جست‌وجوی استاد و پژوهشگر

Maja J Matarićمشاهده پروفایل

استاد

• Socially Assistive Robotics
• Human-Robot Interaction
• Assistive Technology
• +۷ مورد دیگر

Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.

Benjamin Carrison-Schaferمشاهده پروفایل

دانشیار

• Reconfigurable Computing
• FPGAs
• Electronic Design Automation
• +۷ مورد دیگر

Benjamin Carrison-Schafer is an Associate Professor and Assistant Dean for Graduate Student Success at the Department of Electrical and Computer Engineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He leads the Design Automation and Reconfigurable Computing Laboratory (DARClab), focusing on systems, high-level design, and programming methodologies for VLSI computing systems and FPGAs. Education: MBA from McGill University, Canada (2012) PhD in Electrical and Electronic Engineering from University of Birmingham, UK (2003) Research Interests: His work spans modeling, analysis, synthesis, optimization, and implementation of VLSI systems. Current projects include: Approximate Computing, High-Level Synthesis Design Space Exploration, Hardware Security, Behavioral MPSoC Optimization, and Automatic Fault-Tolerant System Generation. His research blends theory and practice, using analytical and experimental techniques to solve real-world problems. Publication Trends: Recent publications demonstrate strong focus on hardware security, FPGA optimization, and automated design methodologies. Key themes include High-Level Synthesis innovations, hardware acceleration techniques, and cross-disciplinary applications of reconfigurable computing. His work frequently addresses challenges in hardware trustworthiness, energy efficiency, and design automation scalability. Professional Activities: Associate Editor: IEEE Transactions on Sustainable Computing (2022-present) Conference Chair: ICCD 2024, DCAS 2024 Program Committee: ASP-DAC, DATE, FCCM, GLSVLSI Advising & Labs: Supervises multiple PhD and Master's students at DARClab. Current research includes domain-specific architecture design, hardware security, sustainable computing, and ML for VLSI design. The lab collaborates with industry partners like Renesas Electronics and develops commercial tools through spin-off company highX Technologies.

Haitao Shiمشاهده پروفایل

پژوهشگر

• Criminal Justice
• Drug Policing
• Research Methods
• +۳ مورد دیگر

Dr Haitao Shi is a Researcher affiliated with the School of Social and Political Science at the University of Edinburgh. His research focuses on criminal justice systems, drug policing in China, and the intersection of technology with crime. He employs advanced research methods including quantitative tools (R, Python, SPSS), qualitative software (Nvivo, MAXQDA), and data visualization techniques (Gephi, QGIS). His current projects include studying Chinese students' experiences at the University of Edinburgh and analyzing rhizomatic networks in online drug trade funded by the Polish Ministry of Science. Haitao's work emphasizes cross-cultural comparisons, particularly in drug trade dynamics across Finnish, Polish, and English-speaking contexts. He explores topics like police professionalization in China, guanxi networks, and quota-driven policing strategies. His methodologies span cloud computing (Azure, AWS), database systems (MySQL), and version control (Git), reflecting a technologically adept approach to criminology research. Notable research themes include the role of marketing in darknet drug trade, generational conflicts in police culture, and community-based drug rehabilitation policies. He collaborates with international teams and utilizes platforms like Microsoft Azure for large-scale data analysis.

Peter A. Flachمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Explainable AI (XAI)
• +۳ مورد دیگر

