جست‌وجوی استاد و پژوهشگر

David Damanikمشاهده پروفایل

استاد

• Spectral Theory
• Dynamical Systems
• Aperiodic Order
• +۳ مورد دیگر

David Damanik is the Robert L. Moody, Sr. Professor of Mathematics at Rice University, where he has established himself as a leading researcher in spectral theory, dynamical systems, and aperiodic order. His work bridges pure mathematics with mathematical physics, focusing on the spectral properties of operators arising in quantum mechanics and quasicrystal theory. Dr. Damanik received his academic training at Johann Wolfgang Goethe-Universität in Frankfurt, Germany, earning a Dipl.-Math. in 1995, Dipl.-Inform. in 1996, and Dr. phil. nat. in 1998. His educational background reflects a strong foundation in both mathematics and computer science, which informs his interdisciplinary research approach. His research interests center around spectral theory of Schrödinger operators, particularly those with ergodic, quasi-periodic, and aperiodic potentials. He has made significant contributions to understanding the spectral properties of operators associated with quasicrystals, substitution sequences, and other aperiodic structures. His work often connects spectral properties with dynamical systems concepts, particularly through the study of rotation numbers, Lyapunov exponents, and gap labeling theorems. Damanik's research has profound implications for understanding quantum transport in aperiodic media and the mathematical foundations of condensed matter physics. Analysis of his recent publications (2022-2024) reveals a continued focus on ergodic Schrödinger operators, with two comprehensive monographs providing a systematic treatment of the field. His work spans both theoretical foundations and specific applications, addressing problems in one-dimensional systems, quasi-periodic potentials, and aperiodic tilings. The research demonstrates strong connections between spectral theory, dynamical systems, and mathematical physics, with particular emphasis on the interplay between spectral properties and the underlying dynamics of the potential. Annales Henri Poincaré Prize (2014) for the paper "Continuum Schrödinger operators associated with aperiodic subshifts" Professor Damanik has mentored numerous PhD students and maintains an extensive network of collaborators across the globe, as evidenced by his long list of coauthors. His research has been supported by various grants that enable him to organize workshops and conferences, fostering collaboration in his field. He has been instrumental in organizing major conferences such as the Spectral Theory and Mathematical Physics conference honoring Barry Simon's 80th birthday (scheduled for 2026) and multiple workshops on aperiodic order at prestigious institutions like Banff International Research Station and Mathematisches Forschungsinstitut Oberwolfach. Through his teaching of specialized courses like "Mathematics of Aperiodic Order" and "Ergodic Theory and Topological Dynamics," Damanik has cultivated the next generation of researchers in his field. His leadership in organizing conferences and workshops has established him as a central figure in the international community studying spectral theory and aperiodic structures.

• Tropical Geometry
• Cluster Algebras
• Combinatorics
• +۵ مورد دیگر

David E Speyer is a Professor in the Department of Mathematics at the University of Michigan . His research focuses on algebraic problems with combinatorial flavors , particularly in tropical geometry , cluster algebras , and geometry of Lie groups . He has supervised multiple PhD students, including Shelby Cox, Will Dana, and John Wiltshire-Gordon, and collaborated on projects with undergraduates like Grant Barkley and Benjamin Branman. Education: PhD in Mathematics from UC Berkeley under Bernd Sturmfels; undergraduate at Harvard. Research: Key areas include tropical geometry , cluster algebras , and flag manifolds . His work often bridges combinatorics, algebraic geometry, and representation theory. Publications: Over 40 papers, including breakthroughs in cluster algebras , affine weak order , and braid variety cluster structures . Awards: Clay Research Fellow (2005-2010). Teaching: Coordinates courses like Math 593 (graduate algebra) and Math 214 , with a focus on inquiry-based learning .

