Find Academic Supervisors

• Artificial Intelligence
• Deep Learning
• Recurrent Neural Networks
• +12 more

Juergen Schmidhuber is Associate Professor at the Faculty of Informatics of Università della Svizzera italiana and a leading researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). He is also Chief Scientist at NNAISENSE, a company dedicated to building practical general-purpose AI. His work has profoundly influenced modern artificial intelligence, particularly through the development of Long Short-Term Memory (LSTM) networks in 1991, now deployed across billions of devices for speech recognition, machine translation, and virtual assistants. His research interests span Artificial Intelligence, Deep Learning, Recurrent Neural Networks, Universal AI, Meta-Learning, Algorithmic Information Theory, Artificial Curiosity, Robotics , and Low-Complexity Art . He has pioneered mathematically rigorous frameworks for self-improving AI systems and formal theories of creativity and beauty. His work bridges theoretical foundations with real-world applications in computer vision, natural language processing, and autonomous robotics. The recent articles reflect a consistent trajectory of innovation, combining deep theoretical insights with scalable machine learning architectures. His publications emphasize sequence modeling, universal learning, intrinsic motivation, and computational creativity , demonstrating both foundational contributions and industrial impact. From LSTM to Goedel machines, his work consistently targets the long-term goal of self-improving general AI. Scientific Awards: Numerous awards in AI and machine learning (specific names not listed) Schmidhuber leads a research group at IDSIA, where he mentors students and researchers in advancing the frontiers of AI. His lab has secured significant recognition and industrial collaboration, though specific grants are not detailed. He promotes the 'New AI'—general, sound, and relevant to physics—and continues to explore the convergence of intelligence, computation, and the universe. Labs and Teams: Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) NNAISENSE (as Chief Scientist)

Dan OlteanuView profile

Professor

• Database Systems
• Database Theory
• Artificial Intelligence
• +7 more

Prof. Dan Olteanu is a full professor at the Department of Informatics, University of Zurich, leading the Data Systems and Theory (DaST) group. He holds visiting professorships at the University of Oxford and is an emeritus fellow of St Cross College. His academic journey includes a PhD from Ludwig Maximilian University (2005), postdoctoral roles at Saarland University and Cornell University, and prior faculty positions at Oxford (2007–2020). He has also worked in industry with companies like LogicBlox and RelationalAI, focusing on database systems and AI. Education: Bachelor’s in Computer Science, Politehnica University of Bucharest (2000) PhD in Computer Science, Ludwig Maximilian University (2005) Professional Roles: Full Professor, University of Zurich (since 2020) Visiting Professor, University of Oxford Emeritus Fellow, St Cross College Editorial Roles: ACM TODS, VLDBJ, SIGMOD Conference Chair: ICDT Council (since 2022) His research focuses on data systems theory, including query optimization, probabilistic databases, factorized databases, and in-database machine learning. He co-authored the seminal book Probabilistic Databases (2011) and has pioneered algorithms for efficient machine learning over relational data and incremental maintenance of analytical workloads. His work emphasizes scalable, theoretically grounded solutions for real-world data challenges. Awards: ICDT 2019 Best Paper Award ACM SIGMOD 2018 Distinguished PC Member Award ERC Consolidator Grant (2016) Oxford Outstanding Teaching Award (2009) Grants & Funding: Supported by Google, Microsoft Azure, Amazon AWS, EPSRC, and the European Commission. His research bridges academia and industry, with contributions to commercial systems like LogicBlox and RelationalAI. Labs & Teams: Heads the DaST group at Zurich, focusing on data systems theory and applications. Collaborates widely in the database and AI communities.

