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Dr. Shirin Nilizadeh is an Associate Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington's College of Engineering. She leads the Security and Privacy Research Lab, conducting interdisciplinary research at the intersection of cybersecurity, privacy, machine learning, and social media analysis. Her work addresses critical societal issues related to online security, privacy, and safety through data-driven approaches. Dr. Nilizadeh received her PhD in Computer Science from Indiana University in 2014, followed by MS in Computer Science from Amirkabir University (2007) and BS in Computer Engineering from Islamic Azad University (2004). Her research focuses on security and privacy in systems and social networks, employing techniques from machine learning and big data analytics. She takes a highly interdisciplinary approach, integrating AI, NLP, social sciences, and public health to address societal issues in cybersecurity and privacy. Her research objectives include: (1) detecting and characterizing emerging threats in online social networks like social engineering attacks, misinformation, and online hate speech; (2) advancing the adversarial robustness and fairness of ML and NLG systems; and (3) studying humans' online behaviors through data-driven interdisciplinary research. Analysis of her recent publications reveals a strong focus on AI-generated security threats, particularly phishing scams using LLMs, NFT fraud detection, social media toxicity analysis, and content moderation systems. Her work bridges theoretical security research with practical applications, often addressing real-world security challenges through innovative technical solutions. Among her notable scientific achievements are the prestigious NSF CAREER award (2023), Comcast Innovation Awards (2022 and 2024), College of Engineering Outstanding Early Career Research award (2024), and IEEE SP 2024 Distinguished Paper Award. Her work has also received best paper and technical poster awards at eCrime 2021 and NDSS 2022. Dr. Nilizadeh has successfully mentored numerous doctoral and master's students while securing significant research funding, including multiple NSF grants and Comcast Innovation Fund awards. She leads a vibrant research group that has produced impactful work cited in official reports submitted to The Supreme Court and the EU Committee on Civil Liberties, Justice, and Home Affairs. Her lab has also received coverage from WIRED, MIT Technology Review, Orange's Hello Future, and Communications of the ACM. She serves on numerous program committees for top international conferences including ACM CCS, USENIX Security, and POPETS, and has organized outreach programs like OurCS@DFW to broaden participation of underrepresented students in computing.

• Cybersecurity
• Privacy
• Social Media Analysis
• +7 more

Ozgur Yilmaz is a Professor in the Department of Mathematics at the University of British Columbia (UBC). He is the Director of the Pacific Institute for the Mathematical Sciences (PIMS) and has held roles such as Interim Deputy Director at PIMS and Deputy Director at the Banff International Research Station (BIRS). His research focuses on applied harmonic analysis, signal processing, compressed sensing, and seismic signal processing. Education: PhD in Applied and Computational Mathematics from Princeton University (2001), B.Sc. in Mathematics and Electrical Engineering from Boğaziçi University (1997). Research Interests: Mathematical problems in analog-to-digital conversion, blind source separation, sparse approximations, compressed sensing, and their applications in seismic exploration. He has contributed to advancements in sigma-delta quantization, low-rank matrix recovery, and compressed sensing algorithms. Funding: Recipient of NSERC Discovery Grants, UBC Data Science Institute grants, and leadership in collaborative research groups (CRGs) on high-dimensional data analysis and applied harmonic analysis. His work bridges theoretical mathematics with practical applications in signal processing and AI-driven medical imaging. Students and Postdocs: Supervised numerous PhD and MSc students in areas like compressed sensing, seismic data reconstruction, and machine learning. Current advisees include Aaron Berk and Xiaowei Li. Former students hold positions at academic institutions and tech companies. Labs and Collaborations: Affiliated with UBC’s Data Science Institute (DSI), Centre for Artificial Intelligence Decision-making and Action (CAIDA), and the Institute of Applied Mathematics (IAM). Collaborates on projects integrating AI with scientific discovery, such as retinal biomarker identification using deep learning.

