جست‌وجوی استاد و پژوهشگر

Karin Nachbagauerمشاهده پروفایل

استاد

• Multibody System Dynamics
• Numerical Mathematics
• Inverse Dynamics
• +۳ مورد دیگر

Karin Nachbagauer is a Professor of Applied Mathematics at the University of Applied Sciences Upper Austria, affiliated with the Faculty for Engineering's Mechanical Engineering Department. She holds a Hans Fischer Fellowship at the TUM Institute for Advanced Study (since 2020). Her research focuses on multibody system dynamics, numerical mathematics, optimal control, and inverse dynamics, with applications in mechanical engineering and robotics. She earned her PhD in Engineering Sciences (2012) and Diploma in Industrial Mathematics (2009) from Johannes Kepler University Linz. Notable awards include the 2020 Best Paper Award for optimal control research and 2019 Excellence in Teaching Award. Her work emphasizes adjoint gradient methods for optimization problems, parameter identification in multibody systems, and time-optimal control applications. Current projects include the VRoboCoop initiative for human-robot collaboration and IOMMS for innovative optimization in multibody systems. Publications span journals like Journal of Computational and Nonlinear Dynamics and Multibody System Dynamics , with over 80 peer-reviewed articles. She actively participates in international conferences and serves on editorial boards.

Andrei Krokhinمشاهده پروفایل

استاد

• Computational Complexity
• Constraint Satisfaction Problems
• Universal Algebra
• +۴ مورد دیگر

Andrei Krokhin is a Professor in the Department of Computer Science at Durham University, UK. His academic roles include being a member of the Algorithms and Complexity Research Group. He holds a PhD in Mathematics from Ural State University (Russia) and has held positions at Warwick University and Oxford University. His research focuses on computational complexity, constraint satisfaction problems (CSP), universal algebra, and combinatorics. Education: PhD in Mathematics, Ural State University, 1990s Research Interests: Professor Krokhin investigates the mathematical and algorithmic foundations of CSP, emphasizing complexity classification and approximation. His work bridges universal algebra, logic, combinatorics, and graph theory. Key themes include algebraic approaches to CSP, constraint optimization, and the interplay between computational complexity and structural mathematics. Awards: EPSRC Advanced Research Fellowship (2006) Principal organizer of the 2006 Oxford Workshop on Mathematics of Constraint Satisfaction Invited plenary speaker at ISMVL 2003 (Tokyo) Invited lectures at NATO ASI Summer School (2003) Advising & Grants: Supervises PhD students (e.g., Yiming Qiu) and leads EPSRC-funded projects like 'Promise Constraint Satisfaction Problems: Structure and Complexity.' He recruits students for research on CSP complexity and approximation. Labs/Teams: Member of the Algorithms and Complexity Research Group at Durham University, collaborating internationally on CSP theory and applications.

• Number Theory
• Algebra
• Diophantine Approximation
• +۱ مورد دیگر

Anton Mosunov is a Lecturer in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds a Ph.D. from the University of Waterloo (2019). His research focuses on Number Theory, particularly Diophantine approximation and computational number theory, involving large-scale calculations to explore equations in integers and conjectures. He teaches courses such as Calculus I and Linear Algebra for Engineers in 2025. His work includes studies on Diophantine equations, automorphism groups of binary forms, and class group tabulations. While no scientific awards are listed, his publications reflect expertise in algebraic structures and computational methods. He is involved in undergraduate education and research mentorship within the Mathematics Department.

