جست‌وجوی استاد و پژوهشگر

Frederick A. A. Kingdomمشاهده پروفایل

استاد

• Perception
• Cognition and Cognitive Neuroscience
• Spatial Vision
• +۸ مورد دیگر

Frederick A. A. Kingdom is a Professor in the Department of Ophthalmology at McGill University's Faculty of Medicine, focusing on Perception, Cognition and Cognitive Neuroscience . His research explores the interplay between early visual feature detection (edges, bars) and intermediate stages forming contours, textures, and surfaces through spatial vision, color vision, stereopsis, texture perception, brightness/lightness perception, and transparency studies . Email: fred.kingdom@mcgill.ca Key research domains include: Perceptual Mechanisms : Lateral inhibition, contrast normalization, spatial bandpass filters, and their role in brightness/lightness perception and illusions like simultaneous brightness contrast. Color Vision : Red-green vs blue-yellow system distribution, chromatic contrast requirements for stereopsis, color-based depth processing limitations, and color-shading effects that parse surfaces vs illumination. Texture Analysis : Detection thresholds for orientation/frequency/contrast modulated textures, co-circularity in texture perception, and texture statistical sensitivity (e.g., kurtosis importance). Shape Processing : Shape-frequency/shape-amplitude aftereffects, global vs local shape coding, and contour inflection adaptation. His work combines psychophysics , fMRI , image processing , and computational modeling to dissect visual system architecture, particularly how color and luminance signals are integrated/separated in early cortical processing.

Josef Dickمشاهده پروفایل

استاد

• Numerical Integration
• Quasi-Monte Carlo Rules
• Number Theory
• +۵ مورد دیگر

Professor Josef Dick serves as a Professor and Deputy Head in the School of Mathematics & Statistics at the University of New South Wales (UNSW). With a distinguished career in computational mathematics, he has established himself as a leading researcher in numerical integration methods and quasi-Monte Carlo theory. His work bridges theoretical mathematics with practical computational applications across various scientific domains. Dr. Dick earned his PhD in Mathematics from UNSW in 2004 and his MSc in Mathematics from the University of Salzburg in 2001. His academic journey reflects a strong foundation in both theoretical and applied mathematics, which has informed his subsequent research contributions. Professor Dick's research primarily focuses on numerical integration and quasi-Monte Carlo rules , employing techniques from number theory , abstract algebra (particularly finite fields), discrepancy theory , wavelet theory , and statistics . His work provides rigorous analysis of practical algorithms for computational problems, with implementations often provided in Matlab to bridge theory and application. His research has successfully addressed point distributions on the unit cube for numerical integration, completely uniformly distributed sequences for Markov chain quasi-Monte Carlo algorithms, and explicit constructions of uniformly distributed points on the sphere. Analysis of his recent publications (2022-2025) reveals a consistent focus on advancing quasi-Monte Carlo methods, with increasing integration of machine learning techniques and applications to complex computational problems. His work demonstrates strong interdisciplinary connections between pure mathematics, computational science, and practical engineering applications, particularly in uncertainty quantification and high-dimensional numerical integration. Discovery project from Australian Research Council (2012-2014): "Mathematics in the round - the challenge of computational analysis on spheres" Queen Elizabeth II Fellowship from Australian Research Council (2010-2014): "Algebraic methods for Markov Chain Monte Carlo and quasi-Monte Carlo" UNSW Vice Chancellor Fellowship (2006-2009) Professor Dick has supervised numerous PhD and Honours students working on topics including Quasi-Monte Carlo methods, Discrepancy Theory, Markov chain Monte Carlo, and Uncertainty Quantification. His research has been supported by significant grants from the Australian Research Council, including serving as Chief Investigator on multiple projects. Beyond his research, he serves as an Editor for the Journal of Complexity and Journal of Approximation Theory, demonstrating his leadership in the mathematical community. He teaches courses in Algebra and Mathematical Computing for Finance at UNSW.

