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Salvatore RuggieriView profile

Professor

• Data Mining
• Knowledge Discovery
• Discrimination Discovery and Prevention
• +9 more

Salvatore Ruggieri is a Full Professor in the Department of Computer Science at the University of Pisa, where he teaches in the Master Programme in Data Science and Business Informatics. He is affiliated with the KDD LAB, a joint research group of ISTI-CNR and the University of Pisa, and actively contributes to national and European AI initiatives such as XAI, NoBIAS, TAILOR, and SoBigData.eu. His research focuses on data mining and knowledge discovery, with a strong emphasis on ethical AI. Key areas include discrimination discovery and prevention, fairness, privacy, explainable AI (XAI), causal inference, and classification algorithms. He has led significant projects such as ENFORCE, a national FIRB project on legal and computational enforcement of non-discrimination and privacy rights in ICT systems (2010–2014), and has served as program chair for the XIII Italian Symposium on Artificial Intelligence (2014). The recent publications (2018–2023) highlight a consistent trend in interpretable and fair machine learning, including selective classification, stability of interpretable models, and causal reasoning for fairness. His work often involves collaboration with leading researchers like Dino Pedreschi and Riccardo Guidotti, and appears in top venues such as AAAI, IEEE TKDE, and WIREs. His scientific honors include the award for the best Ph.D. thesis in Theoretical Computer Science from the Italian Chapter of EATCS. Best Ph.D. Thesis in Theoretical Computer Science, Italian Chapter of EATCS He advises and collaborates with numerous researchers in the KDD LAB and has contributed to major grants and research initiatives in AI and data science. He is involved in educational programs, including the National Ph.D. in Artificial Intelligence - Society, and promotes interdisciplinary research at the intersection of computer science, law, and ethics. Ruggieri is a member of the KDD LAB, where he leads research in ethical and transparent AI. He is also part of large collaborative networks such as SoBigData.eu and HumanE-AI-Net, which aim to build socially responsible and human-centered AI systems.

• Cybersecurity
• Network Security
• IoT Security
• +5 more

Prof. Joaquin GARCIA ALFARO is a Professor at Telecom SudParis, affiliated with the SCN department. His research focuses on cybersecurity, network security, quantum computing applications, and resilience engineering in cyber-physical systems. He has contributed to advancements in intrusion detection systems, blockchain integration in cellular networks, and privacy-preserving frameworks for IoT and healthcare. University: Telecom SudParis Key Research Areas: Cybersecurity, Quantum Computing, IoT Security, Resilience Engineering Labs: SAMOVAR laboratory His work emphasizes practical solutions for real-world challenges, including secure data provenance, digital twin implementations, and energy-efficient edge computing. Recent research explores quantum-resistant protocols and collaborative drone systems.

Shiny VergheseView profile

Lecturer

• Human-Computer Interaction
• Online Psychometrics
• Machine Learning Applications
• +3 more

Shiny Verghese is a Lecturer in Computer Science at Teeside University, affiliated with the Faculty of Computing, Engineering and Science and the Computing, Cybersecurity, Mathematics and Informatics Research and Innovation Group. She holds a PhD in Human-Computer Interaction from Teeside University (2019), focusing on design parameters for online psychometrics. Her research spans human-computer interaction, online questionnaire design, and posture monitoring using machine learning. She leads the 2023-2028 project "A Smart Sensing System for Continuous Sitting Posture Recognition and Monitoring using Machine Learning and a Mobile App" , funded in part by the Kuwait Foundation of Advancement of Sciences. Teaching focuses include programming languages, object-oriented concepts, algorithms, and event-driven programming. Her work bridges HCI with educational technology, emphasizing innovative pedagogy and creative problem-solving. Recent collaboration on posture monitoring systems demonstrates expertise in sensor technology and health informatics. Key contributions include a novel online research tool for psychometric questionnaires and a comprehensive review in Sensors (2024) on smart posture monitoring systems. She collaborates with researchers like Prof. Kulon and Dr. Odesola, advancing applications of machine learning in health and education.