Xuming HeView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +9 more

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Alexander ArtikisView profile

Associate Professor

• Artificial Intelligence
• Distributed Systems
• Complex Event Recognition
• +4 more

Dr. Alexander Artikis is an Associate Professor of Artificial Intelligence at the University of Piraeus and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos". He leads the Complex Event Recognition (CER) group , focusing on symbolic and probabilistic approaches to event recognition and forecasting. University of Piraeus (2025–present) NCSR Demokritos (2017–present) Complex Event Recognition Group (2017–present) His research spans Artificial Intelligence and Distributed Systems , with a focus on: Complex Event Recognition (CER) : Developing logic-based systems for detecting events in real-time data streams Event Calculus : Creating probabilistic and incremental versions for runtime reasoning Multi-Agent Systems : Modeling norm-governed interactions Maritime Informatics : Applying CER to vessel trajectory analysis and fleet management Key publications reveal trends in: Neuro-symbolic forecasting models combining deep learning and logic-based reasoning Symbolic automata with memory for pattern detection Online learning techniques for dynamic event rule generation Tensor-based formalizations for efficient temporal reasoning Handling uncertainty in real-time maritime data streams Optimizing memory usage for scalable stream processing He contributes to open-source tools like RTEC (Run-Time Event Calculus) and holds a European patent on complex event forecasting. His work addresses challenges in: Proactive decision-making systems Knowledge Graph consistency Hybrid human-machine discovery of movement patterns Big Data analytics for time-critical applications

Helen OleynikovaView profile

Lecturer

• Robotics
• Autonomous Systems
• Micro-Aerial Vehicles (MAVs)
• +12 more

Helen Oleynikova is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, where she is part of the Autonomous Systems Lab. She works on the intersection of perception and planning, particularly for micro-aerial vehicles. Her research focuses on real-time onboard mapping, planning, and localization using visual-inertial systems and signed distance fields. Research Interests: Helen's work spans robotics, autonomous systems, and computer vision, with a focus on enabling safe and efficient navigation in complex environments. She specializes in visual-inertial odometry, SLAM, 3D mapping using signed distance fields, and real-time path planning for MAVs. Her projects often involve embedded systems and FPGA-based high-speed vision for obstacle avoidance. Publication Trends: Her recent publications (2023–2019) show a consistent focus on real-time, onboard algorithms for autonomous navigation. Key themes include signed distance function maps, collision-free motion generation, global localization, and efficient exploration. She frequently publishes in top-tier robotics conferences such as ICRA and IROS, and journals like IEEE RA-L and Journal of Field Robotics. Professional Experience: Senior Researcher, Autonomous Systems Lab, ETH Zürich Senior Software Engineer, Isaac 3D Perception, Nvidia Senior Scientist, Microsoft Mixed Reality and AI Lab, Zürich Software Engineer, Google (StreetView) Contributor, Willow Garage (ROS, TurtleBot Arm) Education: PhD in Robotics, ETH Zürich (2019) MSc in Robotics, ETH Zürich BSc in Robotics, Olin College of Engineering (2011) Advising and Grants: While no formal students are listed, she has collaborated extensively with researchers at ETH Zürich and industry labs. Her work has been supported through institutional affiliations and industry research roles. She has contributed to open-source robotics software, particularly in ROS-based systems for manipulation and navigation. Labs and Teams: Helen is a key member of the Mobile Manipulation team at the Autonomous Systems Lab at ETH Zürich. She has also been involved in projects at Nvidia, Microsoft, Google, and Willow Garage, focusing on real-world deployment of autonomous systems.

Laura PozziView profile

Professor

• Embedded Systems
• Compiler Techniques
• Coarse-Grain Reconfigurable Architectures
• +3 more