• Applied and Computational Harmonic Analysis
• Signal Processing
• Compressed Sensing
• +5 more

Federico Tombari is a Director of Research at Google Zurich and a Lecturer (Privatdozent) at the Chair of Computer Aided Medical Procedures (CAMP) at TUM. He leads applied research in Computer Vision and Machine Learning, focusing on 3D vision, robotics, augmented reality, autonomous driving, and healthcare applications. His work emphasizes unsupervised learning, large multimodal models, neural radiance fields, and scene graphs. Education & Professional Background: As PD Dr. Ing. Habil., he holds a habilitation in engineering and has been active in academic and industrial research for over a decade. His roles include Area Chair for top conferences like CVPR and ECCV, and Associate Editorships for journals like IJRR. Research Interests: Federico’s research spans 3D scene understanding, object recognition, SLAM, and novel view synthesis. He explores applications in surgical robotics, autonomous systems, and medical imaging. Recent trends in his work include generative models for scene generation and semantic scene graphs for holistic modeling. Grants & Industry Collaborations: He has led projects with Toyota, BMW, Audi, Zeiss, and others, focusing on 3D perception, autonomous driving, and medical vision. His work bridges academia and industry, emphasizing practical applications. Labs & Teams: He contributes to labs like DHM (Deutsches Herzzentrum München), NARVIS Lab, and RobUSt (Robotics and Ultrasound), advancing interdisciplinary research in healthcare and robotics.

• Computer Vision
• 3D Computer Vision
• Machine Learning
• +8 more

Richard A. Shweder is the Harold H. Swift Distinguished Service Professor of Human Development at the University of Chicago, where he has been since 1972. He is a leading figure in cultural psychology and anthropological research, focusing on moral reasoning, cultural migration, and multiculturalism. His work critiques Western liberal assumptions, emphasizing robust cultural pluralism and the complexities of cross-cultural ethical conflicts. Shweder's research spans decades in Bhubaneswar, India, examining family practices, gender roles, and moral foundations. Education: Ph.D. in Social Anthropology from Harvard University (1972). Notable visiting roles include fellowships at the Center for Advanced Study in the Behavioral Sciences, Stanford University, and the Institute for Advanced Study in Berlin. Research interests include cultural psychology, moral pluralism, and the challenges of multiculturalism in liberal democracies. His books, such as Thinking Through Cultures and Why Do Men Barbecue? , explore how cultural contexts shape thought and emotion. He critiques global human rights campaigns, particularly regarding female genital surgeries, advocating for nuanced cultural understanding. Scientific awards include the Guggenheim Fellowship (1985), Carnegie Scholar (2002), and the Lifetime Achievement Award from the Society for Psychological Anthropology (2016). He has served as President of the Society for Psychological Anthropology and co-chaired projects on law and culture. Recent work focuses on the moral challenges of cultural migration and the limits of tolerance in Western societies. His 2020 lecture on moral absolutes and 2023 publications on gender equality in religious practices highlight ongoing debates in cultural anthropology. Shweder actively engages in public discourse through podcasts, interviews, and critiques of academic politics, such as opposing academic boycotts of Israeli institutions. His academic roles include teaching courses like Moral Psychology and Comparative Ethics and When Cultures Collide . He advises students on cultural migration and ethical pluralism, with notable contributions to encyclopedic references like The Child: An Encyclopedic Companion .

• Cultural Psychology
• Anthropology
• Moral Development
• +5 more

Melanie Campbell is a Professor at the University of Waterloo, cross-appointed to the School of Optometry and Department of Systems Design Engineering. Her research focuses on the optical properties of the eye, developing imaging systems for diagnosing Alzheimer's disease and diabetic retinopathy through polarization techniques and adaptive optics. PhD in Physics from Australian National University (1982) MSc in Physics from University of Waterloo (1977) BSc in Chemical Physics from University of Toronto (1975) Research interests include: Retinal amyloid detection for Alzheimer's diagnosis Adaptive optics for high-resolution imaging Polarimetry in ocular pathology Presbyopia and ophthalmic corrections Her publications (2012-2019) demonstrate interdisciplinary applications of optics in neuroscience and diabetes research, with key contributions to: Retinal polarization imaging Two-photon therapy systems Cone photoreceptor analysis Animal models of neurodegeneration Scientific honors include: 2019 Laird Lecturer 2014 CAP-INO Medal 2004 Rank Prize in Optoelectronics Fellow of Optical Society of America As director of Campbell Labs, she leads research on retinal imaging techniques and their applications in neurological disease diagnosis.

