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David ClewettView profile

Assistant Professor

• Emotion and memory
• Neuroscience of arousal
• Episodic memory
• +4 more

David Clewett, Ph.D. , is an Assistant Professor of Psychology at University of California, Los Angeles (UCLA) , where he leads the Dynamic Arousal and Memory Lab. His research explores how emotional arousal, stress, and neuromodulatory systems like norepinephrine and dopamine shape the way we encode, organize, and retrieve memories. He joined UCLA in July 2020 after completing his Ph.D. in Neuroscience at the University of Southern California and a postdoctoral fellowship at NYU and Columbia University. Education: Ph.D. in Neuroscience, University of Southern California (2016) B.S. in Biopsychology with minor in English, University of California, Santa Barbara Research Focus: Dr. Clewett’s lab investigates how physiological arousal influences attention and memory, using a multimodal approach that includes fMRI, pupillometry, eye-tracking, and pharmacological methods. His work spans four major themes: The role of emotion and arousal in selective memory enhancement or suppression. How contextual shifts and emotional states structure episodic memory into meaningful events. Techniques to weaken or update traumatic or unwanted memories. The dynamic effects of attention and arousal states on neural and memory representations. Publications and Impact: His work has been published in top-tier journals such as Nature Communications , Journal of Neuroscience , Hippocampus , and Trends in Cognitive Sciences . His research has contributed to understanding how neuromodulators like norepinephrine and dopamine interact with memory systems to prioritize salient information, and how these processes can be leveraged for therapeutic intervention in PTSD, depression, and aging-related memory decline. Students and Lab Team: Dr. Clewett mentors a diverse team of graduate students and research assistants. Current graduate students include Jacinda Taggett, Ringo Huang, Erin Morrow, Bailey Harris, and Brandon Katerman. Former lab members have gone on to Ph.D. programs at Harvard, UC Berkeley, and other top institutions. Lab and Facilities: The lab is located in Pritzker Hall at UCLA, and is equipped with tools for fMRI, pupillometry, eye-tracking, and behavioral testing. The lab also collaborates with researchers across UCLA and other institutions, including NYU, Columbia, and USC.

Risto MiikkulainenView profile

Professor

• Neuroevolution
• Evolutionary Computation
• Deep Learning
• +7 more

Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.

• Advanced Materials
• Composite Materials
• Smart Materials
• +8 more

Prof. Raul Fangueiro is a Full Professor and Vice-Dean at the School of Engineering, University of Minho, Portugal. As a Senior Researcher, he leads FIBRENAMICS – Institute of Innovation on Fiber-Based Materials and Composites. His work spans advanced materials (nano, smart, composites) and structures (3D, auxetic, multiscale) with applications in defense, healthcare, construction, and automotive sectors. Supervised over 20 PhD and Post-Doc researchers Scientific coordinator of 20+ national/international research projects Author of 200+ journal papers (H-index: 47), 500+ conference publications, 36 books, 40 patents Research focuses on nanotechnology , electrospinning , and sustainable material systems : Auxetic composites for personal protection Biodegradable nanofibers for medical use Smart textiles for biological/chemical resistance Recycled mineral/wood-based composites Graphene-reinforced green materials Multiscale fiber architectures Scientific recognition includes: Top 2% most influential scientist (Elsevier/Stanford 2020) Founder of AUXDEFENSE and ICNF conferences Editorial board member of leading composite journals Advisor to European Defense Agency/NATO working groups Active in industry-academia partnerships through spin-offs (Sciencentris, B4Logic, Beyond Composites, Givaware, Pixartidea) and collaborative projects with institutions like Instituto Superior Técnico, University of Aveiro, and international universities.

Elisa DonatiView profile

Researcher

• Neuromorphic Engineering
• Biomedical Signal Processing
• Wearable Healthcare Technologies
• +5 more

Dr. Elisa Donati is a researcher and independent group leader at the Institute of Neuroinformatics , affiliated with both the University of Zurich and the Swiss Federal Institute of Technology Zurich . Her work bridges neuromorphic engineering, biomedical signal processing, and wearable healthcare technologies, with a focus on creating brain-inspired systems for neuroprosthetics and rehabilitation. Affiliation: Institute of Neuroinformatics, University of Zurich & ETH Zurich Email: elisa@ini.uzh.ch Elisa’s research emphasizes developing neuromorphic signal processing strategies for wearable and embedded systems, enabling real-time closed-loop interactions with the nervous system. She specializes in translating neuroscience insights into energy-efficient technologies for digital health applications, including neuroprosthetics and personalized biomedical devices using neuromorphic hardware. Her recent publications (2024–2025) highlight advancements in gesture recognition via EMG and event-based systems, neuromorphic heart rate monitoring , and spiking neural network architectures . These works span biomedical signal processing, low-power computing, and adaptive algorithms, reflecting her commitment to robust, real-time, and brain-inspired solutions for healthcare. Elisa’s contributions to neuromorphic computing are evident in her exploration of heterogeneous population encoding , event-driven processing , and ultra-low-power microcontrollers . Her projects often integrate wearable systems with neuroscience, aiming to improve prosthetic control and rehabilitation technologies .

