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Julien AlbertView profile

Researcher

• Explainable Artificial Intelligence
• Human–Computer Interaction
• Recommender Systems
• +4 more

Julien Albert is a researcher within the Namur Digital Institute (NaDI) and the Research Center on Information Systems Engineering at the University of Namur . He completed his master’s degree in computer science with a data-science focus in 2020 and is active in explainable AI, recommender systems, and human–computer interaction. Education: M.S. in Computer Science (data-science focus), University of Namur, 2020 – thesis on scientific-literature recommendation systems. Research Interests: Albert investigates how machine-learning decisions can be made transparent and trustworthy, with a particular emphasis on explainability for computer-vision models and fairness-aware personalized ranking . His work combines rigorous algorithmic development with human-centered empirical studies, bridging machine-learning research and interactive-systems design. Recent publications reveal a clear strand on explainable AI (XAI): evaluating saliency-based explanations, designing human-in-the-loop studies, and accelerating interpretation of nonlinear embeddings. A parallel track focuses on recommender systems , exploring both explanation interfaces powered by large language models and bias-aware Bayesian ranking algorithms. A third emerging theme tackles civic technologies , studying requirements for idea-browsing tools on digital participation platforms. He has (co-)supervised at least three master’s students and regularly participates in international venues such as the Francophone Conference on Human–Computer Interaction and the European Symposium on Artificial Neural Networks, indicating a growing footprint in the XAI and HCI communities.

James GrimmelmannView profile

Professor

• Computer and Internet Law
• Digital Copyright
• Content Moderation
• +4 more

James Grimmelmann is the Tessler Family Professor of Digital and Information Law at Cornell Tech and Cornell Law School. He directs CTRL-ALT, the Cornell Tech Research Lab in Applied Law and Technology. With a background in both law and computer science, he bridges the gap between these fields to examine how software regulation impacts societal structures, freedom, wealth, and power dynamics. Professor Grimmelmann earned his J.D. from Yale Law School and an A.B. in computer science from Harvard College. Before law school, he worked as a programmer for Microsoft. After graduation, he clerked for Judge Maryanne Trump Barry of the U.S. Court of Appeals for the Third Circuit and was a Resident Fellow at Yale's Information Society Project. He previously held faculty positions at the University of Maryland, New York Law School, and Georgetown University Law Center. His research focuses on the intersection of law and technology, particularly examining digital copyright, content moderation systems, search engine regulation, online governance structures, and privacy on social networks. He has authored the widely-used casebook "Internet Law: Cases and Problems" (now in its 15th edition) and over fifty scholarly articles addressing critical issues at this nexus. Professor Grimmelmann's recent work has centered on the legal implications of artificial intelligence, particularly regarding copyright issues in generative AI systems. His publications analyze the complex relationship between AI training data and intellectual property rights, the regulatory challenges of content moderation, and the evolving landscape of digital property. His analyses of how generative AI systems interact with copyright law have been cited in major legal cases involving companies like OpenAI, Meta, and Anthropic. He is a regular contributor to public discourse, writing for outlets like Slate, Salon, Wired, and Ars Technica, and providing expert commentary for major news media including the New York Times, Wall Street Journal, and Washington Post. He maintains an active presence in the academic community through his long-running blog, Laboratorium, which he's maintained since 2000.