Tommaso Calarcoمشاهده پروفایل

استاد

• Quantum Optimal Control
• Quantum Computing
• Quantum Technologies
• +۳ مورد دیگر

Tommaso Calarco serves as Director of the Institute for Quantum Control (PGI-8) at Jülich Research Centre, leading cutting-edge research in quantum optimal control methodologies for next-generation quantum technologies. His work focuses on developing transformative computational frameworks applicable to natural sciences, logistics, and high-performance computing through advanced quantum device engineering. His research spans quantum optimal control for computation and many-body systems, emphasizing physical model development, model reduction techniques, and machine learning integration for scalable quantum hardware. Key focus areas include spin-qubit optimization, diamond quantum register engineering, and error suppression in gate operations, with significant contributions to ultracold atom systems and semiconductor-based quantum platforms. Analysis of his 2024-2025 publications reveals concentrated efforts on hardware-specific challenges across multiple quantum modalities: spin shuttling fidelity in semiconductor systems, gate optimization for nitrogen-vacancy centers, and photon-spin interface engineering. This work demonstrates a unifying thread of optimal control solutions tailored to platform-specific decoherence mechanisms and scalability constraints. As Director of PGI-8 within the Peter Grünberg Institut, Calarco oversees a dedicated research team advancing quantum control theory and applications, contributing substantially to European quantum technology roadmaps including the Quantum Flagship initiative and strategic European Commission reports.

Kristopher Micinskiمشاهده پروفایل

استادیار

• Programming Languages
• Static Analysis
• Formal Methods
• +۴ مورد دیگر

Kristopher Micinski is an Assistant Professor in the Electrical Engineering and Computer Science Department at Syracuse University. His research focuses on scalable program analysis, static analysis, and formal methods applied to computer security and privacy. He holds a PhD in Computer Science from the University of Maryland and a BS in Computer Engineering from Michigan State University. Education: PhD, Computer Science, University of Maryland at College Park BS, Computer Engineering, Michigan State University Research Interests: His work bridges theory and application of program analyses, emphasizing scalable static analysis frameworks, Datalog optimization for distributed systems, and security applications. Recent efforts include GPU-accelerated Datalog engines and large-scale malware analysis pipelines like Assemblage. Grants & Projects: NSF PPoSS Large: $1M grant for declarative analytics DARPA V-SPELLS: $400K for legacy software optimization Assemblage: $350K DoD grant for malware classification Teaching: He teaches undergraduate and graduate courses on programming languages (CIS352) and formal methods (CIS700), with materials publicly available on YouTube.

Peter Dybjerمشاهده پروفایل

استاد

• Intuitionistic Type Theory
• Categorical Type Theory
• Proof Assistants
• +۹ مورد دیگر

Peter Dybjer is a Professor of Computing Science at the Department of Computer Science and Engineering, Chalmers University of Technology in Sweden. He is a leading researcher in the field of type theory, with a focus on intuitionistic type theory, categorical semantics, and proof assistants. His work bridges foundational aspects of mathematics and computer science, emphasizing constructive methods and formal verification. Research Interests: Intuitionistic Type Theory and its categorical semantics Proof assistants and formal verification Inductive and inductive-recursive definitions Game semantics and normalization techniques Dependent type systems and their applications Homotopy Type Theory and univalent foundations Activities & Contributions: Editor of the Bulletin of Symbolic Logic Member of program committees for conferences like TYPES, FSCD, and MFPS Keynote speaker at events including the European Congress of Mathematics and the Hausdorff Research Institute's Summer School on Types, Sets, and Constructions Initiated and directed the Master's Program in Computer Science – Algorithms, Languages, and Logic at Chalmers Teaching & Outreach: Professor Dybjer has taught advanced courses at international summer schools, including: Inductive Definitions in Constructive Type Theory (TYPES Summer School, 2005) Dependent Types at Work (LerNet Summer School, Uruguay, 2008) Normalization by Evaluation (Midlands Graduate School, 2009 and 2015)

Sandra Zillesمشاهده پروفایل

استاد

• Computational Learning Theory
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Sandra Zilles is a Professor and Canada Research Chair (Tier 1) in Computational Learning Theory at the University of Regina's Department of Computer Science. She holds adjunct appointments at the University of Waterloo and collaborates with the Alberta Machine Intelligence Institute (Amii). Her research focuses on theoretical computer science and artificial intelligence, particularly interactive learning models, formal language theory, and heuristic search algorithms. Her research integrates computational learning theory, formal language theory, and discrete artificial intelligence structures. Key interests include: Machine teaching with limited data Learnability of pattern languages and automata Graph-theoretic approaches in AI Her work bridges theoretical frameworks with applications in medical imaging, bioinformatics, and game theory. Zilles has received numerous honors including: NSERC Canada Research Chair (Tier 1, 2022-2029) Royal Society of Canada College membership Best Paper Awards (KI 2012, ALT 2003, COLT 2002) She mentors over 50 students and postdocs through her research group. Current projects explore symbolic automata, collaborative learning, and geometric teaching models. Her lab maintains international collaborations with institutions in Germany, Canada, and the US.

