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Farnoush Rezaei JafariView profile

Researcher

• Explainable AI
• Efficient Machine Learning
• Generative Models
• +3 more

Farnoush Rezaei Jafari is a Researcher and Ph.D. Candidate in Machine Learning at the Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). She is part of the Machine Learning / Intelligent Data Analysis group. Her M.Sc. in Computer Science was completed at TU Berlin in 2021. Her research interests focus on enhancing the transparency and efficiency of machine learning models, particularly in the areas of Explainable AI, Efficient Machine Learning, and Generative Models. She explores innovative methods to make AI systems more interpretable while maintaining computational efficiency. Farnoush has contributed to notable publications, including work on adaptive token sampling techniques for vision transformers and explainable sequence models. Her recent research explores symbolic XAI to bridge the gap between model outputs and human-understandable logic. No scientific awards have been explicitly mentioned in the provided information. She has no recorded advisees or grants listed. She is associated with the BIFOLD institute, contributing to collaborative research efforts in foundational AI and data science.

Remco M. DijkmanView profile

Professor

• Business Process Management
• Data-Driven Process Optimization
• Supply Chain Management
• +3 more

Remco M. Dijkman serves as Full Professor in Information Systems at Eindhoven University of Technology (TU/e), chairing the Information Systems group within the Industrial Engineering and Innovation Sciences school. He additionally holds a Full Professor position at EAISI High Tech Systems and acts as research director for high-tech supply chains at the European Supply Chain Forum—a network of over 50 multinational companies. His research centers on Business Process Management with emphasis on data-driven optimization of business processes. His academic background includes both PhD and Master's degrees in Computer Science from the University of Twente. Publications span Information Systems, Computers in Industry, and Transactions on Software Engineering and Methodology, with over 100 papers and service on the editorial board of Information Systems. He has held visiting positions at New York University, Hasso Plattner Institute, IBM Zurich Research Lab, Humboldt-University Berlin, and Queensland University of Technology. Dijkman's research interests focus on detecting, diagnosing, and predicting optimal execution scenarios in business processes, developing mathematical models for quantitative process analysis , and resource assignment optimization . These are primarily applied in transportation logistics and high-tech supply chains, where he investigates data-driven predictions for transport order assignment and supply chain planning. His work bridges artificial intelligence with practical business applications. Recent publications (2024-2025) reveal concentrated efforts in deep reinforcement learning for resource allocation, process pattern discovery, and software library development (GymPN, SimPN). Key trends include predictive process monitoring for healthcare applications, event data enrichment frameworks, and uncertainty handling in logistics planning—demonstrating strong interdisciplinary integration. Scientific recognition includes: Best Demo Award (2019) Best Reviewer Award (2016) Test of Time Award (2019) He has supervised 150 students, including Lotte Vugs who received the Dow Chemical Best OML Master Thesis Award in 2020. Grant leadership spans eight projects: NXTGEN Smart Industry (2023-2030), CollChain (2023-2029), CERTIF-AI (2020-2025), FENIX (2019-2023), and DynaPlex (2021-2024), focusing on digital twins, federated networks, and AI-driven supply chain solutions. Dijkman directs the Information Systems group at TU/e and leads the European Supply Chain Forum's high-tech supply chain research. His work integrates with semiconductor manufacturing and transportation logistics through collaborations with industry partners, while his 2023 invited talks at Technical University of Munich and Humboldt University Berlin highlight his international engagement.

