Jordan Boyd-Graber is a Full Professor at the University of Maryland, affiliated with the Department of Computer Science and the University of Maryland Institute for Advanced Computer Studies (UMIACS). His research focuses on machine learning, natural language processing, computational linguistics, topic models, and question answering. He has made significant contributions to the development of interactive topic modeling systems and evaluations of large language models. His work often bridges theoretical advancements with practical applications, such as improving human-AI collaboration and enhancing the interpretability of machine learning models. Boyd-Graber’s research also addresses challenges in adversarial examples, calibration of models, and the ethical implications of AI systems. His recent publications span cutting-edge topics like evaluating topic models through ProxAnn, mitigating hallucinations in vision-language models, and exploring human-AI complementarity in question answering. He collaborates extensively with researchers in computer science, linguistics, and social sciences to advance interdisciplinary applications of NLP and ML.
Dr. Richard Gault is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on computer vision and deep learning applied to microscopy data, particularly in medicine, health, and life sciences. He leads a team developing novel methods for medical image analysis, including histopathology and digital pathology, with applications in cancer diagnosis and environmental science. He is actively involved in teaching, having received Excellence in Teaching awards from Queen's University Belfast in 2019 and 2022. His work bridges computational intelligence and healthcare, with notable contributions to AI-driven diagnostics, stain normalization in histopathology, and multimodal data fusion. Dr. Gault's research interests include ensemble learning, fuzzy systems, and generative models like diffusion networks. He supervises multiple PhD students and has mentored graduates now working in machine learning engineering and postdoctoral research. His team’s achievements include awards such as the 2023 Best Oral Presentation at the Pan Ireland Ophthalmology Day and a 2021 Best Paper Award from his school. Key contributions include the LymphoSight AI application for detecting lymphoid structures and HistoClean , open-source software for improving CNN development in histopathology. He has been recognized as a Senior Member of IEEE and a Fellow of the Higher Education Academy. His work is supported by grants such as the R5131ECI project on 3D quantifier approximation via 2D video analysis (2019–2025). He actively engages in academic activities, including conference organization and PhD external examinations across Europe.
Julian Berger is a postdoctoral researcher at the Max Planck Institute for Human Development in the Center for Adaptive Rationality , where he explores how to enhance decision-making through hybrid human-AI systems. He is also a fellow of the Joachim Herz Foundation and has received funding from the Foundation of German Business and the Danish Data Science Academy. Education: M.A. Psychology in Business and Economics, Universidade Catolica Portuguesa (2021) B.A. Politics, Administration and International Relations, Zeppelin Universität (2018) His research spans human-AI collaboration , collective intelligence , and interpretable machine learning . A recurring theme in his work is developing methods to combine human expertise with AI capabilities for accuracy in domains like medical diagnostics , credit scoring , and football analytics . He has authored publications in high-impact venues such as PNAS , Nature Human Behavior , and Science and Medicine in Football . Scientific awards and funding include: Fellowship for interdisciplinary economics, Joachim Herz Foundation (2024) PhD funding from the Foundation of German Business (Stiftung der deutschen Wirtschaft) Research grant from the Danish Data Science Academy His recent article trends emphasize ensembling techniques that leverage complementary human and AI errors, algorithmic fairness, and practical heuristics like Hybrid Confirmation Trees. These works demonstrate significant improvements in diagnostic accuracy and decision cost-efficiency. Beyond academia, Berger works as a consultant and ML engineer with Simply Rational , focusing on interpretable models for financial and sports analytics. His work bridges theoretical research with real-world applications, prioritizing fairness, transparency, and human accountability in AI systems.
Mariya Toneva is a tenure-track faculty member at the Max Planck Institute for Software Systems , conducting groundbreaking research at the intersection of Machine Learning , Natural Language Processing , and Neuroscience . She leads the Bridging AI and Neuroscience (BrAIN) group , focusing on computational models that align AI systems with human brain processes. Her work aims to enhance both AI capabilities and neuroscience understanding through this cross-disciplinary approach. Actively recruiting postdocs, PhDs, and research interns in areas like code/text representation, brain-AI alignment, and neuroimaging data analysis Collaborator on NIH-funded projects using fMRI and neuropixel data Research Themes : Her group explores neural mechanisms of language processing, event segmentation in narratives, memory reactivation via music, and effective human-AI collaboration frameworks. Key methods include LLM analysis, cross-modal similarity metrics, and naturalistic task-based fMRI studies. Key Publications (2024-2025): Brain-tuned speech models (INTERSPEECH 2025) Cognitive event boundaries in LLMs (Behavioral Research Methods 2025) Music-induced memory reactivation (biorxiv 2024) LLM-brain alignment reasons (EMNLP 2024) Advising : Mentors PhD candidates Omer Moussa (speech processing), Camila Kolling (representational similarity), and Gabriele Merlin (LLM alignment). Collaborates with institutions like MIT, NYU, and ETH Zurich.