• Machine Learning
• Reinforcement Learning
• Robotics
• +3 more

Georg Martius is a Full Professor in the Department of Computer Science at the University of Tübingen's Faculty of Science and a Max Planck Research Group Leader at the MPI for Intelligent Systems. Since April 2023, he has been a core member of the DFG-funded Cluster of Excellence 'Machine Learning: New Perspectives for Science,' which received extended funding through 2032 for its mission to integrate machine learning into fundamental scientific discovery processes. His academic foundation includes a PhD from the University of Göttingen and Bernstein Center for Computational Neuroscience (2005), a Diploma in Computer Science from the University of Leipzig (2003), and a visiting research period at the University of Edinburgh's Division of Informatics. Postdoctoral positions followed at the Max Planck Institutes for Dynamics and Self-Organization (Göttingen, 2009), Mathematics in the Sciences (Leipzig, 2010), and IST Austria (2015). Professor Martius's research pioneers the intersection of reinforcement learning, robotics, and tactile sensing, with emphasis on developing autonomous systems capable of natural locomotion, dexterous manipulation, and physical-world understanding. His work bridges theoretical machine learning with practical hardware applications, particularly in creating differentiable simulators, superresolution tactile sensors, and biologically plausible learning frameworks for robotic control. Analysis of his 2024-2025 publications reveals dominant trends in offline reinforcement learning (especially goal-conditioned and diversity-maximization techniques), object-centric representation learning for video understanding, and tactile sensing innovations. A strong thread connects foundation models to world model construction, while his work on differentiable physics engines enables precise collision handling and contact dynamics for real-world robotic control. His leadership roles include directing the Distributed Intelligence research team at Tübingen and contributing to major collaborative initiatives like the Real Robot Challenge and Myochallenge 2022. The Cluster of Excellence appointment represents recognition of his contributions to transforming scientific methodology through machine learning, particularly in automating hypothesis generation and experimental design. Current projects focus on integrating large-scale machine learning with embodied intelligence, advancing tactile perception systems like the Minsight vision-based sensor, and developing neuroplasticity-inspired approaches for robust out-of-distribution detection. His work directly impacts fields requiring physical interaction intelligence, from autonomous navigation to medical robotics, with emphasis on sample-efficient learning from limited real-world data.

Christopher E. NelsonView profile

Assistant Professor

• Gene therapy
• Regenerative medicine
• Genome editing
• +7 more

Christopher E. Nelson is an Assistant Professor in the Department of Biomedical Engineering at the University of Arkansas, College of Engineering. His lab focuses on developing biologically inspired strategies for controlled drug and gene delivery, particularly in the context of gene therapy and regenerative medicine. He is actively supported by the NIH, DoD, and Arkansas Bioscience Institute. Education: Postdoctoral Fellow – Duke University Ph.D. – Vanderbilt University B.S. – University of Arkansas Research Focus: Dr. Nelson’s lab integrates genome editing technologies with targeted delivery systems to address challenges in treating genetic diseases and promoting tissue regeneration. Major themes include CRISPR/Cas9 delivery , gene regulation in wound healing , and safe-harbor genome integration in skeletal muscle. His work spans viral and non-viral delivery vehicles , including lipid nanoparticles and AAV vectors, with a strong emphasis on preclinical validation in models of Duchenne muscular dystrophy and inflammatory disease. Scientific Awards: Controlled Release Society Postdoctoral Fellowship The Hartwell Foundation Postdoctoral Fellowship NIH Pathway to Independence Award (K99/R00) Funding & Support: The Nelson Lab is currently funded by: NIH NIGMS R35 DoD CDMRP DMD IDEA Award Arkansas Bioscience Institute University of Arkansas Engineering & Honors Colleges Lab & Team: The Nelson Lab is a dynamic, interdisciplinary team working at the intersection of gene editing, biomaterials, and regenerative medicine. They regularly present at national conferences such as ASGCT and NCUR, and mentor undergraduate researchers through SURF and Honors College grants.