Emma PiersonView profile

Assistant Professor

• Machine Learning
• Data Science
• Health Equity
• +3 more

Emma Pierson is an Assistant Professor of Computer Science at the University of California, Berkeley, and holds a parallel appointment as Assistant Professor at Cornell Tech (Jacobs Technion-Cornell Institute) with a secondary joint position in Population Health Sciences at Weill Cornell Medical College. She is affiliated with the Berkeley AI Research Lab, Computational Precision Health, and the Center for Human-Compatible AI. Education & Career Path: PhD in Computer Science, Stanford University (Jure Leskovec lab) MSc in Statistics, University of Oxford (as a Rhodes Scholar) Former Senior Researcher, Microsoft Research New England Former Data Scientist, 23andMe and Coursera Research Focus: Professor Pierson develops machine-learning techniques to advance health and social science, with a strong emphasis on reducing inequality and improving healthcare delivery . She leverages large language models, sparse autoencoders, and other modern tools to uncover hidden biases, design fairer algorithms, and generate novel scientific hypotheses from complex datasets. Across more than 50 peer-reviewed publications, her work repeatedly combines computational innovation with real-world impact —from documenting racial inequities in pain management ( Nature Medicine , 2021) to modeling COVID-19 disparities ( Nature , 2021) and quantifying urban segregation via mobility networks ( Nature , 2023). Recent trends show an intensified focus on LLM-driven discovery and evidence-based AI policy , with forthcoming papers in Science (2025) and New England Journal of Medicine AI (2025). Scientific Honors: NSF CAREER Award Rhodes Scholarship & Hertz Fellowship MIT Technology Review 35 Innovators Under 35 Forbes 30 Under 30 in Science AI2050 Early Career Fellowship Samsung AI Researcher of the Year (2023) Best Paper Awards – KDD, AISTATS, CHIL Advising & Funding: Prof. Pierson is actively recruiting PhD students for Fall 2026 and welcomes post-doctoral researchers, master’s students, and undergraduates. Her group is supported by the NSF, the AI2050 program, and various industry and philanthropic partners. Labs & Teams: She leads an interdisciplinary research group spanning UC Berkeley EECS and Cornell Tech. Collaborations include clinicians at UCSF and Weill Cornell, social scientists at Stanford, and policy experts at the Berkeley Center for Human-Compatible AI.

• Machine Learning
• Neural Networks
• Dimensionality Reduction
• +3 more

Dr. Alexander Schulz is a Researcher at the University of Bielefeld within the Faculty of Engineering and its Machine Learning Group . He focuses on machine learning applications across diverse domains including biomedical engineering , fairness evaluation , and data visualization . Research Interests Transfer learning for medical applications Dimensionality reduction techniques Bias detection in language models Classifier visualization tools Collaborations Center for Cognitive Interaction Technology (CITEC) Machine Learning Reports publications His recent publications highlight trends in dynamic graph analysis , semantic bias measurement , and physiological data generation . He has contributed to tools like the Box and Beans test for prosthetics evaluation and DeepView for classifier boundary visualization. Collaborative projects include cardiovascular data synthesis for implantable devices and fairness-aware AI frameworks. Dr. Schulz works at CITEC 2-228 and is reachable at aschulz@techfak.uni-bielefeld.de . His work integrates theoretical machine learning with practical implementations in healthcare and industrial systems.

Elisabeth Maria MayrhuberView profile

Researcher

• Computer Science
• Machine Learning
• Bioinformatics
• +3 more

Elisabeth Maria Mayrhuber serves as a Researcher at the University of Applied Sciences Upper Austria's Hagenberg campus with dual affiliations: Research Center Hagenberg Bioinformatics AIST Center of Excellence Medical Engineering/TIMed Center Her work operates under the Information & Communications Technology (ICT) focal area, leveraging interdisciplinary approaches at the computer science-engineering interface. Research expertise spans core domains with technical depth: Machine Learning (Semi-supervised Learning, Genetic Programming, Explainable AI) Bioinformatics and medical data systems Process Mining and user behavior analytics Precision Livestock Farming applications Cold-start problem solutions in distributed environments Recent publications (2024-2025) demonstrate methodological innovation through evolutionary computing applied to critical domains: interpretable decision systems for livestock farming, symbolic regression in agricultural contexts, obesity risk modeling using genetic programming, and ontology-enhanced process mining for laboratory workflows. These works consistently address scalability challenges while advancing model interpretability standards. Active within the Research Center Hagenberg Bioinformatics and AIST TIMed Center ecosystems, she contributes to cross-disciplinary initiatives integrating computational methods with medical engineering and agricultural technology development.

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Fabian Kovac is a Junior Researcher in the Data Intelligence research group at the Institute of IT Security Research, Department of Computer Science and Security, St. Pölten University of Applied Sciences. His work bridges artificial intelligence, data science, and IT security with a focus on practical implementations and theoretical foundations. His research spans critical AI domains including: Trustworthy and user-centered AI systems Attainable utility preservation for AI safety Sustainable and energy-efficient AI development Active learning methodologies Environmental applications of machine learning Analysis of his 2022-2026 publications reveals a cohesive trajectory toward making AI systems more robust, interpretable, and environmentally responsible. His work consistently addresses the tension between AI capability and safety, with recent contributions focusing on baseline methods for utility preservation and sustainability metrics. The upcoming TrustAI platform (2026) demonstrates his commitment to translating theoretical frameworks into user-centered tools. No scientific awards were documented in the available sources. Regarding academic advising and research funding, no student supervision records or specific grant details were disclosed in the provided materials. Kovac operates within the Data Intelligence research group at the Institute of IT Security Research, contributing to interdisciplinary projects that merge security principles with cutting-edge AI development across academic and industrial collaborations.

