جست‌وجوی استاد و پژوهشگر

Martin Kruczenskiمشاهده پروفایل

استاد

• String Theory
• Gauge Theory
• AdS/CFT Correspondence
• +۵ مورد دیگر

Martin Kruczenski is a Professor of Physics and Astronomy at Purdue University, affiliated with the College of Science. He earned his M.Sc. and Ph.D. in Physics from the University of Buenos Aires, Argentina (1998). His research focuses on theoretical high-energy physics, particularly the intersection of string theory, gauge theory, and integrable systems within the AdS/CFT correspondence framework. Research interests span: String/Gauge duality : Emergence of strings from gauge theory in large N limits Holographic methods : Applications to QCD and confining gauge theories Integrability : Exact solutions in AdS spacetimes and connections to spin chains Bootstrap techniques : Non-perturbative constraints on scattering amplitudes Minimal surfaces : Geometric descriptions of Wilson loops His publications demonstrate sustained focus on bootstrap methods applied to gauge theories, holographic complexity, and integrable structures in string theory. Recent work emphasizes computational approaches to strongly coupled systems and quantum criticality. Major scientific awards include: Ruth and Joel Spira Award for Graduate Teaching (2014, 2008) Purdue University Faculty Scholar (2012-2017) NSF Career Award (2010-2015) Alfred P. Sloan Fellowship (2009) He maintains active collaborations with researchers worldwide on integrability and holography projects. Current work explores quantum complexity measures in holographic systems and novel bootstrap constraints on gauge theories.

• Liquid Crystals
• Partial Differential Equations
• Geometric Variational Problems
• +۳ مورد دیگر

Zhiyuan Geng is a Golomb Visiting Assistant Professor of Mathematics at Purdue University's Department of Mathematics, part of the College of Science. His research focuses on advanced mathematical analysis, particularly in liquid crystals, partial differential equations, geometric variational problems, and materials science. Contact details include his email: geng42@purdue.edu , and office location MATH 409. Research interests span the following key areas: Liquid crystal defect dynamics and stability Phase transitions modeled via Allen-Cahn systems Free boundary problems in materials systems Triple junction configurations and surface tension effects Asymptotic analysis of interfaces and self-similar solutions Mathematical modeling of colloidal interactions in nematic phases Recent publications (2021–2025) emphasize theoretical breakthroughs in understanding complex material systems through PDE analysis and geometric variational principles. His work addresses critical phenomena like eigenframe discontinuities in Q-tensor models, rigidity properties of triple junction solutions, and stability of free boundary flows. These studies highlight interdisciplinary approaches combining pure mathematics with applications in soft condensed matter physics. No scientific awards or grants are explicitly listed in the provided information. He does not currently have documented advisees or students. Laboratory affiliations or collaborative research teams are not indicated in the texts provided.

Rafael Oliveiraمشاهده پروفایل

استادیار

• Computational Complexity
• Algebraic Geometry
• Optimization Algorithms
• +۱ مورد دیگر

Rafael Oliveira is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, conducting research from office DC 1313. His work explores fundamental connections between complexity theory, optimization, and geometry. Research spans: Algebraic complexity barriers Geometric combinatorics Efficient tensor algorithms Proof complexity frameworks His publications demonstrate consistent focus on mathematical foundations of computation , with recent advances in polynomial identity testing, interactive proofs, and geometric optimization. Article analysis shows recurring themes in algebraic complexity (40%), combinatorial geometry (30%), and optimization (30%).

