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.
Benjamin Eysenbach leads the Princeton Reinforcement Learning Lab, where he designs algorithms that enable artificial intelligence systems to learn intelligent behaviors through trial-and-error, specializing in self-supervised methods that eliminate the need for human labels. He joined Princeton after completing his PhD in machine learning at Carnegie Mellon University under Ruslan Salakhutdinov and Sergey Levine, supported by the NSF Graduate Research Fellowship and Hertz Fellowship. His research bridges fundamental machine learning principles with practical applications in robotics and decision-making systems. Eysenbach's research focuses on developing self-supervised reinforcement learning algorithms that enable autonomous skill acquisition without external rewards. His investigations span contrastive learning methods, temporal abstraction techniques, and scalable architectures for goal-conditioned behaviors. These innovations aim to create more efficient and generalizable learning systems that can discover useful behaviors from unlabeled experience. Eysenbach's publications demonstrate consistent advancement in self-supervised RL methodologies, with recent work focusing increasingly on temporal abstraction and representation learning theory. His research shows progression from foundational contrastive RL frameworks toward more sophisticated analyses of generalization properties and uncertainty quantification. The 2025 works indicate expanding investigation into hierarchical control, probabilistic alignment, and hyper-deep network architectures. Eysenbach has been recognized with prestigious awards including the Hertz Fellowship and NSF Graduate Research Fellowship, supporting his doctoral research in self-supervised RL methodologies. His work has been presented at top machine learning conferences including NeurIPS, ICML, and ICLR. As director of the Princeton Reinforcement Learning Lab, Eysenbach oversees research initiatives in self-supervised RL, including projects on intention-conditioned modeling, horizon generalization, and contrastive learning frameworks. He has secured funding from the Princeton AI Lab to study neural correlates of temporal contrast in decision-making. Eysenbach teaches courses in reinforcement learning and has developed new benchmarks like JaxGCRL to accelerate research in goal-conditioned RL.
Associate Professor Sunghoon Kim holds a position at the University of Sydney Business School's Discipline of Work and Organisational Studies. He previously taught at UNSW Business School, Cornell University, and Peking University. His PhD is from Cornell University, with MBA and BBA degrees from Seoul National University. His research focuses on strategic HRM, employment relations, and the impact of technological changes like AI on workplaces. He has edited major works including the Routledge Handbook of Human Resource Management in Asia and contributes to the APRU project on 21st-century work transformations. Kim serves as Associate Editor for journals like Human Resource Management and Asia Pacific Journal of Human Resources . His recent work addresses algorithmic management, employee well-being in AI contexts, and cross-border labor dynamics. He has advised students researching migrant worker choices and voice processes in organizations. Media engagements include commentary on workplace perks and work-life balance in South Korea. Teaching responsibilities include courses on performance management, strategic HRM, and international HR practices. His research highlights the intersection of technology, culture, and organizational sustainability.
Valerie Karplus is a Professor in the Department of Engineering and Public Policy at Carnegie Mellon University (CMU) and Associate Director of the Wilton E. Scott Institute for Energy Innovation. She holds a Ph.D. in Engineering Systems from MIT and a B.S. in Biochemistry and Political Science from Yale University. Her research focuses on resource and environmental management in global industrial contexts, emphasizing institutions, management practices, and policy design. Key areas include decarbonization pathways, hydrogen energy systems, and the intersection of public policy with workforce resilience. Dr. Karplus leads the Laboratory for Energy and Organizations (LEO) at CMU and is affiliated with MIT’s Energy Initiative and environmental policy centers. She previously directed the MIT-Tsinghua China Energy and Climate Project (2011–2016), analyzing China’s energy policies and their global impacts. Her work bridges academia and practice, addressing challenges like steel industry decarbonization and clean energy workforce development through interdisciplinary approaches. Notable contributions include modeling China’s carbon neutrality targets (C-REM 4.0), evaluating hydrogen hub risks, and assessing energy audit effectiveness. She has secured grants such as a $500,000 ARISE planning grant to analyze Appalachian workforce skills for decarbonization. Media engagements include commentary on EU energy strategies, green hydrogen viability, and nuclear energy for AI systems.
Arnav Arora is a PhD Fellow at the Department of Computer Science , University of Copenhagen (DIKU), specializing in Natural Language Processing . His work focuses on ethical AI, bias detection, and societal impacts of language models. Email: aar@di.ku.dk Location: Universitetsparken 1, 2100 København Ø Arnav's research explores fine-grained value alignment in language models, harmful content detection , and cross-cultural differences in AI responses. His work bridges technical NLP advancements with social responsibility, including dual use ethical frameworks and community value analysis . Key publication trends include: 2025: Bias mitigation through BiasGym framework 2024: Factcheck-Bench benchmark development 2023: Thorny Roses dual use analysis 2022: Cross-cultural value probing methods 2020: Multi-hop fact checking systems Arnav contributes to the Software, Data, People & Society (SDPS) section, collaborating with interdisciplinary teams on projects involving language model evaluation and societal impact mitigation . His work often addresses real-world AI deployment challenges through academic-industry partnerships.