Michael J. Shelleyمشاهده پروفایل

استاد

• Applied Mathematics
• Biophysics
• Complex Fluids
• +۴ مورد دیگر

Michael J. Shelley is the Lilian and George Lyttle Professor of Applied Mathematics and holds joint appointments in Mathematics, Neural Science, and Mechanical Engineering at New York University's Courant Institute of Mathematical Sciences. He also serves as Co-Director of the Applied Mathematics Laboratory and Director of the Center for Computational Biology at the Flatiron Institute. Education: PhD (Applied Mathematics) from the University of Arizona (1985), MS (Applied Mathematics) from the University of Arizona (1984), BA (Mathematics) from the University of Colorado (1981). Research: Focuses on complex phenomena in active matter, biophysics, and complex fluids. Key areas include fluid-structure interactions (e.g., swimming/flying mechanics), cytoskeletal dynamics, and collective behavior in biological systems. Collaborates closely with experimentalists through the Applied Math Lab and Flatiron Institute. Labs & Affiliations: Co-Director, Applied Mathematics Laboratory; Director, Center for Computational Biology (Simons Foundation); affiliated with NYU’s Courant Institute and Department of Mathematics. Notable Work: Models for microtubule-motor assemblies, active suspensions, and fluid-structure interactions. Pioneered computational frameworks for Stokes suspensions and fiber dynamics in viscous fluids.

Thomas C. M. Leeمشاهده پروفایل

استاد

• Nonparametric Modeling
• Semiparametric Modeling
• Statistical Learning
• +۴ مورد دیگر

Thomas C. M. Lee is a Distinguished Professor of Statistics and Associate Dean of the Faculty in Mathematical and Physical Sciences at the University of California, Davis, within the College of Letters and Science. He holds a prominent position in the Department of Statistics and serves as a key academic leader at UC Davis. Education: B.App.Sc. (Math) from University of Technology, Sydney, Australia (1992) B.Sc. (Hons) (Math) with University Medal from University of Technology, Sydney, Australia (1993) Ph.D. from Macquarie University and CSIRO Mathematical and Information Sciences, Sydney, Australia (1997) Professor Lee's research spans multiple areas of statistics with a focus on developing innovative methodologies. His work particularly emphasizes nonparametric and semiparametric modeling , statistical learning , and statistical image and signal processing . He has made significant contributions to applying statistical methods across various scientific disciplines, demonstrating the versatility and power of statistical approaches in solving complex real-world problems. His research often bridges theoretical developments with practical applications, creating methodologies that are both mathematically sound and practically useful. Scientific Awards and Honors: Elected Fellow of the American Association for the Advancement of Science (AAAS, 2019) Elected Fellow of the American Statistical Association (ASA) Elected Fellow of the Institute of Mathematical Statistics (IMS) Elected Senior Member of the IEEE Professor Lee has held significant editorial roles including serving as Editor-in-Chief for the Journal of Computational and Graphical Statistics (2013-2015) and currently as Review Editor for the Journal of the American Statistical Association. From 2015 to 2018, he chaired the Department of Statistics at UC Davis. He has taught numerous statistics courses including STA 13 (Elementary Statistics), STA 131C (Introduction to Mathematical Statistics), STA 243 (Computational Statistics), and STA 401 (Statistical Consulting). His leadership extends beyond research to academic administration, where he has shaped statistics education and departmental direction at UC Davis.

Ivon Arroyoمشاهده پروفایل

استاد

• Learning Sciences
• Computer Science
• Data Science
• +۹ مورد دیگر

Ivon Arroyo is a Professor in the Department of Teacher Education & Curriculum Studies (TECS) at the University of Massachusetts Amherst. Her research focuses on integrating novel technologies into math and computational thinking education, emphasizing affective and metacognitive states. She develops intelligent tutoring systems, such as COVES, which personalize learning in real-time and utilize facial expression recognition to enhance engagement. Her work on WearableLearning explores embodied, physically active multiplayer games for K-12 classrooms, leveraging mobile devices and wearable technologies to create immersive learning experiences. Dr. Arroyo holds an Ed.D. (2003) and M.S. (2000) from UMass Amherst and a B.S. from Universidad Blas Pascal in Argentina (1995). She has been recognized with multiple awards, including Best Paper Awards at the 2009 International Conference on Artificial Intelligence in Education and the 2010 Educational Data Mining Conference, a Fulbright Fellowship (1996), and a 1994 undergraduate prize for computer vision research. Her research interests span interdisciplinary areas such as Learning Sciences , Computer Science , Data Science , and Psychology . She prioritizes culturally responsive pedagogical agents and cross-cultural studies in educational technology, particularly in Argentina, India, and the U.S. Her projects often address challenges in developing countries, including localization of tutoring systems to Spanish. Advising and grants are central to her work, with grants like the NSF CAREER Award (2020) supporting embodied math classrooms. She collaborates on teacher dashboard frameworks and explores ethical AI integration in education. Her labs focus on creating tools that merge computational innovation with theoretical learning science principles, emphasizing real-world applications like the WearableLearning Cloud Platform.

