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Ron Dror is the Cheriton Family Professor of Computer Science at the Stanford Artificial Intelligence Lab , with courtesy appointments in Structural Biology and Molecular & Cellular Physiology . He also holds affiliations with Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), the Institute for Computational and Mathematical Engineering (ICME), Sarafan ChEM-H, and the Wu Tsai Neurosciences Institute. Education: PhD in Electrical Engineering and Computer Science, MIT MPhil in Biological Sciences, University of Cambridge (Churchill Scholar) BS in Mathematics and Electrical & Computer Engineering, Rice University (summa cum laude) Ron leads a multidisciplinary research group that combines molecular simulation and machine learning to study biomolecular structure, dynamics, and function. His work focuses on developing computational methods to accelerate drug discovery by predicting molecular interactions and designing more effective therapeutics. Current projects include the PENSA software library for analyzing biomolecular ensembles and FRAME framework for structure-based ligand design. His research has produced groundbreaking work on G-protein-coupled receptors (GPCRs) , RNA structure prediction , and mitochondrial transport mechanisms . Key publications highlight applications of geometric deep learning and molecular dynamics simulations in structural biology. Scientific Awards: Cheriton Family Professorship (2023) Two Gordon Bell Prizes (2014, 2009) Best Paper Awards at NeurIPS (2021), IPDPS (2013), SC11 (2011), SC09 (2009), SC06 (2006) Science Magazine Top 10 Breakthrough (2010) Fulbright Scholarship , NSF Fellowship , DoD Fellowship , Whitaker Foundation Fellowship Ron has advised numerous doctoral and master’s students including EJ Fine , Masha Karelina , and Briana Sobecks . His lab collaborates with experimentalists across academia and industry, applying computational methods to diverse biomedical problems such as RNA structure prediction , GPCR signaling , and mitochondrial metabolism .

• Computational Biology
• Machine Learning
• Molecular Dynamics Simulation
• +6 more

Gerold Schneider is an Associate Professor at the University of Zurich , affiliated with the Department of Computational Linguistics under the Faculty of Arts and Social Sciences and Faculty of Business, Economics and Informatics . He leads the Text Crunching Center (TCC) , focusing on interdisciplinary research at the intersection of NLP, Digital Humanities, and Health Data Science. Research Interests His work spans Text Analytics , Digital Humanities , Corpus Linguistics , and Health Data Science , with applications in: Biomedical NLP (e.g., Alzheimer’s detection, clinical trials) Digital Humanities projects (e.g., analyzing Charles Dickens, UN archives) Migration discourse framing across languages Adversarial data collection for hate speech detection Interdisciplinary methodologies for digital unstructured data Recent Publications 2025–2024 research highlights include annotated corpora for preclinical and neurological studies, AI-driven analysis of historical linguistic variation, and innovative tools for language learners. His NLP applications address health diagnostics, ethical AI, and cross-lingual political discourse. Labs & Teams As TCC leader, he spearheads collaborative projects within the Digital Society Initiative (DSI) communities (AI & Law, Health, Ethics, etc.), integrating computational methods with humanities and health research.

• Computational Linguistics
• Digital Humanities
• Corpus Linguistics
• +3 more

Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, Language Technologies Institute, leading the L3 Lab. His research focuses on bridging informal and formal reasoning with AI, spanning machine learning for mathematics and code, inference algorithms, and AI agents. PhD in Computer Science from New York University (advised by Kyunghyun Cho) Postdoctoral work at University of Washington (advised by Yejin Choi) His work explores AI-driven formal methods for mathematics and code generation, test-time compute scaling, and algorithms enabling AI improvement over time. Recent publications analyze reasoning evaluation, premise selection, and automated proof optimization in systems like Lean. Key article trends include neural theorem proving, code generation, and inference-time compute optimization. Awards: NVIDIA AI Labs Pioneering Research Awards (2017, 2018), NAACL 2025 Best Paper. Current advisees include PhD students Pranjal Aggarwal, Weihua Du (co-advised with Yiming Yang), Andre He (co-advised with Daniel Fried), and Seungone Kim (co-advised with Graham Neubig). He co-organizes workshops like Autoformalization for the Working Mathematician (ICERM 2025) and VerifAI: AI Verification in the Wild (ICLR 2025), and teaches Advanced NLP at CMU.

