Dr. Burkhard Maess is a Research Professor and Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences, leading the Methods and Development Group Brain Networks. His research focuses on auditory and language processing, signal analysis, and dynamic modeling of neuronal networks. He holds a Diploma in Physics (University of Leipzig, 1987) and a PhD in Physics (University of Leipzig, 1990). His career includes postdoctoral positions at the Academy of Sciences of the GDR and the Free University of Berlin before joining the MPI in 1995. Since 2000, he has led research groups on MEG/EEG signal analysis and cortical network dynamics. His work integrates advanced neuroimaging techniques like MEG and EEG to study sensory processing, neural network dynamics, and the effects of aging on auditory attention. Key contributions include developing high-resolution BEM-FMM methods for source localization and analyzing cross-frequency coupling in neuroscience data. His group also explores spinal cord electrophysiology and the neural underpinnings of perceptual decision-making. Dr. Maess’ research spans cognitive neuroscience, biomedical engineering, and computational modeling, with a focus on bridging empirical findings with theoretical frameworks in neuroscience.
Dimitris Samaras is a SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. He leads the Computer Vision Lab and holds adjunct roles in Biomedical Informatics and Radiology. His research focuses on computer vision, machine learning, medical imaging, and computational behavioral sciences, with interdisciplinary collaborations in psychology and neuroscience. Education: Ph.D. in Computer Science (University of Pennsylvania, 2001), M.S. in Computer Science (Northeastern University, 1994), Diploma in Computer Engineering (University of Patras, Greece, 1992). Research Interests: Modeling 3D shape and illumination interactions, facial expression analysis, medical image analysis, and applying machine learning to brain imaging. Current funded projects include NIH/NIDA grants, NSF initiatives, and collaborations with institutions like Brookhaven National Lab and Adobe. Publications: Over 150 peer-reviewed papers in top venues like ICCV, CVPR, and MICCAI, with impactful work on shadow removal, face relighting, and digital pathology. Recent trends emphasize medical AI, generative models, and multimodal interactions. Awards: SUNY Chancellor’s Award (2018), Dean’s Millionaire’s Club (2016), and multiple NIH/NSF grants. Recognized for contributions to scholarship and creative activities in academia. Grants & Teams: Leads over $10M in active grants, including projects on AI for penguin population tracking, histopathology image analysis, and robotic assistance. Collaborates with interdisciplinary teams in medicine, engineering, and cognitive science. Labs & Initiatives: Directs the Computer Vision Lab, contributes to the ColdSteel/NSF CVDI-NY SPIR consortium, and co-leads the Sensor and Transportation Security Center with Farmingdale State College.
Laura K. Nelson is an Associate Professor of Sociology at the University of British Columbia , where she also directs the Centre for Computational Social Science . Her work bridges computational methods with sociological inquiry, focusing on gender inequality, social movements, and organizational dynamics. She previously held faculty roles at Northeastern University and affiliated with institutions like the NULab for Texts, Maps, and Networks and the Network Science Institute . Education: PhD in Sociology (2014), University of California, Berkeley MA in Sociology (2009), University of California, Berkeley BA in Sociology (2006), University of Wisconsin-Madison (Phi Beta Kappa) Research Interests span computational sociology, social movement strategy, intersectionality, and STEM equity. She pioneered frameworks like computational grounded theory and radical objectivity , integrating machine learning with qualitative paradigms. Recent publications analyze gender dynamics in emergency medicine, feminist movement histories, and the NSF ADVANCE program’s impact on equity. Her 2024 Social Science Quarterly paper quantifies ADVANCE’s interdisciplinary reach. Awards include the 2020 Best Meta-Reviewer at SocInfo20 and Outstanding Faculty of the Year at Northeastern University. She serves on editorial boards for American Journal of Sociology , Poetics , and Acta Sociologica . She co-PIs a National Science Foundation grant studying gender-equity dissemination in higher education networks and supervises graduate student Jinyang Yu . Her lab, Centre for Computational Social Science , drives open-source methodological innovation.
Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.