Peter A. Flach is Professor of Artificial Intelligence at the Intelligent Systems Laboratory, School of Computer Science, University of Bristol, where he has been faculty since 1997 and was promoted to Professor in 2003. He currently directs the UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence and serves as Vice-President of the European Association for Data Science (EuADS), having previously served as its President. His research centers on rigorous evaluation and improvement of machine learning systems, with seminal contributions to classifier calibration (including Beta Calibration and Precision-Recall-Gain curves), explainable AI frameworks, and data science methodology. He pioneered the extension of CRISP-DM to data science trajectories and developed Explainability Fact Sheets for systematic assessment of XAI approaches. His work bridges theoretical foundations with practical deployment in real-world systems. Analysis of his recent publications reveals a dominant focus on interpretable and reliable machine learning, with strong emphasis on performance evaluation metrics, model calibration techniques, and human-centered explainability. His research increasingly addresses healthcare applications through projects like SPHERE and clinical decision support systems, while maintaining core contributions to fundamental ML theory. His scientific recognition includes prestigious fellowships: Fellow of the European Lab for Learning and Intelligent Systems (ELLIS) Fellow of the European Association for Artificial Intelligence (EurAI) Professor Flach has secured major research funding including UKRI Centre for Doctoral Training grants and EU network funding (TAILOR). He leads extensive collaborations across Engineering, Population Health Science, and international institutions (Monash University, Polytechnic University of Valencia), plus over 20 industry partners in the Practice-Oriented AI CDT including LV= and QinetiQ. His work with the SPHERE project demonstrates successful translation of AI research into residential healthcare settings. He leads the Intelligent Systems Laboratory at Bristol, which develops influential open-source tools including the FAT Forensics Python toolbox for algorithmic fairness and the Explainability Fact Sheets framework. His lab maintains strong connections with the European AI community through ELLIS and EurAI, positioning Bristol as a hub for human-centered and methodologically rigorous AI research.

Nadia Polikarpovaمشاهده پروفایل

دانشیار

• Program Synthesis
• Program Verification
• Type Systems
• +۵ مورد دیگر

Nadia Polikarpova is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego . She earned her PhD from ETH Zurich in 2014 under Bertrand Meyer , followed by postdoctoral research at MIT CSAIL with Armando Solar-Lezama . Her academic contributions have been recognized with prestigious awards including the 2020 Sloan Fellowship , 2020 Intel Rising Stars Award , and 2020 NSF CAREER Award . Polikarpova's research focuses on program synthesis , program verification , and type systems . She leads the Programming Systems group at UCSD and contributes to the IFIP Working Group 2.8 on Functional Programming since 2022. Her work spans foundational research and practical tools, including projects like Synquid , SuSLik , and Laurel that combine formal methods with machine learning for code generation. Her recent publications in venues like OOPSLA , NeurIPS , and ICFP reveal trends in AI-assisted programming , live programming environments , and formal verification . She has advised numerous PhD and Master’s students including Shraddha Barke , Zheng Guo , and Tristan Knoth , many of whom have moved to prominent academic and industry positions. Notable artifacts from her lab include tools like ColDeco for spreadsheet inspection and Superfusion for eliminating intermediate data structures. 2020 : Sloan Fellow 2020 : Intel Rising Stars Award 2020 : NSF CAREER Award 2021 : Distinguished Paper at POPL 2023 : Distinguished Artifact at PLDI 2023 : Distinguished Paper at OOPSLA Polikarpova actively contributes to academic service, serving on program committees for PLDI , POPL , and OOPSLA , and co-chairing the OOPSLA Review Committee in 2023. She has delivered keynotes at APLAS'20 and PLDI'24 , emphasizing the integration of large language models with formal methods.

Josiah Hesterمشاهده پروفایل

دانشیار

• Sustainable Computing
• Battery-Free Systems
• Intermittent Computing
• +۷ مورد دیگر

Josiah Hester is Associate Professor of Interactive Computing and Computer Science at Georgia Tech's College of Computing, where he also directs the Center for Advancing Responsible Computing and the Ka Moamoa – Ubiquitous and Mobile Computing Lab . His research focuses on sustainable, battery-free computing systems for health, conservation, and education, informed by his Native Hawaiian heritage. Education: Ph.D. in Computer Science, Clemson University, 2017 B.S. in Computer Science, Clemson University Honors College, 2011 Research Interests: Hester’s group pioneers intermittent computing —devices that harvest ambient energy (solar, radio, thermal, microbial) and operate reliably despite frequent power loss. Applications span: Large-scale conservation sensing with Indigenous communities Battery-free health wearables (smart masks, implantables) Educational tools for sustainable design Carbon-aware IoT and recyclable robotics Recent Work & Trends: His 2024–2025 publications unveil soil-powered sensors, recyclable electronics, and culturally-grounded CS education tools. A consistent theme is co-design with underserved communities and embedding sustainability metrics at every layer—from hardware to curriculum. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE) – 2025 Alfred P. Sloan Fellowship – 2022 NSF CAREER Award – 2022 Popular Science “Brilliant 10” – 2021 3M Non-Tenured Faculty Award – 2021 Multiple best-paper & best-presentation awards Grants & Teams: Hester’s lab is supported by >$50 million in active funding from NSF, NIH (ARPA-H), DARPA, DoD, Sloan, VMware, and 3M. He leads interdisciplinary teams on projects such as: $35 million ARPA-H bioelectronics initiative for diabetes and cancer $5 million NSF “Coastlines and People” Hub for Manoomin conservation $2 million NSF-DELPHI sustainable edge device design Laboratory & Mentorship: The Ka Moamoa Lab (named with Hawaiian meaning “lamp of the moamoa fish”) hosts graduate, undergraduate, and post-doctoral researchers. Hester actively recruits Native and Indigenous students and has mentored award-winning advisees such as Nivedita Arora (ACM Doctoral Dissertation Award 2024).