Laura Pozzi is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), Switzerland, since 2015. She previously held positions as Associate Professor (2011–2015) and Assistant Professor (2005–2011) at USI. Prior to joining USI, she was a postdoctoral researcher at EPFL's Processor Architecture Laboratory (2001–2005), a research engineer at STMicroelectronics (2000), and an Industrial Visitor at UC Berkeley (2000). Education: MS and PhD in Computer Engineering from Politecnico di Milano, Italy (1996–2000). Her research focuses on the interaction between compiler and architecture design, particularly in embedded systems , with key areas including approximate computing , coarse-grained reconfigurable arrays (CGRAs) , high-level synthesis (HLS) , and fuzz testing . She has led projects on automated design space exploration, compiler optimizations for reconfigurable architectures, and error estimation in approximate circuits. Recent Publications span topics like SAT-based mapping for CGRAs , grammar-based fuzzing of shell interpreters , and approximate logic synthesis , reflecting her interdisciplinary work bridging hardware/software co-design and software verification. Scientific Awards: Credit Swiss Best Teaching Award IEEE DAC Best Paper Award Leadership Roles: Co-Program Chair, IEEE Symposium on Application Specific Processors (SASP) Editorial Board Member, IEEE Design and Test Students: Current: Rodrigo Otoni (Postdoc), Morteza Rezaalipour (PhD), Riccardo Felici (PhD), Cristian Tirelli (PhD) Alumni: Ilaria Scarabottolo (PhD/Postdoc), Lorenzo Ferretti (PhD/Postdoc), Georgios Zacharopoulos (PhD), Giovanni Ansaloni (PhD/Postdoc), Paolo Bonzini (PhD)

Lukas FesenfeldView profile

Researcher

• Environmental Governance
• Political Economy
• Climate Change Mitigation
• +4 more

Lukas Fesenfeld is an environmental governance and political economy researcher at ETH Zurich and a lecturer at the Oeschger Centre for Climate Change Research and the Policy Analysis and Environmental Governance group at the University of Bern. His interdisciplinary work focuses on identifying drivers and outcomes of transformative processes in environmental policies, particularly in energy and agri-food systems, to accelerate biodiversity protection and climate change mitigation. His research interests span the political economy of climate policy, food system transformation, and the interplay between policy, behavioral, and technological changes in socio-technical systems. Fesenfeld employs innovative mixed-method approaches combining quantitative and qualitative techniques including surveys, field experiments, advanced econometrics, and machine learning to understand complex feedback dynamics in sustainability transitions. His work has been published in leading journals including Nature Climate Change, Nature Food, and Global Environmental Change, addressing critical issues like the political feasibility of transformative climate policies and the governance of food system transformation. His research has identified key levers for systemic change rather than incremental improvements in food systems, with particular focus on meat consumption and food waste reduction. Theodor Kocher Prize (50,000 CHF) from the University of Bern SNIS Award 2021 for best PhD thesis on International Studies PhD scholarship by the Heinrich Böll Foundation Multiple competitive research grants totaling over EUR 1.7 million as principal investigator Fesenfeld has supervised over ten Bachelor's theses and eight Master's theses while teaching diverse courses on environmental governance, political economy, and climate policy. He actively engages with the science-policy interface through collaborations with NGOs, public administration, and participation in global policy assessment reports. His founding of the NAHhaft Institute for Sustainable Food Strategies demonstrates his commitment to translating research into practical solutions for food system transformation.

• New Institutional Theory
• Population Ecology
• Organizational Change
• +3 more

Nikolaus Beck is a Full Professor at the Faculty of Economics of the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes an MA in Sociology from the University of Erlangen-Nuremberg, a PhD in Business Administration from the University of Mannheim, and a habilitation from the University of Erfurt. He previously taught at the Academy of Fashion and Design in Hamburg. His research focuses on New Institutional Theory, Population Ecology, organizational change, and the sociology of the family. He is affiliated with the Centre for Organisational Research (CORe) and the Institute of Management and Organisation (IMO), contributing to empirical research and organizational behavior studies. Contact him at nikolaus.beck@usi.ch or via his office at Blue Building, P103. Research Interests: Beck’s work explores how organizations adapt to institutional pressures, evolve within competitive environments, and navigate identity challenges. His studies often involve case analyses of industries like brewing and hospitality, examining authenticity, community attachment, and market dynamics. He employs methodologies such as event history analysis and organizational ecology to study long-term organizational trajectories. Articles Trends: His publications analyze organizational behavior in sectors like brewing, humanitarian projects, and sports, emphasizing institutional multiplicity, learning mechanisms, and strategic diversification. Recent work investigates how geographic communities shape business strategies and how organizations balance identity preservation with adaptive change. Awards: No scientific awards explicitly mentioned in the provided texts. Advising & Grants: No specific advising or grant information listed here. His academic contributions are primarily through research and teaching at USI’s Faculty of Economics. Labs/Teams: Active in the CORe (Organization and Management Theory) and IMO (Institute of Management and Organisation), focusing on interdisciplinary organizational studies.

