جست‌وجوی استاد و پژوهشگر

Yiping Luمشاهده پروفایل

استادیار

• Machine Learning
• Scientific Machine Learning (AI4Science)
• Numerical Algorithms
• +۷ مورد دیگر

Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford

• Programming Language Theory
• Semantics of Programming Languages
• Domain Theory
• +۷ مورد دیگر

Gordon Plotkin is a Professor at the School of Informatics, University of Edinburgh, where he is affiliated with the Laboratory for Foundations of Computer Science (LFCS). His research lies at the intersection of theoretical computer science and programming language semantics, with a profound influence on the formal understanding of computation. His research interests include Programming Language Theory, Semantics of Programming Languages, Domain Theory, Operational Semantics, Lambda Calculus, Type Theory, Concurrency Theory, and Algebraic Effects. His seminal work on structural operational semantics and domain theory has laid the foundation for modern semantics of programming languages. His publications span over five decades, showing a sustained and evolving research trajectory from foundational work in lambda calculus and domain theory to recent contributions in algebraic effects, probabilistic computation, and biochemical systems modeling. The articles demonstrate a consistent focus on formal methods, mathematical rigor, and the algebraic structure of computational effects. He has collaborated with leading researchers including Martín Abadi, John Power, Glynn Winskel, and John Reynolds. His work continues to influence both theoretical and practical developments in programming languages and systems. Gordon Plotkin has made foundational contributions to computer science, particularly through his development of structural operational semantics and domain-theoretic models of computation. He has advised numerous researchers and supervised many influential PhD theses, though specific student names are not listed in the provided text. His work has been supported by long-standing affiliations with the Laboratory for Foundations of Computer Science and the University of Edinburgh, and he has contributed to major collaborative projects in programming language design and verification. He is associated with several research groups and labs, most notably the Laboratory for Foundations of Computer Science (LFCS), which serves as a hub for theoretical research in programming languages, semantics, and logic at the University of Edinburgh.

Lucia Carichinoمشاهده پروفایل

استادیار

• Mathematical Biology
• Fluid Dynamics
• Numerical Methods
• +۵ مورد دیگر

Lucia Carichino is an Assistant Professor in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT). She holds a PhD in Mathematics from Purdue University and a BS/MS in Mathematical Engineering from Politecnico di Milano, Italy. Her research focuses on mathematical and computational models of multiscale biological systems, particularly fluid-structure interaction in biological contexts like ocular blood flow and microswimmers. She emphasizes integrating experimental data with mathematical models to advance medical understanding. Carichino teaches courses such as Differential Equations, Linear Algebra, and oversees undergraduate research projects. In 2023, she received the National Science Foundation LEAPS-MPS award for her work on computational modeling of eye-contact lens interactions. Her research has been published in high-impact journals and presented at conferences. She actively collaborates on projects addressing glaucoma, ocular hemodynamics, and biomedical applications. Education: PhD in Mathematics, Purdue University BS and MS in Mathematical Engineering, Politecnico di Milano, Italy Research interests include fluid dynamics, numerical methods, and mathematical biology. Her work bridges theoretical models with biomedical applications, such as optimizing gene therapy delivery and analyzing ocular physiology under varying environmental conditions (e.g., altitude). She explores topics like sperm motility, computational simulations of biological systems, and the interplay between fluid dynamics and biological structures. Her recent articles highlight advancements in ocular pharmacokinetics, contact lens interactions, and altitude effects on intraocular pressure. These studies underscore her expertise in multiscale modeling and fluid-structure interaction. Carichino also contributes to educational initiatives, fostering a collaborative classroom environment. Notable awards include the NSF LEAPS-MPS award (2023). She advises student research projects and collaborates with colleagues, such as Maki, on interdisciplinary studies. Her work is supported by grants and has led to presentations at ophthalmology and mathematics conferences. Carichino’s lab focuses on computational modeling of biological systems, particularly in ophthalmology and microscale fluid dynamics. Her team develops tools to simulate complex physiological processes, aiding in medical diagnostics and treatment strategies.