Brandon Graingerمشاهده پروفایل

دانشیار

• Electric Power Conversion
• Medium- and High-Voltage Power Electronics
• HVDC & STATCOM Systems
• +۶ مورد دیگر

Dr Brandon M Grainger is an Eaton Faculty Fellow and Associate Professor of Electrical and Computer Engineering at the University of Pittsburgh’s Swanson School of Engineering, where he also directs the Electric Power Technologies Laboratory, serves as Associate Director of the Energy GRID Institute, and co-directs Pitt AMPED. A key architect of Pitt’s electric power program since 2008, he focuses on advanced power conversion, high-voltage electronics, wide-band-gap semiconductors, and aerospace power systems. Education PhD, Electrical Engineering (Power Conversion), University of Pittsburgh, 2014 MS, Electrical Engineering, University of Pittsburgh, 2011 BS, Mechanical Engineering & Minor in Electrical Engineering, University of Pittsburgh, 2007 Executive Education Certificate, Tepper School of Business, Carnegie Mellon University, 2019 Research Focus Dr Grainger’s work lies at the intersection of power electronics, high-voltage engineering, and sustainable energy systems. He specializes in medium- and high-voltage power electronics (HVDC, STATCOM), resonant converters, and ultra-high-power-density designs leveraging SiC and GaN semiconductors. His investigations extend to electric-vehicle traction drives, solid-state transformers, optimized magnetics for aerospace applications, and resilient microgrids. He and his students routinely collaborate with NASA JPL, Johns Hopkins APL, Honeywell Aerospace, and the Naval Research Laboratory, leveraging Pitt’s NSF SHREC center to push the boundaries of power conversion in space and defense systems. Selected Research Themes High-frequency, high-density DC/DC converters for satellite power systems Radiation-tolerant GaN converters and point-of-load power stages Medium-voltage testbed development (13.8 kV, 5 MVA) Rare-earth-free permanent-magnet machine topologies Model-predictive control of multi-phase drives and microgrids Honors & Awards 2024 IEEE Region 2 Outstanding Educator Award 2024 Pitt STRIVE Outstanding DEI Service Award 2019 ESWP Engineer of the Year 2019 ASEE 2nd Place Best Paper Award 2019 SRI Undergraduate Best Mentor Award Richard K. Mellon Endowed Graduate Fellowship National Academies of Science & Engineering Ambassador Senior Member, IEEE Grants & Industry Partnerships Dr Grainger’s research has been continuously funded by federal agencies and industry partners including NASA JPL, Johns Hopkins APL, Honeywell Aerospace, the Naval Research Laboratory, Eaton, and the National Science Foundation through the SHREC Center. These awards support graduate students and post-docs working on next-generation power systems for aerospace, naval, and terrestrial applications. Laboratories & Teams Director, Electric Power Technologies Laboratory (EPTL) Associate Director, Energy GRID Institute Co-Director, Pitt AMPED (Advanced Multimodal Power and Energy Development) Faculty Affiliate, NSF SHREC Center

Tobias Müllerمشاهده پروفایل

استاد

• Combinatorics
• Probability Theory
• Random Graphs
• +۵ مورد دیگر

Prof. Tobias Müller is a Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. His academic journey includes previous positions at Utrecht University, CWI (Centrum Wiskunde & Informatica), Tel Aviv University, and Eindhoven University of Technology, with a doctorate from the University of Oxford under Colin McDiarmid. His research focuses on combinatorics, probability theory, random graphs, percolation, discrete and stochastic geometry, and combinatorial game theory. He has contributed extensively to understanding complex networks, hyperbolic models, and geometric random structures. Research Interests: Random Graphs and Percolation Theory Discrete and Stochastic Geometry Hyperbolic Network Models Probabilistic Combinatorics Geometric Probability Graph Algorithms and Connectivity Notable Contributions: Analysis of Voronoi and Poisson-Voronoi percolation in hyperbolic planes. Studies on Mallows random permutations and their cycle structures. Research on component games and logical limit laws in graph theory. Investigations into the geometry and properties of random geometric graphs. Grants & Collaborations: Active in organizing workshops and conferences on random graphs and geometric networks, including the BIRS Workshop on Random Geometric Graphs and the STAR Workshops series. Labs/Teams: Member of the Bernoulli Institute’s research groups, focusing on stochastic studies, combinatorics, and algorithmic methods.

• Mathematical Optimization
• Algorithmic Intelligence
• Energy Systems Modeling
• +۵ مورد دیگر

Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.

• Behavioral modeling
• Electromagnetic compatibility
• Macromodeling
• +۷ مورد دیگر

Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.