Antonio CarzanigaView profile

Professor

• Distributed Systems
• Software Engineering
• Content-Based Networking
• +7 more

Antonio Carzaniga is a Full Professor and founding member of the Faculty of Informatics at Università della Svizzera italiana (USI), where he has been active since 2004. Previously, he served as an Assistant Research Professor at the University of Colorado at Boulder from 2001 to 2007. He holds a Ph.D. in Computer Science and a Bachelor’s degree in Electronic Engineering from Politecnico di Milano. Full Professor, Faculty of Informatics, Università della Svizzera italiana (2004–Present) Assistant Research Professor, Department of Computer Science, University of Colorado at Boulder (2001–2007) Ph.D. in Computer Science, Politecnico di Milano Bachelor’s in Electronic Engineering, Politecnico di Milano His research spans distributed systems and software engineering, with a strong focus on content-based addressing networks, publish/subscribe systems, middleware, software fault tolerance, and verification. He has pioneered work in information-centric networking and developed the Siena project, a scalable publish/subscribe service. His recent work extends into programmable networks, GPU-accelerated matching, and performance annotations for cloud systems. The 15 most recent publications highlight a consistent trajectory in scalable, high-performance networking and adaptive software systems. Key themes include content-based communication, packet subscriptions, information-centric networking, and leveraging redundancy for fault tolerance and testing. His work bridges theoretical foundations with practical implementations, often involving system-level software and performance evaluation. Best Paper Award, ACM SIGCOMM Workshop on Information-Centric Networking (ICN'13) Carzaniga has advised multiple graduate students, including Michele Papalini, Koorosh Khazaei, and Daniele Rogora, and has collaborated on funded research projects in distributed systems and networking. He has contributed to software development through projects like the Siena Fast Forwarding engine and the Synthetic Workload Generator. His service includes organizing workshops and contributing to major conferences in software engineering and computer systems. He leads research initiatives such as Siena and Content-Based Networking, focusing on scalable, decentralized communication infrastructures. His lab has developed key tools for evaluating publish/subscribe performance and implementing high-speed forwarding algorithms.

Jonathan KlüserView profile

Researcher

• Political Science
• Public Policy
• Media Governance
• +3 more

Dr. Jonathan Klüser is a Postdoctoral Researcher at the University of Zurich, specializing in political science and public policy. His work explores the interplay between media coverage and governance structures, focusing on platform regulation, ministerial coordination in coalition governments, and bureaucratic influences on policy-making. His research spans parliamentary democracies and cross-party executive dynamics. Research Interests: Dr. Klüser investigates Media-driven platform governance Coalition government coordination Bureaucratic capacity in legislative processes Policy agenda dynamics Ministerial accountability structures Comparative policy analysis . His methodological approach combines empirical studies of social media regulation with theoretical frameworks like Punctuated Equilibrium Theory. Collaborative Networks: He frequently collaborates with scholars such as Christian Breunig and Daniela Beyer, contributing to journals like Political Communication and West European Politics . Current projects examine media-policy feedback loops and institutional resource allocation in European governance systems.

Sébastien DoeraeneView profile

Lecturer

• Language design
• Compilers
• Virtual machines
• +4 more

Sébastien Doeraene is a Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Computer and Communication Sciences and the Information Systems Laboratory . He is best known as the creator of Scala.js and serves as the Executive Director of the Scala Center . His research focuses on cross-platform language design, compiler optimization, and interoperability between statically and dynamically typed languages. PhD in Programming Languages (EPFL, 2018) Master of Science in Computer Science Engineering (Université catholique de Louvain, Belgium) His work bridges theoretical language design with practical implementations, particularly for JavaScript interoperability. Key projects include: Scala.js (compile Scala to JavaScript) Mozart 2 (Oz language implementation) Ozma (Scala extension with Oz concurrency) FunLabyrinthe (customizable maze game) Teaching contributions include courses on algorithmic thinking (C programming) and software development (Python, object-oriented design). As part of the LAMP laboratory , he advances Scala ecosystem tools and education initiatives.