Luke Schaefferمشاهده پروفایل

استادیار

• Quantum Computing
• Theoretical Computer Science
• Combinatorial Game Theory
• +۴ مورد دیگر

Luke Schaeffer is an Assistant Professor at the University of Waterloo, affiliated with the Cheriton School of Computer Science and the Institute for Quantum Computing (IQC). His academic journey includes a BMath and MMath from Waterloo, a PhD from MIT under Scott Aaronson, and postdoctoral positions at Waterloo and UMD. His research bridges quantum computing and theoretical computer science, with focuses on quantum circuit complexity, classical simulation of quantum systems, and combinatorics on words. Notable projects include studying the Clifford group's structure, low-depth quantum vs. classical circuits, query complexity of regular languages, and fermion-to-qubit encodings. He also explores non-quantum areas like combinatorial game theory and cellular automata. His work on interactive quantum advantage protocols demonstrated separations between quantum and classical circuit models, including results against AC⁰[p] and NC¹ classes. He co-developed sample-optimal classical shadow algorithms for pure states and classified Clifford gates over qubits into 57 distinct classes. Education: BMath and MMath, University of Waterloo PhD in Computer Science, MIT (advisor: Scott Aaronson) Key Research Themes: Quantum advantage and circuit complexity Automata and combinatorics on words Classical simulation techniques for quantum systems Algorithmic foundations of quantum computing He advises students in the Cheriton School of Computer Science, emphasizing quantum computing and theoretical computer science. His contributions include foundational results in quantum-classical separations and decidability of word properties via first-order logic.

Zajj Daughertyمشاهده پروفایل

دانشیار

• Combinatorial Representation Theory
• Diagram Algebras
• Lie Algebras and Quantum Groups
• +۳ مورد دیگر

Zajj Daugherty is an Associate Professor in the Department of Math & Statistics at Reed College. Their research focuses on combinatorial representation theory, specializing in diagram algebras such as Temperley-Lieb, Brauer, and BMW algebras, with connections to Lie algebras, quantum groups, and statistical mechanics. They hold a Ph.D. from the University of Wisconsin-Madison (2010) and an undergraduate degree from Harvey Mudd College. Research interests include representation theory of diagram algebras, centralizer algebras in tensor spaces, and applications to knot theory and statistical physics. Key areas of exploration involve combinatorial structures like partitions, tableaux, and lattice models to encode algebraic properties. Recent work includes studies on periplectic Lie superalgebras, affine VW supercategories, and quasisymmetric functions. Publications emphasize algebraic structures and their representations, with notable contributions to Temperley-Lieb algebras, Brauer diagram techniques, and symmetric function theory. Talks span international venues including the University of Melbourne, UC Berkeley, and Dartmouth College.

• Topology
• Geometry
• Algebraic Geometry
• +۴ مورد دیگر

Luigi Grasselli is a Senior Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia. His research spans topology, geometry, and their applications in engineering and art history. He focuses on metamaterials design, geometric structures, and historical mathematical contributions of Renaissance artists like Piero della Francesca and Albrecht Dürer. Grasselli also investigates environmental risks in recycling electronic waste. His academic career includes roles in computational topology, geometric modeling, and interdisciplinary studies linking mathematics with art. Key areas of expertise include crystallization theory, 3-manifold topology, and tessellation patterns applied to engineering materials. Grasselli has contributed to over 70 publications since 1978, exploring topics from Seifert manifolds to risk assessment methodologies. His work bridges pure mathematics with practical applications, such as additive manufacturing and sustainable waste management. He collaborates internationally, emphasizing geometric foundations in both historical and modern contexts.

• Cognitive Neuroscience
• Neuroimaging Techniques
• Psychiatric Disorders
• +۳ مورد دیگر

Laurel S. Morris is a cognitive neuroscientist affiliated with the University of Oxford's Nuffield Department of Clinical Neurosciences (NDCN), where she leads the NeuroSCOPE Lab. Her work focuses on developing novel cognitive and digital measures of motivation, self-agency, and reward/punishment processing, alongside optimizing high-resolution neuroimaging techniques for precision medicine and biofeedback interventions targeting psychiatric disorders like depression, anxiety, and addiction. Affiliations: Nuffield Department of Clinical Neurosciences, University of Oxford Her research integrates advanced neuroimaging methods (e.g., fMRI, DTI) with clinical applications, investigating brain regions such as the ventral tegmental area and subgenual cingulate cortex. Recent studies explore KCNQ potassium channel modulation and ketamine’s effects on brain activity in depression. She actively collaborates across disciplines to advance understanding of neurobiological mechanisms underlying mental health conditions. Dr. Morris is open to supervising new graduate students in her lab. Her work has been published in high-impact journals including Molecular Psychiatry and Neuropsychopharmacology, with a focus on translational neuroscience and precision medicine approaches.

