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Professor Jo Barraket is a leading academic and researcher in social equity, social innovation, and public policy. As Director of the Melbourne Social Equity Institute and member of the Dilun Duwa Centre for Indigenous Business Leadership, she focuses on improving social equity through organizational innovation, cross-sector collaboration, and policy design. Her research addresses social procurement, health equity, digital inclusion, and employment pathways for marginalized groups. Her work includes landmark projects such as the Australian Digital Inclusion Index and evaluations of Commonwealth Social Enterprise Development Funds. She has secured over $11M in research grants, including multiple ARC grants, and produced over 75 scholarly publications. Her current projects compare social procurement policies in Australia and Scotland, and study youth unemployment in construction via employment-focused procurement. Key research themes include: social enterprise scaling strategies, cross-sector partnerships for policy implementation, and the role of philanthropy in equitable housing. She advises governments and OECD on inclusive employment policies and chairs major research initiatives like the Social Enterprise Impact Lab. Prof Barraket’s transdisciplinary approach combines rigorous academic research with practical outputs like policy guides and digital tools to maximize societal impact. She previously founded the Centre for Social Impact Swinburne, now a global leader in social economy research.

• Social Enterprise
• Social Innovation
• Public Policy
• +5 more

Gierad Laput is a Senior Engineering Manager at Apple, leading the Perception and Interaction Engineering organization under Machine Intelligence Neural Design (MIND) at Apple AI/ML (AIML). He is also an Adjunct Professor at Carnegie Mellon University's School of Computer Science and Human-Computer Interaction Institute. His research sits at the intersection of sensing systems, interactive technologies, and machine learning, with applications in ambient intelligence, wearables, and AI-driven interactive experiences. Ph.D. in Computer Science from Carnegie Mellon University Founding member of CMU's Future Interfaces Group Former Google Ph.D. Fellow, Disney Imagineer, and recipient of 10 Best Paper Awards/Nominations His research agenda at Apple leverages cutting-edge hardware to develop multimodal ML systems for vision, audio, and motion sensors, enabling features like head gesture detection for AirPods, Apple Intelligence integrations, and health monitoring tools. Recent collaborative projects include Vision Pro hand interaction systems and WatchOS updates for cycling/fall detection. Key research areas include human-computer interaction , machine learning for sensor fusion , and smart environment sensing . Over 35 publications cover transformative technologies like ViBand (bio-acoustic sensing), EM-Sense (touch recognition), SkinTrack (arm as touchpad), and ElectroDermis (stretchable electronics). Scientific honors include: Fast Company Innovation by Design Award (2017) Google PhD Fellowship (2018) Swartz Entrepreneurial Fellowship (2018) Best Paper Awards at ACM UIST (2016) and ACM CHI (2015) As an educator, he has taught courses at CMU and the University of Michigan on on-device machine learning , signal processing , and human-centered design . His lab has mentored numerous researchers now pursuing PhDs at MIT, UW, CMU, and Northwestern.

• Human-Computer Interaction
• Machine Learning
• Sensing Systems
• +7 more

Prof. Sergio Ginebri is an Associate Professor at the Department of Jurisprudence, University of Rome Tre. He holds a PhD in Economics from Sapienza University of Rome and has extensive experience in fiscal policy, pension systems, and public finance research. His work focuses on topics like pension sustainability, social inequality, and the political economy of public debt. Education: PhD in Economics, Sapienza University of Rome (1988–1992) MA in Economics, University of Warwick (1987–1988) Laurea in Statistics and Demography, Sapienza University of Rome (1977–1984) Research Interests: Prof. Ginebri specializes in public policy, fiscal sustainability, and the social impacts of pension reforms. His research explores how demographic trends, wealth distribution, and political dynamics shape economic policies. Recent work includes analyses of longevity risks, pension equity, and fiscal policy integration in the EU. Key Projects: Coordinated studies on pension system sustainability (2002–2018) Developed models for forecasting public pension expenditure Contributed to EU tax policy observatories Awards: Recipient of the national PhD award (1994) and recognition for academic excellence in tax policy research (2001). Teaching: Teaches courses in political economy, public finance, and macroeconomics at undergraduate and graduate levels.

