جست‌وجوی استاد و پژوهشگر

Stefano Ermonمشاهده پروفایل

دانشیار

• Machine Learning
• Generative AI
• Computational Sustainability
• +۷ مورد دیگر

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

Tommi Jaakkolaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۷ مورد دیگر

Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.

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 .

Qianwen Wangمشاهده پروفایل

استادیار

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +۵ مورد دیگر

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

Wengong Jinمشاهده پروفایل

استادیار

• AI for drug discovery
• Generative AI models
• Geometric deep learning
• +۵ مورد دیگر

Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.

Weiping Tangمشاهده پروفایل

استاد

• Targeted Protein Degradation
• PROTACs
• Medicinal Chemistry
• +۵ مورد دیگر

Weiping Tang is a Professor of Pharmaceutical Sciences and Chemistry at the University of Wisconsin-Madison, holding the Janis Apinis Professorship in the School of Pharmacy and the Vilas Distinguished Achievement Professorship. He also serves as Director of the Medicinal Chemistry Center at the School of Pharmacy and maintains a faculty appointment with the Department of Chemistry in the College of Letters and Science. Janis Apinis Professor of Pharmaceutical Sciences Vilas Distinguished Achievement Professor Director of Medicinal Chemistry Center Faculty Appointment with Department of Chemistry Dr. Tang received his B.S. in Chemistry from Peking University in 1997, M.S. in Chemistry from New York University in 1999, Ph.D. in Organic Chemistry from Stanford University in 2005, and completed a postdoctoral fellowship in Medicinal Chemistry, Chemical Biology and Drug Discovery at Harvard University in 2007. Dr. Tang's research program focuses on drug discovery for cancer, infectious diseases, and neurodegenerative disorders through three interconnected areas: Organic Synthesis (advancing glycoscience through novel carbohydrate synthesis technologies), Medicinal Chemistry (developing small molecules that selectively remove disease-associated proteins), and Chemical Biology (dissecting biological pathways using novel small molecule probes). His group operates as an interdisciplinary team where chemists and biologists collaborate closely on drug discovery projects, with particular emphasis on developing novel degraders for disease-causing proteins. Analysis of Dr. Tang's publication record reveals a significant shift toward targeted protein degradation technologies, particularly PROTACs and molecular glues, while maintaining strong foundations in carbohydrate chemistry. His most impactful recent work includes developing degraders for extracellular and membrane proteins (previously considered 'undruggable'), creating rapid synthesis platforms like Rapid-TAC and Rapid-Glue, and advancing understanding of ternary complex formation for novel PROTAC design. His research spans both chemical methodology development and therapeutic applications across multiple disease areas. Vilas Distinguished Achievement Professorship Janis Apinis Professorship Numerous high-impact publications in leading chemistry and pharmacology journals Editor's pick and hot paper designations for significant contributions Dr. Tang mentors a diverse team of graduate students, postdoctoral fellows, and staff scientists with expertise spanning synthetic chemistry, medicinal chemistry, carbohydrate chemistry, computational chemistry, biochemistry, and cell biology. His group has developed innovative platforms for the rapid synthesis of protein degraders and has made significant contributions to understanding the mechanisms of action for these novel therapeutics. Current research includes developing selective degraders for cancer targets like RIPK1, BRD4, and CARM1, as well as advancing delivery systems for clinical translation. The Tang Research Group maintains state-of-the-art facilities within the School of Pharmacy at UW-Madison, equipped for comprehensive chemical synthesis, compound characterization, and biological evaluation. The group actively collaborates with researchers across campus and with industry partners to advance discoveries toward clinical applications, with particular focus on cancer therapeutics and protein degradation technologies.