Gheorghe Craciunمشاهده پروفایل

استاد

• Mathematical Biology
• Dynamical Systems
• Chemical Reaction Networks
• +۳ مورد دیگر

Gheorghe Craciun is a Professor in the Department of Mathematics and the Department of Biomolecular Chemistry at the University of Wisconsin-Madison. His research focuses on mathematical and computational models in biology and medicine, particularly dynamical systems models of biological interaction networks. He has been a visiting researcher at the Max Planck Institute for Mathematics in the Sciences during the 2019-2020 academic year and has organized the Madison Workshops on Mathematics of Reaction Networks. Craciun's primary research interests include Mathematical Biology, Dynamical Systems, Chemical Reaction Networks, Computational Biology, Systems Biology, and Algebraic Geometry. He investigates systems of differential equations with polynomial right-hand sides, which are common in biochemical reaction networks, ecological interactions, and epidemiological models. His work often involves proving global stability, analyzing multistability, and characterizing steady states using tools from algebraic geometry and combinatorics. Recent publications demonstrate his focus on toric differential inclusions, endotactic networks, and the global attractor conjecture, extending to applications in biochemical networks and discrete Boltzmann equations. His extensive publication record reveals a strong trend toward algebraic and geometric methods for analyzing complex biological networks, with significant contributions to reaction network theory, stability analysis, and parameter characterization. Craciun's work bridges abstract mathematical concepts with practical applications in biochemistry, ecology, and medicine, including modeling vitellogenin production in trout and peptide mass distributions. He has collaborated extensively with international researchers including Alicia Dickenstein, Anne Shiu, Bernd Sturmfels, Casian Pantea, and Miruna-Stefana Sorea. In education, Craciun teaches graduate courses such as Math 703 and mentors students through the Madison Math Circle and Putnam Club, while organizing specialized workshops that foster collaboration in reaction network theory.

Roummel F. Marciaمشاهده پروفایل

استاد

• Optimization
• Signal Processing
• Data Science
• +۵ مورد دیگر

Roummel F. Marcia is a Professor and current Chair of the Department of Applied Mathematics at the University of California, Merced, within the School of Natural Sciences. He received his Ph.D. from UC San Diego under Professor Philip Gill and previously held postdoctoral positions at the San Diego Supercomputer Center and University of Wisconsin-Madison, as well as a research scientist position in electrical engineering at Duke University. His research spans multiple areas in optimization and its applications, with a focus on signal processing, data science, machine learning, linear algebra, and mathematical biology. Dr. Marcia's work has significant interdisciplinary impact, particularly in biomedical imaging, computational biology, and quantum computing applications. His research methodology often combines theoretical optimization approaches with practical applications in data-intensive fields. Dr. Marcia's recent publications demonstrate a strong trend toward integrating optimization theory with deep learning architectures, particularly in applications requiring sparse data handling, biomedical imaging, and quantum computing. His work shows increasing focus on developing novel optimization algorithms specifically designed for machine learning contexts, including quasi-Newton methods adapted for deep learning and specialized techniques for handling non-convex optimization problems. School of Natural Sciences Faculty Award for 'Developing or Improving Academic Programs and Tracks' (2021-22) Leadership roles in SIAM Activity Group on Applied Mathematics Education Recognition as a Math Alliance Mentor for supporting underrepresented students Dr. Marcia has successfully mentored numerous doctoral students to completion, with graduates moving to positions at Meta, Johns Hopkins University Applied Physics Laboratory, Lawrence Livermore National Laboratory, and other prestigious institutions. His research has been consistently funded by major agencies including NSF (with grants IIS 1741490, DMS 1840265, DMS 2229495, CCF 2343610), DARPA, and ARPA-E. As the current graduate chair of the Applied Math Graduate Program, he plays a key role in shaping the next generation of mathematical scientists. His work with the SMaRT (Scientific Mathematics Research and Training) team demonstrates his commitment to collaborative, interdisciplinary research.

