legacy systems for microservices, EV charging network design, and adversarial ML in cybersecurity. Labs/Teams: Active in QUT’s Centre for Data Science, focusing on service science and cyber-physical systems integration.
Krzysztof J. Kochut is a Professor in the School of Computing at the University of Georgia. His career at UGA spans from Limited Term Assistant Professor (1987–1988) to his current professorial role since 2001. He holds a Ph.D. in Computer Science from Louisiana State University (1987) and an M.S. from the University of Warsaw (1982). Education: Ph.D., Computer Science, Louisiana State University (1987) M.S., Computer Science, University of Warsaw (1982) His research focuses on knowledge graphs, ontologies, semantic web technologies, bioinformatics, distributed systems, and computational intelligence. He has developed frameworks for knowledge graph embedding, entity summarization, and semantic resource federation. Recent publications highlight knowledge graph mining, random walk-based embeddings, and relation prediction using large language models. Funded by NIH and NSF, his work addresses challenges in biomedical data integration and dark proteome annotation. Scientific Awards: Student Career Success Influencer Award 2024 Student Career Success Influencer Award 2022 Outstanding Faculty Service Award 2016 Best Paper Award 2015 Professor Kochut has received grants for projects like Annotating dark ion-channel functions using knowledge graph mining (NIH, 2021–2025) and Integrated Technology Resource for Biomedical Glycomics (NIH, 2002–2008). He leads the development of tools such as GTXplorer for glycosyltransferase analysis and participates in semantic web initiatives.
Dr. Jose Paolo Talusan is a Research Scientist at the Department of Computer Science and Computer Engineering , Vanderbilt University, specializing in smart transportation systems , distributed computing , and cyber-physical systems . He is affiliated with ScopeLab , a research group focused on smart cyber-physical systems. Education: PhD from Nara Institute of Science and Technology, Japan (2020) Research Interests: His work addresses challenges in urban mobility through middleware architectures, optimization algorithms, and machine learning. Key areas include incident detection in transportation systems, privacy-preserving route planning, and vehicle-to-building charging optimization. Publication Trends: Recent publications focus on real-time transit optimization (2024-2025), leveraging reinforcement learning for heterogeneous agents in vehicle-to-building systems, and privacy-aware route planning in smart cities. His work integrates IoT , edge computing , and graph neural networks to tackle imbalanced data and sparsity issues in transit analytics. Labs & Teams: Actively contributes to ScopeLab at Vanderbilt University, collaborating on interdisciplinary projects with researchers in computer science, electrical engineering, and urban planning.
Dr. Jeffery D. Weir is an Adjunct Professor in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT). A former U.S. Air Force officer, he specializes in decision analysis, risk analysis, and multi-objective optimization, with applications spanning military logistics, energy systems, and transportation modeling. His research addresses complex operational challenges such as aircraft scheduling, RNA foldability prediction, and cost analysis for military installations. Doctor of Philosophy (Ph.D.), Industrial Engineering & Operations Research, Georgia Institute of Technology, 2002 Master of Science (M.S.), Operations Research, Air Force Institute of Technology, 1995 Bachelor of Science in Electrical Engineering (B.S.E.E.), Georgia Institute of Technology, 1988 Dr. Weir's research bridges theoretical and applied domains, focusing on metaheuristics, data envelopment analysis, and robust optimization. His recent work includes RNA structure prediction frameworks, multi-modal distribution networks, and energy model recommendation systems. His publications span journals like BMC Bioinformatics , European Journal of Operations Research , and IEEE Transactions on Evolutionary Computation , reflecting interdisciplinary expertise in computational biology, military logistics, and building energy systems. Scientific awards include the Toulmin Medal for best article in 2004. He has received grants from the Defense Intelligence Agency, United States Transportation Command, Air Force Materiel Command, and other military organizations. Dr. Weir is a member of the Institute for Operations Research and the Management Sciences (INFORMS), Military Operations Research Society, and Institute of Industrial Engineers. His work emphasizes practical decision support tools for military personnel and resource management.