David R. Koesمشاهده پروفایل

دانشیار

• Computational Drug Discovery
• Machine Learning in Biology
• Open Source Software
• +۳ مورد دیگر

David R. Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the School of Medicine. He holds roles such as Associate Director of the Joint CMU-Pitt Computational Biology PhD Program (CPCB) and is involved in multiple graduate programs including Intelligent Systems and Computational Biomedicine. His research focuses on developing computational algorithms and systems for drug discovery, emphasizing open-source software and machine learning applications in biomedical data. Koes teaches courses like MSCBIO2025 (Bioinformatics Programming in Python) and MSCBIO2065 (Scalable Machine Learning for Big Data Biology). He has secured NIH funding (R35GM140753) and collaborated on projects with institutions like NVIDIA and Google Cloud. His lab develops tools such as GNINA, Pharmit, and 3Dmol.js, and actively contributes to open drug discovery initiatives. Education: PhD in Computer Science from Carnegie Mellon University (CMU). Research Interests: Leveraging computation and AI for drug design, molecular docking, pharmacophore modeling, and open science. Specific areas include developing scalable machine learning pipelines, virtual screening systems, and tools for 3D molecular analysis. Grants and Funding: Current NIH R35 grant and prior support from NSF, Relay Therapeutics, and others. His work emphasizes translating computational methods into practical drug discovery solutions. Lab and Teams: Directs a lab focused on computational drug discovery, collaborating with multiple academic and industry partners. Supervises graduate students and postdocs in projects spanning AI-driven drug design, molecular modeling, and software development.

Antti Honkelaمشاهده پروفایل

استاد

• Privacy-preserving machine learning
• Differential Privacy
• Bayesian Machine Learning
• +۳ مورد دیگر

Antti Honkela is a Professor of Data Science at the University of Helsinki's Department of Computer Science, within the Faculty of Science. He also serves as the Coordinating Professor for the Privacy-preserving and Secure AI Research Programme at the Finnish Center for Artificial Intelligence (FCAI), and as Deputy Director of the Master's Programme in Data Science. His roles include membership in the Health and Social Data Permit Authority (Findata) and as an Action Editor for Transactions on Machine Learning Research. Honkela's research focuses on privacy-preserving machine learning, differential privacy, Bayesian methods, and their applications in computational biology and healthcare. He leads projects such as the European Lighthouse in Secure and Safe AI (ELSA) and the Data Literacy for Responsible Decision-making initiative. His work emphasizes developing robust frameworks for privacy-aware AI, including differentially private synthetic data and federated learning. Honkela has advised numerous PhD and Master's students, and his contributions span theoretical advancements and practical implementations, such as the D3p Python package for differentially private probabilistic programming. Key contributions include advancements in Bayesian inference from synthetic data, privacy accounting mechanisms, and computational methods for genomic epidemiology. His interdisciplinary approach bridges machine learning, statistics, and healthcare, addressing challenges in data privacy and secure AI deployment.

• Trustworthy Machine Learning
• Conformal Prediction
• Explainable Artificial Intelligence
• +۲ مورد دیگر

Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.