Chen Sunمشاهده پروفایل

استادیار

• Computer Vision
• Deep Learning
• Machine Learning
• +۸ مورد دیگر

Chen Sun is an Assistant Professor of Computer Science at Brown University and a part-time Staff Research Scientist at Google DeepMind . His research bridges computer vision, machine learning, and artificial intelligence , focusing on multimodal representation learning, visual commonsense, and controllable video generation . He directs the PALM🌴 research lab , which explores scalable models for robotic planning, video understanding, and human activity recognition . Chen Sun earned a Ph.D. in Computer Science from the University of Southern California (2016) , advised by Professor Ram Nevatia , and a Bachelor of Science in Computer Science from Tsinghua University (2011) . His lab's work has been supported by Adobe, Honda, Meta, NASA, and Samsung , and he is affiliated with the NSF AI Research Institute on Interaction for AI Assistants . His research spans multimodal transformers, embodied agents, and physics-informed video generation . Key trends include Learning from unlabeled videos for human activity recognition Developing controllable generation techniques using motion trajectories and physics-based signals Advancing scalable frameworks for video-language tasks Scientific awards include the Brown University Richard B. Salomon Faculty Research Award and Samsung Global Research Outreach Award . He has served as Workshop Chair (CVPR 2025) , Action Editor (TMLR) , and Area Chair for top conferences like ICLR, CVPR, and NeurIPS . Chen Sun mentors a dynamic team including Ph.D. students Apoorv Khandelwal (Presidential Fellow) Calvin Luo (Research Mobility Fellow) Nate Gillman (Math Department) Shijie Wang Tian Yun (co-advised with Ellie Pavlick) Yuan Zang Zilai Zeng Zitian Tang and alumni now pursuing Ph.D. programs at Princeton, Cornell, UBC, and UNC . His teaching portfolio includes graduate-level courses on Deep Learning (CSCI 2470) , Advanced Topics in Deep Learning (CSCI 2952N) , and a short course on Multimodal Transformers at ICASSP 2022 and AAAI 2023 .

Daniel Frischemeierمشاهده پروفایل

استاد

• Mathematics Education
• Statistics Education
• Data Science Education
• +۵ مورد دیگر

Daniel Frischemeier is a Professor of Mathematics Didactics with a focus on Primary Education at the University of Münster's Faculty of Mathematics and Computer Science. He has established himself as a leading researcher in statistics and data science education for primary school students, with extensive contributions to educational methodology and teacher training. University of Münster (2021-present) TU Dortmund (2020-2021) University of Paderborn (2009-2020) Ludwig-Maximilians-Universität München (2017-2018) Dr. Frischemeier completed his doctoral studies at the University of Paderborn with a dissertation on statistical thinking and research using TinkerPlots software. His educational background includes graduate studies in Mathematics and undergraduate studies in Mathematics and Physics for teaching at various school levels. His research focuses on the design and testing of teaching-learning environments for primary mathematics education, particularly in the areas of data analysis, probability, and statistics. He conducts qualitative analysis of learners' cognitive processes related to the guiding principle of 'data and chance' in primary education. His work also includes the design and evaluation of teaching materials in data science and civil statistics, the use of learning videos to promote process-related skills, and the implementation of Fermi tasks and computer science education within primary mathematics lessons. Analysis of Dr. Frischemeier's recent publications reveals a strong emphasis on data literacy development in primary education, with increasing focus on the integration of digital tools and the conceptual understanding of data as models. His work bridges mathematics education with emerging fields of data science, addressing both theoretical frameworks and practical classroom applications. The research demonstrates a progression from basic statistical concepts toward more complex data modeling approaches suitable for young learners. Elected member of the International Statistical Institute (ISI) Chair of the Local Organizing Committees for IASE Satellite 2025 Conference Council-Member of the International Statistical Institute Special Edition Editor of the Statistics Education Research Journal Member of International Program Committees for major statistics education conferences Co-Leader of CERME Thematic Working Group 5 on Probability and Statistics Education Dr. Frischemeier serves in numerous editorial capacities and review roles for prominent journals in mathematics and statistics education. He leads significant research projects including 'Promoting Data Science Education for Teacher Education at the University level (DataSETUP)' and 'Data Science Education in STEAM for Civic Engagement and Social Justice from the Early Years (DataScEd4CiEn)'. His work has substantial impact on teacher education programs and curriculum development in statistics and data science for primary schools. He is actively involved in the development and leadership of the Math Center Münster (MaZ), which promotes mathematical potential for all students. His team includes numerous research assistants and doctoral candidates working on various aspects of mathematics education research, particularly focusing on data literacy and statistical reasoning in primary education contexts.