Alex Blumenthalمشاهده پروفایل

استادیار

• Dynamical Systems
• Smooth Ergodic Theory
• Chaos Theory
• +۳ مورد دیگر

Alex Blumenthal is an Assistant Professor in the School of Mathematics at the Georgia Institute of Technology since Fall 2020. His academic background includes a Ph.D. from New York University (2016) with a dissertation titled 'Nonuniformly hyperbolic theory for Banach space mappings.' Prior to joining Georgia Tech, he held positions as an instructor at the University of Maryland, teaching courses in probability theory, linear algebra, and precalculus, and served as a recitation leader at New York University for courses in chaos theory, differential equations, and analysis. Blumenthal's research focuses on dynamical systems and ergodic theory, with specialization in: Chaotic behavior in deterministic and stochastic systems Smooth ergodic theory and SRB measures Lyapunov exponents in random dynamical systems Stochastic fluid mechanics and turbulence modeling Infinite-dimensional dynamical systems on Banach spaces Statistical properties of complex systems His work bridges abstract mathematical theory with physical applications like fluid dynamics and statistical mechanics. Analysis of his recent publications shows strong emphasis on stochastic dynamics, Lyapunov exponents, and fluid mechanical systems, with mathematical techniques drawn from ergodic theory, functional analysis, and probability theory. His publications frequently appear in top mathematical physics and dynamics journals. No scientific awards or honors are mentioned in the source materials. Similarly, no information is available regarding research grants, student advising, or laboratory affiliations.

• Mathematics
• Machine Learning
• Quantum Topology
• +۹ مورد دیگر

Sergei Gukov is the John D. MacArthur Professor of Theoretical Physics and Mathematics at the California Institute of Technology (Caltech), where he has been a faculty member since 2005. He serves in the Division of Physics, Mathematics and Astronomy, with primary affiliation in the Department of Mathematics. His research bridges the fields of mathematics and theoretical physics, focusing on deep connections between geometry, topology, and quantum field theory. Gukov received his B.S. from Moscow Institute of Physics and Technology in 1997, followed by an M.S. and Ph.D. from Princeton University in 2001. He joined Caltech as an Associate Professor in 2005, was promoted to Professor in 2007, and was named the John D. MacArthur Professor in 2021. His research spans several interconnected areas at the frontier of mathematics and physics. A central theme is the exploration of quantum topology and its connections to mathematical physics. He has made significant contributions to the geometric Langlands program, gauge theory, and the categorification of knot and 3-manifold invariants. His recent work increasingly incorporates machine learning approaches to mathematical problems, reflecting his interest in the intersection of traditional mathematical research and modern computational techniques. Gukov's work often reveals deep connections between seemingly disparate areas of mathematics and physics, such as the relationship between Rozansky-Witten geometry and Coulomb branches in supersymmetric gauge theories. Gukov's publications demonstrate a consistent focus on the mathematical structures underlying quantum field theories and their topological implications. His recent work shows an increasing emphasis on computational approaches to mathematical problems, particularly through his interest in mathematics and machine learning. The recurring themes across his research include the application of physical insights to solve mathematical problems and the discovery of new mathematical structures through physical reasoning. He serves on the editorial boards of several prestigious journals including the Journal of Knot Theory and Its Ramifications, Communications in Mathematical Physics, and Letters in Mathematical Physics. Gukov is also active in the academic community, having delivered plenary talks at major conferences such as the First International Congress of Basic Science and presenting at String Math 2023 on the potential impact of AI on mathematical research. Gukov teaches Ma 146 ab, Introduction to Knot Theory and Quantum Topology, a course that reflects his research interests. He also runs a seminar on Mathematics and Machine Learning, held Tuesdays from 2-3pm in East Bridge Conference room 114, demonstrating his commitment to fostering interdisciplinary research at the intersection of mathematics and computational methods.