Paul E. Handمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۳ مورد دیگر

Paul E. Hand is an Associate Professor of Mathematics and Computer Science at Northeastern University, affiliated with both the College of Science and the Khoury College of Computer Sciences. He holds a Bachelor of Science in Applied and Computational Mathematics from the California Institute of Technology (2004) and a PhD in Mathematics from the Courant Institute at New York University (2009), where he received the Kurt O. Friedrichs Prize for outstanding dissertation. PhD in Mathematics, Courant Institute, NYU (2009) BS in Applied and Computational Mathematics, Caltech (2004) His research focuses on developing theoretical frameworks and algorithms for machine learning and artificial intelligence, particularly in signal recovery, phase retrieval, and vision/imaging. He also explores intersections of deep learning with convex optimization and has contributed to bilinear recovery problems. Recent publications emphasize generative models, inverse problems, and robust optimization techniques. Key themes include deep learning with provable recovery guarantees , convex programming for signal inversion , and manifold-based optimization . Kurt O. Friedrichs Prize for Outstanding Dissertation (2009) NSF CAREER Grant DMS-1848087 He has taught courses on Deep Learning, Machine Learning, and Signal Processing at Northeastern University since 2016, previously holding academic roles at Rice University (2016-2018) and MIT (2009-2016). He directs educational outreach initiatives and developed the educational resource Leading Lesson for multivariable calculus problem-solving.

• Combinatorics
• Graph Theory
• Algebraic Combinatorics
• +۴ مورد دیگر

Lluis Vena Cros is a distinguished researcher in the Department of Mathematics at Universitat Politècnica de Catalunya - BarcelonaTech (UPC-BarcelonaTech), affiliated with the Faculty of Mathematics and Statistics. He is a core member of the GAPCOMB research group, which specializes in Geometric, Algebraic and Probabilistic Combinatorics. His research spans multiple domains within combinatorics, with particular emphasis on graph theory, algebraic combinatorics, geometric combinatorics, and probabilistic combinatorics. Vena Cros has made significant theoretical contributions to the understanding of graph homomorphisms, Tutte polynomials, Ramsey theory, and extremal combinatorial structures. His work often explores the deep connections between combinatorial objects and other mathematical disciplines such as topology, algebra, and discrete geometry. An analysis of his recent publications reveals a consistent focus on combinatorial structures with applications across multiple mathematical domains. His work on Tutte polynomials for various graph structures demonstrates a unifying approach to graph invariants, while his research in Ramsey theory explores structural properties of large combinatorial objects. The recurring theme across his publications is the investigation of extremal properties and computational aspects of combinatorial configurations. Vena Cros actively participates in multiple research projects including 'Structure, Randomness and Computational Methods in Extremal Combinatorics,' 'COntemporary COmbinatorics and Applications (COCOA),' and the 'Grup GAPCOMB de la UPC.' These projects involve collaborations with numerous researchers across Spain and internationally, reflecting his significant standing in the combinatorial mathematics community. His work has been supported by various funding bodies including the Spanish State Research Agency and the European Union's Horizon 2020 program. As a member of the GAPCOMB research group, Vena Cros contributes to a vibrant mathematical community focused on advancing combinatorial theory and its applications. The group maintains strong connections with other research centers and regularly participates in international conferences and collaborative projects, fostering the exchange of ideas across the global combinatorial mathematics community.