• Biomedical Optics
• Retinal Imaging
• Alzheimer's Disease Diagnosis
• +4 more

Esfandiar Mohammadi is an Associate Professor at the Institute for IT Security, University of Lübeck, leading the Privacy & Security (PrivSec) group and directing the AnoMed competence cluster. He has held tenured faculty positions since 2019 after postdoctoral research at ETH Zürich (2016-2019) and a PhD at Saarland University (2015). University of Lübeck (2015-present) ETH Zürich (2016-2019) Saarland University (2015) His research focuses on privacy-preserving technologies in machine learning, anonymous communication protocols, and formal verification of security properties. Recent work includes advancements in Mixnet scalability and federated learning with differential privacy guarantees. Key publication trends reveal a strong emphasis on privacy-preserving algorithms for machine learning (2024), cryptographic protocols for anonymous communication (2025), and security analysis of decentralized systems (2023-2025). Collaborations span institutions like ETH Zürich, Saarland University, and industry partners EnergieDock/NAECO Blue for the VeDS project. His group includes 11 researchers (5 PhD students) and software engineers working on topics like Differential Privacy Secure Multi-Party Computation Trusted Execution Environments

• Privacy & security in machine learning
• Differential privacy
• Anonymous communication
• +1 more

Craig A. Chin is an Associate Professor in the Department of Electrical Engineering at Kennesaw State University. He holds a Ph.D. and M.S. in Electrical Engineering from Florida International University (2006, 2001) and a B.S. in Electrical and Computer Engineering from the University of the West Indies (1995). Research Interests: Digital Signal Processing for Biomedical Applications Machine Learning in Biomedical Signal/Image Analysis Wireless Body Area Networks (WBANs) for Healthcare Network Security in Resource-Constrained Environments Biometric Authentication for Wireless Sensor Networks Innovations in Engineering Education (Cooperative/Active Learning, Humanities Integration) Article Trends: His publications (2001–2019) focus on Biomedical Signal Processing (EMG/Eye-Gaze Integration, Stress Detection), WBANs for mHealth , and Engineering Education Pedagogy . Recent work (2013–2019) emphasizes Cooperative Learning Strategies and Wireshark-based Network Security Labs . Academic Contributions: Craig has taught courses such as Biometrics, Data Communications, Biomedical Instrumentation, and Medical Electronics. He actively explores future research on biometric-driven security for Body Area Sensor Networks and active learning strategies in online education.

• Digital Signal Processing
• Machine Learning
• Biomedical Signal/Image Processing
• +7 more

Dr. Elizabeth Lipke is the Mary and John H. Sanders Professor of Chemical Engineering at Auburn University’s Samuel Ginn College of Engineering. Her lab focuses on tissue engineering, particularly in cancer and cardiovascular systems, with a focus on 3D in vitro models for drug screening and disease mechanisms. She leads interdisciplinary research combining biomaterials, microfluidics, and stem cell technologies to model complex biological systems. Education & Affiliations: PhD in Chemical Engineering (assumed based on role) Director of the Lipke Lab at Auburn University Collaborator with Dr. Arnold (cancer biology) and Dr. Mistriotis (biomedical engineering) Research Interests: Engineered cancer models to study obesity-related colorectal cancer progression Cardiac tissue engineering using pluripotent stem cells Microfluidic platforms for scalable production of hydrogel microspheres Drug efficacy testing in vascularized tumor models Awards & Recognition: 2024 AIMBE College of Fellows Inductee (top 2% in medical/biological engineering) NSF Grant ($621,934) for cardiac tissue manufacturing research (2018) Alabama Launchpad $25,000 award for Vivosphere technology (2023) Lab Highlights: Developed tumor-mimetic microvascular platforms for anti-cancer drug screening Engineered insulin-sensitive/insulin-resistant colorectal cancer models Pioneered scalable production of cardiac tissue microspheres from hiPSCs Grants & Funding: NSF RECODE Grant (2021) LAUNCH Innovation Grant (2023) Labs/Teams: Lipke Lab at Auburn University Collaborations with Arnold Lab (cancer biology) and Mistriotis Lab (biomedical engineering)

• Tissue Engineering
• Cancer Research
• Cardiac Tissue Engineering
• +5 more

Simon Hayhoe is a Reader in Education at the University of Exeter's School of Education and an Associate of the Scottish Sensory Centre, University of Edinburgh. His work focuses on sensory impairment, ageing, inclusive technologies, and participatory research methodologies. He has held roles at the University of Bath (2016-2023), Canterbury Christ Church University, and Sharjah Women’s College, UAE. His research has been supported by a Horizon 2020 grant and a Fulbright Fellowship, among others. Education: PhD in Education (University of Birmingham, 2006), PGCE in Secondary Technology (University of Bath, 1997), MEd (University of Leicester, 1995), BA in Technical Communication (Coventry Polytechnic, 1993). His research interests include sensory impairment philosophies, accessible museum technologies, and inclusive education policies. Notable projects include the ARCHES initiative on cultural heritage accessibility and the Sound Pad Project, which co-created inclusive technologies with marginalized communities. He has delivered keynotes at institutions like MIT, Harvard, and the Metropolitan Museum of Art, and serves on editorial boards for journals such as British Journal of Visual Impairment and Discover Global Issues . His awards include the Fulbright Scholarship and fellowships from the British Computer Society and Metropolitan Museum of Art.