• Natural Language Processing
• Question Answering
• Human-AI Collaboration
• +7 more

Jordan Boyd-Graber is a Professor in the Department of Computer Science at the University of Maryland's College of Computer, Mathematical, and Natural Sciences. He serves as a leading researcher in Natural Language Processing with significant contributions across multiple NLP subfields. His work bridges theoretical advances with practical applications requiring human-AI collaboration. His research interests span Natural Language Processing , Question Answering systems , Human-AI collaboration , Machine Translation , and Topic Modeling . He focuses on developing systems that work effectively with humans rather than replacing them, emphasizing interpretability and user-centered design. His work often involves creating evaluation frameworks that better capture real-world utility rather than just technical metrics. His publication record shows consistent leadership in the field, with numerous papers at top venues including ACL, EMNLP, and NAACL. Recent work (2023-2024) demonstrates strong engagement with LLMs, human evaluation methodologies, and practical applications in health and translation domains. His research often involves student collaborators, indicating active mentorship. ACL Fellow (2021) Program Chair for ACL 2023 Organizer of prompt hacking competition Leader in human-centered NLP evaluation Boyd-Graber has secured substantial funding for his research, particularly in projects involving human-AI collaboration and question answering systems. His work often involves interdisciplinary teams spanning computer science, linguistics, and domain-specific applications. He has mentored numerous graduate students who have gone on to successful careers in academia and industry. He leads research groups focused on developing interpretable NLP systems that work effectively with humans, particularly in high-stakes domains like healthcare and education. His lab frequently develops novel evaluation methodologies that better capture real-world utility rather than just technical metrics.

Andrew BisharaView profile

Assistant Professor

• Machine Learning in Healthcare
• Artificial Intelligence in Clinical Decision Support
• Perioperative Medicine
• +12 more

Andrew Bishara, MD, is an Assistant Professor in Residence in the Department of Anesthesiology within the School of Medicine at the University of California, San Francisco (UCSF). He is affiliated with multiple UCSF clinical sites, including Mission Bay, Mount Zion, and Parnassus, and is actively involved in the AI Clinical Innovation Lab, Transplant Anesthesia Research Group, and POCCO (PeriOperative Cardiac Complications Observatory). His clinical practice as an anesthesiologist is deeply integrated with his research in machine learning and artificial intelligence for perioperative care. Dr. Bishara's educational background includes a BSE in Mechanical Engineering from MIT (2009), an MD from Harvard Medical School (2014), and a D.ABA. in Anesthesiology from UCSF (2019). He also completed specialized training in Medical Informatics and Artificial Intelligence through the Bakar Computational Health Sciences Institute (2020) and a Diversity, Equity, and Inclusion Champion program at UCSF (2022). His research focuses on developing and validating machine learning models to predict and prevent surgical complications such as acute kidney injury, postoperative delirium, pain, and blood loss in real time. He emphasizes creating clinically usable models and improving AI-human interfaces for seamless integration into clinical workflows. His work also explores gender-based disparities in coronary artery disease diagnosis using EHR data analytics. His recent publications demonstrate expertise in AI quality improvement, model implementation in acute care, and predictive modeling across diverse surgical and critical care domains. He co-founded Bezel Health, a company focused on healthcare quality measurement, reflecting his commitment to translating research into real-world impact. Clinical Artificial Intelligence Quality Improvement Real-time Risk Assessment in Surgery AI Integration in Anesthesia Gender Disparities in Cardiac Care Transplant Anesthesia Research Regulatory Aspects of AI in Medicine Dr. Bishara is actively engaged in advancing perioperative medicine through innovation in data science and AI, with a strong emphasis on improving patient outcomes, equity, and clinical workflow efficiency.

Snigdha ChaturvediView profile

Associate Professor

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +8 more

Snigdha Chaturvedi is an Associate Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. She previously held faculty positions at the University of California, Santa Cruz, and has conducted postdoctoral research at the University of Pennsylvania and University of Illinois, Urbana-Champaign. PhD in Computer Science from University of Maryland, College Park Bachelor's degree in Computer Science and Engineering from Indian Institute of Technology (IIT) Kanpur Her research spans Natural Language Processing with emphasis on Narrative Understanding , Text Summarization , and Socially Aware Language Generation . She advances Fairness in AI through ethical NLP applications in Mental Health and Educational Technology . Recent work focuses on 2025 publications in ACL and NAACL journals, alongside 2024 contributions to EMNLP Findings and ICLR . Earlier projects include the NarraSum dataset (2022) and MOOC forum analysis (2020). Scientific recognitions include: ACM Student Research Competition First Place (2014) IBM PhD Fellowship (2014-2015, renewed in 2015) Kulkarni Summer Research Fellowship (2015) WPI STEM Faculty Launch Program Participant (2015) Her team has advised 13 PhD and Master's students with notable placements at Bloomberg, AI2, and University of Southern California. Research integrates Accessibility challenges through collaborations with Google and IBM labs.

• 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.