Reed Maxwellمشاهده پروفایل

استاد

• Hydrology
• Water Resources
• Groundwater Modeling
• +۸ مورد دیگر

Reed Maxwell is the William and Edna Macaleer Professor of Engineering and Applied Science in the Department of Civil and Environmental Engineering and the High Meadows Environmental Institute at Princeton University. He serves as Director of the Integrated Groundwater Modeling Center (IGWMC) and leads a research group comprising graduate students, postdoctoral researchers, and staff. His academic appointments include concurrent roles in both the School of Engineering and Applied Science and the High Meadows Environmental Institute. Maxwell's research focuses on understanding connections within the hydrologic cycle and how they relate to water quantity and quality under anthropogenic stresses. His work centers on hard problems in hydrology including groundwater, evapotranspiration and snow. His research group uses integrated hydrologic modeling, field observations, and remote sensing products to study terrestrial freshwater systems. Key research areas include surface water and the terrestrial hydrologic cycle; interactions of the land-surface, surface water and groundwater; and human health risk assessment. Maxwell has authored more than 185 peer-reviewed journal articles with an H-Index of 66 and over 19,000 citations. His recent work emphasizes machine learning applications in hydrology, continental-scale modeling, and physically rigorous scenario generation through projects like HydroFrame and HydroGEN. He teaches courses including CEE 306/ENV 318 Hydrology: Water and Climate and CEE 586/ENV 586 Physical Hydrology. 2020 Distinguished Henry Darcy Lecturer American Geophysical Union Fellow (2019) 2018 Boussinesq Lecturer Belle van Zuylen Chair (visiting), University of Utrecht 2017 School of Mines Research Award recipient Maxwell has mentored 17 PhD students and 20 MS thesis students throughout his career. His current research group includes multiple postdocs, research software engineers, and graduate students working on projects spanning continental-scale hydrologic modeling, groundwater-stream interactions, and machine learning applications in hydrology. The IGWMC maintains an active education and outreach program including STEM fairs, school visits, and digital educational tools like the HydroFrame Education Team's virtual sandtank aquifer model.

Ann B Leeمشاهده پروفایل

استاد

• Scientific Machine Learning
• Trust-Worthy UQ
• Likelihood-Free Inference
• +۱ مورد دیگر

Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۶ مورد دیگر

Chris J. Maddison is an Assistant Professor at the University of Toronto, holding joint appointments in the Department of Computer Science and the Department of Statistical Sciences. He is also a CIFAR AI Chair at the Vector Institute and a member of the ELLIS Society. Maddison earned his DPhil from the University of Oxford and previously worked as a Senior Research Scientist at Google DeepMind and a member at the Institute for Advanced Study. His research focuses on advancing machine learning methodologies, particularly in leveraging data’s natural structure for efficient learning, with applications in drug discovery, causal inference, and AI safety. Education: DPhil in Computer Science, University of Oxford His research interests span machine learning, AI safety, reinforcement learning, and the integration of logical reasoning into large language models. Maddison has contributed to foundational work on gradient estimation techniques and was a key member of the AlphaGo project. He actively explores how statistical structures in real-world data influence AI capabilities. Recent publications emphasize evaluating conversational agents, mitigating AI safety risks, and enhancing logical reasoning in LLMs. His work bridges theoretical advancements with practical applications, such as code generation and multi-agent systems. Awards: NeurIPS Best Paper Award (2014), Open Philanthropy AI Fellowship Maddison advises multiple PhD students and postdoctoral researchers, fostering collaborations across academia and industry. His former advisees now hold roles at institutions like OpenAI, Stanford, and Magic AI. He teaches advanced courses in machine learning and statistical methods, including CSC 2541 (Large Models) and STA 314 (Machine Learning). Maddison is affiliated with the Schwartz Reisman Institute for Technology and Society, extending his impact to societal implications of AI. His lab’s interdisciplinary approach combines algorithmic innovation with real-world problem-solving.

Matthias Feurerمشاهده پروفایل

استاد

• Automated Machine Learning
• Hyperparameter Optimization
• Bayesian Optimization
• +۵ مورد دیگر