Paata Ivanisviliمشاهده پروفایل

دانشیار

• Analysis
• Partial Differential Equations
• Probability
• +۵ مورد دیگر

Paata Ivanisvili is an Associate Professor at the University of California, Irvine (UCI), Department of Mathematics, School of Physical Sciences. His research focuses on Analysis, Probability, Harmonic Analysis, and Functional Analysis, with a particular emphasis on isoperimetric inequalities, functional inequalities, and discrete structures such as the Hamming cube. He has held visiting positions at institutions including the Hausdorff Research Institute for Mathematics and Princeton University. Ivanisvili has organized conferences such as the Dual Trimester Program at the Hausdorff Institute on Boolean Analysis in Computer Science (2024) and annual Summer/Fall Schools since 2021. He earned his PhD in Mathematics from Michigan State University (2015) and a BS from Saint Petersburg State University (2011). His research interests include sharp inequalities in analysis (e.g., Poincaré, Beckner, Ehrhard), hypercontractivity, and applications to discrete mathematics and probability. He has collaborated with prominent mathematicians such as Fedor Nazarov, Alexander Volberg, and Roman Vershynin. Notable awards include the NSF CAREER Award (2021–2025) and Simons Fellowship in Mathematics (2025–2026). Ivanisvili’s recent work explores the interface between harmonic analysis and discrete mathematics, including studies on additive energies, convex hulls of space curves, and learning theory. His articles frequently address foundational questions in geometric functional analysis, often using tools like Bellman functions and optimal control theory. He actively advises PhD students and has mentored visiting researchers at UCI.

Cezary Kaliszykمشاهده پروفایل

استاد

• Automated Reasoning
• Formal Methods
• Formalized Mathematics
• +۷ مورد دیگر

Cezary Kaliszyk is a Professor in Theoretical Computer Science at the University of Melbourne, previously affiliated with the University of Innsbruck. He is actively involved in research and leadership in formal methods, automated reasoning, and machine learning for theorem proving. Research Interests: Automated Reasoning and Interactive Theorem Proving Formalized Mathematics and Proof Automation Machine Learning for Logic and Theorem Proving Integration of AI with Proof Assistants (Coq, Isabelle) Dependent Type Theory and Higher-Order Logic His recent publications (2023–2025) span topics in dependently-typed logic, learning for proof guidance, formalization of surreal numbers, and blockchain-based formal methods. The works consistently bridge formal logic with machine learning, emphasizing automation, explainability, and cross-system integration. Scientific Leadership and Projects: Principal Investigator, ERC project FormalWeb3 Lead Developer, CoqHammer , Tactician , ProofWeb WG5 Leader, COST Action EuroProofNet (until 2024) Contributor to HOL(y)Hammer , Isabelle Enigma He supervises multiple PhD students and has mentored several graduates in formal methods and AI. He teaches courses in theoretical computer science, logic, and machine learning. There are no listed awards in the provided data, but his extensive publication record and project leadership indicate significant recognition in the field. Labs and Research Groups: He leads a research group focused on formal methods and learning-based reasoning, collaborating internationally on projects involving proof automation, formal libraries, and semantic technologies.

Mike Kirbyمشاهده پروفایل

استاد

• Scientific Computing
• Physics-Informed Machine Learning
• Computational Science and Engineering
• +۴ مورد دیگر

Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).

Valter Morettiمشاهده پروفایل

استاد

• Algebraic Quantum Field Theory
• Black Holes
• Functional Analysis
• +۱۰ مورد دیگر

Valter Moretti is a Full Professor in the Department of Mathematics at the University of Trento. His academic career spans roles from Research Fellow to Full Professor, focusing on Mathematical Physics and Quantum Field Theory (QFT) in curved spacetime. He earned an MSc in Physics from Genova University and a PhD in Theoretical Physics from Trento University. Research Interests : Algebraic QFT, General Relativity, Quantum Mechanics, Operator Algebras, and Spectral Theory. His work bridges mathematical rigor with physical applications, particularly in quantum localization, entanglement, and curved spacetime phenomena. Publications : Authored 15+ recent papers on topics like quantum particle localization, entanglement certification, and QFT on curved backgrounds. Collaborated on a 2022 patent for generating entangled photon states. Awards : Holds a patent for a quantum-certified random number generator (2022). Supervision : Advised 8 PhD students, including N. Pinamonti, L. Franceschini, and C. van de Ven. Coordinated national and international research projects (e.g., H2020-MSCA-COFUND-2015). Labs & Collaborations : Affiliated with INFN, TIFPA-INFN, and Q@TN (Quantum@Trento). Organized conferences like Quantum Physics and Geometry (2014) and Quantum Machine Learning (2023). Teaching : Lectures on Analytical Mechanics, Quantum Relativistic Theories, and Special Relativity. Authored textbooks on Spectral Theory and Quantum Mechanics.