Sebastian SauppeView profile

Researcher

• Linguistic diversity and language processing
• Incremental processing
• Event cognition
• +5 more

Sebastian Sauppe is a Senior Assistant and Ambizione Fellow at the University of Zurich's Department of Psychology, where he leads the research area 'Cognitive and Developmental Neuroscience of Language' within the Developmental Psychology: Infancy and Childhood unit. His work bridges language processing, cognitive neuroscience, and ontogenetic development, with fieldwork conducted on under-researched languages including Äiwoo in Solomon Islands. Education: Recipient of Max Planck Society's Otto Hahn medal for psycholinguistic dissertation work Current Roles: SNSF Ambizione grant holder, National Geographic Explorer Dr. Sauppe's research focuses on linguistic diversity and language processing , examining how grammatical structures interact with real-time comprehension and production. His work on incremental processing investigates neural mechanisms of sentence construction, while event cognition studies explore how language shapes semantic role representation. He also examines child development of language-cognition interfaces and interactive language use in social contexts. His methodology combines mobile EEG, eye-tracking, and behavioral paradigms across diverse linguistic environments. Analysis of his recent publications reveals strong emphasis on cross-linguistic validation of processing principles (particularly agent preference phenomena), neural correlates of syntactic variation, and methodological innovation in field psycholinguistics. His work consistently bridges theoretical linguistics with cognitive neuroscience, with increasing focus on developmental trajectories. National Geographic Explorer (awarded by National Geographic Society) Otto Hahn Medal (Max Planck Society for outstanding dissertation work) As an Ambizione grant holder, Dr. Sauppe leads independent research while mentoring junior researchers. His fieldwork in Solomon Islands demonstrates commitment to ecological validity and linguistic diversity. Current projects involve mobile EEG studies of language processing in naturalistic settings and cross-species comparisons of event cognition with great apes. His research group collaborates extensively across Europe and with field linguists worldwide, developing novel paradigms for studying language in under-documented contexts. Dr. Sauppe maintains active laboratory work at the University of Zurich while conducting periodic field expeditions. His team utilizes mobile neuroimaging equipment for cross-cultural studies and collaborates with primatology researchers on comparative cognition projects. The lab emphasizes methodological rigor in both controlled laboratory and naturalistic field settings.

Kevin J LiangView profile

Researcher

• Deep Learning
• Computer Vision
• 3D Computer Vision
• +5 more

Dr. Kevin J Liang is a Research Scientist at Meta Platforms, Inc. , specializing in Deep Learning , Computer Vision , and 3D Reconstruction . He earned his PhD in Electrical & Computer Engineering from Duke University in 2020, with a dissertation on Deep Automatic Threat Recognition for Airport X-Ray Baggage Screening . His research focuses include: 3D Computer Vision (ICON, Fast3R) Few-Shot Learning (Sylph, HyperMix) Federated Learning (WAFFLe) Object Detection (EgoTracks, Self-Supervised Methods) Recent publications demonstrate his leadership in Egocentric Vision (Ego-Exo4D) and Transformer Applications (GliTr). He has received numerous awards including the E Bayard Halsted Fellowship (2017) and Summa cum laude (2015), and serves on program committees for major conferences like NeurIPS and CVPR . As an educator, he developed and taught tutorials for Duke University's Machine Learning School and Coursera courses, covering TensorFlow, PyTorch, and foundational ML concepts for over 600 students.