Matthias Feurer is a Thomas Bayes Fellow and interim professor at the Chair of Statistical Learning and Data Science, funded by the Munich Center for Machine Learning (MCML) at Ludwig Maximilian University of Munich. He is a member of the Department of Statistics at LMU Munich, working under Prof. Dr. Bernd Bischl. His academic background includes: PhD in Computer Science from Albert-Ludwigs-Universität Freiburg, supervised by Prof. Dr. Frank Hutter M.Sc. in Computer Science from the University of Freiburg B.Sc. in Computer Science and Media from the Media University Stuttgart Feurer's research focuses on simplifying machine learning usage through Automated Machine Learning (AutoML). His work encompasses hyperparameter optimization, meta-learning, and model selection, with increasing emphasis on multi-objective AutoML that considers factors beyond predictive performance such as interpretability, deployability, and fairness. He actively develops open-source tools to advance the field. His recent publications demonstrate a strong trajectory in practical AutoML systems, with growing attention to tabular machine learning, foundation models integration, and addressing real-world constraints in optimization. His work consistently bridges theoretical advances with practical implementations through several widely-used open-source projects. Notable achievements include: 1st place in the warmstarting-friendly leaderboard of the BBO NeurIPS challenge Winner of the 2nd AutoML challenge Winner of the kdnuggets blog contest on AutoML Feurer is actively mentoring and teaching, having advertised PhD positions focused on AutoML, optimization, and benchmarking. He co-founded the Open Machine Learning Foundation supporting OpenML.org. His upcoming move to TU Dortmund as an assistant professor in AutoML and Optimization signals continued growth in his academic career while maintaining his research focus on making machine learning more accessible and rigorous.

Caroline Lemieuxمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Fuzz Testing
• +۶ مورد دیگر

Caroline Lemieux is an Assistant Professor at the Department of Computer Science, University of British Columbia (UBC), with research focused on advancing software correctness, security, and performance through innovative testing and synthesis techniques. Her work bridges Programming Languages and Software Engineering , particularly in fuzz testing, specification mining, and program synthesis. PhD from University of California, Berkeley (2021), advised by Koushik Sen Postdoctoral researcher at Microsoft Research, NYC (2021-2022) Key contributions: FuzzFactory , CodaMOSA , Arvada , and Gauss Her research integrates machine learning with traditional testing methods, exemplified by projects like RLCheck (reinforcement learning for test generation) and AutoPandas (neural synthesis for dataframes). Recent publications analyze generator-based fuzzing challenges and propose hybrid strategies combining coverage feedback with AI-driven insights. Scientific Awards : ACM/SIGSOFT Best Paper Award (ESEC/FSE 2019) ACM/SIGSOFT Tool Demonstration Award (ISSTA 2019) ACM/SIGSOFT Distinguished Artifact Award (ISSTA 2019) NSERC Postgraduate Scholarship-Doctoral (PGS D) UBC Governor General's Silver Medal (2016) Teaching roles include: 2025W2: CPSC 539L - Topics in Programming Languages 2024W2: CPSC 410 - Advanced Software Engineering 2023W2: CPSC 410 (with Alex Summers) She supervises graduate and undergraduate researchers working on projects like ExploTest (automated unit test generation) and GRIMOIRE (grammar extraction from pseudo-rules). Her research team collaborates with institutions including Microsoft Research, Google, and academic partners in systems security and AI-driven testing.

Lorenzo M. Polvaniمشاهده پروفایل

استاد

• Atmospheric Science
• Climate Modeling
• Arctic Studies
• +۴ مورد دیگر

Lorenzo M. Polvani is the Maurice Ewing and J. Lamar Worzel Professor of Geophysics at Columbia University, with dual appointments in the Department of Applied Physics and Applied Mathematics and the Department of Earth and Environmental Sciences. He has taught at Columbia for over 30 years and received multiple teaching awards. PhD in Physical Oceanography, MIT/Woods Hole Joint Program (1988) MSc and BSc in Physics, McGill University His research spans Atmospheric Science , Climate Modeling , and Arctic Studies , focusing on geophysical fluid dynamics, radiative forcing, and climate sensitivity. Recent work examines nonlinear climate responses to CO2 variations, Arctic amplification mechanisms, and volcanic aerosol impacts. Scientific publications reveal trends in radiative forcing , Arctic sea ice dynamics , stratospheric ozone effects , and climate feedback interactions . Key tools include the ClimKern Python package for radiative feedback analysis. Notable research includes: Nonlinear precipitation responses to volcanic eruptions High CO2 forcing effects on North Atlantic Oscillation Cloud feedbacks reducing hydrological sensitivity Arctic amplification through radiative-advective equilibrium Stratospheric ozone impacts on tropical climate patterns