Yu-Run Linمشاهده پروفایل

دانشیار

• Social Networks
• Political Networks
• Computational Social Science
• +۷ مورد دیگر

Yu-Run Lin is an Associate Professor at the University of Pittsburgh's School of Computing and Information, holding secondary appointments in the Political Science and Computer Science departments. He serves as Research & Academic Director at the Institute for Cyber Law, Policy, and Security (Pitt Cyber). His research focuses on computational social science, social/political networks, and visual analytics for network data, with applications in disaster response, misinformation detection, and public algorithm accountability. He leads the PICSO Lab and has contributed to over 100 publications across top venues like ICWSM, WWW, and IEEE Transactions. Key awards include the Best Paper Award (2020) and Honorable Mention (VAST 2014). Education: PhD in Informatics, Arizona State University (2010). Postdoctoral training at Harvard University and Northeastern University. Research Interests: Lin investigates how groups react to social/political events using social media, cellphone data, and mixed methods. His work bridges computational methods with social theory to understand network dynamics, algorithmic fairness, and crisis communication. Recent projects include modeling food insecurity, analyzing conspiratorial narratives, and developing tools like FairSight and TribalGram. Grants & Awards: NSF-funded work on digital accountability of public officials, grants for opioid overdose forecasting (CASTNet), and support from the KDD Humanitarian Mapping Workshop. Awards include the 2020 Best Paper Award and multiple conference recognitions. Labs & Teams: Directs the PICSO Lab, collaborating on AI ethics, disaster analytics, and computational social science. Engages with interdisciplinary teams in Pitt Cyber and the Collaboratory Against Hate.

• Oncology
• Pharmacology
• Molecular Biology
• +۳ مورد دیگر

Yao Li is a Postdoctoral Associate in the Department of Pathology at Yale School of Medicine. They are affiliated with the Yan Lab and collaborate with researchers such as Qin Yan, David Rimm, and Sabine Martha Lang. Education: MD from Central South University (2020) Research interests include oncology , pharmacology , and neuroscience . Their work spans cancer immunology , chromatin remodeling , and neurodegenerative imaging , with a focus on melanoma , BRD4 inhibitors , and brain iron deposition . Recent publications highlight drug synergy in melanoma , epigenetic mechanisms in neurodegeneration , and neuroimaging in schizophrenia . Scientific awards are not explicitly listed in the provided data. Yao Li mentors students in cancer biology and neuroscience . Their lab collaborates with institutions like Yale Cancer Center and Yale Neuroimaging Lab. Future work may integrate biomedical data science and immunotherapy for cancer-patient outcomes.

• Neuroimaging
• Functional Magnetic Resonance Imaging (fMRI)
• Brain Connectivity
• +۳ مورد دیگر

Vince Calhoun, PhD, is a Research Professor at Yale School of Medicine. His research integrates neuroimaging, machine learning, and neuroscience to study brain connectivity in disorders like Alzheimer's disease and schizophrenia. He earned his MS from Johns Hopkins University (1996) and PhD from the University of Maryland (2002). Research Focus: Dr. Calhoun's work centers on developing advanced fMRI analysis methods, including dynamic functional connectivity and multimodal MRI techniques. His interests span neuroinformatics, brain network modeling, and AI-driven diagnostics for neurological and psychiatric conditions. Recent Publications: His 15 latest articles (2024-2025) focus on machine learning applications in neuroimaging, such as federated learning for data privacy, graph neural networks for intelligence mapping, and predictive modeling of cognitive decline. Key themes include explainable AI, neurodevelopmental disorders, and cross-modal data fusion.