• Machine Learning for Mathematics
• Machine Learning for Code
• AI Reasoning and Agents
• +6 more

Dr. Heather Culbertson is an Assistant Professor in the Department of Computer Science at the University of Southern California, leading the HaRVI (Haptics Robotics and Virtual Interaction) Laboratory. Her research focuses on designing haptic hardware and algorithms to create intuitive human-robot interactions through touch, emphasizing realism in virtual environments and social connectedness. Key areas include mediated touch, wearable haptic systems, and multisensory integration in VR/AR applications. The lab's work spans robotics, human perception modeling, and cross-disciplinary solutions for healthcare and immersive technologies. Research interests revolve around tactile feedback systems, social robotics, and the psychological impact of haptic interfaces. Recent work explores real-time haptic texture rendering, affective communication via touch, and motion platforms for VR. Culbertson's projects often integrate human factors early in design to ensure usability and immersion. Publications highlight advancements in haptics coding standards, drone-human interaction, and emotion regulation systems. Her contributions bridge engineering and cognitive sciences, addressing challenges in wearable tech, surgical training simulations, and assistive robotics. The HaRVI Lab collaborates across disciplines to advance tactile interfaces for virtual reality, social robotics, and medical applications.

• Haptics
• Robotics
• Virtual Reality
• +3 more

Michael Groll serves as Professor and Chair of Biochemistry at the Technical University of Munich (TUM), where he leads structural biology and enzymology research with a focus on proteasome mechanisms and inhibitor development. His laboratory, located at the Ernst-Otto-Fischer-Str. 8 campus in Garching, maintains active collaborations in drug discovery for cancer and infectious diseases. His primary research domains include proteasome inhibition, enzyme catalysis, and natural product biosynthesis, employing X-ray crystallography, biochemical assays, and bioengineering to dissect molecular mechanisms. Recent work emphasizes AI-guided enzyme optimization, bacterial stress response targeting, and structural characterization of halogenation enzymes, reflecting interdisciplinary approaches bridging chemistry and biology. Analysis of his 2023-2025 publications reveals consistent innovation in proteasome-targeted therapeutics, with 15 high-impact papers featuring structural insights into enzyme-inhibitor complexes and biosynthetic pathways. Key trends include engineering megasynthetases for immunoproteasome inhibitors, optical control of protein degradation, and elucidating metal-dependent mechanisms in antibiotic biosynthesis. No scientific awards were documented in the provided source material. While specific grant details and student mentorship records were not disclosed, his extensive publication record indicates leadership in collaborative research projects involving structural biology and chemical biology methodologies. The Chair of Biochemistry under Prof. Groll operates as a hub for structural enzymology, housing facilities for protein crystallography, enzyme kinetics, and natural product characterization. His team actively contributes to TUM's research ecosystem through partnerships with pharmaceutical groups and international structural biology consortia.

• Proteasome Research
• Structural Biology
• Enzyme Mechanisms
• +2 more

Hyowon Gweon is an Associate Professor in the Department of Psychology at Stanford University. As the leader of the Social Learning Lab, her research focuses on how humans learn from others and help others learn, employing interdisciplinary methods including developmental, computational, and neuroimaging approaches. She holds a PhD in Cognitive Science from MIT (2012) and joined Stanford in 2014 after a postdoc at MIT. Her research interests span computational approaches to social learning, developmental psychology, neuroimaging, and education. She has received notable awards such as the APS Janet Spence Award (2020), James S. McDonnell Scholar Award (2018), and Marr Prize (2010). Her work explores topics like counterfactual reasoning, social cognition in infants, and embodied AI benchmarks. Labs/Teams: Social Learning Lab Key Themes: Prosocial behavior, theory of mind, cognitive development, and human-AI interaction. Her recent articles investigate infant gaze behavior, temporal reasoning in children, and strategic communication in preschoolers. Grants and advising details are not explicitly listed in the provided text.