• Transportation Engineering
• Location Optimization
• Geospatial Information Science
• +4 more

Tae J. Kwon is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Alberta . His research spans Winter Transportation Engineering , Intelligent Transportation Systems (ITS) , Traffic Optimization , and Geospatial Information Science . Developed LoRWIS , a web-based decision support system for road weather sensor optimization Recipient of multiple awards including ITS Canada Excellence in Research and Development Award (2024) , KOFST Scientist of the Year (2024) , and Early Career Research Award (University of Alberta, 3 times) Active in professional committees like the TRB Surface Transportation Weather Research Group (2016–present) Research Highlights : Winter Road Maintenance : Advanced geostatistical and deep learning techniques for road condition monitoring Location Optimization : Pioneered models for RWIS sensor placement using hybrid geostatistical methods Intelligent Transportation Systems : Focused on big data applications for traffic safety and connected vehicles Climate Resilience : Integrated ecological vulnerability into fire hazard modeling Scientific Awards : ITS Canada Excellence in Research (2024) KOFST Scientist of the Year (2024) Donald Stanley Best Paper Award (2023) AKCSE Early Achievement Award (2021) University of Alberta Early Career Research Award (2020) Great Supervisor Award (2019) Teaching & Leadership : Teaches courses like CIV E 419 (Transportation Engineering: Highway Planning and Design) and CIV E 612 (Transportation Planning: Methodology and Techniques) Leads the Geospatial Transportation System & Sensing Solutions Lab (GeoTrans) , mentoring PhD students Juan Cuellar and Jiehao Bi Collaborates with agencies including Alberta Transportation , NSERC , and Iowa DOT

Ashok GoelView profile

Professor

• Artificial Intelligence
• Cognitive Science
• Learning Technology
• +4 more

Ashok Goel is a Professor of Computer Science and Human-Centered Computing at Georgia Institute of Technology and Chief Scientist at Georgia Tech’s Center for 21st Century Universities (C21U). He also serves as Executive Director of the NSF-funded National AI Institute for Adult Learning and Online Education (AI-ALOE). His research spans cognitive systems, artificial intelligence, and education, with a focus on computational design, creativity, and AI-driven educational technologies. Professor, School of Interactive Computing, Georgia Tech Chief Scientist, Center for 21st Century Universities Executive Director, NSF’s National AI Institute for Adult Learning and Online Education Goel’s research explores the intersection of AI and cognitive science, particularly in computational design, creativity, and biologically inspired design. His recent work emphasizes AI in education, including virtual teaching assistants like Jill Watson (powered by ChatGPT) and frameworks for scalable, human-centric AI-augmented learning. He investigates explainable AI, multimodal educational systems, and bidirectional feedback mechanisms to enhance personalized learning experiences. Award highlights include: AAAI’s Outstanding AI Educator Award Fellow of AAAI and Cognitive Science Society University System of Georgia Regent’s Award for Scholarship of Teaching and Learning Goel leads the Design Intelligence Laboratory at Georgia Tech, mentoring a team of graduate and undergraduate researchers. His contributions to AI education include pioneering Georgia Tech’s Online Master of Science in Computer Science (OMSCS) program and developing blended learning frameworks. He also co-founded the AI-based educational startup Beyond Question (LLC) in 2020.

Gabriella CasalinoView profile

Assistant Professor

• Computational Intelligence
• eHealth
• Data Stream Mining
• +5 more

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

Femi OlanView profile

Senior Lecturer

• Digital Entrepreneurship
• Knowledge Management
• Performance Management
• +5 more

Femi Olan is a Senior Lecturer at the Strategy, Operations and Entrepreneurship department within Essex Business School at the University of Essex. His academic work bridges the intersection of digital entrepreneurship, knowledge management, and artificial intelligence in organisational contexts. Educational Background: PhD, University of Plymouth Research interests focus on digital entrepreneurship , artificial intelligence , and knowledge management , particularly examining how these technologies enhance organisational resilience and sustainability. His publications in journals like Information Systems Frontiers and Technological Forecasting & Social Change highlight applications of AI in supply chains, crisis information management, and sustainability. Recent editorial roles include Cogent Business & Management Journal (2024) and guest editorships for special issues on fake news, sustainable agriculture supply chains, and crisis information management. His 15 most recent articles (2022-2025) span topics such as explainable AI in B2B marketing, CSR implementation in African SMEs, and Industry 4.0 adoption in agriculture. He supervises PhD candidates in entrepreneurship and has secured funding from the UK Higher Education Academy and European Commission . As a coordinating board member of the Euro Working Group – Decision Support Systems , he contributes to international research networks and conferences.