• Sensory Impairment
• Ageing and Cultural Inclusion
• Accessible and Inclusive Technology
• +7 more

Jianwen Su is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where he has been since 1990. He holds a Ph.D. in Computer Science from the University of Southern California and B.S./M.S. degrees from Fudan University in China. His research focuses on databases, formal verification, web services, business process management (BPM), and workflow systems. He has contributed to data-centric workflow modeling, artifact-based BPM frameworks, and tools like the Web Service Analysis Tool (WSAT). Adjunct professorships at Peking, Fudan, and Donghua Universities in China. Key roles: General co-chair of ICSOC 2013, PC chair of PODS 2009, and general chair of SIGMOD 2001. Recipient of the 2000 Outstanding Faculty Award (UCSB College of Engineering) and IBM Faculty Awards (2007, 2008). Research spans database query languages, incremental query evaluation, spatial databases, and formal verification techniques for software systems. Current emphasis is on data modeling for workflows and BPM systems.

• Database Systems
• Formal Verification
• Web Services
• +5 more

Gonzalo Nápoles is an Assistant Professor at Tilburg University's School of Humanities and Digital Sciences, Department of Cognitive Science and AI. He holds a Doctoral Degree in Computer Science from Rough Cognitive Networks (2014–2017). His research focuses on AI applications in cognitive modeling, pattern classification, and neural networks with interdisciplinary applications in healthcare, finance, and social sciences. Key research areas include Fuzzy Cognitive Maps, data augmentation techniques for neuroimaging, and interpretable machine learning systems. He actively contributes to UN Sustainable Development Goals related to education and innovation. Recent work explores AI ethics, financial risk assessment using dynamic networks, and sensory processing disorder analysis through neural networks. Education: Doctoral Degree in Computer Science, 2017 (Thesis: Rough Cognitive Networks) Prize-winning research includes Best Paper Awards at CIARP 2021 and IWAIPR 2023. He collaborates internationally, hosting academic visitors and serving on multiple PhD committees. Current projects involve stock prediction using graph neural networks and fMRI data augmentation methodologies. Awards: Best Paper Award - CIARP 2021 Best Paper Award - IWAIPR 2023 Nápoles advises on PhD theses in cognitive science and AI applications. His work bridges theoretical advancements with practical implementations in healthcare, finance, and urban systems.

• Artificial Intelligence
• Machine Learning
• Cognitive Science
• +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.

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

Professor Liu Hongyan is a full-time Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management, where he has served since 1994, achieving the rank of Professor in 2011 after previously holding positions as Associate Professor (2003-2011) and Teacher. His research bridges theoretical data science with practical applications across e-commerce, healthcare, and social media platforms. Education: PhD in Management, School of Economics and Management, Tsinghua University (2001) His research focuses on big data management , machine learning , and business intelligence with specialized expertise in personalized recommendation systems , medical/financial data analysis , and computer vision applications . Recent work integrates large language models and causal inference to solve complex problems in short video platforms, live streaming, and healthcare analytics, emphasizing real-world impact through industry collaborations. Analysis of his 15 most recent publications (2023-2025) reveals a strong trajectory toward multimodal AI systems combining recommendation engines with computer vision, particularly in 3D animation for advertising and healthcare. Key trends include LLM-enhanced display advertising, emotion-aware facial animation, and medical image annotation using adversarial learning, while maintaining core contributions to behavioral data mining in social networks. Scientific recognition includes: National Archives Administration's Outstanding Scientific and Technological Achievement Award Multiple Best Paper Awards at international conferences Outstanding Doctoral Dissertation Supervisor designation from the Society for Management Science and Engineering Special Award for National Natural Science Foundation project on user behavior pattern discovery Professor Liu has secured leadership roles in major National Natural Science Foundation projects including Innovation Research Groups and international cooperation initiatives. His industry impact is demonstrated through patented recommendation systems adopted by multiple companies, particularly in personalized content delivery for live streaming and short video platforms. As an Outstanding Doctoral Dissertation Supervisor, he mentors the next generation of data science researchers. He serves as Deputy Director of Tsinghua University's Center for Artificial Intelligence and Management Research and holds key positions in national academic societies including the E-Commerce and Cyberspace Management Committee (China Management Modernization Research Association) and the Information Systems Engineering Committee (Chinese Society for Systems Engineering).