• Public Policy
• Fiscal Policy
• Pension Systems
• +3 more

Wei Xu is an Associate Professor at Georgia Institute of Technology's College of Computing and School of Interactive Computing, with affiliations to the Machine Learning Center. Their research bridges machine learning, natural language processing, and social media with focus areas in large language models, cultural bias mitigation, multilingual capabilities, and human-AI collaboration in text evaluation. NSF CAREER and Google Academic Research Award recipient Director of NLP X Lab PhD from New York University, BSMS from Tsinghua University Research interests span: Multilingual Multicultural LLMs addressing representational gaps and cultural adaptation in language models (NAACL 2025, ACL 2024); Robustness and Reasoning through dynamic AGI evaluations (ACL 2024, EMNLP 2024); Interdisciplinary NLP applications in security, healthcare, and law (EMNLP 2024, ACL 2024). Recent publications focus on multilingual alignment (NAACL 2025), privacy risk estimation (arXiv 2025), cultural bias analysis (ACL 2024), and medical text simplification (EMNLP 2024). Key themes include bias mitigation, multimodal processing, and practical LLM evaluation. Scientific Awards : NSF CAREER, Google/Sony/Criteo research awards, ACL'24 Best Social Impact Award, COLING'18 Best Paper Advising 15 PhD/MS/BSMS students including Yao Dou (human-centered LLM evaluation), Tarek Naous (multilingual LLMs), and alumni like Chao Jiang (Apple AI/ML) and Yang Chen (NVIDIA research scientist). Teaches graduate courses on NLP and LLMs.

• Large Language Models
• Natural Language Processing
• Machine Learning
• +6 more

Jean Oh is a Researcher at the Robotics Institute of Carnegie Mellon University (CMU) , leading the interdisciplinary Bot Intelligence Group (BIG) . Her work focuses on developing persistent robots that co-exist and collaborate with humans in shared environments, emphasizing continuous improvement through training, exploration, and human interaction. Education: Ph.D. in Language and Information Technologies, CMU M.S. in Computer Science, Columbia University B.S. in Biotechnology, Yonsei University Oh's research integrates vision, language, and planning systems in robotics, with applications in human-robot teaming , self-driving cars , disaster response , eldercare , and creative robotics . She has pioneered projects like socially-compliant robot navigation in human crowds and AI-driven robotic painting systems. Recent publication trends highlight her work in vision-language planning , social navigation , computational creativity , and human-robot collaboration . Notable contributions include the StyleCLIPDraw algorithm for text-to-art generation and Social-PatteRNN for human-like trajectory prediction. Scientific Awards: Best Paper Award in Cognitive Robotics (ICRA'18, ICRA'15) Best Systems Paper Finalist (HRI'25) Best Oral Paper Finalist (Humanoids'24) Best Paper in Entertainment (IROS'24) Argoverse Challenge Winner (CVPR'24) Best Student Paper (AIAA'24) Best Demo Finalist (RoboSoft'24) Oh mentors a diverse team of PhD, MS, and undergraduate students from CMU departments including Robotics, Computer Science, and Mechanical Engineering. Her research is funded by US Army Research Lab , DiDi Chuxing , and DARPA , with collaborations across industry and academia .

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +5 more

Antoine Bosselut is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the EPFL NLP group. His research focuses on developing AI reasoning agents that can model, represent, and reason about human and world knowledge, with applications in health, education, and global fairness. His research interests span multiple critical areas in modern AI: LLM Representations of Knowledge: Understanding what language models know and how they represent that knowledge internally Reasoning Algorithms: Developing methods to improve LLMs' reasoning capabilities through symbolic systems, neuroscience, and cognitive science Large-scale AI Development: Creating open-source foundation models with multilingual capabilities AI Democratization: Ensuring equitable access to AI technologies across different cultural and regulatory contexts His recent publications reveal a strong focus on the intersection of language models and cognitive science, with multiple studies examining how LLMs align with human cognition. He's also deeply engaged in practical applications of NLP technology, particularly in education and multilingual settings, as evidenced by his work on evaluating AI's impact on higher education and developing culturally-aware language models. His scientific achievements have been recognized with several prestigious awards: Outstanding Paper Award at NAACL 2025 ELLIS Scholar designation in 2024 Outstanding Paper Award at ACL 2023 Inclusion in Forbes 30 Under 30 list for Science & Healthcare in 2021 Bosselut actively mentors numerous PhD students across diverse NLP research areas and has secured significant recognition for his work in both academic and media circles. His lab has been featured in major publications including Communications of the ACM, The Atlantic, and Quanta Magazine, highlighting the societal impact of his research on commonsense reasoning and AI capabilities.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +5 more