• Process Management
• Business Process Optimization
• Agile Methodologies
• +3 more

Professor Matthias Lederer serves as a faculty member at the Weiden Business School of Ostbayerische Technical University Amberg-Weiden, specializing in Business Informatics with a focus on Process Management. His academic position is complemented by extensive industry experience across multiple sectors including IT services, manufacturing, and consulting. Dr. Lederer's research spans four interconnected domains: process analysis and optimization, IT process management, agile process transformation, and didactics for process digitization. His work bridges theoretical frameworks with practical applications, particularly in business process management (BPM), digital transformation, and the integration of agile methodologies into organizational structures. His research demonstrates a clear trajectory toward increasingly sophisticated applications of data science and artificial intelligence in process optimization. Analysis of his recent publications reveals a strong focus on practical implementations of business process management, with particular emphasis on agile transformations, data-driven process design, and the application of AI in business contexts. His work consistently addresses the intersection of academic research and industry practice, with publications appearing in both academic journals and professional conference proceedings. The research demonstrates growing attention to digital platforms, smart manufacturing applications, and sustainable business practices. His scientific recognition includes: Best Program Director award from ISM International School of Management Project Award 'Innovative LernOrte' from OTH Award for Good Teaching from the Bavarian State Ministry Best Paper Award at the 2014 International Conference on Information Systems Multiple professional certifications including Lean Six Sigma Black Belt and OMG-Certified Expert in Business Process Management Professor Lederer has developed significant academic leadership through his role as Chairman of the Institute of Innovative Process Management. His teaching approach integrates practice-integrated methodologies, reflecting his belief in connecting academic concepts with real-world applications. His extensive consulting background with organizations like REHAU AG + Co. and the Bavarian Ministry of Justice informs his academic work and student mentorship.

Sagar SamtaniView profile

Associate Professor

• Artificial Intelligence for Cybersecurity
• Cyber Threat Intelligence
• Deep Learning
• +5 more

Sagar Samtani is an Associate Professor and Weimer Faculty Fellow at the Kelley School of Business , Indiana University. He serves as Director of the Kelley’s Data Science and Artificial Intelligence Lab (DSAIL) . His research focuses on Artificial Intelligence for Cybersecurity , including cyber threat intelligence, deep learning, and dark web analytics. He holds a PhD from the University of Arizona (2018), and has received prestigious awards such as the Indiana University Outstanding Junior Faculty Award (2023) and IEEE Big Data Security Junior Research Award (2023). Education : PhD in Information Systems, University of Arizona, 2018 MSMIS, University of Arizona, 2014 BSBA, University of Arizona, 2013 Research Interests : Samtani’s work addresses cybersecurity challenges through AI, including proactive threat detection, vulnerability assessment, and healthcare analytics. He emphasizes explainable AI (XAI) for transparency in cybersecurity systems. Grants & Awards : NSF Grant: CyberCorps SFS Program ($2.3M, 2020–2025) NSF Grant: AI4Cyber Research Education ($300K, 2020–2022) Multiple teaching awards, including the Trustees Teaching Award (2023) and recognition as one of Top 50 Undergraduate Professors (2022) Labs & Teams : Leads the DSAIL lab, focusing on AI-driven solutions for business and cybersecurity. Collaborates with NSF-funded initiatives on cyber AI education and threat intelligence.