• Big data management and analysis
• machine learning
• data mining
• +7 more

Aurélien Bornet is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), specifically within the Institute of Chemical Sciences and Engineering (ISIC). He serves as the Platform Leader for the Nuclear Magnetic Resonance Platform at EPFL, where he oversees advanced NMR facilities and research. Dr. Bornet's research focuses on Nuclear Magnetic Resonance (NMR) and Dynamic Nuclear Polarization (DNP) techniques. His work spans several key areas including hyperpolarization methodologies, development of NMR instrumentation, and applications in both chemistry and biomedical fields. His research has led to significant advancements in dissolution DNP, long-lived nuclear spin states, and hyperpolarized metabolite imaging. His recent publication record demonstrates strong activity in developing new NMR techniques and applications, with particular emphasis on hyperpolarization methods that dramatically enhance NMR sensitivity. His work bridges fundamental physics with practical applications in medical imaging and materials science. The research outputs include numerous high-impact publications in journals like Nature Communications, Journal of the American Chemical Society, and Physical Chemistry Chemical Physics, as well as several patents related to NMR technology. Dr. Bornet has received recognition through multiple patents for his innovations in NMR technology, including patents related to polarizing agents, dissolution DNP methods, and NMR instrumentation. His work has important implications for biomedical imaging, particularly in the development of hyperpolarized metabolic imaging for cancer diagnostics and other medical applications. As an educator, Dr. Bornet teaches courses on Basic and Advanced NMR at multiple levels (Level 1 A, Level 1 B, and Level 2) at EPFL and in Sion. His teaching focuses on both theoretical and experimental aspects of NMR, providing students with hands-on experience with modern NMR spectrometers. His academic journey includes completing his PhD at EPFL in 2015 with a thesis on hyperpolarized protons for enhancing NMR sensitivity, advised by G. Bodenhausen and S. Jannin. Prior to this, he completed earlier research on long-lived states as probes of protein stability in 2010 under the supervision of G. Bodenhausen and P. Vasos.

• Nuclear Magnetic Resonance
• Dynamic Nuclear Polarization
• Hyperpolarization Techniques
• +5 more

Marina Milovanović is a Professor at the University of Singidunum, Faculty of Informatics and Computing, Department of Mathematics. She holds dual doctoral degrees from the Faculty of Science, University of Kragujevac (Department of Mathematics, 2014) and Faculty of Entrepreneurial Business, Union University (2008), along with Master's and Bachelor's degrees from the Faculty of Mathematics, University of Belgrade (2000-2005 and 1995-2000 respectively). Faculty of Science, University of Kragujevac, Department of Mathematics (PhD, 2014) Faculty of Entrepreneurial Business, Union University (PhD, 2008) Faculty of Mathematics, University of Belgrade (Master's, 2000-2005) Faculty of Mathematics, University of Belgrade (Bachelor's, 1995-2000) Svetozar Marković High School, science and mathematics major (1991-1995) Professor Milovanović specializes in Mathematics Education and Educational Technology, with particular expertise in interactive multimedia applications for teaching mathematics. Her research consistently bridges theoretical mathematics with practical educational technology solutions, evolving from traditional multimedia approaches to incorporating cutting-edge AI and machine learning techniques. She has authored multiple books including 'Interactive multimedia in mathematics teaching' (2015) and collections of solved mathematics problems for entrance exams. Her recent publication record through 2025 demonstrates active engagement in interdisciplinary research, particularly at the intersection of educational technology, artificial intelligence, and practical applications in fields ranging from software engineering to medical diagnostics. Her work shows a clear trajectory from foundational educational technology research toward more sophisticated AI-enhanced learning systems. Professor Milovanović has made significant contributions to semantic web applications in education, particularly through Moodle LMS enhancements, and has explored SCADA applications in industrial contexts. Her collaborative research spans multiple countries and institutions, reflecting an international scholarly network. She has extensive experience developing computer tools for engineering education and has published on diverse topics including petroleum industry processes, environmental management, and financial mathematics. Her work demonstrates consistent application of computational approaches to solve domain-specific problems across multiple disciplines.

• Mathematics Education
• Interactive Multimedia in Teaching
• Educational Technology
• +5 more