• Natural Language Processing
• Computational Linguistics
• Dependency Parsing
• +7 more

Carlos Gómez Rodríguez is a Full Professor (Catedrático de Universidad) in the Department of Computer Science at the University of A Coruña, Spain, where he leads the FASTPARSE Lab within the LyS Research Group at the CITIC Research Center. His work bridges theoretical and practical aspects of computational linguistics with significant applications in natural language processing. His primary research interests focus on natural language parsing algorithms, with particular emphasis on improving parsing speed for web-scale applications, handling non-projective dependency structures, analyzing morphologically-rich and low-resource languages, and exploring the cognitive aspects of syntax. He maintains a critical perspective on the field's current LLM-driven revolution, advocating for continued research into alternative approaches that prioritize efficiency, explainability, and scientific insight beyond user-facing applications. His recent publications reveal a strong trend toward examining the capabilities and limitations of large language models, particularly in comparison to human language processing, while continuing to advance traditional parsing techniques. His work spans theoretical foundations, practical implementations, and diverse applications including sentiment analysis, opinion mining, and creative writing evaluation. National Young Researcher Price "María Andresa Casamayor" in Mathematics and Information Technologies by Spain's Ministry of Science Honorary Member of the Royal Spanish Mathematical Society CAEPIA 2024 Best Paper Award for research on evaluating creative writing in LLMs Professor Gómez Rodríguez has successfully led significant research projects including the ERC Starting Grant project FASTPARSE, which focused on techniques to improve the speed of parsing algorithms. His work demonstrates consistent funding support for innovative research at the intersection of computational linguistics, cognitive science, and practical NLP applications. He actively collaborates with researchers across Spain and internationally, as evidenced by his extensive publication record with diverse co-authors. He leads the FASTPARSE Lab and is a core member of the LyS Research Group, focusing on advancing both theoretical understanding and practical implementations of language processing technologies. His team explores diverse aspects of natural language processing, from fundamental syntactic analysis to applied sentiment analysis and innovative applications of language technology in domains like healthcare.

Tom HopeView profile

Assistant Professor

• Artificial Intelligence
• Scientific Knowledge Discovery
• Text Mining
• +7 more

Tom Hope is an Assistant Professor (Senior Lecturer) at the Hebrew University of Jerusalem's School of Computer Science and Engineering, and a Research Scientist at The Allen Institute for AI (AI2). His work focuses on developing AI methods that augment and scale scientific knowledge discovery by harnessing vast repositories of scientific knowledge through literature, knowledge bases, and electronic medical records. His educational background includes a PhD with Dafna Shahaf, followed by postdoctoral research at AI2 and the University of Washington working with Daniel Weld and Eric Horvitz. Prior to his PhD, he led an applied AI research team at Intel that published award-winning work. Hope's research spans a full-stack spectrum from constructing new datasets and machine learning models to designing user-facing systems. His primary focus areas include text mining, knowledge graphs, information extraction, multimodal models, LLMs, and human-computer interaction for scientific discovery. He explores how computational approaches can transform the scientific process by helping researchers explore literature, generate hypotheses, and make informed decisions. His work has been featured in top venues including NAACL, EMNLP, ACL, CHI, AAAI, KDD, CACM, and PNAS, with coverage in Nature and Science. His recent publications reveal a strong emphasis on scientific idea generation systems, literature-based hypothesis formation, and tools for navigating scientific literature. His work often bridges AI techniques with real-world scientific challenges, particularly in biomedicine. 2022 Azrieli Early Career Faculty Fellowship (awarded to eight scientists across all fields) KDD 2017 Best Research Paper Award AKBC 2021 Outstanding paper award Selected for 2021 Global Young Scientists Summit Selected for 2019 Heidelberg Laureate Forum ELLIS Society member Member of KDD 2020 Best Paper Selection Committee Hope has advised numerous students from leading institutions including Carnegie Mellon, University of Washington, Georgia Tech, and others. His research is organized around five main thrusts: AI Inspiration systems for idea generation, Biomed Predictions for clinical applications, Knowledge Graph construction, Scientific Information Extraction, and Scientific Search engines for discovery. His work has significant implications for accelerating scientific discovery and improving clinical decision-making through evidence-based AI systems.