جست‌وجوی استاد و پژوهشگر

Kenji Kawaguchiمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +۱۲ مورد دیگر

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

Han Liuمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Systems
• Statistical Machine Learning
• +۴ مورد دیگر

Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).

Jimeng Sunمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +۵ مورد دیگر

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

Wojciech Matusikمشاهده پروفایل

استاد

• Computer Graphics
• Computational Design and Fabrication
• Computer Vision
• +۷ مورد دیگر

Wojciech Matusik is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Computational Design and Fabrication Group and is a member of the Computer Graphics Group. His research spans computer graphics, robotics, and AI-driven manufacturing, with a focus on computational design, tactile sensing, and material science. Matusik holds a PhD in Computer Science from MIT (2003), an MS from MIT (2001), and a BS from UC Berkeley (1997). His work includes groundbreaking projects like differentiable cloth simulation (DiffCloth), AI-enhanced molecular design, and tactile sensing gloves. He has received prestigious awards such as the MIT TR35 (2004), DARPA Young Faculty Award (2012), and Ruth and Joel Spira Teaching Award (2014). Matusik teaches courses on computer graphics, machine learning, and computational fabrication at MIT. Key research themes include: Robotics: Robotic assembly, tactile interaction, and soft robotics Graphics: 3D holography, procedural material generation Manufacturing: Additive fabrication, topology optimization His recent articles explore AI-driven molecular synthesis, holographic displays, and tactile-enabled VR systems. Matusik collaborates on open-source tools like the WiReSens tactile platform and Simit language for sparse systems.

Todd Squiresمشاهده پروفایل

استاد

• Fluid Mechanics
• Transport Science
• Microfluidics
• +۷ مورد دیگر

Professor Todd Squires is a distinguished faculty member in the Department of Chemical Engineering at the University of California, Santa Barbara, within the Robert Mehrabian College of Engineering. His research focuses on the fundamental principles of transport phenomena as applied to interfaces, membranes, and complex fluids, employing theoretical, computational, and experimental approaches to address significant challenges in micro-scale fluid mechanics. Dr. Squires' educational background includes: BS in Physics, UCLA (1995) BA in Russian Language and Literature, UCLA (1995) PhD in Physics, Harvard University (2002) His research interests span microfluidics and electrokinetics, active and nonlinear microrheology of complex materials, polymer dynamics and sensors, with particular emphasis on non-linear electrokinetic flows, interfacial rheology, and the self-assembly of nanostructured materials. His work bridges fundamental fluid mechanics with practical applications in microfluidic devices, energy storage, and biomedical systems, demonstrating the versatility of this fascinating field. Analysis of Professor Squires' recent publications reveals a consistent focus on interfacial phenomena, with particular attention to the rheological properties of fluid interfaces, particle dynamics in complex fluids, and novel microfluidic techniques for measuring and manipulating these systems. His research demonstrates strong interdisciplinary connections between chemical engineering, physics, and materials science, with applications spanning energy storage, biomedical engineering, and environmental systems. Professor Squires has received numerous prestigious awards and honors: 2018 Robert W. Vaughan Lecture in Chemical Engineering, Caltech 2015 Elected Fellow of the American Physical Society 2013 Mid-Career Award, American Electrophoresis Society 2012 The Dudley Saville Memorial Lecture at Princeton 2010 Pierre Gilles de Gennes Prize 2010 Allan P. Colburn Memorial Lectureship, University of Delaware 2009 Francois Frenkiel Award for Fluid Mechanics 2009 Camille Dreyfus Teacher-Scholar Award 2008 Beckman Young Investigator 2007 NSF CAREER Award 2005 'Rising Star' - Chronicle of Higher Education As principal investigator of the Squires Group, Professor Squires leads a dynamic research team that combines experimental, theoretical, and computational approaches to investigate transport phenomena at interfaces. His work has been supported by major funding agencies including the National Science Foundation, with his CAREER award indicating early recognition of his potential as both researcher and educator. While specific grant details aren't provided in the source material, his extensive publication record and prestigious awards suggest robust and sustained research funding. The Squires Group maintains state-of-the-art laboratory facilities for studying micro-scale fluid mechanics, including specialized equipment for microrheology measurements, microfluidic device fabrication, and interfacial characterization. Their research environment fosters collaboration across disciplines, with connections to materials science, physics, and biomedical engineering researchers at UCSB and beyond.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +۶ مورد دیگر

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Henry Mossمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bayesian Optimization
• Theoretical Physics
• +۳ مورد دیگر

Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk

• Machine Learning
• Deep Learning
• Gaussian Processes
• +۵ مورد دیگر

Geoffrey Pleiss is an Assistant Professor in the Department of Statistics at the University of British Columbia's Faculty of Science. He is also a CIFAR AI Chair at the Vector Institute and an inaugural member of CAIDA's AIM-SI (AI Methods for Scientific Impact) cluster. His work bridges statistical theory, machine learning, and computational methods with applications across various scientific domains. Pleiss received his PhD from the Computer Science department at Cornell University in 2020, where he was advised by Kilian Weinberger and worked closely with Andrew Gordon Wilson. Prior to his faculty position at UBC, he was a postdoctoral researcher at Columbia University with John P. Cunningham. His research focuses on the intersection of deep learning and probabilistic modeling, particularly on developing heuristic and approximate notions of uncertainty from machine learning models. His work has significant implications for reliable and optimal decision-making in experimental design and scientific discovery. Major research thrusts include neural network uncertainty quantification, Bayesian optimization, Gaussian processes, and ensemble methods. Pleiss develops theoretical frameworks while maintaining strong connections to practical applications across scientific domains. An analysis of his recent publications reveals a strong focus on uncertainty quantification in deep learning models, with particular attention to the limitations and capabilities of ensemble methods in the era of overparameterized models. His work increasingly addresses practical challenges in Bayesian optimization for scientific discovery, especially in materials science. There's also a growing emphasis on computational efficiency in Gaussian process methods, reflecting his commitment to making advanced statistical techniques accessible for real-world applications. CIFAR AI Chair Pleiss currently advises several graduate students including Donney Fan (PhD, Computer Science), Tim G. Zhou (MSc, Computer Science), Zachary Lau (MSc, Statistics), Nathan Cantafio (BSc, Statistics), and Tristan Cinquin (Research Intern at Vector Institute). His research is supported by multiple funding sources including his CIFAR AI Chair position, which provides significant research resources for advancing machine learning methodologies with scientific impact. Pleiss co-created and maintains GPyTorch, a highly efficient and modular implementation of Gaussian processes in PyTorch designed for speed, modularity, and prototyping. He is also involved with CoLA (Compositional Linear Algebra), a library for structured linear algebra operations in JAX and PyTorch that enables fast linear algebra computations by automatically exploiting matrix structure.

David Duvenaudمشاهده پروفایل

دانشیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۵ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.

Robert J. Hamersمشاهده پروفایل

استاد

• Electrochemistry
• Nanotechnology
• Materials Science
• +۹ مورد دیگر

Robert J. Hamers is a Professor of Chemistry and the Steenbock Professor of Physical Science at the University of Wisconsin-Madison . He serves as the Director of the Center for Sustainable Nanotechnology , a multi-institutional collaboration, and is a Senior Editor for Accounts of Chemical Research . Additionally, he co-founded the startup Silatronix, Inc. and leads the ACS/UW-Madison Bridge to the Chemistry Doctorate Program . B.S. in Chemistry, University of Wisconsin-Madison (1980) Ph.D. in Chemistry, Cornell University (1986) Hamers' research focuses on surface chemistry, nanotechnology, and renewable energy , with specific interests in electrochemical energy storage, photoelectron emission mechanisms, and environmental impacts of nanomaterials . His group develops ultra-stable surface chemistries for energy devices and investigates charge-transfer processes at material interfaces . Recent publications highlight advances in diamond-based materials , organosilicon electrolyte additives , and environmental fate of nanomaterials . Scientific recognitions include the Wisconsin Distinguished Professor title. His work bridges fundamental surface science with applied technologies through collaborations with academic institutions, national laboratories, and industry partners like Dow Chemical . The Hamers Group actively trains graduate students and postdoctoral researchers in multidisciplinary approaches.

• Stelae
• Offering Tables
• Scarabs
• +۷ مورد دیگر

Professor Alexander Ilin-Tomich serves as an Acting Professor at the Institute of Egyptology within the Department of History and Cultural Studies at the Free University of Berlin. His academic career spans multiple prestigious institutions including Johannes Gutenberg-Universität Mainz and the Russian Academy of Sciences. His educational background includes: 2022: Habilitation in Egyptology at Johannes Gutenberg-Universität Mainz with thesis "Egyptian name scarabs from the 12th to the 15th Dynasty: Geography and chronology of production" 2007-2011: Doctoral studies at Lomonosov University Moscow, earning "kandidat istoriceskich nauk" (equivalent to PhD) 2002-2007: Undergraduate studies in Ancient History (including Egyptology) at Lomonosov University Moscow Professor Ilin-Tomich specializes in Egyptian stelae, offering tables, scarabs, personal names, prosopography, and administration during the Middle Kingdom and Second Intermediate Period. His research combines philological, archaeological, and historical approaches to examine ancient Egyptian society, with particular focus on administrative structures, material culture, and regional variations. His work on scarab production and distribution has established him as a leading expert in this specialized field. His publication record demonstrates consistent focus on Middle Kingdom administration, with recent expansion into material analysis of Egyptian blue pigment and methodological approaches to studying small archaeological finds. He has developed significant digital resources including the open-access "Personen und Namen des Mittleren Reiches" database. Professor Ilin-Tomich has secured multiple competitive research grants: Fritz Thyssen Foundation project "Ancient Egyptian titles in official and familial contexts, 2055-1352 BC" (2021-2024) Fritz Thyssen Foundation project "Transformation and variability in the corpus of ancient Egyptian personal names 2055-1550 BC" (2017-2021) Alexander von Humboldt Foundation postdoctoral fellowship (2015-2017) Russian Foundation for the Humanities research grant (2013-2015) He actively contributes to the academic community through editorial work, including co-editing the 2023 Festschrift "Schöne Denkmäler sind entstanden. Studien zu Ehren von Ursula Verhoeven," and has established himself as a key researcher in Middle Kingdom studies through his monographs and numerous journal articles.