• Computational Approaches
• Developmental Approaches
• Education
• +6 more

Dylan Hadfield-Menell is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, holding the Bonnie and Marty (1964) Tenenbaum Career Development Professorship. His research focuses on AI alignment and human-AI interaction within MIT's School of Engineering. His research interests center on agent alignment problems in AI systems, particularly examining uncertainty in objective optimization for human-robot teams and societal oversight of machine learning systems. Key areas include the principal-agent alignment problem , assistance games frameworks , and robust preference learning that accounts for hidden contextual factors in reinforcement learning from human feedback. His recent publications reveal strong trends toward multi-agent cooperation , formal contract mechanisms for resolving social dilemmas, and advanced evaluation methodologies for AI safety. The research spans theoretical frameworks like open-universe assistance games while addressing practical challenges in language model alignment and cultural bias assessment. Scientific awards include: AI2050 Early Career Fellowship from Schmidt Futures Berkeley Fellowship NSF Graduate Research Fellowship C.V. Ramamoorthy Distinguished Research Award His work bridges theoretical computer science with real-world AI governance challenges, as demonstrated through MIT's participation in AI policy white papers. Current research directions include developing frameworks for transparent AI systems and addressing fundamental limitations in aligning recommender systems with human values through interdisciplinary synthesis.

• AI Alignment
• Human-AI Interaction
• Reinforcement Learning
• +3 more

Prof. Dr. Nicole Ondrusch is a Professor at Heilbronn University (HHN) within the Faculty of Computer Science. She specializes in teaching methodological fundamentals of software engineering, including ERP systems, Design Thinking, Change Management, and digital transformation. Her research focuses on educational concepts for computer science technologies and future-relevant skills. Key Roles and Projects: Member of BW-CAR doctoral center Organizer of Barcamp Heilbronn, an unconference teaching project fostering student teamwork Principal investigator in funded projects like DigiTrans (digital transformation education) and DEV:GAMES (educational game development) SAP Academic Board DACH spokesperson since 2021 Research Interests: Her work emphasizes innovative teaching methods such as Design Thinking, agile project implementation, and digital experimentation. She explores ethical AI education, interdisciplinary collaboration, and the societal impact of digital transformation. Nicole also develops practical teaching tools like virtual escape rooms and game-based learning frameworks. Grants & Partnerships: Funded by DAAD (DigiTrans), Stiftung Innovation, and others Collaborates with industry partners like SAP and local IT organizations (ConnectIT) Labs & Teams: She leads the Lab:D initiative, developing digital teaching platforms, and co-organizes the Barcamp, a student-led event integrating real-world project management and community building.