Dr. Sikha S Bagui is a Distinguished University Professor in the Department of Computer Science at the Hal Marcus College of Science and Engineering, University of West Florida . She served as the former Chair of Computer Science and was the Founding Director of the Center for Cybersecurity . Her research spans database design, Big Data analytics, machine learning, and cybersecurity . Research Focus: Machine Learning, Data Mining, Network Traffic Analysis, Graph Databases, and Resampling Techniques for Imbalanced Data Awards: Askew Fellow (2018–2021), multiple Excellence in Teaching and Distinguished Research Awards (2001–2024) Contributions: Authored books on databases/SQL (translated internationally), developed the UWF-ZeekData datasets for cybersecurity research, and served as Associate Editor for multiple journals Tools & Frameworks: Active in Hadoop, Spark, Memgraph, and MITRE ATT&CK-based threat modeling Publication Trends: Recent work focuses on MITRE ATT&CK datasets , graph-based cybersecurity , resampling rare attacks , and educational impacts in computing . Her research bridges theoretical and applied domains, including clinical decision support systems and K-12 computer science education. Scientific Awards: Askew Fellow (Reubin O’D. Askew Institute for Multidisciplinary Studies, 2018–2021) Excellence in Teaching and Advising Award (UWF, 2012) Distinguished Research and Creative Activities Award (UWF, 2007, 2012) Excellence in Undergraduate Teaching and Advising Award (UWF, 2001–2006) Leadership & Service: Directed the Center for Cybersecurity, contributed to journals as Associate Editor, and engaged in initiatives like NCWIT Aspirations in Computing and the Association for Women in Computing.
Tossapon Boongoen is a Professor in the Department of Computer Science at Aberystwyth University, with over a decade of experience in artificial intelligence and machine learning. Previously, he served as Associate Professor at Mae Fah Luang University (2017-2022) and Royal Thai Air Force Academy (2011-2017), where he also directed the MFU Research and Innovation Institute. His research spans ensemble clustering for privacy-preserving data fusion deep learning in remote sensing and sky survey data network security applications for ransomware and intrusion detection forest fire risk modeling using spatial-temporal data Recent publications focus on convolutional neural networks, adversarial attack classification, and collaborative filtering algorithms. He leads international projects funded by the British Council, FCDO, and Academy of Medical Sciences, including collaborations with institutions in Thailand, Korea, Vietnam, France, and Czech Republic. Professional engagements include editorial roles in journals like Knowledge-Based Systems Frontiers in Neurorobotics PeerJ Computer Science ICT Express and partnerships with GISTDA, GOTO Observatory, and Imperial College London.
Amir Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, and mobility in distributed systems for emerging technologies like IoT, Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). University: University of Oslo Department: Informatics Academic Rank: Professor His research spans IoT, Edge/Fog Computing, and Cyber-Physical Systems, emphasizing energy efficiency, privacy preservation, and self-adaptive architectures. Key areas include network traffic classification, computation offloading, and federated learning applications in vehicular systems. Recent publications highlight advances in latency-aware IoT data transmission , federated vehicular networks , energy-efficient wireless charging , and privacy-preserving data integration . These works often integrate machine learning with network optimization. Projects include the CPS Lab at UiO, DILUTE (Fluid Service Abstraction), and the Gemini Centre on IoT . He collaborates on initiatives like PACE for energy informatics curricula development.
Dr. Tieming Liu is an Associate Professor in the School of Industrial Engineering and Management at Oklahoma State University , where he has served since 2005, first as Assistant Professor and then promoted to Associate Professor in 2011. His expertise bridges operations research, supply-chain coordination, healthcare analytics, renewable-energy policy, and production scheduling. Education Ph.D. in Transportation and Logistics, Massachusetts Institute of Technology, 2005 M.S. in Industrial Engineering and Management Science, Northwestern University, 2001 M.S. in Control Theory and Control Engineering, Tsinghua University, 2000 B.S. in Control Theory and Control Engineering, Tsinghua University, 1997 Research Interests Dr. Liu’s scholarship is organized around three pillars: Supply-Chain & Logistics: coordination contracts, inventory bounds, responsive pricing, channel rebates, and production flexibility under uncertainty. Healthcare Analytics: machine-learning models for diabetic retinopathy and sepsis risk prediction, clinical decision-support systems, and handling imbalanced EHR data. Energy & Sustainability: renewable portfolio standards, capacity coordination with renewable energy certificates, and incentive mechanisms for renewable and conventional generators. Recent methodological contributions include hidden Markov models for continuous mortality prediction, tree-augmented Bayesian networks for sepsis risk, and tensor-completion-driven convolutional networks for longitudinal medical data. Scientific Awards & Honors EJOR Reviewer Award, 2019 IEM Faculty Award, 2019 Halliburton Outstanding Faculty Award, OSU, 2014 Merrick Foundation Teaching Award, OSU, 2013 Riata/Koch Faculty Fellow, OSU, 2012 Lockheed Martin Teaching Award, OSU, 2011 Student Organization Faculty Advisor of the Year, OSU, 2010 Student Mentorship & Collaboration Dr. Liu has advised or co-advised a large cohort of doctoral and master’s students whose names appear as first or co-authors on his publications. His collaborative network spans MIT, Northwestern, IBM T. J. Watson Research Center, and multiple departments across OSU, fostering interdisciplinary projects that integrate operations research with real-world healthcare, transportation, and energy challenges. Laboratories & Teams He conducts research within the analytics and optimization laboratories of the School of Industrial Engineering and Management, directing projects funded by federal agencies and industry partners aimed at next-generation decision-support systems for healthcare providers, logistics operators, and energy market regulators.