Ken Koedingerمشاهده پروفایل

استاد

• Cognitive Science
• Educational Technology
• Intelligent Tutoring Systems
• +۳ مورد دیگر

Ken Koedinger is a University Professor at Carnegie Mellon University (CMU), holding appointments in the School of Computer Science (Human-Computer Interaction Institute) and the Dietrich College of Humanities and Social Sciences (Department of Psychology). He is the co-founder of Carnegie Learning, Inc., and directs the LearnLab, now the scientific arm of CMU’s Simon Initiative. His research focuses on cognitive modeling, intelligent tutoring systems, and the science of learning. Dr. Koedinger has over 250 peer-reviewed publications and has been awarded prestigious titles such as the Hillman Professorship of Computer Science and Fellow of the Cognitive Science Society. Education: Ph.D. in Cognitive Psychology (CMU, 1990), M.S. in Computer Science (University of Wisconsin, 1986). Prior to academia, he taught in an urban high school, informing his interdisciplinary approach to education research. Research Interests: Cognitive modeling, educational technology, learning analytics, and the integration of AI into learning systems. Key projects include Cognitive Tutors, LearnLab, and the Pittsburgh Science of Learning Center. Awards: Over 30 grants from NSF, Department of Education, and private foundations, including major contributions to K-12 math education and AI-driven personalized learning. Advising: Supervised over 30 graduate students, many now leading roles in academia and industry. Current students focus on AI ethics, scalable education, and mixed-reality learning.

• Artificial Intelligence
• Optimization
• Machine Learning
• +۷ مورد دیگر

Sebastian Pokutta is a Professor at Technische Universität Berlin, Vice President at the Zuse Institute Berlin (ZIB), and Chair of the Cluster of Excellence MATH+ and MODAL. His research lies at the intersection of Artificial Intelligence, Optimization, and Machine Learning, with applications in sustainability, quantum computing, and mathematical discovery. Research Interests: Development of novel optimization algorithms, particularly Frank-Wolfe and Conditional Gradient methods. Integration of machine learning with decision-making and combinatorial optimization. AI for Science (AI4Science), including applications in quantum mechanics and ecology. AI and creativity, human-AI co-creativity, and social science modeling using multi-agent LLMs. His recent publications (2025) demonstrate a strong focus on scalable optimization, interpretability, and algorithmic foundations. The work spans theoretical advances in convergence analysis, practical implementations in Julia (FrankWolfe.jl), and real-world deployments in biomass estimation and quantum certification. Scientific Awards: Gödel Prize (2023) STOC Test of Time Award (2022) Science Prize of the Association for Pediatric Orthopedics (2025) Google Research Awards (2021, 2020) NSF CAREER Award (2015) He advises a vibrant research group, with former students and postdocs securing faculty positions at institutions like Inria, Carlos III University, and James Madison University. His group has received funding from Google, DFG, and Math+, and he leads major collaborative efforts such as the Thematic Einstein Semester on Mathematical Optimization for Machine Learning. Labs and Teams: Interactive Optimization and Learning Lab at TU Berlin and ZIB. Leadership in MODAL and MATH+ research clusters, fostering interdisciplinary collaboration in mathematical optimization and AI.

Kenneth Chelstمشاهده پروفایل

استاد

• Decision Analysis
• Operations Research
• K-12 Mathematics Education
• +۲ مورد دیگر

Dr. Kenneth Chelst is a Professor in the Department of Industrial and Systems Engineering at Wayne State University , where he directs the Engineering Management Program. His work bridges operations research with engineering management and K-12 mathematics education . A Rabbinic Ordination holder from Yeshiva University, he combines analytical rigor with pedagogical innovation. Educational Background: Ph.D., M.S. in Operations Research from MIT B.A. in Mathematics and Physics from Yeshiva College His research focuses on structured decision-making , OR applications in emergency services , and math curriculum development . The NSF-funded Project MINDSET ($3.3 million) and Project MEDeATe highlight his efforts to integrate OR into high school education. His From Percentage to Algebra middle school book and Ford-funded workshops have impacted hundreds of teachers. Scientific Awards: INFORMS President's Award (2011) for K-12 OR outreach Edelman Prize Laureate (2000) for Ford's OR application Multiple teaching awards from Ford Motor Company Dr. Chelst consults with Urban Science Applications Inc. and previously led the INFORMS Roundtable. His courses include Decision and Risk Analysis and Leadership Projects in Engineering Management , emphasizing corporate partnerships and immediate ROI.