Ambuj K. Singhمشاهده پروفایل

استاد

• Machine Learning
• Social Networks
• Network Science
• +۷ مورد دیگر

Ambuj K. Singh is a Distinguished Professor of Computer Science at the University of California, Santa Barbara (UCSB), with a part-time appointment in the Biomolecular Science and Engineering Program. He holds a PhD from the University of Texas at Austin (1989), an MS from Iowa State University (1984), and a BTech from the Indian Institute of Technology, Kharagpur (1982). His campus affiliations include the Center for Bio-Image Informatics, Information Network Academic Research Center, and IGERT on Network Science. PhD, University of Texas at Austin, 1989 M.S., Iowa State University, 1984 B.Tech., Indian Institute of Technology, Kharagpur, 1982 Research interests span network science, machine learning, and bioinformatics, with a focus on graph-based methodologies. His work addresses: Data-centric modeling of dynamic networks Representation learning and explainability in graph neural networks Network analysis in social systems and biological networks Applications in drug discovery and brain sciences Geometry-preserving distance metrics for data integrity Recent publications highlight advancements in counterfactual explanations, GNN benchmarking, molecular graph pretraining, and self-attention for event detection. Scientific contributions include: Founding Acelot, Inc., an in silico drug discovery company Editorial roles at IEEE Transactions on Knowledge & Data Engineering and BMC Journal of Clinical Bioinformatics NSF-IGERT (2013-2018), ARL-funded Information Networks Academic Research Center (2009-2014), and US Army MURI grants Advising has involved mentoring over 50 graduate/postdoctoral students, including 30+ PhD candidates. He leads a multidisciplinary research group at UCSB and collaborates with off-campus entities like Acelot, Inc.

Martin Burkeمشاهده پروفایل

استاد

• Organic Chemistry
• Small-Molecule Synthesis
• Molecular Prosthetics
• +۷ مورد دیگر

Martin Burke is the May and Ving Lee Professor for Chemical Innovation and Professor of Chemistry at the University of Illinois Urbana-Champaign , with additional appointments in Biochemistry, Biomedical & Translational Sciences, and multiple campus institutes including the Beckman Institute and the Carl R. Woese Institute for Genomic Biology. Education B.S. Johns Hopkins University , 1998 Ph.D. Harvard University , 2003 M.D. Harvard Medical School , 2003 Research Interests Burke’s program centers on molecular prosthetics : the design, synthesis and application of small molecules that replicate or replace missing or dysfunctional proteins. His group pioneered iterative cross-coupling (ICC) using MIDA-protected haloboronic acids to automate the construction of complex natural products and function-oriented small molecules. Current projects target ion-channel replacement in cystic fibrosis, iron-transport restoration in anemia, and non-toxic antifungals that overcome drug resistance. Scientific Awards & Honors National Academy of Medicine (2021) AAAS Fellow (2021) ASCI Member (2021) iCON Award (2019) Mukaiyama Award, Japan (2019) ACS Nobel Laureate Award for Graduate Education (2017) Thieme-IUPAC Prize in Synthetic Organic Chemistry (2014) Elias J. Corey Award (2013) Arthur C. Cope Scholar Award (2011) Research Output & Impact Burke has authored >120 peer-reviewed articles, >30 patents, and his work has been cited >20,000 times. High-impact publications in Nature , Science , and Angewandte Chemie have advanced automated synthesis, molecular prosthetics, and cystic fibrosis therapeutics. Laboratory & Training The Burke Laboratories house a multidisciplinary team of graduate students, post-doctoral researchers, and physician-scientists developing next-generation molecular prosthetics. The group is supported by NIH, NSF, private foundations, and industry partnerships aimed at democratizing molecular innovation.