Mark D. Gross is a Professor of Computer Science and Director of the ATLAS Institute at the University of Colorado Boulder, where he leads an interdisciplinary hub for creativity and invention. His academic journey began at MIT with BS and PhD degrees, followed by faculty roles at Carnegie Mellon University (2004-2013), University of Washington Seattle (1999-2004), and CU-Boulder (1990-1999, 2014-present). As co-founder of Modular Robotics Incorporated and Blank Slate Systems LLC, he bridges academia and entrepreneurship. Education: BS and PhD in Computer Science from MIT Gross’ research spans design methods, modular robotics, computational design tools, and tangible interaction. He pioneered sketch recognition software like 'The Electronic Cocktail Napkin' and explores physical computing through projects such as shape-changing interfaces, interactive construction kits, and augmented reality systems. His work integrates IoT, digital fabrication, and educational technology. Recent publications highlight innovations in AR/VR collaboration, shape-changing robotics, and interactive fabrication. Key themes include climate communication through data physicalization, AI-driven creative systems, and soft robotics for dynamic interfaces. Despite no explicit awards listed, his career demonstrates sustained impact through ACM conference leadership (Creativity and Cognition 2009, TEI 2011) and industry partnerships. Gross’ prior industry experience includes positions at Atari Cambridge Research and Logo Computer Systems. His lab at ATLAS fosters radical creativity through projects like PaperMech, DynaBlock, and WearAir, emphasizing hands-on learning and cross-disciplinary experimentation.
Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.
Dr. Jeffrey Morgan is a Researcher at Cardiff University's School of Social Sciences, specializing in multidisciplinary research at the intersection of computer science, social science, and geography. His work emphasizes human-computer interaction, visualization, and big data analytics. He holds a Research Software Engineer role, combining technical expertise with academic inquiry. Key research interests include AI-driven patent analysis, IoT applications in rural citizen science, and geospatial Twitter demographics. He has contributed to studies on Hadoop infrastructure optimization and social media conflict detection, often collaborating with institutions like Xiamen University and the University of Bremen. His publications span topics like energy-efficient big data processing, digital geography of Welsh identity, and scalable social media analysis frameworks. Notable projects include COSMOS (a cloud-based social media analysis platform) and studies on post-devolution cultural narratives in Wales. Award-winning work includes computational Twitter analysis for detecting online community tensions and geotagging behavior patterns. His research often bridges technical innovation with societal impact, addressing challenges in rural technology deployment and digital sociology.
Petar Kormushev is a Senior Lecturer (Associate Professor) in Robotics at the Dyson School of Design Engineering, Imperial College London, and the founder/director of the Robot Intelligence Lab. He holds a PhD in Computational Intelligence from Tokyo Institute of Technology. His research focuses on robotics and machine learning, particularly reinforcement learning for autonomous robots. Key projects include the WALK-MAN humanoid robot for disaster response and contributions to the PANDORA and STIFF-FLOP EU projects. He has received the 2013 John Atanasoff Award for scientific excellence in ICT. His lab develops machine learning algorithms applied to humanoid robots like COMAN and iCub, with interests in robot learning, compliant control, and autonomous systems. He has supervised numerous PhD students and led research teams at IIT and Imperial College.
Venkatram Ramaswamy is the Hallman Fellow of Electronic Business and Professor of Marketing at the Ross School of Business, University of Michigan. He is a globally recognized thought leader in innovation, strategy, and co-creation, with expertise spanning marketing, branding, IT, and organizational dynamics. His work emphasizes the transformative potential of co-creating value with stakeholders across industries and sectors. Education: PhD in Marketing, The Wharton School, University of Pennsylvania (1989). Research Interests: Venkat’s work focuses on co-creation paradigms, experience innovation, digital ecosystems, and stakeholder collaboration. He pioneered the concept of co-creation as a revolutionary approach to business value, detailed in his seminal books The Future of Competition (2004) and The Power of Co-Creation (2010). His recent work explores AI-human interactional creation and platformed ecosystems. Key Contributions: He has advised global firms like Mahindra, Hitachi, and Infosys on co-creation strategies. His frameworks integrate strategic risk management, digital platforms, and lived-experience ecosystems to drive sustainable value. He has delivered keynotes at events worldwide, including the Drucker Forum and InnovationsKongress. Awards: Recipient of the MIT-PwC Award (2003), Emerald Literati Award (2009), and Best Speaker Award at the Baptie Marketing Focus (2005). Grants & Labs: Collaborations with institutions like the International University of Japan (IUJ) and involvement in initiatives like the India Philanthropic Co-Creation Project reflect his commitment to systemic social impact. His research is supported by grants exploring digitalized societal ecosystems and wellbeing-focused platforms.
Ben Green is an Assistant Professor in the University of Michigan School of Information and a courtesy Assistant Professor in the Gerald R. Ford School of Public Policy. He holds a PhD in Applied Mathematics from Harvard University with a secondary focus on Science, Technology, and Society. His research examines algorithmic ethics, fairness, and governance, aiming to reduce harms and advance social justice. Notable works include The Smart Enough City (2019) and his forthcoming Algorithmic Realism . He is affiliated with the Berkman Klein Center for Internet & Society at Harvard and the Center for Democracy & Technology. Education: PhD in Applied Mathematics, Harvard University (with secondary field in Science, Technology & Society) BS in Mathematics & Physics, Yale University Research Interests: Algorithmic fairness in public policy Human-algorithm interaction dynamics Regulatory frameworks for AI Equity-centered data science practices Urban technology policy His recent publications explore themes like the limitations of human oversight in algorithmic systems, the sociotechnical challenges of implementing ethical AI, and the intersection of legal reasoning with computational systems. His writing emphasizes actionable solutions to systemic biases in algorithmic governance. Ben’s current projects include advancing algorithmic realism – a framework for grounding data science in socially just practices – and analyzing how counterfactual explanations influence judicial decisions. He serves on multiple interdisciplinary advisory boards and frequently collaborates with policymakers to translate research into actionable strategies.