Dr. Kate Farrahi is an Associate Professor in the ECS department at the University of Southampton, where she leads research in the Vision, Learning and Control (VLC) Group. Previously, she was a Research Assistant at the Idiap Research Institute and earned her PhD in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her work focuses on the intersection of machine learning and digital health, particularly in developing human sensing methods using vision and wearable technologies. She currently supervises four PhD students in Computer Science and actively accepts new PhD applications. Her research interests span machine learning applications in healthcare, including wearable device analytics, epidemiological modeling via AI, and drug discovery through generative methods. She has been recognized with a Best Paper Award (2022) and contributes to interdisciplinary research groups such as the Institute for Life Sciences and Centre for Machine Intelligence. Her work bridges computational methods with real-world health challenges, emphasizing practical deployment of AI solutions in clinical and public health contexts. Research Groups: Vision, Learning and Control; Institute for Life Sciences; Centre for Health Technologies; Centre for Machine Intelligence Key Collaborations: Cross-disciplinary projects combining computer science with biomedical engineering and public health
Bruna Damiana Heinsfeld serves as Assistant Professor of Learning Technologies within the Department of Curriculum & Instruction at the University of Minnesota's College of Education and Human Development. Her research critically examines the intersections of technology, society, and education through the lens of Critical Studies of Education and Technology (CSET), with emphasis on power dynamics and social justice implications. Her academic credentials include: Ph.D. in Interdisciplinary Learning and Teaching (Learning Design and Technologies concentration) from UTSA M.A. in Education (Digital Languages, Media and Education concentration) from PUC-Rio, Brazil Multiple Postgraduate Certificates from Brazilian institutions: Media Discourse Analysis, Ontology and Epistemology, Philosophy/Sociology/Social Sciences, and Distance Learning Management B.A. in English Language and Literature from UERJ, Brazil Dr. Heinsfeld's research program centers on critical discourse analysis of educational technology narratives across corporate, policy, and educational contexts. She investigates how technological discourses are constructed and operationalized, particularly regarding digital equity, participatory exclusion, and the sociopolitical dimensions of technology adoption. Her methodological approach combines Critical Discourse Analysis (CDA) with critical pedagogy to unpack ideological, cultural, and economic factors shaping educational technology implementations. She examines epistemological foundations of technological beliefs and challenges neoliberal narratives in EdTech through rigorous deconstruction of policy documents and marketing materials. Analysis of her recent publications (2019-2023) reveals consistent focus on corporate influence in educational technology, with particular attention to Google and Microsoft's marketing narratives, pandemic-era remote learning disparities, and Latinx student experiences. Her work demonstrates how technological solutions often reinforce existing inequalities through participatory exclusion mechanisms, while her policy analyses expose contradictions between equity rhetoric and actual implementation. Recurring methodological threads include discourse analysis of educational landscape reports and critical examination of public policy frameworks. No scientific awards were documented in the provided materials. As an advisor, Dr. Heinsfeld cultivates a supportive environment grounded in social justice principles, emphasizing critical consciousness development and student autonomy. Her advising philosophy centers on accessible feedback, interdisciplinary exploration, and preparation for meaningful societal engagement. She teaches foundational courses including CI 4311W Technology and Ethics in Society (examining algorithmic bias, privacy concerns, and AI ethics) and CI 8147 Critical Discourse Analysis in Educational Research (focusing on language-power dynamics in educational contexts). While specific research laboratories aren't detailed, her collaborative work spans international contexts with Brazilian institutions and U.S. colleagues, particularly evident in joint publications with researchers like V. Marone and M. Pischetola. Her research trajectory indicates ongoing critical engagement with emerging technologies like AI in education policy, as evidenced by upcoming 2025 conference presentations.