Levent Burak Karaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Design
• +۱۲ مورد دیگر

Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He is a leading researcher in AI-driven computational design, additive manufacturing, and intelligent engineering systems, leading the Visual Design and Engineering Lab (VDEL) at CMU. Education: B.S., Mechanical Engineering, Middle East Technical University (1998) M.S., Mechanical Engineering, Carnegie Mellon University (2000) Ph.D., Mechanical Engineering, Carnegie Mellon University (2005) His research focuses on integrating machine learning, optimization, and geometric modeling to revolutionize engineering design and manufacturing. Key areas include topology optimization, CAD intelligence, digital twins, generative design, bioengineering, and electronic design automation. His work enables automation of traditionally labor-intensive design processes using deep learning and reinforcement learning. His recent publications reveal a strong trend toward physics-informed surrogate modeling, real-time simulation, manufacturability prediction, and AI-driven automation in mechanical, biomedical, and electronic systems. These works frequently appear in top journals such as Journal of Mechanical Design and Journal of Applied Mechanics , and at premier conferences like NeurIPS and DAC. Scientific Awards: National Science Foundation CAREER Award ASME Design Automation Society Young Investigator Award Google AI for Social Good Impact Scholar Kara advises several Ph.D. students and has secured significant funding from federal agencies such as the NSF and the U.S. Army Research Laboratory, as well as collaborations with industrial leaders including Cadence Design Systems and NVIDIA. His research is also supported by CMU’s NextManufacturing Center and the Critical Technology Initiative. He is actively involved in developing intelligent design systems that leverage AI to automate product design, optimize manufacturing processes, and improve medical diagnostics, particularly in oral cancer screening and organ preservation. His lab, VDEL, is a hub for innovation in AI-enabled engineering.

• Nuclear Magnetic Resonance (NMR)
• In vivo imaging
• MRI pulse sequence development
• +۷ مورد دیگر

Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.

Markus Heinonenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +۵ مورد دیگر

Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

Klavs F. Jensenمشاهده پروفایل

استاد

• Materials Science
• Chemical Engineering
• Machine Learning
• +۴ مورد دیگر

Professor Klavs F. Jensen is the Warren K. Lewis Professor of Chemical Engineering and Professor of Materials Science and Engineering at MIT. His research focuses on integrating automation, machine learning, and robotics to accelerate materials discovery and pharmaceutical synthesis. He leads the Jensen Research Group, pioneering automated reaction systems with online analytics and optimization algorithms. Education: MS in Chemical Engineering (Technical University of Denmark, 1976); PhD in Chemical Engineering (University of Wisconsin, 1980). Research Interests: Thermochemistry, electrochemistry, photochemistry, Bayesian optimization, high-throughput experimentation, and AI-driven synthesis planning. He collaborates with MIT’s Machine Learning for Pharmaceutical Discovery Consortium to develop algorithms for drug development and process chemistry. Awards: Member of National Academy of Sciences (2017), Member of National Academy of Engineering (2002), Fellow of the American Association for the Advancement of Science (2007), and Fellow of the National Academy of Inventors (2022). Grants & Labs: Editor-in-Chief of Reaction Chemistry and Engineering ; holds 63 US patents and over 490 journal articles. His lab’s innovations include ASKCOS (open-source synthesis planning software) and automated platforms for closed-loop molecular discovery.

Brian Hieمشاهده پروفایل

استادیار

• Machine Learning
• Computational Biology
• Protein Engineering
• +۶ مورد دیگر

Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University , a Dieter Schwarz Foundation Stanford Data Science Faculty Fellow , and an Innovation Investigator at Arc Institute . He leads the Laboratory of Evolutionary Design , focusing on the intersection of biology and machine learning . His prior roles include a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI . Education: Ph.D. , Electrical Engineering and Computer Science , Massachusetts Institute of Technology (2021) Bachelor’s Degree , Stanford University Research Interests: Brian’s work bridges machine learning and computational biology , with a focus on protein engineering , single-cell RNA sequencing , and viral evolution . His Evolutionary velocity framework predicts protein evolutionary dynamics across timescales, while his Scanorama algorithm enables efficient integration of heterogeneous single-cell datasets. He also develops structure-informed language models for antibody optimization and uncertainty-aware ML for biological discovery. Publication Trends: His recent work (2023) emphasizes structure-based inverse folding for antibody evolution, evolutionary scale modeling , and unsupervised optimization . Earlier studies (2022-2021) cover evolutionary velocity , multi-modal single-cell analysis , and viral escape prediction using natural language analogies. Scientific Awards: Stanford Science Fellow (2021) National Defense Science and Engineering Graduate Fellowship (2019) Advising: He mentors doctoral students including Brandon Ameglio , Garyk Brixi , and Chang M. Yun , with a focus on biological design and computational methods . Labs & Collaborations: His lab collaborates with Bio-X and the Institute for Human-Centered Artificial Intelligence (HAI) , and he maintains affiliations with Sarafan ChEM-H and Stanford Data Science .