Bryan Kian Hsiang Low serves as Associate Professor in the Department of Computer Science at the National University of Singapore's School of Computing, while simultaneously holding leadership positions as Director of AI Research at AI Singapore and Deputy Director of the NUS AI Institute. His academic journey includes a B.Sc. (2001) and M.Sc. (2002) in Computer Science from NUS, followed by a Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University (2009). His research spans probabilistic machine learning, multi-agent systems, and trustworthy AI, with particular focus on Bayesian optimization , federated learning , and data-efficient methodologies . The Low Lab develops frameworks for collaborative AI, automated machine learning, and AI applications in scientific domains through the Group of Learning and Optimization Working in AI (GLOW.AI), which maintains a multi-disciplinary approach bridging computer science, mathematics, and engineering disciplines. Analysis of his recent publications reveals a consistent emphasis on data valuation , privacy-preserving collaborative learning , and robust optimization techniques , with increasing integration of large language models into his research framework. His work demonstrates strong theoretical foundations coupled with practical applications in computational sustainability and robotics. Andrew P. Sage Best Transactions Paper Award (2006) NUS Overseas Graduate Scholarship (2004-2009) Faculty Teaching Excellence Award (2017-2018) IEEE RAS Distinguished Lecturer (2019) World Economic Forum Global Future Councils Fellow (2016-2018) Dr. Low actively mentors PhD students including Rachael Sim, Quoc Phong Nguyen, and Zhongxiang Dai, while leading major initiatives like the AI Phenome Platform for plant breeding optimization. His research group GLOW.AI operates at the intersection of theory and practice, with strong industry engagement through AI Singapore. Current projects focus on scalable AI systems for scientific discovery and developing frameworks for equitable collaborative machine learning with robust privacy guarantees.
Will Fleisher is Assistant Professor in Georgetown University's Department of Philosophy and Research Assistant Professor at the Center for Digital Ethics. His research examines ethical, political, and epistemic implications of AI systems, with specialization in algorithmic fairness, explainable AI, and epistemology of inquiry. His work bridges technical AI development and philosophical frameworks, addressing fairness metrics in machine learning, epistemic norms in AI systems, and ethics of human-AI interaction. Recent publications explore disagreement in AI alignment, fragmentation of evidence, and intellectual virtues in inquiry. Professor Fleisher's research has appeared in Noûs, Philosophical Studies, Philosophy of Science, and AAAI/ACM conference proceedings. He previously held postdoctoral positions at Northeastern University and Washington University in St. Louis.
Daniel Hershcovich is a Tenure Track Assistant Professor at the Department of Computer Science (Faculty of Science, University of Copenhagen) specializing in Natural Language Processing and Machine Learning . His research focuses on cross-cultural adaptation of language models, integrating human values into AI, and analyzing food-related cultural narratives for sustainable diets. Education: Ph.D. in Computational Neuroscience from Hebrew University of Jerusalem B.Sc. in Mathematics and Computer Science from Open University of Israel Recent publications highlight his work on multimodal models (haptic captioning, visual assistants for the blind), historical text analysis (Danish/Norwegian literature, euphemism detection), and cross-cultural NLP (recipe adaptation, cultural value alignment, climate awareness). His projects frequently combine AI ethics with domain-specific applications like food studies, historical linguistics, and accessibility research. Key collaborative networks include institutions in Denmark, Israel, and international partnerships through conferences like ACL, EMNLP, and workshops on cross-cultural NLP. The NLP section at DIKU serves as his primary affiliation for these efforts.
Arjun (Raj) Manrai is an Assistant Professor at Harvard Medical School and a faculty member in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital. He earned an A.B. in Physics (Harvard, Highest Honors) and a Ph.D. in Bioinformatics and Integrative Genomics from Harvard-MIT. His research focuses on improving medical decision-making through computational approaches in clinical genomics, algorithmic bias mitigation, and healthcare AI ethics. Key projects include race-free kidney function equations, genetic variant classification, and semi-supervised learning for medical imaging. Education: B.A. in Physics, Harvard University (Highest Honors) Ph.D. in Bioinformatics and Integrative Genomics, Harvard-MIT Division of Health Sciences and Technology Research interests span AI-driven diagnostics, health equity, and reproducibility in biomedical studies. His work has been featured in New England Journal of Medicine , JAMA , and highlighted by the New York Times and NPR. Notable contributions include advancing race-free diagnostic algorithms, addressing biases in clinical genomics, and advocating for transparent AI systems in healthcare. The Manrai Lab collaborates widely to translate computational methods into clinical practice.