Rianne ConijnView profile

Assistant Professor

• Learning Analytics
• Human-Technology Interaction
• Artificial Intelligence
• +6 more

Rianne Conijn is an assistant professor in the Human-Technology Interaction group at Eindhoven University of Technology (TU/e), Netherlands. Her research bridges data-driven methodologies (machine learning, statistical modeling) with human-centered design to enhance learning analytics, explainable AI, and writing process analysis. She holds a joint PhD (cum laude) from Antwerp University and Tilburg University, and an MSc (cum laude) in Human-Technology Interaction from TU/e. Academic Background: MSc (2015, TU/e, cum laude), PhD (2020, Antwerp University & Tilburg University, cum laude). Research Focus: Learning analytics, keystroke logging, explainable AI for education, data dashboards, and self-regulated learning dynamics. Teaching: Courses in Advanced Research Methods, Human-AI Interaction, Behavioral Research Methods, and AI ethics in education. Her recent publications explore parallel language planning in writing, longitudinal self-regulated learning strategies, and generalizability of academic performance prediction models. She leads an NWO Veni project on Human-Centered AI in education, emphasizing tailored explanations for student-AI collaboration. Scientific awards include cum laude distinctions for her MSc and PhD, and the NWO Veni grant. Collaborative work spans institutions in the Netherlands, Norway, and the U.S., with applications in intelligent tutoring systems and ethical AI deployment in exams. Key trends across her work: integration of machine learning with educational theory, leveraging keystroke data for cognitive process insights, and prioritizing actionable, explainable AI systems for student support. Publications span journals like the Journal of Experimental Psychology: General , Computers and Education , and IEEE Transactions on Learning Technologies . Scientific Awards: NWO Veni grant for Human-Centered AI in education Cum laude for MSc and PhD Grants & Collaborations: National Science Foundation grants (2016868, 2302644) for biometric feedback in writing UK Research and Innovation grant (ES/W011832/1) for real-time AI scaffolding TU/e Boost! Program grant for self-regulated learning analysis Labs & Teams: EAISI Foundational (Eindhoven AI Systems Institute) Human Technology Interaction group at TU/e Collaboration with Norwegian Reading National Center (University of Stavanger) Project teams for Waterproof ITS and ProWrite grants

Bruce StephenView profile

Senior Lecturer

• Data-Driven Methods
• Power Systems
• Smart Grids
• +12 more

Bruce Stephen is a Senior Lecturer and Strathclyde Chancellor's Fellow in the Department of Electronic and Electrical Engineering at the University of Strathclyde, where he has been since 1999. His work lies at the intersection of data science and power systems engineering, with a strong focus on real-world industrial applications. His educational background includes a BSc in Aeronautical Engineering from the University of Glasgow (1997), an MSc from the University of Strathclyde (1998), and a PhD in Electronic and Electrical Engineering (2005) from the University of Strathclyde. Dr. Stephen's research centers on data-driven methodologies for solving complex engineering challenges in power systems, particularly under conditions of limited data or domain knowledge. His applications span the entire energy value chain—from generation (nuclear, wind, solar) to transmission, distribution, and end-use. He develops software solutions for condition assessment, anomaly detection, and predictive modeling to support asset management and future grid planning. Notably, he co-founded Silent Herdsman Ltd, a spin-out company applying intelligent systems to precision livestock farming. His recent publications highlight a strong trend toward advanced machine learning techniques such as transfer learning, surrogate modeling, and synthetic data generation (e.g., using CTGANs) to improve reliability and decision-making in power systems. These works emphasize explainability, uncertainty quantification, and scalability, particularly in renewable-rich and data-scarce environments. Dr. Stephen is currently the Principal Investigator on the EPSRC-funded Analytical Middleware for Informed Distribution Networks (AMIDiNe) project, aiming to identify barriers to Net Zero through improved data modeling of unmonitored networks. He has also contributed to major projects including EU FP7 ORIGIN, EPSRC APAtSCHE, AGILE, and Transactive Energy Supply Arrangements. He actively advises students and collaborates on interdisciplinary research. His professional activities include organizing the QFF Quarterly Forecasting Forum (2018) and delivering invited talks at industry workshops. He has supervised datasets and research involving structural health monitoring and industrial diagnostics. His work supports UN Sustainable Development Goals related to affordable and clean energy, industry innovation, and climate action.