• Digital Transformation
• Innovation Management
• Design Thinking
• +3 more

Professor Markos Zachariadis is the Chair in Financial Technology (FinTech) and Full Professor of Information Systems at Alliance Manchester Business School (AMBS), University of Manchester. He also directs the Centre for Financial Technology Studies (CFTS) and holds roles such as FinTech Research Fellow at the Cambridge Centre for Digital Innovation and member of the World Economic Forum’s Global Future Council on Financial Systems. His research focuses on the intersection of digital innovation, financial technology, and network economics, with notable studies on payment systems (e.g., SWIFT), Open Banking, and blockchain. He has authored/co-authored influential works like The Society for Worldwide Interbank Financial Telecommunication (SWIFT) (2014) and co-edited The PAYTECH Book (2020). Education: PhD in Management (Economics of Payment Systems) from the London School of Economics (2011), MSc in ADMIS (2004), and BSc in Economics from the University of Patras (2003). Prior academic roles include Associate Professor at Warwick Business School and Visiting Professorships at Ivey Business School and Cambridge Judge Business School. Industry experience includes roles at UBS Investment Bank and Hewlett Packard. Research Interests: Economic impacts of ICT adoption in banking, diffusion of payment networks, data standards, Open Banking, blockchain, and regulatory frameworks. Awards include the NET Institute Award and SWIFT Institute Awards for research on payment systems and digital transformation. He advises global organizations like the Hellenic Competition Commission and frequently engages with media outlets such as BBC, CNN, and The Financial Times. Key Projects: Organized an international PayTech conference funded by Gates Foundation and MasterCard. Co-led the Centre for Digital Trust and Society (2021–present) and the Fintech and Financial Crimes project (2021–2022). His work contributes to UN SDGs related to innovation and financial inclusion. Labs/Teams: Founded the Centre for Financial Technology Studies (CFTS) at AMBS, part of interdisciplinary initiatives like the Centre for Digital Trust and Society.

• Financial Technology (FinTech)
• Digital Innovation
• Payment Systems
• +5 more

Xianguo Li is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Canada. He holds prestigious fellowships including Fellow of the Canadian Academy of Engineering (FCAE), Fellow of the Engineering Institute of Canada (FEIC), and Fellow of the Canadian Society for Mechanical Engineering (CSME). His primary research focuses on thermal fluid science, energy systems, and fuel cell technology, with a strong emphasis on green energy solutions. **Education**: 1989: Doctorate in Mechanical Engineering, Northwestern University, USA 1986: Master's in Mechanical Engineering, Northwestern University, USA 1982: Bachelor's in Thermal Energy Engineering, Tianjin University, China **Research Interests**: His work spans fuel cells, spray dynamics, fluid dynamics, heat and mass transfer, power generation, and renewable energy systems. He leads the Fuel Cell and Green Energy Lab, advancing innovations in energy storage, propulsion systems, and sustainable technologies. **Awards**: Outstanding Performance Award (University of Waterloo, 2007) Frank Walk Service Award (2001) Best Paper Award (2003) Recipient of the Simpson Fellowship (1988) **Advising & Grants**: He supervises graduate students and research associates in projects funded by NSERC, Auto 21, CFI, and industry partners. His lab collaborates on fuel cell durability, thermal management, and green energy policy initiatives. **Editorial Roles**: Founding Editor-in-Chief of the International Journal of Green Energy , Field Chief Editor of Frontiers in Thermal Engineering , and serves on dozens of editorial boards. He chairs major conferences like the International Green Energy Conference and the World Fuel Cell Conference series.

• Fuel Cells
• Energy Systems
• Thermal Fluid Science
• +7 more

Bertrand Schneider is an Associate Professor of Education at Harvard University's Graduate School of Education (GSE). He holds a Ph.D. in Learning Sciences and Technology Design from Stanford University (2015) and previously served as a postdoctoral researcher at Stanford's Transformative Learning Technologies Laboratory. His research focuses on educational interfaces (e.g., augmented reality, tangible tools) and multimodal learning analytics, particularly in collaborative and informal learning environments. He investigates how technologies like gaze-tracking and motion sensors can assess student learning trajectories and improve instructional strategies in makerspaces and remote teaching contexts. Education : Ph.D., Learning Sciences and Technology Design, Stanford University (2015) Postdoctoral Researcher, Transformative Learning Technologies Laboratory, Stanford University Research Interests : Educational technologies (AR, tangible interfaces, multitouch) Multimodal data analysis (gaze, body movement, speech) Collaborative learning in formal/informal settings Remote teaching strategies using shared gaze visualizations AI applications in personalized feedback and misinformation discernment Key Contributions : Developed methodologies for capturing joint visual attention in collaborative learning Advocated for ethical implementation of multimodal learning analytics Explored AR's dual-edged impact on student engagement and collaboration Labs/Teams : Current research aligns with GSE's focus on innovative learning technologies Prior affiliation with Stanford's Transformative Learning Technologies Lab