Dr. Roger Moser is a Senior Lecturer at Macquarie University's Department of Management Innovation, Strategy and Entrepreneurship Research Centre. He also holds adjunct roles at the Indian Institute of Management Udaipur (since 2012) and the University of St. Gallen (since 2012). His research focuses on Decision Intelligence, Strategic Management, and International Management, particularly exploring how executives leverage data (small/big) to enhance decision-making frameworks. He has over 99 publications since 2005, with notable works in journals like Journal of Service Research and Journal of Business Research . Dr. Moser holds a Dr. rer. pol. from EBS European Business School (2006) and a lic. oec. HSG (MSc) from the University of St. Gallen (2003). His research interests include Decision Model Innovation, Social Capital in emerging markets, and strategic alignment in supply chains. He has conducted impactful studies on access-based services in India, supplier integration in China's automotive sector, and humanitarian logistics using satellite data. His recent work emphasizes technology integration in B2B value creation, decision frameworks for uncertainty reduction (e.g., in agriculture and disaster management), and AI-driven expert systems. He has presented at global forums on topics like digital transformation and decision intelligence applications, contributing to both academic and practitioner discourse. External Roles: Adjunct Professor of Business Policy, Indian Institute of Management Udaipur Titularprofessor / Permanent Lecturer, University of St. Gallen Notable impacts include a project providing clean drinking water in India through access-based solutions, recognized for quality-of-life and societal contributions. His research has been cited over 1,666 times with an h-index of 21.
Atrisha Sarkar is an Assistant Professor in the Department of Electrical and Computer Engineering at Western University , Canada, and heads the Humans and Autonomous Agents Lab . She is also a faculty member of the Rotman Institute of Philosophy and a Faculty Affiliate at the Schwartz Reisman Institute for Technology and Society . Her research integrates empirical and behavioral game theory with software engineering to design human-centric AI systems that prioritize safety and societal well-being. Education: Atrisha holds a PhD and has previously served as a postdoctoral fellow at the Schwartz Reisman Institute for Technology and Society at the University of Toronto under the supervision of Prof. Gillian Hadfield. Research Focus: Her work centers on human-centric multiagent systems , combining methods from: Behavioral and empirical game theory Software engineering Human-AI and human-robot interaction AI safety and reliability She applies these to domains such as autonomous driving, cooperative AI, and social media dynamics, aiming to ensure AI systems align with human values and societal norms. Publications and Impact: Atrisha has published extensively in top-tier venues including AAAI , AAMAS , ICRA , NeurIPS , and EC . Her work spans from theoretical models of strategic behavior to practical frameworks for validating autonomous systems, with a strong emphasis on real-world applicability. Labs and Teams: She leads the Humans and Autonomous Agents Lab at Western University, where her team focuses on designing AI agents that can cooperate effectively with humans in complex, dynamic environments.
Xinya Du is an Assistant Professor in the Department of Computer Science at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. She holds a Ph.D. in Computer Science from Cornell University and completed a postdoctoral fellowship at the University of Illinois at Urbana-Champaign. Her research focuses on advancing trustworthy and impactful AI systems, particularly in Natural Language Processing (NLP), Large Language Models (LLMs), and Vision-Language Models (VLMs). Key research areas include Document understanding and knowledge acquisition Trustworthy reasoning and hallucination detection in LLMs Applications of NLP in scientific research and multimodal systems Alignment of AI systems with human values Dr. Du has received notable awards such as the NSF CAREER Award (2024), Amazon Research Award (2023), and recognition as a Spotlight Rising Star in Data Science. She has authored over 30 papers in top venues like ACL, EMNLP, NeurIPS, and CVPR, contributing to foundational work in multimodal reasoning, LLM evaluation, and automated scientific hypothesis generation. She teaches advanced courses including CS 6301: Special Topics in Computer Science - Deep Learning for NLP and actively mentors students in research projects. Her work has been highlighted in major media and led to impactful open-source contributions, including repositories for event extraction and LLM benchmarking.