• Explainable AI
• Human-AI Interaction
• Machine Learning
• +7 more

Andreas Holzinger is a Professor at Graz University of Technology, with additional affiliations at Medical University Graz and University of Natural Resources and Life Sciences Vienna in Austria. He is recognized as an IFIP Fellow (2021) for his significant contributions to information processing and computer science. His work spans multiple institutions across Europe, with notable collaborations extending to the University of Alberta in Canada. Professor Holzinger's research focuses on Human-Centered AI, Explainable AI (XAI), and their practical applications across diverse domains. His work bridges theoretical AI advancements with real-world implementations in healthcare, forestry, and human-robot interaction. He has pioneered approaches in counterfactual explanations, graph neural networks, and human-in-the-loop systems that emphasize transparency and trustworthiness in AI decision-making processes. His recent publications demonstrate a strong trend toward integrating large language models with traditional AI systems while maintaining explainability. Holzinger's work consistently emphasizes the human element in AI systems, ensuring that technological advancements serve human needs rather than obscuring decision processes. His research in medical AI, smart forestry, and agricultural applications shows a commitment to solving practical problems with human-centered technological solutions. Scientific Awards: IFIP Fellow (2021) Professor Holzinger has been instrumental in establishing design guidelines for explainable AI systems, particularly through his work on post-hoc versus ante-hoc explanations. His research on Kandinsky Patterns has provided valuable experimental frameworks for pattern analysis and machine intelligence. He has secured significant research funding for projects bridging AI with practical applications in healthcare and environmental monitoring. His leadership extends to the organization of major conferences and workshops, including the CD-MAKE conference series, where he has fostered interdisciplinary collaboration between AI researchers and domain experts. His work on the CLARUS platform demonstrates practical implementations of interactive explainable AI for medical applications.

Bharanidharan ShanmugamView profile

Associate Professor

• IoT Security
• Cyber Risk Management
• Network Security
• +3 more

Dr. Bharanidharan Shanmugam is an Associate Professor in Information Technology at Charles Darwin University's Faculty of Science and Technology. He specializes in cybersecurity, IoT security, and cyber risk management in microgrids. His research focuses on addressing real-world challenges in IoT, smart grids, and medical devices to enhance community impact. **Research Interests:** - IoT Security - Cyber Risk Assessment in Microgrids - Network and Information Security - Applied Cybersecurity Solutions - Blockchain and Privacy-Preserving Technologies **Key Projects (2019–2025):** - Cyber Territory Skills Hub (2023–2025) - Renewable Energy Microgrid Hub (2021–2024) - Blockchain-Based Digital Identity (2019–2020) **Publications:** Focuses on IoT security frameworks, intrusion detection systems, and AI-driven cybersecurity solutions. Recent works include studies on smart grid load forecasting, medical IoT threat detection, and water leakage detection using machine learning. **Grants & Supervision:** Principal Investigator on multiple ARC-funded projects. Supervises PhD students in DevSecOps and IoT security. Active in organizing workshops on digital awareness for indigenous communities.

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +6 more

Talal Shaikh is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences in Dubai. He serves as Director of Undergraduate Studies and Programme Director for BSc Computer Science, BSc CS (AI), and MSc Software Engineering. With a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. Research Interests: Pervasive Computing, IoT/M2M, AI/ML, WiFi Sensing for Healthcare, Financial Machine Learning, Educational Technology Awards: Teaching Excellence Awards (2017/18), Fellow of the Higher Education Academy (FHEA), multiple Learning and Teaching Oscars (2016, 2017, 2018) His work spans Ubiquitous Computing and IoT , focusing on sensor networks and WiFi-based sensing for healthcare. In Artificial Intelligence , he applies ML to robotics, financial analytics, and educational innovation. Recent articles analyze Reinforcement Learning , Emotion Recognition , and WiFi Sensing applications. His teaching emphasizes student-centric learning, with over 100 supervised dissertations achieving distinctions. Collaborations include international conferences and interdisciplinary research in smart environments and adaptive systems.