Michele Dolceمشاهده پروفایل

مدرس

• Partial Differential Equations
• Fluid Dynamics
• Kinetic Theory
• +۴ مورد دیگر

Michele Dolce is a Lecturer and Scientist at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB) and the Chair of Mathematical Analysis, Calculus of Variations and PDEs (AMCV). He previously held positions as a Postdoc at EPFL and a Research Associate at Imperial College London, where he worked under Prof. Michele Coti Zelati. His academic journey includes a PhD from the Gran Sasso Science Institute. Current affiliation: EPFL, School of Basic Sciences (SB), Department of Mathematics (MATH), AMCV Past affiliation: Imperial College London His research focuses on the mathematical analysis of Partial Differential Equations (PDEs) in fluid dynamics and kinetic theory. Key areas include hydrodynamic stability, long-time behavior of viscous vortex systems, and time-decay properties of kinetic models like the Boltzmann and Wave Kinetic Equations. Recent work explores vortex merging phenomena and Taylor dispersion in rotationally symmetric flows. Scientific activities include organizing workshops such as "Long time dynamics in random and deterministic systems" (2025) and co-organizing events like the "Deterministic and random features of fluids" summer school (2023) and the "Enjoying Probability and Fluids in Lausanne" workshop (2023). His publications span journals including Communications in Mathematical Physics, Archive for Rational Mechanics and Analysis, and Journal of Mathematical Fluid Mechanics. Supported by Swiss National Science Foundation (SNF Ambizione grant PZ00P2_223294) Partially funded by GNAMPA (INdAM group)

• Numerical analysis
• Partial differential equations
• Fluid mechanics
• +۵ مورد دیگر

Dr. Jean-Christophe Nave is an Associate Professor in the Department of Mathematics and Statistics at McGill University. He holds a PhD from the University of California, Santa Barbara (2004), under advisors Xu-Dong Liu and Sanjoy Banerjee. Prior to McGill, he served as a Lecturer and Instructor at MIT's Mathematics Department (2005-2010). His research focuses on numerical analysis, partial differential equations, fluid mechanics, and computational methods for interface problems. He has led research groups involving postdocs, PhD, and undergraduate students, collaborating on projects like the Correction Function Method for PDEs and the Characteristic Mapping Method for advection problems. Education: Ph.D. in Applied Mathematics from UCSB (2004). Affiliations include the Institut des Sciences Mathematiques Steering Committee, Centre de Recherches Mathematiques Applied Math Lab, and CNRS-UMI. Active in teaching courses like Numerical Analysis I/II and Non-Linear Dynamics at McGill, with sabbatical periods noted in recent years. Research interests span numerical methods for PDEs, fluid-structure interaction, and multi-phase flows. His work integrates computational geometry and invariant numerical techniques, addressing challenges in complex fluid dynamics and interface-driven phenomena. Over 40 peer-reviewed publications and continuous contributions to the field of computational applied mathematics. Scientific advising includes over 20 graduate and undergraduate students, with notable alumni now in academia and industry. Collaborations include projects on volcano dynamics, fiber drawing instabilities, and concentrated solar power systems. His methods have advanced numerical simulations for engineering and physical systems involving discontinuous coefficients and sharp interfaces.

Brendan Dolan-Gavittمشاهده پروفایل

دانشیار

• Cybersecurity
• Program Analysis
• Virtualization Security
• +۵ مورد دیگر

Brendan Dolan-Gavitt is an Associate Professor in the Computer Science and Engineering Department at NYU Tandon School of Engineering and part of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from Georgia Tech (2014) and a BA in Math and Computer Science from Wesleyan University (2006). His research spans cybersecurity, program analysis, virtualization security, memory forensics, and embedded/cyber-physical systems, focusing on automating the understanding of large software systems to develop novel defenses. Research interests include developing techniques for static and dynamic analyses of real-world software to reveal hidden design assumptions. His work has been presented at top security conferences like USENIX Security, ACM CCS, and IEEE Security & Privacy. He led the development of the open-source PANDA platform for dynamic analysis. His publications primarily focus on AI-driven security solutions, vulnerability discovery, and automated testing tools. Recent work explores LLMs in offensive security, fuzzing enhancements, and secure code generation, emphasizing practical applications in cybersecurity. Scientific Awards: NSF CAREER Award for improving software vulnerability testing and education He leads the OSIRIS Lab, a student-run cybersecurity group, and collaborates on interdisciplinary projects addressing emerging security challenges through grants and industry partnerships.