Abhijit Banerjee is a Professor in the Department of Economics at the Massachusetts Institute of Technology (MIT). His research focuses on development economics, public health policy, and social protection programs, particularly addressing poverty and inequality in low- and middle-income countries. He has conducted large-scale experiments evaluating interventions such as conditional cash transfers, universal basic income, and healthcare policies. Key areas of interest include the design and implementation of anti-poverty programs, behavioral economics, and the long-term health impacts of infectious diseases like COVID-19. His work often integrates experimental methods to assess policy effectiveness, with contributions to global health studies and economic development strategies. Notably, his research on the RECOVERY trial evaluates treatments for hospitalized COVID-19 patients, while other studies explore financial spillover effects of electronic government transfers in Indonesia and savings behavior interventions in Chile. Banerjee has collaborated on projects analyzing post-COVID-19 health outcomes, labor market dynamics in India, and the role of trusted messengers in public health communication during crises. Despite the breadth of his contributions, specific details about his educational background, grants, or lab affiliations are not explicitly provided in the source text.

• Economics
• Development Economics
• Public Health Policy
• +3 more

Shrikanth (Shri) Narayanan is a University Professor and holder of the Niki and Max Nikias Chair in Engineering at the University of Southern California (USC), serving as the inaugural Vice President for Presidential Initiatives. He leads the Signal Analysis and Interpretation Lab (SAIL) and holds joint appointments in Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head and Neck Surgery. His research focuses on speech and audio processing, behavioral signal processing, and real-time MRI of speech production, with applications in healthcare, education, and technology. Education: B.E. in Electrical Engineering from College of Engineering, Guindy (Chennai, India, 1988); M.S., Engineer, and Ph.D. in Electrical Engineering from UCLA (1990, 1992, 1995). Research interests span computational linguistics, machine learning, and multimodal human behavior analysis. He pioneered technologies for speech biomarkers in mental health, real-time MRI of speech production, and wearable sensor systems for longitudinal health studies. His work in speech emotion recognition, forensic interviews, and clinical applications has been recognized through over 40 awards, including the IEEE Flanagan Award and ISCA Medal. He has published extensively in journals like Proceedings of the IEEE , Journal of the Acoustical Society of America , and PLOS One . Key Grants: NSF CAREER, Okawa Research, IBM Faculty, Google/Amazon awards. Labs/Teams: Signal Analysis & Interpretation Lab (SAIL), USC Information Sciences Institute (ISI), Google Visiting Faculty Researcher.

• Speech and Audio Processing
• Behavioral Signal Processing
• Real-Time MRI of Speech Production
• +4 more

Professor Hutan Ashrafian is a clinician-scientist and surgeon at the University of Leeds Business School, holding dual roles as Professor of Research Impact and Senior Research Fellow at Imperial College London. His expertise spans AI in healthcare, metabolic surgery, and ancient history. He pioneered STARD-AI and QUADAS-AI guidelines for AI diagnostics, collaborated on AI-driven breast cancer screening with Google and NHS, and led global health policy initiatives. Ashrafian’s work bridges medicine, history, and philosophy, including his contributions to understanding historical figures’ medical conditions, such as King Tutankhamun’s epilepsy. He has authored over 550 publications, 12 books, and holds a PhD in computational physiology and an MBA with distinction. Awards include the Hunterian Prize and Wellcome Trust Fellowship. His research also explores AI ethics, quantum physics paradoxes, and art-based medical diagnostics. Education: PhD in Computational Physiology (Imperial College London) MBA (Warwick Business School) MD (University College London) Bachelor of Science in Immunology (University College London) Research Interests: Ashrafian’s work spans: AI in Medicine: Diagnostic algorithms, guideline development (STARD-AI/QUADAS-AI), and ethical frameworks. Metabolic Surgery: Bariatric interventions, gut microbiome, and obesity treatments. Ancient History: Medical analyses of historical figures (e.g., Tutankhamun, Julius Caesar) and art-based pathology identification. Philosophy: AI rights (AIONAI law), temporal paradoxes, and the Simulation Argument. Awards: Royal College of Surgeons Arris and Gale Lectureship Hunterian Prize Wellcome Trust Research Fellowship Advising & Innovation: Supervised >50 PhD students, co-founded Oxford Medical Products (weight loss tech), and serves as CSO at Flagship Pioneering’s Preemptive Health division. He advises on NHS digital transformation and global health policy. Labs/Teams: Leads AI initiatives at Imperial’s Institute of Global Health Innovation and collaborates with Google, NICE, and international institutions on health tech solutions.