Kimberly B. Rogers is an Associate Professor of Sociology at Dartmouth College , where she has been employed since 2015. She is affiliated with the Quantitative Social Science Program and the Neukom Institute for Computational Science . Her research focuses on how inequalities are produced, maintained, and resisted through behavior and emotion dynamics in social interactions , with particular attention to status and power hierarchies , occupational inequality , and mental health outcomes . She teaches courses such as Introductory Sociology , Research Methods , and Status and Power in Social Interaction , integrating computational tools into her pedagogy. PhD in Sociology, Duke University (2013) MA in Sociology, Duke University (2008) MA in Psychology, Wake Forest University (2005) BA in Psychology, Randolph-Macon Woman's College (2003) Rogers' research spans three key areas: cultural consensus in identity sentiments , behavioral/emotional responses to inequality , and computational modeling of social processes . Her work reveals how micro-social mechanisms perpetuate or disrupt status hierarchies across contexts like occupational roles , racialized interactions , and digital collaboration . Recent publications examine technological co-diffusion , pandemic-induced identity shifts , and gendered occupational perceptions . Her scholarly output follows trends in computational social science and cross-cultural emotion analysis . She employs Bayesian affect control theory to model dynamic identity processes and uncertainty in interactions , while her 2025 work on violence against women demonstrates applications of general strain theory to contemporary social issues. Senior Faculty Grant (2023-24) , Dartmouth College Wilson Fellowship (2021-22) , Dartmouth College Outstanding Article Award (2017) , American Sociological Association Seed Funding Grant (2016-17) , Dartmouth Provost Doctoral Dissertation Grant (2010-11) , National Science Foundation Rogers has received multiple grants for collaborative projects like THEMIS.COG , examining identity and sentiment modeling in groups . She mentors students through engaged scholarship and social impact practicums , though specific advisees aren't listed. Her 2019-2020 involvement with Campus Compact and 2016 DCSI teaching grant highlight her commitment to community-engaged pedagogy .
Dr. Leah Perlmutter is a tenure-track Assistant Professor in the Computer Science Department at Grinnell College, focusing on inclusive pedagogy and student belonging in post-secondary CS education. She earned her Ph.D. (2023) and M.S. (2020) in Computer Science and Engineering from the University of Washington, and her B.A. (2012) in Computer Science and Engineering from Colby College. Her research examines how course policies and teaching assistant interactions impact student inclusion in computer science, with a particular emphasis on resubmission opportunities and justice-centered approaches to teaching. Her work also includes human-robot interaction projects like GestureCalc (an eyes-free calculator for touchscreens) and EMAR (a social robot for emotional clarity support). Recent publications explore themes such as algorithmic ethics in education, student belonging in CS, and accessible interface design. She received the NSF Graduate Research Fellowship (2017) and the Outstanding Female Engineer Award (2018). At Grinnell, she teaches courses like Functional Problem Solving, Software Design, and Algorithms, Ethics, and Society.
Slava Jankin is a Professor of Data Science and Government at the University of Birmingham’s School of Government, where he also serves as Deputy Director of the Institute for Data and AI and Founding Director of the Centre for Artificial Intelligence in Government. He is concurrently a Fellow and Founding Director of the Data Science Lab at the Hertie School in Berlin. Previously, he held a Professorship at the University of Essex and has worked at University College London (UCL) and the London School of Economics (LSE). His research bridges computational methods, governance, and climate policy, with a focus on AI applications in public institutions, climate-health surveillance, and misinformation resilience. Jankin earned a PhD in Political Science from Trinity College Dublin (2009), a Postgraduate Diploma in Statistics (2006), and a BSc from Belarus State Economic University (2002). **Education**: • PhD in Political Science, Trinity College Dublin (2009) • Postgraduate Diploma in Statistics, Trinity College Dublin (2006) • BSc Econ with Distinction, Belarus State Economic University (2002) **Research Interests**: Jankin’s work integrates AI and computational methods with governance challenges, including climate policy, health surveillance, and institutional effectiveness. He leads initiatives like the Lancet Countdown’s climate-health monitoring and the CATALYSE project on climate impacts. His research also explores digital twins for governance systems and the role of cultural diversity in societal resilience against misinformation. **Grants & Collaborations**: He advises the UN and EU on AI and data science, co-leads the Lancet Countdown, and collaborates with institutions like the Alan Turing Institute. His applied work includes developing AI tools for public service optimization and policy simulations. **Labs & Teams**: Directs the Centre for AI in Government (University of Birmingham) and the Hertie School’s Data Science Lab, fostering interdisciplinary teams to advance computational methods in public policy.