Xiaoxiao Liمشاهده پروفایل

استادیار

• Federated Learning
• Medical Imaging Analysis
• AI Fairness and Trustworthiness
• +۷ مورد دیگر

Dr. Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC), with joint appointments in Computer Science (Associate Member) and the School of Medicine at Yale University (Adjunct Assistant Professor). She is also a Canada CIFAR AI Chair and Canada Research Chair (Tier II) in Responsible AI. Her research focuses on enhancing trustworthiness, fairness, and efficiency in AI algorithms and foundation models, particularly in healthcare applications. Education: B.S. (Honors) in Zhejiang University (2015), Ph.D. in Biomedical Engineering from Yale University (2020), Postdoc at Princeton University (2020-2021). She leads the Trusted and Efficient AI (TEA) Lab at UBC, which develops algorithms for federated learning, medical imaging analysis, and interpretable AI systems. Research interests include federated learning, generative models, medical image analysis, AI fairness, and graph-based methods for neuroimaging. Recent projects include GMValuator (data valuation for generative models), FairMedFM (fairness benchmarking in medical AI), and FedTextGrad (textual gradient-based FL optimization). Grants: Canada Foundation for Innovation Grant (2023), UBC Green Lab Fund (2023), Vector Institute funding Teaching: Courses on machine learning, federated learning, and AI ethics at UBC Awards & Recognition: Best Paper Award at FL@FM WWW 2024, Editorial Board Member of Medical Image Analysis , multiple top-tier conference acceptances (NeurIPS, ICLR, CVPR, MICCAI). Lab & Teams: TEA Lab collaborates with industry and hospitals to translate AI research into clinical tools. Current projects address AI fairness in healthcare, federated learning for medical data, and multimodal medical analytics.

• Lithium-Ion Battery Production
• Energy Storage Systems
• Production Engineering
• +۳ مورد دیگر

Prof. Dr.-Ing. Rüdiger Daub serves as Professor and Chair of Production Engineering and Energy Storage Systems at the Technical University of Munich (TUM), operating within the Department of Mechanical Engineering. His leadership encompasses research direction, academic supervision, and strategic development of battery production technologies at TUM's Garching campus (Boltzmannstr. 15), with active industry collaborations driving innovation in sustainable manufacturing. Daub's research program pioneers advanced production methodologies for lithium-ion and solid-state batteries, focusing on electrode manufacturing, electrolyte filling, and cell assembly processes. His work investigates critical parameter interdependencies affecting battery safety and performance, developing inline monitoring systems and digital twin technologies for real-time process optimization. Key contributions include moisture control in electrode production, electrochemo-mechanical characterization of solid-state systems, and robotics solutions for deformable object assembly, all integrated with machine learning for quality assurance in industrial settings. Analysis of his 2023-2025 publications reveals a dominant research trajectory toward solving production bottlenecks in next-generation energy storage. The work demonstrates increasing integration of computational modeling with empirical validation, particularly in solid-state battery manufacturing and high-voltage electrolyte systems. A notable trend is the cross-pollination of robotics, computer vision, and uncertainty quantification techniques to address complex assembly challenges and distribution shifts in quality monitoring, reflecting industry's urgent need for adaptable, data-driven production systems. Leading TUM's specialized laboratories for battery cell production, Daub's team maintains comprehensive facilities for electrode calendering, electrolyte filling, and cell assembly with integrated tracking and tracing capabilities. The research infrastructure supports collaborative projects with automotive OEMs and battery manufacturers to develop scalable production processes, emphasizing environmental sustainability through water-based electrode production and footprint optimization. Current initiatives focus on digital factory modeling and prelithiation technologies for next-generation battery systems.

Yunjie Yangمشاهده پروفایل

دانشیار

• AI-powered sensing and imaging
• Machine learning
• Soft sensors & electronics for robotics
• +۵ مورد دیگر

Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.

Andrew D. Whiteمشاهده پروفایل

دانشیار

• Large language models
• Deep learning in chemistry
• Molecular dynamics
• +۵ مورد دیگر

Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.