• Micrometeorology
• Biogeochemical Cycles
• Atmospheric Sciences
• +3 more

Timo Vesala is Professor of Meteorology and Academy Professor at the University of Helsinki’s Faculty of Agriculture and Forestry, Institute for Atmospheric and Earth System Research (INAR). He is also affiliated with the Viikki Plant Science Centre (ViPS) and serves as a supervisor in the Doctoral Programme in Atmospheric Sciences. Research Interests: Micrometeorology and biogeochemical cycles Ecosystem–atmosphere exchanges of greenhouse gases Eddy-covariance methodology and flux networks Boreal lakes, wetlands, and forests as components of the climate system Development of long-term observational infrastructures such as ICOS-Finland Recent research output (2023–2025) is dominated by high-impact articles in Advances in Atmospheric Sciences , Biogeosciences , Agricultural and Forest Meteorology , and Geophysical Research Letters , reflecting a balanced portfolio of process understanding, methodological advances, and large-scale synthesis studies. Scientific Awards: Academy Professor (Akatemiaprofessori) – awarded by the Academy of Finland Doctoral Advising & Grants: Supervised or co-supervised doctoral theses of Sheila Wachiye, E. Lopez-Blanco, and X. Li Principal Investigator on Academy of Finland project “The Hidden Role of Gases in Trees” (2021–2025) Project leader for “Kasvihuonekaasujen maa-ilmakehävaihto järvi- ja suoekosysteemeille” (2024–2026) Co-leader of the art-science initiative “Periferia – Metsätaiteeellinen asema” (2021–2031) Participant in EU flagship EMME-CARE (2017–2026) Labs & Teams: Timo Vesala heads the Micrometeorology Group at INAR and leads the Finnish ICOS (Integrated Carbon Observation System) network node. His team operates multiple eddy-covariance towers across boreal lakes, wetlands, and forests, integrating field observations with modeling and remote-sensing data.

• Computer Vision
• Autonomous Driving
• Robotics
• +7 more

Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools

George BritovsekView profile

Professor

• Catalysis
• Sustainable Carbon Management
• Transition Metal Complexes
• +3 more

Professor George Britovsek (FRSC) is a leading figure in catalysis and sustainable carbon management at Imperial College London . As Director of the MRes in Catalysis & Engineering and Head of Teaching in Inorganic Chemistry, he bridges academic leadership with cutting-edge research. His work focuses on transition metal complexes for converting ethylene , alkanes , biomass , and CO₂ into valuable chemicals and fuels through industrial collaborations. Education : M.Sc. (Technical University of Aachen, 1990), Ph.D. (Aachen, 1993) under Prof. W. Keim Postdoctoral Training : University of Tasmania (1994-1996), Imperial College London (1996-2000) His research interests span: Selective oxidation of alkanes using bio-inspired iron complexes Alkene conversions to functional polymers via novel catalysts CO₂ valorization into polymers and cyclic carbonates Biomass-derived feedstocks for chemical synthesis Recent catalysis trends highlight his work on: Designing Fe-N/C catalysts for epoxidation Developing PN3P pincer ligands for H₂ activation Creating degradable polyethylene via iron-catalyzed chain growth Modeling alternating α-olefin distributions in chromium systems Awards : Fellow of the Royal Society of Chemistry (FRSC) Students & Collaborators actively engage in: Photocatalytic polymer degradation Electrocatalytic CO₂ conversion Functionalized polymeric materials 3D-printed catalytic scaffolds His Britovsek Research Group operates at the Molecular Sciences Research Hub, White City Campus, advancing both homogeneous and heterogeneous catalysis through experimental and computational approaches.