David Chalmers is a University Professor of Philosophy and Neural Science at New York University and co-director of the Center for Mind, Brain, and Consciousness. He is also an Honorary Professor of Philosophy at the Australian National University and co-director of the PhilPapers Foundation. His work bridges philosophy, cognitive science, and emerging technologies. Research Interests: Chalmers is best known for his work on the 'hard problem of consciousness'—the challenge of explaining subjective experience. His research spans philosophy of mind, metaphysics, epistemology, philosophy of language, and the foundations of physics and AI. He actively explores the implications of virtual reality, simulation theory, and large language models for philosophy and consciousness studies. Recent Research Trends: His recent publications focus on AI consciousness, the ethical treatment of AI systems, the simulation hypothesis, and the nature of thought in language models. These works reflect a growing engagement with artificial intelligence and digital metaphysics, positioning philosophy at the forefront of technological inquiry. Scientific Awards: While no specific awards are listed in the provided text, Chalmers is widely recognized as one of the most influential contemporary philosophers, particularly in philosophy of mind. Advising and Grants: He mentors students and postdocs, though specific names are not listed. His leadership in the Center for Mind, Brain, and Consciousness and the PhilPapers Foundation suggests active grant-funded research and academic collaboration. Labs and Teams: He co-directs the Center for Mind, Brain, and Consciousness at NYU and the PhilPapers Foundation , both of which support research, publications, and global philosophical discourse in philosophy of mind and related fields.
Juan Carlos De Martin is a Full Professor of Computer Engineering at the Polytechnic of Turin, where he is also co-founder and co-director of the Nexa Center for Internet & Society. He holds a Faculty Associate position at the Berkman Klein Center for Internet & Society at Harvard University and is a member of the Scientific Council of the Treccani Institute and the Steering Committee of Biennale Democracy. He previously served as Vice Rector for Culture and Communication at the Polytechnic of Turin (2018–2023) and as president of its libraries (2007–2015). His research centers on the societal implications of digital technologies, with a strong emphasis on algorithmic and data justice, digital power, and the democratic challenges posed by modern technology. He advocates for a more democratic and ethical technological future, particularly critiquing the dominance of smartphones and promoting digital sovereignty. His recent publications reflect a clear trend toward ethical AI, data protection, and the social impact of algorithms. He has published on gender bias in language models, GDPR compliance tools, and non-discrimination audits in software, demonstrating a sustained commitment to fairness, transparency, and accountability in digital systems. Best Student Paper Award IEEE ISCC 2011 Best Student Paper Award IEEE ICME 2005 Fellow at Harvard University (Berkman Klein Center) (2011–2015, 2016–2024) Faculty Associate at Collège d'études mondos, France (2016) De Martin has advised PhD students like Marco Rondina on Responsible AI and has led numerous EU-funded research projects such as COMMUNIA and DECODE. He has also played a key role in public policy, serving on ministerial working groups on AI and online hate. He is the founder of the Biennale Tecnologia and has authored influential books on the future of universities and technology, all published under Creative Commons licenses. He leads the Nexa Center for Internet & Society, a multidisciplinary research group focused on the legal, economic, and social aspects of the Internet. The center fosters collaboration between computer scientists, legal scholars, and social scientists to address pressing digital challenges.
Yoshua Bengio is a Full Professor at the Université de Montréal, affiliated with the Department of Computer Science and Operations Research at the Faculty of Arts and Sciences. He is a pioneer of deep learning and a leading figure in AI safety. He co-founded Mila – Quebec Institute of Artificial Intelligence and serves as its scientific director. His work focuses on advancing AI technology while addressing ethical and safety challenges, including AI governance and catastrophic risk mitigation. Education: Ph.D. in Computer Science from McGill University (1991), postdoctoral studies at MIT. Research interests include deep learning, causal inference, AI ethics, and responsible AI development. He contributed to the Montreal Declaration for Responsible AI and leads the International Scientific Report on AI Safety. Recent articles emphasize AI safety frameworks, governance, and technical advancements in machine learning. Awards include the Turing Award (2018), Killam Prize (2019), and recognition as TIME's Most Influential Person (2024). He holds prestigious fellowships and is a member of the UN Scientific Advisory Board for Breakthrough Science and Technology. Affiliations include Mila, IVADO (as founding scientific director), and CIFAR programs. His work bridges academia, industry, and policy to ensure AI benefits humanity while minimizing existential risks.