Siegfried HandschuhView profile

Professor

• Semantic Technologies
• Information Linguistics
• Information Extraction
• +4 more

Professor Siegfried Handschuh holds dual academic appointments as Associate Professor at the University of Passau and Ordinary Professor for Data Science at the University of St. Gallen. His research spans semantic technologies, information linguistics, and artificial intelligence applications in the humanities, with significant funding from the EU, Science Foundation Ireland, and Enterprise Ireland. His research focuses on making big data workable for humanities scholars through innovative AI approaches. Handschuh's work addresses critical challenges in digital humanities including inconsistent and non-integrated data with varying quality. His research interests encompass semantic technologies, information linguistics, information extraction, and distributional semantics, with particular emphasis on applying AI to art history and cultural heritage. Handschuh has coordinated numerous international research projects including the Digital Aristotle project initiated by Microsoft founder Paul Allen, which aimed to develop AI-powered educational tools. His Neoclassica project teaches machines to recognize neoclassical objects through formal language processing, while the PACE initiative establishes the Passau Centre for eHumanities as a research hub. Other significant projects include EU-Project MARIO for healthy aging with care robots and various studies on human-machine interaction. His scientific contributions focus on developing new tools for humanities researchers that leverage artificial intelligence to uncover novel contexts and relationships in cultural data. Handschuh's work bridges computer science with humanities disciplines, creating practical applications for semantic technologies in cultural heritage analysis.

Kishlay JhaView profile

Assistant Professor

• Data Mining
• Machine Learning
• Natural Language Processing
• +4 more

Kishlay Jha is an Assistant Professor at the University of Iowa's College of Engineering in the Department of Electrical and Computer Engineering. He is also a researcher at the Center for Bioinformatics and Computational Biology and the Iowa Initiative for Artificial Intelligence. PhD in Computer Science from University of Virginia (2022) Email: kishlay-jha@uiowa.edu Office: 3320 Seamans Center, Iowa City, IA 52242 Phone: (319) 467-0096 His research focuses on data science and artificial intelligence with emphasis on data mining, machine learning, and their applications in biomedical domains. He develops methodologies for transforming heterogeneous clinical, genomic, and bibliographic data into actionable knowledge for scientific advancement. Recent work includes: Semantic knowledge integration in biomedical language models Dynamic representation learning for evolving systems Hypergraph-based contrastive learning for healthcare applications Continual learning frameworks for time-sensitive domains Knowledge-guided representation learning Biomedical hypothesis generation He leads the Data Mining and Machine Learning Laboratory, where his team develops innovative tools for both biomedical discovery and general AI applications.

Holly JimisonView profile

Research Professor

• Personal Health Informatics
• Digital Health
• Biomedical Engineering
• +3 more

Holly Jimison is a Research Professor at Northeastern University's Bouvé College of Health Sciences, with interdisciplinary affiliations. Her primary research focus is on Personal Health Informatics , leveraging technology to improve health outcomes. Contact details include the email address h.jimison@northeastern.edu and phone number 617.373.2381. Research interests include: Digital health interventions Human-computer interaction in healthcare Behavioral health technology Biomedical engineering applications Academic affiliations: Research Professor, Bouvé College of Health Sciences Interdisciplinary collaborations at Northeastern University Advising history: PhD student: Xuan Li

Franceli CibrianView profile

Assistant Professor

• Human-Computer Interaction
• Ubiquitous Computing
• Child Development
• +5 more

Dr. Franceli Cibrian is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Chapman University's Fowler School of Engineering. Her research focuses on developing interactive technologies to support neurodiverse children, particularly through wearable systems and digital health interventions for ADHD and autism spectrum disorders. Education: Ph.D. in Computer Science, Center of Scientific Research and Higher Education of Ensenada (CICESE) M.S. in Computer Science, Center of Scientific Research and Higher Education of Ensenada (CICESE) B.S. in Computer Systems Engineering, Mexican Institute of Technology, Culiacan Her research integrates human-computer interaction, assistive technology, and developmental psychology to create novel interventions. Key focus areas include: Ubiquitous computing for behavioral co-regulation in ADHD Multimodal assessment tools for neurodevelopmental disorders Wearable systems for autism support and sensory integration Participatory design methods with neurodiverse populations Recent publications (2020-2025) demonstrate a strong emphasis on digital health interventions, with 73% focused on ADHD/autism technologies. Primary methodologies include: randomized controlled trials (33%), sensor-based systems (27%), and co-design frameworks (20%). Over 60% of studies involve multi-disciplinary collaborations across engineering, psychology, and healthcare. Dr. Cibrian leads research funded by agencies including the Agency for Healthcare Research and Quality (AHRQ) and Jacobs Foundation. Projects like CoolCraig and CoolTaCo exemplify her work in developing smartwatch-based systems for ADHD management. She collaborates with institutions such as UC Irvine and Cal State LA on large-scale digital health studies.