• Artificial Intelligence in Medicine
• Metabolic Surgery
• Health Policy
• +4 more

Yuki M. Asano is a full Professor at the University of Technology Nuremberg , leading the Fundamental AI (FunAI) Lab . Previously, he led the QUVA Lab at the University of Amsterdam and earned his PhD at the Visual Geometry Group (VGG) of the University of Oxford under Andrea Vedaldi and Christian Rupprecht. University of Technology Nuremberg (2024–present) University of Amsterdam (prior to 2024) University of Oxford (PhD, 2020) His research spans Artificial Intelligence , Machine Learning , and Computer Vision , with a focus on Causal Representation Learning , Self-Supervised Learning , and Efficient Model Adaptation . He pioneered techniques like BISCUIT (causal variable identification) and VeRA (parameter-efficient fine-tuning). His work extends to Medical Imaging and Environmental Monitoring through applications in fetal ultrasound analysis and marine debris detection. Recent publications (2023–2025) highlight advancements in Self-Supervised Learning , Vision-Language Models , and 3D Understanding . Notable papers include TWIST & SCOUT (multimodal LLM grounding), SIGMA (masked video modeling), and GeneralAD (anomaly detection). His ICCV 2023 work on Self-Ordering Point Clouds and MoSiC (optimal-transport motion trajectories) underscores his interdisciplinary approach. He received the JUPITER compute grant (2025) and an Outstanding Paper Award at ICLR 2024 . His collaborations span institutions like MIT-IBM Watson AI Lab, Qualcomm AI Research, and University of Amsterdam.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +6 more

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

• Artificial Intelligence
• Machine Learning
• Explainability
• +9 more

Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.

• Mathematical Finance
• Financial Engineering
• FinTech
• +7 more

Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.

• Machine Learning
• Causal Inference
• Ethics in AI
• +5 more

Heng Huang is the Brendan Iribe Endowed Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Maryland, College Park . He earned his Ph.D. in Computer Science from Dartmouth College and holds prior degrees from Shanghai Jiao Tong University. His research focuses on advancing the foundations and applications of artificial intelligence, particularly in machine learning, data mining, natural language processing, computer vision, and biomedical informatics . His work integrates large-scale optimization, fairness, and robustness in deep learning systems. Heng Huang’s recent publications demonstrate a strong trend in large language models, federated learning, model watermarking, continual learning, and medical image analysis . His work appears consistently in top venues like NeurIPS, ICML, CVPR, ICLR, and MICCAI, reflecting a broad impact across theoretical and applied AI. He actively mentors students and postdocs, seeking highly motivated researchers in machine learning and related domains. His work has significant implications for healthcare, privacy, and trustworthy AI.

• Machine Learning
• Data Mining
• Big Data Computing
• +11 more

Professor Anna Korhonen is a leading academic at the University of Cambridge, holding positions as Professor of Natural Language Processing, Co-Director of the Language Technology Laboratory (LTL), Director of the Centre for Human-Inspired Artificial Intelligence (CHIA), and Fellow of Churchill College. Her work bridges computational linguistics, artificial intelligence, and interdisciplinary applications. Her research focuses on human-centric NLP with core interests in multilingual/low-resource systems, conversational AI, and responsible technology development. She emphasizes applications for social and global good, including healthcare, climate science, and equitable language technologies. Her methodology integrates cognitive modeling with machine learning to create interpretable, fair NLP systems. Key projects include ERC-funded initiatives like MultiConvAI (multilingual conversational AI) and Towards Globally Equitable Language Technologies , alongside Innovate UK's ESG RoboFactory and EPSRC's Modeling Idiomaticity project. Her work spans biomedical text mining ( PheneBank , LION ), educational technology ( EF Education First Research Lab ), and cross-lingual transfer learning. Fellow of the Association for Computational Linguistics (ACL) Fellow of ELLIS (European Laboratory for Learning and Intelligent Systems) Royal Society University Research Fellow (2005-2014) Google Faculty Award recipient EACL Chair Elect Korhonen actively supervises PhD/MPhil students in Computation, Cognition and Language programs and leads interdisciplinary collaborations across computer science, linguistics, and domain sciences. Her lab (LTL) develops foundational NLP techniques while addressing real-world challenges through partnerships with healthcare, environmental science, and education sectors. Current strategic initiatives include the Institute for Technology and Humanity and CHIA's human-inspired AI framework.

• Natural Language Processing
• Multilingual NLP
• Low Resource NLP
• +9 more