• Computational Mathematics
• Numerical Analysis
• Isogeometric Analysis
• +۳ مورد دیگر

Mattia Tani is a researcher at the Department of Mathematics, University of Pavia. He is affiliated with the Scientific Computing research group focusing on Numerical Methods and Applications. His work centers on developing advanced numerical techniques for computational mechanics, particularly Isogeometric Analysis (IGA), space-time methods, and efficient solvers. Tani’s research emphasizes computational efficiency through low-rank approximations, preconditioning strategies, and parallelization techniques. His academic contributions include pioneering work on matrix-free methods, Tucker tensor-based solvers, and domain decomposition approaches for multi-patch geometries. Notable methodologies include the IETI-DP method for discontinuous Galerkin discretizations and weighted quadrature rules for hierarchical B-splines. Tani also explores applications in parabolic problems, Schrödinger equations, and thermal engineering simulations. Publications highlight a focus on bridging theoretical numerical analysis with practical computational tools, with emphasis on scalability and robustness for large-scale problems. His work is disseminated across top journals and conferences, reflecting interdisciplinary collaboration between applied mathematics and engineering.

Jin-Yi Caiمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Complexity Theory
• Holographic Algorithms
• +۳ مورد دیگر

Jin-Yi Cai is a distinguished Professor of Computer Science and Steenbock Professor of Mathematical Sciences at the University of Wisconsin at Madison, where he has been a faculty member since 2000. Previously, he held academic positions at State University of New York at Buffalo (Professor 1996-2000, Associate Professor 1993-1996), Princeton University (Assistant Professor 1989-1993), and Yale University (Assistant Professor 1986-1989). He has also been a Radcliffe Institute Fellow at Harvard University (2007-2008) and a Guggenheim Fellow and Visiting Professor at the University of Toronto (1999-2000). Dr. Cai earned his Ph.D. in Computer Science from Cornell University in 1986, an M.A. in Mathematics from Temple University in 1983, and a Certificate in Mathematics from Fudan University in 1981. His academic journey spans prestigious institutions across the United States and demonstrates a consistent trajectory of scholarly excellence. Professor Cai's research focuses on theoretical computer science, particularly computational complexity theory, with significant contributions to holographic algorithms, counting constraint satisfaction problems, and graph homomorphisms. His work bridges computer science and mathematics, developing sophisticated algorithms and proving fundamental complexity results. His research has evolved from foundational work in structural complexity and oracle separations to specialized work in holographic algorithms and counting problems, demonstrating both depth and breadth in theoretical computer science. His publication record shows a consistent output of high-impact research, with major contributions spanning over three decades. His work on holographic algorithms represents a particularly innovative strand of research that has opened new avenues in computational complexity. The progression of his research demonstrates increasing specialization in counting problems while maintaining connections to broader theoretical frameworks in computer science and mathematics. 2022 Simons Fellowship 2022 CCF Award for Overseas Outstanding Contribution 2022 Fellow, American Mathematical Society (AMS) 2021 Fulkerson Prize in Discrete Mathematics 2021 Gödel Prize in Theoretical Computer Science 2014 Steenbock Professorship, UW Madison 2001 ACM Fellow 1998 John Simon Guggenheim Fellowship 1994 Sloan Fellowship Professor Cai has served as Editor of the Journal of Computer and System Sciences and Associate Editor of the Journal of Computational Complexity. His work has been recognized with numerous prestigious fellowships including the Guggenheim Fellowship, Sloan Fellowship, and Humboldt Research Award. His research has had significant impact in theoretical computer science, earning him the Gödel Prize and Fulkerson Prize, two of the most prestigious awards in theoretical computer science and discrete mathematics respectively.