Ahmed HassoonView profile

Academic

• Artificial Intelligence
• Multi-modal reasoning
• Diagnostic Safety
• +9 more

Ahmed Hassoon is an Assistant Research Professor at the Johns Hopkins Bloomberg School of Public Health, with a primary appointment in the Department of Epidemiology and joint appointments in the Department of Neurology at the School of Medicine and the School of Engineering. He is affiliated with several key research centers, including the Welch Center for Prevention, Epidemiology and Clinical Research, the Center for Diagnostic Excellence, and the Center for Humanitarian Health. MPH, Johns Hopkins Bloomberg School of Public Health, 2014 MD, Baghdad College of Medicine, 2004 Dr. Hassoon’s research is centered on leveraging data science and artificial intelligence to improve healthcare quality and safety, particularly in diagnostic accuracy. His work spans applications in lung cancer, stroke, and emergency medicine, with a strong emphasis on reducing diagnostic errors using frameworks like symptom-disease pair analysis (SPADE). He is deeply engaged in developing AI models that reason across multiple clinical modalities to enhance patient outcomes. His recent publications highlight a strong trend in diagnostic safety, AI-driven interventions, and epidemiological analysis of misdiagnosis harms. Key themes include the validation of computable phenotypes, measurement of diagnostic error burden, and the impact of mental health on acute care diagnostics. Hubert Humphrey Fellowship Award, U.S. Department of State (2010) US President Appreciation Certificate, The White House (2011) Atlas Corps Award (2012) Sommer Scholarship Award (2013) State of Maryland Cigarette Restitution Fund Faculty and Translational Research Awards (2016–2017) Institute for Healthcare Improvement (IHI) Fellowship (2024) Dr. Hassoon is a recipient of the AHRQ K08 Career Development Award focused on improving cancer diagnostic safety at Johns Hopkins Hospital. He co-instructs graduate-level courses in data science and AI with Professor Brian Caffo, contributing to the training of the next generation of public health data scientists. He has not formally advised students listed in the provided texts. His collaborative research spans multiple institutions and has been widely disseminated through policy, news, and social media, indicating significant real-world impact. His work is deeply integrated with the Welch Center and the Center for Diagnostic Excellence, where he contributes to translational research initiatives aimed at improving clinical decision-making and patient safety through innovative data-driven approaches.

Martin LarssonView profile

Professor

• Mathematical Finance
• Stochastic Analysis
• Probability Theory
• +4 more

Martin Larsson is a Professor in the Department of Mathematical Sciences at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. He holds a Ph.D. from Cornell University and completed a postdoctoral appointment at the Swiss Finance Institute at EPFL, Lausanne, Switzerland. His research focuses on Mathematical Finance, stochastic analysis, probability, and statistics, with emphasis on affine and polynomial processes, stochastic portfolio theory, and sequential statistics. Key research domains include modeling interest rate term structures, large-scale equity market dynamics, and statistical testing in online settings. He serves as the Departmental representative for the Master of Science in Computational Finance (MSCF) program at CMU. Larsson has received the Bruti-Liberati Visiting Fellowship from the University of Technology Sydney. His work bridges theoretical probability with applications in finance, including contributions to stochastic volatility modeling, optimal contracts in trading, and robust portfolio optimization under uncertainty. Publications span topics such as martingale exit times, Wasserstein distance convergence, and ergodic control in stochastic systems, reflecting his interdisciplinary approach to mathematical finance and probability theory. His research often combines analytical techniques with stochastic control and geometric flows.

Hongye ZhangView profile

Lecturer

• Superconductor Technology
• Cryogenic Techniques
• Electric Machines and Drives
• +3 more

Dr. Hongye Zhang serves as a Lecturer in Superconducting and Cryogenic Electric Machines at the School of Engineering, University of Edinburgh, while maintaining a Visiting Research Fellow position at the University of Manchester. He actively contributes to the European Society for Applied Superconductivity (ESAS) as a Board Member and chairs the international HTS 2026 workshop. His educational foundation includes: BSc and MSc in Electrical Engineering from Xi’an Jiaotong University (2015, 2018) Diplôme d’ingénieur (MEng) from École Centrale de Lyon (2018) PhD in Applied Superconductivity from the University of Edinburgh (2021) Dr. Zhang’s research centers on decarbonizing transport through superconducting/cryogenic electric machines for hydrogen-powered aircraft, integrating artificial intelligence with superconductor technology and cryogenic techniques. His work targets net zero emissions by developing high-power-density propulsion systems that leverage hydrogen energy and advanced numerical modeling of superconductors. Analysis of his 2022-2025 publications reveals dominant themes in superconducting machine design for wind energy and electric aviation, with significant contributions to loss mitigation, flux pump technology, and trapped field magnet applications. His research bridges fundamental superconductor characterization with practical system integration for renewable energy. Recognized with the 2021 IEEE Council on Superconductivity Graduate Study Fellowship, his professional engagements include: Early Career Editorial Board Member for Elsevier’s Superconductivity journal Technical Editor for IEEE Transactions on Applied Superconductivity Program Committee Member for SMT 2023 He leads critical research within the £54-million H2GEAR project developing hydrogen-electric aircraft propulsion, while teaching Power Engineering 2 and Electrical Machines courses. His advisory roles span doctoral supervision and industry collaboration through Energy Systems research institute. Based at the University of Edinburgh’s Faraday Building, Dr. Zhang directs a research group focused on hydrogen energy applications and superconducting machine testing, with strong ties to the H2GEAR consortium and ESAS working groups.