Tilman Börgersمشاهده پروفایل

استاد

• Mechanism Design
• Game Theory
• Economic Theory
• +۳ مورد دیگر

Tilman Börgers is the Samuel Zell Professor of the Economics of Risk at the Department of Economics, University of Michigan. His research focuses on mechanism design, game theory, and economic decision-making. He is affiliated with Lorch Hall, Ann Arbor, and can be reached at tborgers@umich.edu. Current Position: Professor of Economics Institution: University of Michigan Department: Department of Economics His research interests include mechanism design, information aggregation, and theoretical foundations of economic behavior. Recent work explores topics such as undominated mechanisms, diversity in decision-making, and algebraic closure in economic models. Selected papers include contributions to the Review of Economic Design and journals like Journal of Economic Theory . His book An Introduction to the Theory of Mechanism Design (Oxford University Press) is a key reference in the field. Teaching materials include notes on microeconomic theory, game theory, and general equilibrium theory. No awards or grants are explicitly listed in the provided content.

Erin R Ottmarمشاهده پروفایل

دانشیار

• Mathematics Education
• Educational Psychology
• Perceptual Learning
• +۲ مورد دیگر

Dr. Erin R. Ottmar is an Associate Professor of Psychology and Learning Sciences at Worcester Polytechnic Institute (WPI), affiliated with the Department of Social Science & Policy Studies. She directs the MAPLE (Math Abstraction Play Learning Embodiment) Lab, focusing on educational technology and mathematics cognition. She holds a BA in Psychology and Elementary Education from the University of Richmond (2005) and a PhD in Educational Psychology: Applied Developmental Science from the University of Virginia (2011). Her postdoctoral training was completed at the University of Richmond. Her research integrates educational, cognitive, and developmental psychology to design interventions improving mathematics teaching/learning. Key projects include developing dynamic educational technologies like From Here to There and Graspable Math that embed perceptual learning and gesture-based interaction. Her current work examines how cognitive and non-cognitive pathways combine to enhance K-12 mathematics education, using classroom observations, longitudinal data, and multi-level modeling. Publications demonstrate consistent focus on: Perceptual learning mechanisms in mathematics Educational technology efficacy Classroom implementation studies Game-based learning analytics Cognitive factors in mathematical reasoning Awards include: NSF CAREER Award (2022) Sigma Xi Outstanding Junior Faculty Researcher Award (2022) AERA Early Career Fellow (2016) She actively mentors graduate/undergraduate students in the LST program and Psychology department. Major grants include NSF funding for projects on perceptual cues in algebra learning ($668K) and AI-supported teacher tools. She emphasizes interdisciplinary collaboration and real-world applications in her lab.

Tom Henzingerمشاهده پروفایل

استاد

• Formal Methods
• Software Engineering
• Computer Science
• +۵ مورد دیگر

Tom Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA), leading the Tom Henzinger Group. His research focuses on formal methods to enhance software quality, addressing challenges in concurrent systems, real-time and embedded systems, quantum computing, neural networks, and biochemical reaction networks. He has advised numerous PhD students and postdoctoral researchers, contributing to advancements in these fields. His work emphasizes mathematical rigor in software analysis and verification. Current projects include VAMOS (Middleware for best-effort third-party monitoring) and SPyCoDe (Semantic and Cryptographic Foundations of Security and Privacy by Compositional Design). Past projects involved model checking and runtime verification techniques. Research interests span concurrent software synthesis, quantitative modeling of reactive systems, predictability of real-time systems, and formal methods applied to quantum computation and neural networks. He is also engaged in exploring hardware-optimal solutions for quantum algorithms. Administrative Assistant Ksenja Harpprecht supports the group. Courses taught include 'Formalisms Every Computer Scientist Should Know,' focusing on logics, automata, and semantics. The group hosts regular seminars, such as the Tuesday group seminar and the Wednesday joint formal methods seminar with TU Wien's FORSYTE group.