Lasse LensuView profile

Professor

• Computer Vision
• Pattern Recognition
• Machine Learning
• +5 more

Lasse Lensu is a Professor of Machine Vision and Data Analysis at Lappeenranta-Lahti University of Technology LUT (LUT University), Finland. He is affiliated with the Department of Computational Engineering within the LUT School of Engineering Sciences. His research spans multiple domains including computer vision, pattern recognition, and machine learning with applications in marine biology, medical imaging, and spectroscopy. Prof. Lensu received his M.Sc. degree in data communications in 1991, followed by Lic.Sc. and D.Sc. degrees in computer science in 2001 and 2002 respectively, all from LUT. His academic journey has been deeply rooted in the same institution where he now serves as a professor. His primary research interests focus on computer vision and pattern recognition with machine learning approaches. Specifically, he has made significant contributions to plankton recognition using deep learning techniques, medical image analysis particularly in retinal imaging, and signal processing in Raman and CARS spectroscopies. Prof. Lensu has also contributed to energy forecasting and battery state-of-charge prediction using advanced machine learning models. Analysis of his recent publications reveals a strong trend toward applying computer vision and deep learning to specialized domains. His work spans from fundamental research in machine learning techniques to practical applications in marine biology (plankton recognition), medical diagnostics (retinal imaging), and chemical analysis (spectroscopy). A significant portion of his recent work focuses on addressing challenges in open-set recognition, domain adaptation, and uncertainty quantification in deep learning models. Prof. Lensu is an active member of the Computer Vision and Pattern Recognition Laboratory at LUT. He has been instrumental in technology transfer initiatives, contributing to the creation of multiple spin-off companies from the university. His research has practical implications across several industries including environmental monitoring, healthcare diagnostics, and energy management.

Pankush KalgotraView profile

Associate Professor

• Healthcare Analytics
• Network Science
• Dark Side of Information Technology
• +4 more

Pankush Kalgotra is an Associate Professor in the Department of Business Analytics and Information Systems at Auburn University's Harbert College of Business, where he conducts cutting-edge research at the intersection of healthcare analytics, network science, and information systems. His work leverages advanced computational methods to address critical health disparities and disease interaction challenges. His academic credentials include: PhD in Management Science and Information Systems from Oklahoma State University MS in Management Information Systems from Oklahoma State University BTech in Information Technology from National Institute of Technology Raipur, India Dr. Kalgotra's research program centers on healthcare analytics, network science, and neuroimaging in information systems, with particular emphasis on predictive modeling of disease comorbidities and the dark side of technology. He employs deep learning techniques to analyze electronic medical records and mobile health applications, revealing patterns in health disparities across demographic groups. His neuroimaging studies investigate cognitive impacts of technology interruptions on knowledge workers. Analysis of his publication trajectory shows a consistent focus on applying network analytics to healthcare data science, particularly in modeling disease interactions and predicting clinical outcomes. Recent work increasingly integrates AI-driven approaches for early disease detection and examines pandemic-related digital health behaviors, demonstrating methodological evolution from traditional statistical modeling to advanced deep learning frameworks. His scholarly recognition includes: Best Paper Award by AIS SIGHealth (2017) for multimorbidity network analysis of electronic medical records Dr. Kalgotra actively mentors students in business analytics research and has secured funding for health informatics projects, though specific grant details aren't publicly enumerated. His collaborative work spans medical schools, computer science departments, and healthcare institutions, reflecting the interdisciplinary nature of his research agenda. Current projects focus on generative AI applications in clinical decision support and network-based modeling of long-COVID complications.