Eugene DemlerView profile

Professor

• Theoretical Condensed Matter Physics
• Atomic and Molecular Physics
• Quantum Optics
• +1 more

Eugene Demler is a Full Professor at the Department of Physics, ETH Zurich. Previously, he held academic positions at Harvard University from 1998 to 2021, including Assistant Professor (2001-2004), Associate Professor (unspecified dates), and Full Professor (2005-2021). His work bridges theoretical condensed matter physics, atomic and molecular physics, quantum optics, and quantum simulations. Education: MSc in Physics, Moscow Institute of Physics and Technology (1993) Diploma work, Lebedev Physics Institute (1992-1993) PhD in Theoretical Physics, Stanford University (1998), supervised by S.C. Zhang Demler's research focuses on strongly correlated quantum systems, spintronics, quantum sensing, and photo-induced phase transitions. His recent publications explore topics such as quantum polarons, Josephson plasmons, magnon dynamics, and terahertz spectroscopy in superconductors. He has pioneered hybrid quantum-classical methods for electron-phonon systems and cavity-mediated quantum materials. His Google Scholar articles (2023-2025) span theoretical and experimental domains, with keywords including Quantum Physics , Condensed Matter Physics , and Quantum Optics . Subfields include Quantum Control , Superconductivity , Spin Waves , Quantum Sensing , Non-Equilibrium Dynamics , and Quantum Simulation . Scientific Awards: Hamburg Prize for Theoretical Physics (2021) Simons Investigator (2021) Moore Distinguished Scholar (2020) Hanna Visiting Scholar (2019) Highly Cited Researcher (2017-2020) Senior Fellow at ETH Zurich's Institute for Theoretical Studies (2015) Simons Fellowship (2015) Distinguished Scholar at Max Planck Institute of Quantum Optics (2015) Thomson Reuters Highly Cited Researcher (2014) Siemens Research Award (2006) Johannes Gutenberg Lecture Award (2006) NSF Career Award (2002) Sloan Fellowship (2002) Demler teaches courses such as Statistical Physics and Strongly Correlated Systems in Atomic and Condensed Matter Physics . His work integrates theoretical modeling with experimental collaborations, particularly in quantum optics and condensed matter systems.

Sai RavelaView profile

Researcher

• Nonlinear Stochastic Dynamics
• Coherent Fluids
• Uncertainty Quantification
• +6 more

Sai Ravela is a Principal Research Scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT). His research focuses on nonlinear stochastic dynamics, coherent fluid systems, uncertainty quantification, and autonomous observing technologies. He specializes in developing data-driven methodologies for natural hazard detection, climate change impacts, and environmental risk assessment. Ravela’s work integrates computational science with geophysical applications, including storm surge modeling, extreme rainfall analysis, and geothermal exploration. He pioneers techniques like neural dynamical systems and adversarial learning to improve predictive accuracy in nonstationary climate regimes. His contributions span environmental monitoring systems, autonomous aircraft resilience frameworks, and policy-informed climate vulnerability assessments. Key research areas include: Coastal flood risk in Bangladesh and Vietnam Dynamic data-driven applications systems (DDDAS) Machine learning for geosciences and environmental systems Uncertainty quantification in complex fluid dynamics He leads interdisciplinary projects at MIT’s Computational Science and Engineering (CSE) program, advancing methods for data assimilation, surrogate modeling, and real-time environmental observatories. His innovations bridge theoretical frameworks with practical solutions for climate adaptation and disaster resilience.

Li BaowenView profile

Professor

• Phononics
• Thermal Metamaterials
• Nano Heat Transfer
• +2 more

Dr. Li Baowen is Chair Professor at Southern University of Science and Technology (SUSTech) with joint appointments in the Department of Physics and Department of Materials Science and Engineering. A pioneer in phononics and thermal metamaterials, he previously held endowed professorships at University of Colorado Boulder and UC Berkeley. His research focuses on controlling heat transfer at nano scales, developing thermal metamaterials like thermal cloaks, and applying complex networks to physical systems. Dr. Li has published over 400 papers including 3 in Reviews of Modern Physics and 30 in Physical Review Letters, with more than 34,800 citations (H-index 98). He is recognized with the Brillouin Medal from the International Phononics Society and is a Fellow of the American Physical Society. He founded research centers including the China-EU Joint Lab for Nanophononics at Tongji University. His current research explores quantum phononics, machine learning applications in thermal materials, phonon lasers, and quantum sensing technologies.