• Computational Linguistics
• Psycholinguistics
• Language Processing
• +۴ مورد دیگر

Richard Futrell is an Associate Professor at the University of California, Irvine (UCI), affiliated with the Department of Language Science. He leads the Language Processing Group, focusing on computational models of human and machine language processing. His work bridges information theory, Bayesian cognitive modeling, and natural language processing (NLP) interpretability. University of California, Irvine Department of Language Science Language Processing Group leader His research examines how linguistic structures emerge from cognitive and communicative pressures. Key areas include dependency locality, surprisal theory in sentence processing, and efficiency-driven language evolution. He investigates how memory constraints, predictability, and information density shape syntactic and morphological patterns across languages. Recent publications analyze code-switching efficiency, syntactic priming, ERP component modeling, and agent-based language contact simulations. His work frequently employs Bayesian modeling, neural network analysis, and cross-linguistic corpora to uncover universal principles in language processing. ACL Best Paper Award (2024) Best Paper Award for Computational Modeling of Language (2023) Marr Prize for Best Student Paper (2017) He has developed datasets like SPACER for error repair analysis and contributed to phonotactic learning frameworks. His collaborations span cognitive scientists, computational linguists, and neuroscientists, advancing understanding of language production, comprehension, and structural optimization.

• Quiver Representation Theory
• Cluster Algebras
• Kac-Moody Lie Algebras
• +۲ مورد دیگر

Xiuping Su is Senior Lecturer in the Department of Mathematical Sciences at the University of Bath, specializing in quiver representation theory and its connections to cluster algebras, Lie theory, and algebraic geometry. Her research explores combinatorial structures underlying abstract algebraic systems with applications to mathematical physics. Research Focus: Degeneration orders in derived categories Combinatorial aspects of Grassmannian cluster algebras Tame behavior in wild quiver representations Interactions between Schur algebras and quantum groups Recent work resolves conjectures by Geiss-Leclerc-Schröer regarding affine root systems and string algebra components. Current projects investigate categorification approaches to mirror symmetry and combinatorial foundations of quantum flag varieties.

Peter Avitabileمشاهده پروفایل

استاد پژوهشی

• Structural Dynamics
• Vibrations
• Modal Analysis
• +۵ مورد دیگر

Peter Avitabile is a Research Professor in the Mechanical and Aerospace Engineering Department at Michigan Technological University and a Professor Emeritus at the University of Massachusetts Lowell. With nearly five decades of experience, his expertise spans structural dynamics, vibrations, and modal analysis. He has contributed over 350 technical papers and authored Modal Testing: A Practitioner’s Guide . Dr. Avitabile holds a DEng from UMass Lowell and has led the Structural Dynamics and Acoustic Systems Laboratory. He is a Fellow of the Society for Experimental Mechanics (SEM) and served as its President in 2016. Education: DEng, Mechanical Engineering, University of Massachusetts Lowell (1998) MS, Mechanical Engineering, University of Rhode Island (1982) BS, Mechanical Engineering, Manhattan College (1974) Research Interests: Structural dynamic modeling techniques, experimental modal analysis, system modeling, reduced order modeling, and model correlation. Awards: SEM DeMichele Award (2004) Fellow, Society for Experimental Mechanics (2022) UMASS Lowell Pillars of Excellence Award (2016) Grants & Funding: Over $9.3 million in research funding since 2000, alongside substantial hardware/software donations. His work has been supported by industries and government agencies including NASA, the Department of Energy, and the Navy. Teaching & Mentorship: Taught over 30 semesters of graduate courses in structural dynamics and modal analysis at UMass Lowell. Advised 18 PhD and 19 MS students. Conducted international seminars on structural dynamics for companies like NASA, General Motors, and Siemens. Labs & Affiliations: Co-Director, Structural Dynamics and Acoustic Systems Lab (UMass Lowell) Director, Modal Analysis and Controls Lab (UMass Lowell)

• Data Mining
• Machine Learning
• Clustering Algorithms
• +۳ مورد دیگر

Anna Beer is a researcher in the Faculty of Computer Science, specializing in data mining and machine learning with a focus on density-based clustering, spectral clustering, and interactive clustering frameworks. She holds a BSc and MSc in computer science and maintains an ORCID profile (https://orcid.org/0000-0002-6890-997X) for her research contributions. Research Themes: Development of clustering algorithms (e.g., DISCO, Scar, LUCKe), fairness in density-based clustering (FairDen), and applications to molecular dynamics and climate research (DROPP). Collaborations: Works with colleagues like Ira Assent, Christian Plant, and Lars Krieger, with recent contributions to conferences like ICLR 2025. Activities: Presented research on density-connectivity distance at a 2023 oral contribution. Publications: 9 publications since 2019, including 3 in 2025 and 6 in 2024, covering topics from cluster evaluation to deep active learning strategies.