Scott E. Denmarkمشاهده پروفایل

استاد

• Organic Chemistry
• Synthetic Organic Chemistry
• Organoelement Chemistry
• +۵ مورد دیگر

Scott E. Denmark is the Reynold C. Fuson Professor of Chemistry at the University of Illinois, Department of Chemistry, within the College of Liberal Arts & Sciences. He earned his S.B. from MIT (1975) and D.Sc. Tech. from ETH-Zürich (1980) under Albert Eschenmoser. His research focuses on synthetic organic chemistry, organoelement systems (Si, P, Sn, S, Li), palladium catalysis, and chemoinformatics. He has pioneered methods in asymmetric catalysis, total synthesis of natural products, and tandem cycloaddition reactions. Denmark chairs editorial boards for top journals and leads the Denmark Group, mentoring over 100 students. Awards include the Paracelsus Prize (2020), Noyori Prize (2019), and membership in the National Academy of Sciences (2018). Education: S.B., Massachusetts Institute of Technology, 1975 D.Sc. Tech., ETH-Zürich, 1980 (Advisor: Albert Eschenmoser) Research Interests: Design of new organic reactions and catalysts Structure-reactivity relationships in organoelement systems Total synthesis of alkaloids, polyenes, and glycosides Machine learning for catalyst optimization Asymmetric phase transfer catalysis Green chemistry using water-based systems Recent Research Trends: Recent work emphasizes indium-catalyzed allylations of carbohydrates (Nature, 2025) and chemoinformatics-driven catalyst design. Collaborations with MIT and industry (e.g., Pfizer, Amgen) highlight applied impact. Awards: Paracelsus Prize (2020) Ryoji Noyori Prize (2019) Member, National Academy of Sciences (2018) Member, American Academy of Arts and Sciences (2017) Advising & Grants: Mentored 40+ PhD students and 60+ postdocs. Active in funding initiatives for chemoinformatics and sustainable catalysis. Recent grants include Pines Fellowship (2025) for student Matthew Albritton. Labs/Teams: Leads the Denmark Group at UIUC, known for interdisciplinary projects merging organic synthesis with computational methods. Collaborates globally, including with ETH-Zürich and Hiroshima University.

Hang Luمشاهده پروفایل

دانشیار

• Science Communication
• Health Communication
• Environmental Communication
• +۴ مورد دیگر

Hang Lu is an Associate Professor in the Department of Communication and Media at the University of Michigan's College of Literature, Science, and the Arts. He specializes in science, health, environmental, and risk communication (ComSHER), with a focus on media psychology. His research explores audience responses to media messages about sensitive topics and strategies to enhance message effectiveness. Lu holds a Ph.D. in Communication from Cornell University (2018), along with advanced degrees from Cornell, Marquette University, and Central South University in China. He directs the Media and Risk (MaR) Lab and previously served as a postdoctoral fellow at the Annenberg Public Policy Center. His research spans four main areas: 1) emotion dynamics in media responses, 2) predictors of information behaviors, 3) media effects on stigmatization, and 4) AI applications in sensitive domains. He has published in journals like Journal of Communication , Risk Analysis , and Public Understanding of Science , earning multiple top paper awards. As Vice Chair of the Environmental Communication Division at the International Communication Association, Lu contributes to interdisciplinary dialogue. His work addresses critical societal issues such as climate change communication, vaccination hesitancy, and emerging technology ethics.