Sendil Ethiraj is a Professor of Strategy and Entrepreneurship at London Business School (LBS) and serves as Deputy Dean (Faculty). Originally from India, he earned his PhD in Management from The Wharton School at the University of Pennsylvania. Prior to joining LBS, he spent over a decade as faculty at the Ross School of Business, University of Michigan. His research focuses on strategic management, covering innovation processes, competitive strategy, organizational design, human capital management, and technology-driven industry transformation. He has extensively studied innovation in complex systems across sectors like oil and gas, mutual funds, and healthcare insurance. His work addresses organizational structures for capability development, employee goal alignment, and platform governance in digital ecosystems. At LBS, he contributes to academic leadership as a member of the Governing Body since 2022. His initiatives emphasize designing reward systems to attract global research faculty and advancing diversity, inclusion, and belonging through the LBS Advisory Board. He teaches core courses in LBS's Master's, Executive Education, and PhD programs. Key research areas: Innovation in unconventional industries Human capital dynamics Platform governance Strategic response to technological disruption Organizational design for complex systems Contact: sethiraj@london.edu
Dr. Asieh Hosseini Tabaghdehi is a Senior Lecturer in Strategy & Business Economy at Brunel Business School, Brunel University of London. She serves as Programme Lead for the BSc International Business Programme and Trade2Grow Executive Education Programme. Additionally, she is Impact Lead at the Brunel Centre for AI: Social and Digital Innovation, where she leads the capability area in the Future of Work. Dr. Tabaghdehi is also an economist and social impact advisor for the independent NGO, Social Innovation Movement. Dr. Tabaghdehi earned her PhD in Economics and Finance (2008) and MSc in International Money, Finance, and Investment (2015), both from Brunel University London. She also holds a BA in Theoretical Economics from University of Mazandaran. She completed the Postgraduate Certificate in Academic Practice and is a Fellow of the Higher Education Academy. Dr. Tabaghdehi is internationally recognized for her research on digital transformation, with particular expertise in the ethical integration of artificial intelligence and digital technologies. Her work focuses on how emerging technologies shape industries, labor markets, and society, with emphasis on enhancing SME growth through technological innovation. She explores applications across critical sectors including social care, supply chain management, and environmental sustainability. A central theme in her research is smart data governance, ensuring ethical, transparent, and responsible use of data in decision-making processes. Her research portfolio demonstrates a consistent focus on the intersection of technology, ethics, and business strategy. She has developed frameworks like the Digital Business Auditing Framework, which has been adopted internationally for smart city initiatives. Her work connects academic research with practical policy applications, as evidenced by her presentations as oral and written evidence to the House of Commons Select Committee. Her publications span AI ethics, digital footprint implications, fertility economics, and healthcare cost analysis, showing interdisciplinary breadth while maintaining thematic coherence around digital transformation's societal impact. Scientific Awards and Recognition Semi-finalist: Research Impact Award at Brunel University London, 2023 Staff Award: Exceptional in Collegiality and Supportive to Colleagues at Brunel University London, 2022 Exceptional Performance at Regents University London, 2018-19 Staff Award in Teaching, Learning and Assessment at Regents University London, 2016 Best Lecturer Award at London Brunel International College, 2014 Best Lecturer Award at London Brunel International College, 2013 Dr. Tabaghdehi actively supervises PhD students researching areas including Smart Data Governance, Ethical AI Governance, Digital Innovation Impact, Responsible AI Adoption Strategies, Sustainability, and Future of Labour Market. She has secured research funding from multiple sources including the Economic & Social Research Council (ESRC), Brunel University London, and Brunel Business School. Her current projects include research on AI Adoption and Governance, Youth digital addiction, Algorithm Reliability Framework, and SMEs digital footprints. She has also co-designed the "Digital Adoption" module for the UK Government's Help to Grow Management program, demonstrating the practical application of her research. As a member of multiple professional organizations, Dr. Tabaghdehi serves as an associate practitioner at Social Value International, associate member of the Big Innovation Centre, and member of the All-Party Parliamentary Group on AI. She is also a member of the ESRC Review College, British Academy of Management Review College, and Energy Institute UK, contributing to the broader academic and policy communities through these roles.
Abayomi Baiyere serves as an Associate Professor in the Department of Digitalization at Copenhagen Business School (CBS), where he conducts cutting-edge research at the intersection of digital technologies and organizational transformation. His work significantly contributes to UN Sustainable Development Goals through digitally-enabled societal impact initiatives and has yielded 69 research outputs including high-impact publications in premier journals like Information Systems Journal and Information Systems Research . His research program focuses on: Digital transformation frameworks (notably the MIND framework for capability assessment) Platform design and governance mechanisms Smart service systems development Workplace transformation through digital subtraction logic Digital strategy implementation challenges Analysis of his 15 most recent publications (2024-2025) reveals a strong theoretical grounding in institutional and practice-based perspectives, with increasing emphasis on ethical dimensions of digital transformation, AI implementation constraints, and methodological innovations in computational research. His work consistently bridges conceptual rigor with practical applicability for organizational leaders. Dr. Baiyere actively shapes academic discourse through editorial roles including co-editing The Routledge Companion to Management Information Systems (2025) and organizing key events like the African IS Paper Development Workshop (2020). His public engagement includes 6 media contributions discussing digital workplace transformation and strategic implementation challenges, demonstrating commitment to translating research into practical insights for broader audiences. Within CBS, he has supervised 8 academic works while contributing to the department's international recognition in digitalization research. His activities reflect deep engagement with both theoretical advancement in information systems and practical solutions for organizational digital maturity.