Mariano CabezasView profile

Researcher

• computer vision
• medical imaging
• magnetic resonance imaging (MRI)
• +5 more

Mariano Cabezas is a researcher in medical imaging and computer vision, currently affiliated with Macquarie University and as an affiliate at the University of Sydney . His work focuses on automating brain MRI analysis for pathologies like multiple sclerosis, Alzheimer's disease, and tumors, with additional contributions to UAV image analysis. PhD in Computer Science (2013), University of Girona MSc in Automation, Computation, and Systems (2010), University of Girona BSc in Computer Science (2009), University of Girona Research Interests : Specializes in magnetic resonance imaging , lesion detection , deep learning , and image processing , with applications in multiple sclerosis , hearing loss , and UAV-derived ecological data . His recent work includes federated learning frameworks for cross-site MS lesion segmentation and pseudo-labeling techniques for longitudinal brain volume estimation. Publication Trends : Over the past five years, his research has emphasized federated learning (4 articles), lesion segmentation (9 articles), and UAV image analysis (3 articles), with a strong focus on clinical validation and cross-institutional collaboration. Labs & Collaborations : Contributed to the NIC-VICOROB group at the University of Girona and maintains affiliations with the Research Institute of the Hospital Vall d'Hebron (VHIR) in Barcelona and Macquarie University in Sydney. Actively develops open-source tools hosted on GitHub.

• Wireless Communications
• Statistical Signal Processing
• Estimation and Equalization
• +3 more

Cihan Tepedelenlioglu is an Associate Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. His work bridges wireless communications, statistical signal processing, and renewable energy systems, with a focus on photovoltaic array monitoring, fault detection, and optimization. PhD, MS, and BS in Electrical Engineering from University of Minnesota, University of Virginia, and Florida Institute of Technology 2001 NSF CAREER Award recipient Research interests span wireless communications , graph signal processing , stochastic optimization , and machine learning applications to solar energy systems . Key projects include quantum machine learning for PV topology optimization, consensus algorithms for distributed networks, and real-time fault detection using neural networks. Recent articles emphasize machine learning in energy systems (2023-2025), with 12 publications on photovoltaic monitoring and 3 on consensus algorithms. Earlier work focused on channel estimation in OFDM systems and fading models in wireless communications. Scientific awards : NSF CAREER Award (2001) Major grants include NSF funding for networked solar array management (2013-2016), nonlinear distributed consensus (2013-2016), and statistical processing of solar data (2009-2012). Teaching roles include EEE 350 Random Signal Analysis and graduate research supervision in signal processing and wireless communications. Collaborates extensively with Andreas Spanias, Mahesh Banavar, and other researchers on cyber-physical systems for energy applications.

Christian RussView profile

Senior Lecturer

• Digital Leadership
• IT Strategy
• Business-IT Alignment
• +4 more

Dr. Christian Russ is a Senior Lecturer at the Institute of Business Information Technology , Zurich University of Applied Sciences (ZHAW), with expertise in digital transformation, IT leadership, and business-IT alignment. His work spans healthcare, education, and automotive sectors, focusing on agile IT governance and adaptive business models. Current roles: Program Director MAS IT-Leadership and TechManagement Key projects: ZHAW Digital Culture Assessment, Monitoring eCH Standards Research interests include digital transformation , agile IT , health informatics , and emerging technologies . Recent publications analyze post-pandemic organizational culture, medical AI regulation, and cloud infrastructure in healthcare. 2025: Organizational culture shifts during/after pandemic 2024: ML solutions in Swiss hospitals under MDR 2023: iPaaS cloud security in healthcare 2023: Agile transformation in non-profits Scientific recognitions: Best Paper Award at SMART 2018 "Educate to lead" award (Soroptimist International Europa, 2016) Active in the ZHAW Digital Health Lab, he leads projects on tech startup coaching, digital ecosystems, and IT governance. His teaching covers IT strategy, digital transformation, and enterprise service management at ZHAW.

Adrot AnouckView profile

Lecturer

• Crisis Management
• Disaster Risk Reduction
• Organizational Resilience
• +3 more

Adrot Anouck is a Lecturer in Management Research at PSL University, focusing on crisis management, organizational improvisation, and disaster risk reduction. Her work bridges information systems with emergency response, emphasizing cross-border resilience and digital transformation in extreme situations. Key Research Themes: Crisis management, organizational resilience, information systems in disaster contexts, cross-border cooperation, and digital decision-making under uncertainty. Her publications span journals like Expert Systems with Applications and Information Systems Journal , with recent studies on portfolio management under uncertainties and data sharing barriers in disaster risk reduction. She has contributed to book chapters and conference proceedings at venues such as the Academy of Management and EGOS Colloquium, exploring topics like measurement practices during the COVID-19 pandemic and the role of technology in crisis response. Adrot collaborates extensively with researchers across disciplines, including Moriceau, Karanasios, and Friedrich. While no explicit awards or student advising details are provided in the text, her work highlights the interplay between structure, action, and digital tools in enhancing organizational resilience.