Y. Samuel Wangمشاهده پروفایل

استادیار

• Causal Discovery
• Graphical Models
• High-Dimensional Statistics
• +۳ مورد دیگر

Y. Samuel Wang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. He holds a PhD in Statistics from the University of Washington and a BS in Applied Mathematics and Economics from Rice University. Prior to academia, he worked as a management consultant and served as a postdoctoral researcher at the University of Chicago’s Booth School of Business. His research focuses on causal discovery, graphical models, mixed membership models, and high-dimensional data analysis, with applications in network science, environmental studies, and healthcare. Education: PhD in Statistics, University of Washington BS in Applied Mathematics & Economics, Rice University Research Interests: Development of interpretable statistical methods for causal inference and graphical model structures High-dimensional data analysis, particularly in non-Gaussian settings Applications in collaborative networks, environmental microbiology, and healthcare outcomes Recent Research Trends: Recent publications emphasize causal discovery under latent confounding, functional graphical models, and gender dynamics in scholarly collaborations. Methodological contributions include robust high-dimensional inference techniques and computational tools for cyclic structural equation models. Professional Activity: Licensed on GitHub, Google Scholar, and ORCID GitHub repositories include projects on causal discovery (highDNG), gender homophily analysis (genderHomophily), and mixed membership models (mixedMem)

Corentin Coulaisمشاهده پروفایل

استاد

• Mechanical Metamaterials
• Active Matter
• Topological Materials
• +۵ مورد دیگر

Dr. Corentin Coulais is a Professor in the Department of Physics at the University of Amsterdam's Faculty of Science. He leads the Machine Materials Laboratory, focusing on cutting-edge research at the intersection of physics, materials science, and engineering. His work explores the fundamental mechanics of structured materials and their applications in robotics, sensing, and programmable matter. Dr. Coulais's research interests center on mechanical metamaterials, active matter, and topological materials. His laboratory investigates how to design materials with unprecedented properties by leveraging mechanical instabilities, nonlinearity, and topology. His work spans from fundamental theoretical concepts to practical applications in soft robotics, shape-morphing structures, and energy absorption systems. The research combines experimental approaches with computational modeling and increasingly incorporates machine learning techniques for design optimization. His recent publications reveal a strong trend toward increasingly sophisticated control of material behavior through topological principles, active components, and learning algorithms. His work has evolved from basic mechanical metamaterials to systems incorporating non-reciprocity, active matter principles, and machine learning for design. The research spans fundamental physics of mechanical systems to practical applications in robotics and programmable matter. Dr. Coulais has established the Machine Materials Laboratory as a leading center for mechanical metamaterials research. His team develops innovative experimental setups and theoretical frameworks to explore the rich behavior of structured materials. The laboratory maintains strong connections with other research groups internationally and collaborates across disciplines including physics, engineering, and computer science.

Jean-Jacques Heringsمشاهده پروفایل

استاد

• Game Theory
• Microeconomics
• Networks
• +۴ مورد دیگر

Jean-Jacques Herings is a Full Professor of Quantitative Microeconomics at Tilburg University's Tilburg School of Economics and Management (TiSEM), specifically within the Department of Econometrics and Operations Research. He has held this position since March 2022, having previously been a Professor of Economics at Maastricht University from September 1999 to February 2022. Herings' research focuses on five primary themes: Cooperative game theory, non-cooperative game theory, networks, general equilibrium theory, and equilibrium computation. His work spans theoretical foundations and practical applications, particularly in financial networks, matching markets, and strategic decision-making. He has published over 200 research outputs including articles in top journals such as Econometrica, Journal of Economic Theory, Management Science, and Mathematics of Operations Research. His recent publications reveal a continued focus on game-theoretic applications to economic problems, with particular attention to financial networks, bankruptcy problems, matching theory, and information design. There's a clear trend toward computational methods for finding equilibria and increasingly sophisticated modeling of strategic interactions in networked environments, with over 13 publications in 2024 alone demonstrating his active research program. Johannes Cornelis Ruigrok Prize (1997) Lionel W. McKenzie Prize (2008) VICI grant from NWO Fellow of the Game Theory Society (2017) Secretary-Treasurer of Game Theory Society (2014-2022) Fellow of the Society for the Advancement of Economic Theory (2019) As an advisor, Herings has supervised over 20 PhD students to completion, with research spanning game theory applications in economics, finance, and network theory. His editorial service includes membership on the boards of Games and Economic Behavior, Journal of Mathematical Economics, International Journal of Game Theory, and Mathematics of Operations Research. He also serves on the International Advisory Board of the Center for Mathematical Research in Economics and Finance at the University of Manchester.