• Assessment
• Cognitive Development
• Evidence-Based Intervention
• +10 more

Heather Knight is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Oregon State University, part of the College of Engineering. Her research focuses on social robotics and human-robot interaction, emphasizing non-verbal communication and the design of non-anthropomorphic robots. She directs the CHARISMA Research Lab, which applies principles from performing arts to enhance robot charisma through expressive motion and interaction design. Dr. Knight holds a Ph.D. in Robotics from Carnegie Mellon University (2016), and dual M.S. degrees in Robotics (Carnegie Mellon) and Electrical Engineering & Computer Science (MIT, 2008), alongside a B.S. in Electrical Engineering from MIT (2006). She also leads Marilyn Monrobot, a robot theater company producing comedic performances and an annual Robot Film Festival. Her work bridges technical innovation with societal impact, exploring how robots can better communicate intent through motion and interaction. Notable contributions include the 'Sanitizerbot' project for playful public health support and the development of Laban-based motion frameworks for expressive robotics. Awards include being named to Forbes' 30 Under 30 in Science (2011) and her TED talk on robot comedy. Key Research Themes: Multi-robot formations, expressive motion design, human-robot social dynamics, and ethical integration of AI in robotics. Recent Projects: VR storytelling with social robots, service robots in workplace environments, and motion-based affective communication. Her articles span topics from robot humor generation to industrial light communication systems, reflecting a commitment to both foundational research and applied solutions for human-robot coexistence.

• Social Robotics
• Human-Robot Interaction
• Non-Verbal Machine Communication
• +3 more

Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.

• Deep learning
• Trustworthy machine learning
• Computer security
• +4 more

Zhiling Gu is a Research Fellow at Yale School of Public Health, having earned her Ph.D. in Statistics at Iowa State University. Her work integrates statistical theory with applications in public health and medicine. Her research spans Functional Data Analysis Network Analysis Spatiotemporal Modeling Statistical AI Foundations Nonparametric Learning applied to neuroimaging, electronic health records, and environmental health studies. Recent publications focus on Adaptive spatiotemporal models Neuroimaging data processing Pandemic forecasting frameworks Environmental exposure modeling with methodological rigor and practical implementation. Scientific achievements include Runner-up in SMI 2023 Student Paper Competition She has taught STAT 305: Engineering Statistics (ISU) STAT 226: Business Statistics Statistical Computing Statistical Learning and actively engages in academic presentations at conferences like SMI 2024 and CMStatistics 2022.

• Statistics
• Public Health
• Medicine
• +11 more

Dr. Dicle Yagmur Ozdemir is an Assistant Professor of Business Information Management at the Rotterdam School of Management (RSM), Erasmus University. She joined RSM in September 2023 after earning a PhD in Management Science (Information Systems concentration) from the University of Texas at Dallas, a Master's in Industrial Engineering from Sabanci University, and a Bachelor's in Industrial Engineering from Istanbul Technical University. Her research focuses on user-generated content dynamics in online platforms and the application of generative AI in healthcare. She employs econometric modeling and natural language processing to study how novel information in reviews influences stakeholders' decisions, and designs algorithms to mitigate information overload. In healthcare AI, she experiments with generative AI's impact on patient-provider interactions. Her work has been presented at top conferences including CIST, WITS, ICIS, WCBA, and INFORMS. Key themes in her research include algorithmic fairness in content selection, the psychological effects of AI-driven health advice, and the mediation effects of review novelty on consumer behavior. She has collaborated internationally, with research outputs including 6 works since 2023. Notable contributions address the moderating role of review dissimilarity in credibility assessments and the paradoxical effects of positive/negative review valence in decision contexts.

• User-Generated Content Analysis
• Generative AI in Healthcare
• Online Platforms and Decision Making
• +3 more