Claire O'Brien is a Senior Lecturer in Computer Animation at Teesside University, affiliated with the Department of Digital Arts and Animation within the School of Computing, Engineering & Digital Technologies. She serves as Course Leader for the MA Animation program and coordinates Animex Screen, the international student film festival linked to the annual Animex event. Her academic journey includes roles at Northumbria University and South Tyneside College before returning to Teesside in 2022. Education: Bachelor of Fine Art (Painting and Printmaking), University of East London, 1997 Post-Graduate Diploma in Arts Management, Northumbria University, 2000 Master of Computer Animation, Teesside University, 2005 Postgraduate Certificate in Teaching and Learning in Higher Education (PgCTLHE), Teesside University, 2024 Fellow of Advance Higher Education (FHEA), awarded November 2024 Claire's research focuses on Animation Studies , Immersive Technologies (AR/VR/MR) for education, healthcare, and industrial training, Full-dome 360 filmmaking , and pedagogical innovation in digital arts. Her recent work explores how animated visualizations in film convey complex information through interfaces like HUDs and holograms, and how animation functions in public and cultural spaces such as cruise ships and heritage exhibitions. Her recent publications from 2023–2024 demonstrate a consistent engagement with animation as a tool for communication, cultural storytelling, and technological critique. Themes include representational vehicles, folklore in digital art, and the integration of generative AI in creative processes, particularly in health awareness contexts like menopause support. Scientific Awards and Recognitions: Fellow of Advance Higher Education (FHEA), 2024 Postgraduate Certificate in Teaching and Learning in Higher Education, Teesside University, 2024 Claire leads the MenopauseXR project funded by Innovate UK (2024–2025), which investigates the use of Generative AI and Extended Reality to support menopause awareness and education. She has no listed formal students, but her role as course leader and lecturer implies significant student mentorship. Her enterprise activity includes participation in the XR Stories XR Accelerator in 2024, focusing on commercializing mobile augmented reality research. Claire is actively involved in Animex , a major animation and games festival, where she coordinates Animex Screen. She also collaborates with cultural institutions such as the National Trust , having contributed animation and prints to the exhibition "Washington: Fact, Fiction and Folklore." Her work bridges academia, creative practice, and public engagement.
Stéphane Doncieux is a University Professor in Computer Science at Sorbonne University, where he is affiliated with the Institute of Intelligent Systems and Robotics (ISIR), a joint research laboratory with CNRS. Since January 2024, he has served as Director of ISIR, following a term as Deputy Director from 2019 to 2023. He leads the ASIMOV research team and is based at the Pierre and Marie Curie Campus in Paris. His primary research interests lie in cognitive and developmental robotics, with a strong focus on open-ended learning, evolutionary algorithms, and adaptive systems. He investigates how robots can autonomously learn diverse skills through mechanisms such as novelty search, quality-diversity optimization, and intrinsic motivation. His work bridges theoretical foundations in artificial life and practical applications in robotic manipulation, perception, and control. The recent publications highlight a consistent trend in advancing robotic learning under sparse rewards and in open-ended environments. Key themes include quality-diversity optimization for grasping, state representation learning, sim-to-real transfer, and the development of behavioral repertoires. These works are published in high-impact journals such as IEEE Transactions on Robotics, Evolutionary Computation, and Frontiers in Robotics and AI. Coordinator, DREAM FET H2020 project (2015–2018) Principal Investigator, ANR projects on Creative Adaptation by Evolution, Learning Movement Skills, and Grasping with Multimodal Feedback Involved in European initiatives including VeriDREAM and HumanE-AI-Net He has supervised numerous PhD and Master’s students, including Leni Le Goff, Giuseppe Paolo, Alban Laflaquière, and Achkan Salehi, often in collaboration with leading researchers like Olivier Sigaud and Jean-Baptiste Mouret. He teaches computer science and robotics at both undergraduate and graduate levels at Sorbonne University. Doncieux has been instrumental in shaping research directions in evolutionary and developmental robotics, notably through his leadership in the IEEE Task Force on Evo-Devo-Robotics and his editorial contributions. His lab, ASIMOV, fosters interdisciplinary research integrating computer science, neuroscience, and engineering to create more autonomous and intelligent robotic systems.