Markus Landمشاهده پروفایل

عضو هیئت علمی

• Algebraic Topology
• Homotopy Theory
• K-Theory
• +۳ مورد دیگر

Markus Land is a Tenure Track Professor at the Department for Mathematics, Ludwig Maximilian University of Munich, affiliated with the Algebraic Geometry Working Group. His research bridges algebraic topology, homotopy theory, and K-theory, focusing on their interplay with L-theory and manifold topology. He has held postdoctoral positions at the University of Copenhagen (supported by an EU Marie Curie Fellowship and DFG Grant) and the University of Regensburg, and earned his PhD at the University of Bonn under Wolfgang Lück. Education : PhD in Mathematics (University of Bonn, 2016) Positions : Postdoctoral (University of Copenhagen, 2019–2022; University of Regensburg, 2016–2019) Research Interests : Markus Land explores algebraic topology and homotopy theory , particularly algebraic K-theory , hermitian K-theory , and their connections to L-theory and manifold topology . His work often applies infinity-categories to foundational problems in operator K-theory and geometric topology. Recent Publications : His research includes groundbreaking studies on chromatic localization in K-theory, Grothendieck-Witt groups in ring theory, and additivity in cobordism categories . Collaborations with leading mathematicians like Georg Tamme and Ulrich Bunke highlight interdisciplinary approaches to algebraic and geometric problems. Awards & Grants : EU Marie Curie Individual Fellowship (2020–2022) DFG Individual Research Grant (2019–2020) Teaching & Academic Leadership : He has designed advanced courses including Topology I–V , Algebraic K-Theory , and Condensed Mathematics , emphasizing homotopy theory and infinity-categories . Future initiatives include student seminars on arithmetic and algebraic geometry to foster academic collaboration. Working Group : As part of the Algebraic Geometry Working Group at LMU Munich, he contributes to seminars and research projects on arithmetic geometry , manifold classification , and spectral algebra .

Christian Bargetzمشاهده پروفایل

استاد

• Nonlinear Functional Analysis
• Banach Spaces
• Locally Convex Spaces
• +۴ مورد دیگر

Christian Bargetz is a Professor of Functional Analysis at the University of Innsbruck, Austria, affiliated with the Faculty of Mathematics, Computer Science, and Physics (MIP). His primary research focuses on nonlinear functional analysis, Banach space theory, and distribution theory. He teaches advanced courses such as Optimization, Distribution Theory, and Functional Analysis, demonstrating his expertise in both theoretical and applied aspects of his field. Education: Completed his PhD in 2012 at the University of Innsbruck under the supervision of Norbert Ortner. His diploma thesis (2008) explored differential behaviors with Ulrich Oberst. Bargetz has held continuous academic positions since 2008, including roles as a lecturer and researcher. Research Interests: Specializes in iterative projection methods, generic properties of nonexpansive mappings, Fréchet spaces, and vector-valued distributions. His work bridges functional analysis with geometric measure theory and optimization, with applications in metric geometry and topological tensor products. Publications: Over 30 peer-reviewed articles in prestigious journals such as Canadian Journal of Mathematics , Journal of Mathematical Analysis and Applications , and Proceedings of the American Mathematical Society . Recent work includes studies on extremal nonexpansive mappings and Lipschitz function spaces. Grants & Projects: Principal investigator in FWF-funded projects on nonexpansive mappings and Banach spaces. Collaborates internationally, including with institutions in Israel, Poland, and Serbia. Teaching: Leads advanced courses in functional analysis, optimization, and distribution theory. Supervises bachelor's theses and master's projects on topics like extension operators for Lipschitz functions. Affiliations: Active member of the Functional Analysis working group and regularly participates in international conferences such as the Banach Afternoon, Winter School in Abstract Analysis, and DMV-ÖMG Annual Conferences.