• Model Theory
• Shelah's Classification Theory
• Stability/NIP/NTP2 Theories
• +۶ مورد دیگر

Artem Chernikov is a Professor and the Michael Brin Endowed Chair in Mathematics at the University of Maryland, College Park. Previously, he held positions at UCLA from 2015-2023, including roles as Assistant Professor (2015-2018), Associate Professor (2018-2022), and Professor (2022-2023). His research focuses on model theory, particularly Shelah's classification theory (stability, simplicity, NIP/NTP2 properties) and its applications to algebra, geometry, combinatorics, and computer science. He has supervised numerous graduate students and postdoctoral researchers, including notable advisees like Alex Mennen and Chuyin Jiang. Education includes a Ph.D. in Mathematical Logic from Université Claude Bernard Lyon 1 (2012), advised by Itaï Ben Yaacov, and a M.Sc. from Humboldt University Berlin (2009). He also held postdoctoral fellowships at the Hebrew University of Jerusalem, MSRI Berkeley, and Université Paris Diderot. Key awards include the 2023 NSF Research Grant, 2020 Simons Fellowship, 2017 NSF CAREER Award, and the 2013 Sacks Prize for best doctoral dissertation in mathematical logic. His work bridges model theory with combinatorics, topological dynamics, and valuation theory, yielding results in hypergraph regularity, invariant measures, and algebraic structures. Teaching includes advanced courses on model theory, stability, and combinatorics. He co-organized major conferences like the 2025 Model Theory and Applications conference in Warwick and the 2023 EMS Summer School in Leeds. His research outputs include over 30 peer-reviewed papers and invited talks globally, reflecting interdisciplinary impact.

Ben Kravitzمشاهده پروفایل

دانشیار

• Climate Modeling
• Climate Engineering
• Geoengineering
• +۹ مورد دیگر

Dr. Ben Kravitz is an Associate Professor in the Department of Earth and Atmospheric Sciences at Indiana University Bloomington, where he also serves as Director of Graduate Studies. His research focuses on climate dynamics, particularly using climate models to explore radiative forcing, climate response, and climate system feedbacks. He leads the Kravitz Research Group which specializes in combining global climate, regional climate, and process model simulations with mathematical and engineering techniques to understand Earth system responses to perturbations. Dr. Kravitz received his educational training at prestigious institutions: Ph.D. in Atmospheric Sciences, Rutgers University (2011) M.S. in Atmospheric Sciences, Rutgers University (2009) M.S. in Mathematics, Purdue University (2007) B.A. in Mathematics, Northwestern University (2004) His primary research interests center on climate engineering using stratospheric sulfate aerosols, reduced order modeling of the climate system, high latitude climate teleconnections, and uncertainty quantification for wind energy. He applies engineering techniques such as control theory, system identification, and linear systems theory to climate modeling problems. His work spans multiple areas including geoengineering, climate dynamics, teleconnections, climate model emulators, dynamic downscaling, and wind energy applications. His Erdös number is three, making him the academic grandson of Ed Lorenz. Dr. Kravitz's recent publications demonstrate a strong focus on stratospheric aerosol injection strategies, marine cloud brightening, and the climate response to various geoengineering interventions. His research examines how different injection locations, timing, and strategies affect climate outcomes including extreme events, atmospheric circulation patterns, and regional climate impacts. His work is characterized by sophisticated modeling approaches that integrate multiple Earth system components to assess potential climate intervention strategies. As Director of Graduate Studies, Dr. Kravitz mentors graduate students in the Department of Earth and Atmospheric Sciences. His research involves extensive collaboration through the Geoengineering Model Intercomparison Project (GeoMIP) and other international research initiatives focused on climate intervention strategies. His work bridges climate science and engineering to develop innovative approaches for understanding Earth system responses to perturbations. The Kravitz Research Group maintains strong connections with the broader climate modeling community through participation in major intercomparison projects. They focus on understanding how the Earth system responds to perturbations, with particular expertise in developing methods to quantify risks and side effects of climate engineering approaches while exploring fundamental climate dynamics and teleconnections.