Uday Banerjeeمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +۳ مورد دیگر

Uday Banerjee is a Professor in the Department of Mathematics at Syracuse University, affiliated with the College of Arts & Sciences. His research focuses on numerical solutions of elliptic PDEs, particularly using finite element methods, meshless methods, and generalized finite element techniques to address non-smooth coefficients and interface problems. He holds a Ph.D. in Applied Mathematics from the University of Maryland (1985), an M.Sc. from the Indian Institute of Technology (1978), and a B.Sc. from Patna University (1975). His recent work emphasizes approximation properties, error estimation, and implementation challenges in numerical methods. Key contributions include penalty-free SGFEM for interface problems and stable generalized FEM for crack analysis. Banerjee has served as Chair of the Department of Mathematics (2014–2020) and holds roles in university governance, including serving on promotion and tenure committees. He teaches courses like MAT 331 (Linear Algebra) and MAT 581 (Numerical Methods).

Madhur Tulsianiمشاهده پروفایل

استاد

• Theoretical Computer Science
• Complexity Theory
• Algorithm Design
• +۳ مورد دیگر

Madhur Tulsiani is a Professor at the University of Chicago's Department of Computer Science and a researcher at the Toyota Technological Institute at Chicago (TTIC). His research focuses on theoretical computer science, particularly complexity theory and algorithm design, with applications in coding theory and information theory. He has been supported by NSF grants 1254044, 1816372, and 2326685. Education: Bachelor’s in Computer Science, IIT Kanpur (2001-2005) Ph.D. in Computer Science, UC Berkeley (2005-2009), advised by Luca Trevisan Postdoctoral fellowships at the Institute for Advanced Study (IAS) and Princeton University Research Interests: Mathematical foundations of computation Complexity theory and algorithm design Coding theory and error-correcting codes Sum-of-Squares hierarchies and approximation algorithms Recent Contributions: Pioneering work on list decodable codes and expander-based constructions Advances in approximation algorithms for high-dimensional expanders Lower bounds for Sum-of-Squares algorithms using high-dimensional expanders Teaching: Information and Coding Theory Mathematical Toolkit (linear algebra/probability) Summer REU programs in theoretical computer science Students: Advised PhD students including Fernando Granha Jeronimo, Goutham Rajendran, and Shashank Srivastava Co-advised students with Sasha Razborov, Janos Simon, and others Labs/Groups: Member of the Theoretical Computer Science Group at TTIC and UChicago, contributing to cross-disciplinary research in algorithms and complexity.

Christian Muiseمشاهده پروفایل

استادیار

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +۶ مورد دیگر

Christian Muise is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. He holds a PhD (2014) in Artificial Intelligence from the University of Toronto, where he was advised by Sheila McIlraith and J. Christopher Beck. His research focuses on automated planning under uncertainty, combining planning with learning for applications like goal-oriented dialogue systems and multi-agent coordination. He previously held postdoctoral roles at the University of Melbourne's Agentlab and MIT's CSAIL, and was a Research Staff Member at the MIT-IBM Watson AI Lab. Education: PhD in Artificial Intelligence, University of Toronto (2014) MSc in Computer Science, University of Toronto (2009) BSc in Computer Science, Carleton University (2007) Research Interests: His work bridges automated planning and machine learning, emphasizing robustness in uncertain environments. Key areas include non-deterministic planning, model acquisition with large language models (LLMs), and human-aware planning. He explores applications in healthcare (e.g., treatment response prediction), robotics (autonomous navigation), and dialogue systems for safety-critical domains. Current projects include developing explainable planning systems and mitigating bias in AI decision-making. Awards: Scotiabank Scholar (Scotiabank Centre for Customer Analytics) Advising & Labs: Leads the Mu Lab, supervising PhD and Master's students in topics like model acquisition, dialogue systems, and planning bias. Active in open-source tools (e.g., L2P, MACQ library) to democratize planning research. Collaborates on projects like PRP Rebooted and FixMyPlan to advance FOND planning and LLM integration. Labs/Teams: Mu Lab at Queen’s University, focusing on planning under uncertainty, AI safety, and neuro-symbolic systems.