Vladimir Kazeevمشاهده پروفایل

استادیار

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

Aaron Smithمشاهده پروفایل

دانشیار

• Applied Probability
• Computational Statistics
• Monte Carlo Methods
• +۳ مورد دیگر

Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.

Elias Jarlebringمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Matrix computations
• Nonlinear eigenvalue problems
• +۱۰ مورد دیگر

Elias Jarlebring is a Professor in Numerical Linear Algebra at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. He has held the position of Full Professor since 2021, following his tenure as Associate Professor (2013-2021) and Dahlquist Research Fellow (2011-2013). His research focuses on numerical analysis, numerical linear algebra, matrix computations, and scientific computing. Jarlebring develops linear algebra algorithms to solve problems from various fields including systems and control, acoustics, electromagnetics, data science, quantum mechanics, and quantum chemistry. He is a core developer of NEP-PACK, a scientific computing software package for nonlinear eigenproblems. His recent publications demonstrate significant contributions to computational methods for nonlinear eigenvalue problems, matrix functions, and parameterized linear systems. The research shows a clear trajectory toward increasingly complex applications in quantum computing, data science, and wave propagation problems. Project grant, Swedish research council (2019) Ruth och Nils-Erik Stenbäcks foundation, junior grant (2019) Göran Gustafsson Prize for junior researchers (2014) Project grant for junior researchers, Swedish research council (2014-2018) Professor Jarlebring has supervised numerous PhD students including Vilhelm Peterson Lithell, Gustaf Lorentzon, Siobhán Correnty, Parikshit Upadhyaya, Emil Ringh, Antti Koskela, and Giampaolo Mele. He has received multiple research grants from the Swedish Research Council and serves as editor for BIT Numerical Mathematics, Linear and Multilinear Algebra, NACO Numerical Algebra Control and Optimization, and CALCOLO. He is actively involved in the numerical linear algebra community as a member of ILAS (International Linear Algebra Society), GAMM Activity Group on Numerical Linear Algebra, and the Nordic Numerical Linear Algebra Association. He also contributes to open source projects, particularly in the Julia programming language ecosystem.

Dan Jiaoمشاهده پروفایل

استاد

• Computational Electromagnetics
• Computational Multiphysics
• Electronic Design Automation (EDA)
• +۸ مورد دیگر

Prof. Dan Jiao is the Synopsys Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School. She leads the Rapid-Heterogeneous Integration (Rapid-HI) Design Institute and serves as Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques. Her research focuses on computational electromagnetics, multiphysics modeling, and AI-driven design automation for advanced integrated circuits and quantum systems. She has held academic positions since 2005, progressing from Assistant to Full Professor, and has extensive industry experience at Intel Corporation (2001–2005). Education: PhD in Electrical Engineering, University of Illinois at Urbana-Champaign (2001) Senior Staff Engineer at Intel Corporation (2001–2005) Research Interests: Fast numerical methods for large-scale electromagnetic analysis AI/ML integration in design automation (EDA/MDA) Quantum circuits and spin qubit systems Heterogeneous integration and advanced packaging Multiphysics co-simulation for nano-scale devices Signal/power integrity in high-speed systems Key Projects: Leads the NSTC AIDRFIC program (first NSTC R&D Jump Start project), the DARPA NGMM Rapid-HI Design Institute, and the GENIE-RFIC generative design tool initiative. Also directs the Consortium for Electromagnetic Science and Technology. Awards & Honors: 2022 ACES Computational Electromagnetics Award IEEE Fellow (2016) Intel Outstanding Researcher Award (2019) MTT-S Distinguished Microwave Lecturer (2020–2023) 2013 Schelkunoff Prize Paper Award Advising & Grants: Advised over 30 PhD/master's students and led projects funded by NSF, DARPA, Intel, SRC, and industry partnerships. Key grants include NSF CAREER (2008), ONR Young Investigator (2006), and multiple industry-sponsored initiatives. Labs & Teams: Rapid-HI Design Institute (DARPA NGMM) Quantum device co-design group Multiphysics modeling team