• Human Factors Psychology
• Human-Machine Interaction
• Trust in Automation
• +7 more

Dr. Qiyuan Zhang is a Lecturer in Human Factors at Cardiff University's School of Psychology , specializing in human-machine systems with emphasis on human-robot interaction , trust in automation , and human-AI collaboration in safety-critical contexts including transportation, emergency services, and cybersecurity. His research integrates Cognitive Psychology and Social Psychology theories to address real-world challenges. PhD in Cognitive Psychology from Durham University Focus on human-automation interdependence , auditory communication , and cognitive workload effects Key grants: £800k ESRC-JST (Rule of Law in AI Age), £5.6M Innovate UK (Flourish CAV Project), and £75k Airbus (XAI Research) Email: ZhangQ47@cardiff.ac.uk His work explores anthropomorphism in autonomous vehicles , noise impact on cognition , and human error in digital security , with methodological approaches spanning simulations, AR integration, and experimental paradigms.

• Emergency Imaging
• Imaging Utilization
• Machine Learning
• +1 more

Dr. Zachary Scott Delproposto is a Clinical Associate Professor in the Department of Radiology at the University of Michigan Medical School. His affiliations include: Current primary appointment: University of Michigan Past training positions: Intern at St. Joseph Mercy Hospital (Internal Medicine), Resident at Detroit Medical Center (Radiology), Fellow at Beth Israel Deaconess Medical Center (Body MRI) His research explores emergency imaging with emphases on: Acute abdominal/pelvic conditions Imaging utilization efficiency Machine learning applications Dynamic contrast-enhanced techniques Recent publications (2023-2025) focus on AI consensus frameworks, trauma imaging CAD tools, and interactive clinical interfaces. He co-developed ASER AI/ML guidelines and validation studies for emergency radiology applications.

Paul KennedyView profile

Professor

• Biomedical Data Analytics
• Bioinformatics
• Machine Learning
• +4 more

Professor Paul Kennedy is the Head of the School of Computer Science at the University of Technology Sydney (UTS) Faculty of Engineering and Information Technology. He has made significant contributions to data analytics education, receiving a 2013 OLT Citation for Outstanding Contributions to Student Learning and multiple UTS teaching awards. His research focuses on biomedical data analytics, particularly in pediatric cancer treatment outcomes, bioinformatics pipelines for vaccine discovery, and visual analytics for explainable AI in healthcare. His recent publications highlight advancements in multi-criteria decision-making for vaccine candidates, edge-cloud frameworks for omics data, and immersive VR environments for genomic data visualization. He leads the Biomedical Data Science Laboratory at UTS Australian Artificial Intelligence Institute and co-directs the UTS-Queens University AI-driven clinical tools initiative. A key collaborator with the Children’s Hospital at Westmead, he has secured over $2.4 million in research funding and served on international data mining conference committees since 2006. Scientific Awards: 2013 OLT Citation for Outstanding Contributions to Student Learning 2012 UTS Learning and Teaching Award 2020 Team Teaching Award 2019 Team Teaching Citation His methodological innovations span topological data analysis for stress detection, feature-ranking in RNA sequencing, and domain-adaptation techniques in machine learning. He also contributes to global AI ethics standards through ISO/IEC SC42 committee membership.

• AI Explainability
• AI Transparency
• Web Science
• +3 more

Marco Brambilla is a Full Professor of Computer Science and Engineering at Politecnico di Milano, Italy. He leads the Data Science Lab within the DEIB (Department of Electronics, Information and Bioengineering) and serves as director of the Computer Science and Engineering B.Sc. and M.Sc. curricula at the university. His research focuses on AI Explainability and Transparency , Web Science , Big Data Analysis , Social Media Analytics , and Model-driven Development . He is the co-inventor of the Interaction Flow Modeling Language (IFML) standard by the OMG and holds 2 patents on crowdsourcing and multi-domain search. His recent scholarly work demonstrates expertise across multiple domains of computer science, with particular emphasis on graph-based RAG architectures for energy efficiency, human-AI interaction studies, and interpretable network visualizations for CNN-based image classification. His publications show a consistent focus on practical applications of theoretical computer science concepts. Co-inventor of IFML standard by OMG 2 patents on crowdsourcing and multi-domain search GSE Academic Award for Excellence for correlating twitter sentiment analysis and stock price variations Author of multiple books including Model Driven Engineering in Practice , Web Information Retrieval , and Model-Driven UI Engineering of Web and Mobile Apps with IFML Professor Brambilla has been involved in the creation of four startups: WebRatio, Servitly, Fluxedo, and Quantia. He teaches Enterprise ICT Architectures, Systems and Methods for Big and Unstructured Data, Web Science, Digital Innovation Lab, and Model-driven Engineering courses. His Data Science Lab at Politecnico di Milano drives research in multiple cutting-edge areas of computer science and AI.