• Theoretical Physics
• Lattice Gauge Theory
• Quantum Gravity
• +۳ مورد دیگر

Hirasawa Mitsuaki is an Assistant Professor (2023-2026) and Fixed-term researcher at the University of Milan-Bicocca's Department of Physics "Giuseppe Occhialini". His research focuses on theoretical particle physics and quantum gravity, with specialization in non-perturbative methods for quantum field theories. Research interests span several interconnected domains: Lattice gauge theories : Investigating CP restoration in Yang-Mills theories at finite temperature using imaginary θ simulations Matrix models : Studying emergent spacetime dynamics in Lorentzian type IIB matrix models Computational methods : Developing complex Langevin techniques to overcome sign problems in quantum systems Quantum gravity : Exploring spacetime emergence mechanisms in high-energy physics frameworks Recent publications (2019-2025) demonstrate consistent focus on non-perturbative aspects of quantum field theories and quantum gravity. Primary research threads include: 1) Numerical investigation of CP-violating phases in gauge theories, 2) Spacetime emergence in matrix models using novel regularization techniques, and 3) Development of advanced complex Langevin methods for lattice simulations. The work frequently combines theoretical formalism with large-scale computational approaches. No scientific awards, prizes, or fellowships are mentioned in available sources. Available records show no information regarding student advising, research grants, laboratory affiliations, or team collaborations.

Lizhong Chenمشاهده پروفایل

استاد

• Computer Architecture
• Machine Learning Accelerators
• GPU Architecture
• +۶ مورد دیگر

Lizhong Chen is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University and a core AI faculty member in the Collaborative Robotics and Intelligent Systems (CoRIS) Institute. He leads the STAR Lab which focuses on computing systems and AI applications with emphasis on computing efficiency across various computing platforms from embedded devices to supercomputers. Ph.D., Computer Engineering, University of Southern California, 2014 M.S., Electrical Engineering, University of Southern California, 2011 B.S., Electrical Engineering, Zhejiang University, 2009 Chen's research focuses on efficient computer systems (GPUs, accelerators, HPCs, IoT devices) and their applications in machine learning and natural language processing, especially large language models. His work spans machine learning accelerators, GPU architecture, AI-assisted design for computer architecture, and energy-efficient computing systems. He has made significant contributions to NoC (Network-on-Chip) power-gating research and developed the Agate simulator for simulating NoC power-gating. His recent publications (2023-2025) show a strong focus on large language models, particularly for simultaneous translation tasks, Kolmogorov-Arnold networks, and efficient model architectures. His work bridges computer architecture design with AI applications, creating synergies between hardware efficiency and machine learning performance. Scientific Awards: NSF CRII Award (2016) NSF CAREER Award (2018) Best Paper Nomination at IEEE NAS (2018) Best Paper Runner-up Award at HPCA (2020) Chu Kochen Award from Zhejiang University IEEE HPCA Hall of Fame (2020) Chen has served as an Associate Editor of IEEE Transactions on Computers and as program committee member for top computer system and machine learning conferences. He is the founder and organizer of the Annual International Workshop on AIDArc (AI-assisted Design for Architecture). His research is supported by multiple grants from NSF, NIH, Department of Energy, and the Northwest-AI-Hub supported by the CHIPS and Science Act. He teaches courses in computer architecture, high-performance computing, and specialized topics in AI accelerators and GPU architecture. As director of the STAR Lab, Chen leads research on computing efficiency across the spectrum from embedded and mobile devices to supercomputers and data centers. The lab's recent focuses include machine learning accelerators, GPU architecture, applications of AI in architecture designs, and improving the computing efficiency of machine learning and natural language processing models.

Anne Marie Svaneمشاهده پروفایل

دانشیار

• Topological Data Analysis
• Probability Theory
• Mathematical Statistics
• +۳ مورد دیگر

Anne Marie Svane is an Associate Professor at the Department of Mathematical Sciences, Aalborg University, specializing in topological data analysis, stochastic geometry, and probability theory. Her research bridges theoretical mathematics with applied statistics, focusing on geometric functionals and computational methods. Education: PhD in Mathematics from Aarhus University. Her recent work explores cobordism obstructions, Gibbs processes, and applications in climate science and medical research. Key contributions include: Advancing kernel persistence methods for topological data analysis. Developing central limit theorems for spatial point processes. Modeling moisture dynamics in building physics with climate data. Investigating the interplay between digital algorithms and geometric structures. She collaborates on interdisciplinary projects like AI-Aalborg Intelligence and contributes to educational initiatives such as Girls' Day in Science.