Samar Sabie is an Assistant Professor at the Institute of Communication, Culture, Information and Technology (ICCIT) at the University of Toronto, where she also serves as Program Director for the Technology, Coding, and Society (TCS) program. She holds a graduate appointment at the Daniels Faculty of Architecture, Design, and Landscape, reflecting her interdisciplinary work bridging technology, design, and social justice. She leads the Open Design Collaboratory, a research space focused on critical and community-centered design practices. Education: Doctor of Philosophy, Department of Information Science, Cornell University/Cornell Tech, 2022 Master of Science, Department of Computer Science, University of Toronto, 2017 Master of Architecture, John H. Daniels Faculty of Architecture, Landscape, and Design, University of Toronto, 2015 Honors Bachelor of Science (Architecture and Computer Science), University of Toronto, 2011 Her research investigates design as a socio-material practice that fosters community adaptive capacity toward sustainable change. Drawing from architecture, software engineering, ethnography, and philosophy, she explores participatory design , unmaking , and design for social justice . Her work critically engages with how communities resist, adapt, and reimagine technology in contexts of displacement, scarcity, and inequality. Her recent publications, appearing in top venues like CHI , CSCW , and DIS , reveal a strong thematic focus on unmaking as a design strategy for emancipation and agonism, mobility justice , e-waste practices , and cultural memory through design. These works collectively emphasize community agency, ethical ambiguity, and the political dimensions of design. Scientific Contributions: Director, Open Design Collaboratory Program Director, Technology, Coding, and Society (TCS) Graduate Faculty, Daniels Faculty of Architecture, Design, and Landscape Current Courses: CCT204 Design Thinking I, CCT477 Understanding Users Samar Sabie actively mentors students and collaborates on research focused on humanitarian technology, critical making, and design education. Her projects often involve community engagement, participatory methods, and interdisciplinary teams. She has collaborated with and advised early-career researchers such as Dina Sabie, Awais Hameed Khan, and Taneea Agrawal on topics ranging from IDP shelter dynamics to mobility justice advocacy. Her research is supported by her deep engagement with socio-technical challenges in marginalized contexts, and she continues to push the boundaries of how design can serve as a tool for equity, care, and resistance. Future work appears to be evolving toward deeper philosophical inquiries into destruction, care, and the limits of technology.
Malvina Nissim is a leading researcher in computational linguistics and NLP at the University of Groningen's Department of Artificial Intelligence, with a focus on multilingual modeling, bias mitigation, and human evaluation frameworks. Key Contributions : Developed CALAMITA (Italian LLM benchmark), IT5 models for Italian language processing, and ReproHum framework for NLP evaluation reproducibility Research Pillars : Multilingual reasoning consistency, perspective-based text analysis, and figurative language modeling Her work spans activation steering techniques, cross-lingual transfer learning, and the creation of specialized language resources like the EurekaRebus dataset and MAGPIE idiom corpus. She pioneered methods for gender bias measurement in BERT and developed the SocioFillmore tool for perspective visualization. Recent publications explore model uncertainty as MCQ difficulty proxy, Italian headline generation benchmarks, and multilingual multi-figurative language detection. She actively participates in teaching initiatives like the "NLP with Bracelets" workshop for Italian high school students. Scientific Awards : ACL Best Paper Award (2025) EMNLP Outstanding Reviewer (2023) EVALITA Leadership Recognition (2024) She advises PhD students in model bias analysis and has contributed to the development of the Dutch Abusive Language Corpus (DALC) and the ReproNLP reproducibility framework. Her collaborations span institutions in Italy, Netherlands, and international NLP communities.
Benjamin Bach serves as a Reader (equivalent to Associate Professor) in Data Visualization and Design within the School of Informatics at the University of Edinburgh, where he maintains active faculty status as of the page's publication date (October 24, 2024). He is formally affiliated with the Institute for Language, Cognition and Computation, contributing to the university's interdisciplinary research ecosystem in computational sciences. His research spans Data Visualization, Design, Computational Linguistics, Cognitive Science, and Human-Computer Interaction, with emphasis on developing visual representation frameworks that enhance human interpretation of complex data systems—particularly in language processing and cognitive modeling contexts. This work bridges theoretical design principles with practical applications in data-intensive domains. Dr. Bach's professional contact includes the email address bbach@exseed.ed.ac.uk and a personal website, though specific educational credentials remain undocumented in available sources. Regarding academic contributions, no details about graduate student supervision, research funding, or laboratory infrastructure are provided; however, his Institute for Language, Cognition and Computation affiliation indicates collaborative engagement with researchers exploring language, cognition, and computational methodologies.
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