• Human-AI Interaction
• Explainable AI
• Ethical AI
• +5 more

Chiara Natali is a PhD Student in Computer Science at University of Milan-Bicocca (2022-present) and a Visiting Research Fellow at the Dalle Molle Institute for Artificial Intelligence USI-SUPSI, supported by a Swiss Government Excellence Research Fellowship. She serves as a Lecturer for Interaction Design Lab and Human-Computer Interaction courses at University of Milano-Bicocca, and as a Tutor for Advanced Data Management and Decision Support Systems and Human-System Interaction courses across multiple Italian universities. Her educational background includes: MA in Politics, Philosophy and Public Affairs at University of Milan (2020-2022) Master's in Digital Communication Strategy at IED, Milan (2019-2020) BSc in International Politics and Government at Bocconi University, Milan (2016-2019) Natali's research centers on the complex relationship between humans and AI systems, with particular focus on Human-AI Interaction, Explainable AI (XAI), Ethical AI, and her signature concept of Frictional AI. She investigates the multidirectional effects of AI on human cognitive faculties, examining the tension between Augmentation and Deskilling. Her work explores how intentional design friction can serve as a debiasing strategy against Automation Bias, promoting more thoughtful human-AI collaboration while preserving human agency and critical thinking. Her publication record reveals a strong emphasis on practical applications of XAI in high-stakes domains like healthcare, with particular attention to medical decision-making processes. Her research consistently addresses the challenge of designing AI systems that support rather than replace human expertise, exploring how explanations impact accuracy in hybrid decision-making and how to measure technology dominance in AI-supported environments. Her significant contributions have been recognized with: Best Paper Award at the World Conference on Explainable Artificial Intelligence (2024) Best Doctoral Consortium Award at CEUR Workshop Proceedings (2023) Natali actively shapes her field through academic service, serving as PUBLICITY & PROCEEDINGS CHAIR for HHAI 2025 and organizing multiple workshops on Human-Centred Machine Learning, Algorithmic Authority, and Frictional AI. She also contributes to gender equality in STEM as a Science Ambassador for her institution's Gender Equality Plan and previously served as PhD co-representative at the Department Board. Her interdisciplinary approach extends into creative domains, where she is developing an Interactive AI Opera on 'The Garden of (Un)Earthly AI's' funded by the University of Edinburgh's Generative AI Laboratory, and has curated projects exploring Human-AI Music Co-Creation and live-coding music performances.

Ninghao LiuView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Explainable AI (XAI)
• +6 more

Dr. Ninghao Liu is an Assistant Professor of Computer Science in the School of Computing at the University of Georgia, part of the Franklin College of Arts & Sciences - Division of Physical & Mathematical Sciences. He holds a Ph.D. in Computer Science from Texas A&M University (2021) and an M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2015). His research focuses on Explainable AI (XAI), Graph Mining, Model Fairness, Recommender Systems, and Outlier Detection, with notable contributions to foundational AI techniques and their applications in education, healthcare, and environmental sciences. Dr. Liu has secured significant funding, including a three-year NSF grant (2022–2025) for 'Graph-Oriented Usable Interpretation' and a five-year $10 million grant from the U.S. Department of Education (2024–2029) for the GenAI Empowered National Initiative for STEM+C Education. He has also been honored with the Outstanding Paper Award at ICML 2022, Best Paper Award Shortlist at WWW 2019, and other distinctions. His work emphasizes interpretable machine learning, graph neural networks, and addressing algorithmic bias. He collaborates across disciplines, contributing to radiology AI, climate-smart forestry, and pandemic prediction through knowledge-enhanced deep learning. His lab is based at the Boyd Research and Education Center, where he advances research in trustworthy AI systems and data-centric solutions.