• Information Visualization
• Machine Learning
• Data Mining
• +5 more

Gilles Venturini is a Professor in Computer Science at the University of Tours, France, affiliated with PolytechTours and the Computer Science Lab. He has held this position since 1998 following his appointment as Assistant Professor from 1994-1998. His academic credentials include a Research Supervisor Degree (HdR) from the University of Tours (1997), a PhD in Computer Science from the University of the South of Paris (1994) supervised by Yves Kodratoff, and dual qualifications in Computer Science (MSc, 1990) and Electrical Engineering (ESIEE, 1990). Professor Venturini's research concentrates on information visualization, machine learning, and data mining with emphasis on genetic algorithms, neural networks, and computer vision. His work develops practical tools for visual data exploration, automatic dashboard generation, and analysis of large datasets, particularly addressing usability challenges for novice users in open data contexts. His 2023-2025 publications reveal sustained innovation in ternarization of vision-language models for edge devices, few-shot object detection, and backpropagation alternatives for binary neural networks. These contributions bridge theoretical machine learning advancements with applications in cultural heritage visualization and human resources analytics. No scientific awards were documented in the source materials. While specific grant details and student supervision records are absent from the provided text, his professorial role implies active involvement in research funding acquisition and academic mentoring. His work frequently intersects with interdisciplinary teams, particularly in digital humanities projects. He remains operationally active within the University of Tours' Computer Science Lab, driving research in visualization techniques and machine learning applications through this primary institutional affiliation.

Steffen HeberView profile

Professor

• Bioinformatics
• Computational Biology
• Algorithms and Theory of Computation
• +7 more

Steffen Heber is a Professor in the Department of Computer Science at NC State University with a joint appointment in the College of Sciences to support the Bioinformatics Research Center. His work bridges computational methods with biological discovery, focusing on algorithm development for biological data analysis and translation regulation in plants. Ph.D. in Mathematics (2001) Staatsexamen in Mathematics and Biology (1998) Diploma in Mathematics (1995) His research interests span Bioinformatics , Computational Biology , Alternative Splicing , and Translation Regulation . He develops novel algorithms for analyzing genomic data and investigates translational control mechanisms in plants using ribosome footprinting technologies. Recent publications focus on ChIP-seq processing , Ribo-seq analysis , and translational regulatory elements in Arabidopsis. Key themes include data quality control, ribosome positioning algorithms, and plant hormone signaling networks. Key Scientific Awards: IBM Faculty Award (2008) Carol Miller Graduate Lecturer Award (2018, 2013) Thank a Teacher Recognition (multiple years) Best Paper Award, ACMSE (2007) Heber has secured significant funding from the National Science Foundation, including grants for RiboPI technology development and translational hormone-response networks . His work also emphasizes undergraduate research training and K-12 science outreach programs.

Daniel FranzenView profile

Researcher

• Privacy Preservation
• Human-Centered Computing
• Data Science
• +4 more

Daniel Franzen is an affiliated researcher at the Human-Centered Computing group within the Department of Mathematics and Computer Science . His work bridges technology functionality with privacy concerns, focusing on parameters that influence whether users perceive digital systems as intrusive or beneficial. University of Edinburgh PhD in Computer Science (2016) Master of Science in Computer Science (2012) and Media Informatics (2012) from RWTH-Aachen Bachelor of Science in Computer Science (2010) and Mathematics (2011) from RWTH-Aachen His research explores privacy-preserving data donation , human-AI collaboration, and the design of systems that balance utility with ethical considerations. Key projects include delirium prevention in clinical settings, reflective practice in data science, and adversarial interface design patterns. Recent publications highlight trends in privacy visualization , LLM-based conversational interfaces , and hybrid intelligence systems . He investigates how thresholds in machine learning, user onboarding libraries, and visual storytelling impact decision-making and data interpretation. Daniel contributes to educational initiatives through courses like Human-Centered Data Science and Interactive Intelligent Systems , emphasizing critical thinking and visualization literacy. The HCC lab focuses on designing technologies that prioritize user sovereignty and ethical frameworks, such as the Meta-Consent System for health data sharing.