Rishabh Iyer is an Assistant Professor at the University of Texas at Dallas (UT Dallas) and a Visiting Assistant Professor at the Indian Institute of Technology Bombay (IIT Bombay). He leads the CARAML Lab at UT Dallas, focusing on machine learning, computer vision, and natural language processing. His research emphasizes data-efficient learning, subset selection, and combinatorial optimization techniques such as submodular functions. Education: Ph.D. and M.S. from the University of Washington (2011-2015), B.Tech from IIT Bombay (2011). He has held roles including Senior Research Scientist at Microsoft (2016–2019) and Postdoctoral Researcher at University of Washington (2015–2016). Research Interests: Combinatorial loss functions, compute-efficient learning via subset selection, robust deep learning, weak supervision, and continuous learning. Key projects include GLISTER, GRADMATCH, and SMILE frameworks for subset selection and active learning. Awards: NSF Medium Grant (2021), Adobe and Amazon Research Awards (2022), Best Paper Awards at ICML/NeurIPS (2013), Microsoft Research Fellowship (2014). Grants: Supported by NSF, Adobe, Google, Amazon, and UT Dallas startup funds. Active in teaching courses like Machine Learning (CS 6375) and Optimization in Machine Learning (CS 7301). Labs/Teams: CARAML Lab, collaborating on subset selection, active learning, and submodular optimization. Open-source tools include CORDS, DISTIL, and submodlib.
Ioannis Maniadis Metaxas is a lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. He teaches undergraduate and postgraduate courses including Artificial Intelligence, Machine Learning, and Machine Learning for Visual Data Analysis. His research focuses on: Unsupervised representation learning Computer vision systems Large vision-language models Deep clustering techniques Generative models for data augmentation E-commerce personalization systems Recent publications demonstrate consistent focus on efficient learning methods for visual data, particularly investigating unsupervised pretraining, cluster optimization, and model fine-tuning approaches. His work shows progression from foundational 3D data augmentation to cutting-edge vision-language model optimization.
Yingtao Liu is an Associate Professor and holds the Benjamin H. Perkinson Chair & William H. Barkow Presidential Professor at the University of Oklahoma's Aerospace & Mechanical Engineering Department. He specializes in advanced composites, multifunctional materials, intelligent sensors, structural health monitoring, and biomedical applications of shape memory polymers. His research integrates additive manufacturing, nanotechnology, and material science to develop innovative materials and devices. Education: Ph.D., Mechanical Engineering, Arizona State University (2012) M.S., Mechatronics Engineering, Harbin Institute of Technology (2006) B.S., Mechanical Engineering, Harbin Institute of Technology (2004) Research Interests: Development of smart materials with sensing and adaptive capabilities Nondestructive testing and structural health monitoring 3D printing of advanced composites and polymers Biomedical devices for intracranial aneurysm treatment Defect analysis in additive manufacturing processes Recent Contributions: Recent publications focus on shape memory polymers, defect analysis in metal additive manufacturing, and advanced composites for biomedical and structural applications. His work bridges materials science, mechanical engineering, and AI-driven characterization techniques. Awards: Best Paper Award, ASME IMECE 2018 OU VPR Faculty Investment Program Award (2015) Journal of Aerospace Engineering Best Paper Award (2012) Teaching & Outreach: Teaches courses in statics, solid mechanics, and structural health monitoring. Engages in educational initiatives integrating 3D printing and advanced materials into undergraduate curricula.