Joel Greenhouse is a Professor of Statistics at Carnegie Mellon University (CMU), affiliated with the Department of Statistics & Data Science. He has been on the faculty since 1983 and held leadership roles, including serving as Associate Dean of the College of Humanities and Social Sciences from 1997 to 2002. He also holds an adjunct appointment as Professor of Epidemiology and Psychiatry at the University of Pittsburgh. His expertise spans statistical methodology, clinical trial design, and meta-analysis, with a focus on integrating data from multiple sources to address complex healthcare and public health challenges. Greenhouse earned his Ph.D. in Biostatistics from the University of Michigan and completed a postdoctoral fellowship at CMU. His research emphasizes developing statistical tools for observational studies, clinical trials, and meta-analytic frameworks, particularly in neurology, mental health, and public policy contexts. Notable contributions include analyzing the impact of media on youth suicide rates, improving aphasia classification through automated speech analysis, and evaluating highway safety through driver health data. Education: Ph.D. in Biostatistics, University of Michigan Affiliations: Adjunct Professor at University of Pittsburgh, Member of National Academy of Sciences’ committees Professional Service: Data and safety monitoring boards for NIH/VA studies, co-chair of Federal Motor Carrier Safety Administration review panels His awards include CMU’s Doherty Award for Education, Ryan Teaching Award, and E. Dunlop Smith Award for teaching excellence. His work bridges theoretical statistics with real-world applications, particularly in interdisciplinary collaborations across medicine, psychology, and public policy. Greenhouse’s recent articles highlight trends in leveraging large datasets for clinical insights (e.g., aphasiaBank), re-evaluating environmental and behavioral health associations, and advancing causal inference methods. His interdisciplinary approach ensures statistical rigor addresses societal challenges, from suicide prevention to highway safety.
Prof. Dr. Julius Schöning is a Professor at the Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences. His research focuses on Artificial Intelligence , Human-Computer Interaction , and Computer Vision within agricultural contexts. 2019–Present: Professor, Hochschule Osnabrück 2018–2019: System Architect, ZF Friedrichshafen AG 2014–2018: Researcher, University of Osnabrück 2009–2013: Project Lead/System Engineer, CLAAS Harsewinkel Education: M.Sc. in Intelligent Embedded Microsystems (Freiburg), B.Eng. in Mechatronics (DHBW Stuttgart) His research spans smart agriculture , quantum NLP , and explainable AI systems , with recent work on vibrotactile warning systems, AI compliance frameworks, and hybrid dataset applications in farming. Publications emphasize interdisciplinary approaches bridging technology and agricultural practice . Scientific honors include: DAAD Stipendium for conference participation Best Paper Award (2018) IEEE GHTC Student Paper Contest Winner (2016) Sonderpreis für gute Lehre (2017) Finalist/Falling Walls Lab (2015)
Ming Li is a Professor at the University of Waterloo , holding the Canada Research Chair in Bioinformatics . Affiliated with the David R. Cheriton School of Computer Science , his research spans Bioinformatics , Kolmogorov Complexity , Deep Learning , Natural Language Processing , and Computational Biology . His contact details include office Davis Center 3355 and email mli@uwaterloo.ca . Research Interests include Bioinformatics (protein/antibody sequencing, structure analysis), Deep Learning , Natural Language Processing , Kolmogorov Complexity and Applications , and Algorithms & Complexity (average-case analysis, information distance). Editorial Roles : Co-Managing Editor of the Journal of Bioinformatics and Computational Biology , Associate Editor-in-Chief for Journal of Computer Science and Technology , Editorial board member of multiple journals including SIAM Journal on Computing and Information and Computation . Professional Activities : Served on scientific advisory committees for Genome Prairie and Tsinghua University, and numerous international conference program committees (e.g., KDD 2007, CPM 2007, FOCS'99). Awards & Honors : Killam Prize (2010) IEEE Granular Computing Outstanding Contribution Award (2010) Premier's Discovery Award (2009) Fellow of Royal Society of Canada, ACM, and IEEE (2006) Killam Fellowship (2001) E.W.R. Steacie Memorial Fellowship (1996) Multiple Best Paper Awards Students & Postdocs : Mentored over 30 graduate students and postdocs, including current advisees Guangyu Feng, Anqi Cui, and alumni like Babak Alipanahi, Xin Chen, and Brona Brejova.
