جست‌وجوی استاد و پژوهشگر

Mathias Wellerمشاهده پروفایل

استاد

• Parameterized Algorithmics
• Genome Scaffolding
• Phylogenetic Networks
• +۳ مورد دیگر

Mathias Weller is a Professor and Chair of the AKT group at Université Gustave Eiffel (Paris), France. Previously, he held a Full-Time Researcher position at CNRS/LIGM (2018–2022) and postdoctoral roles at LIRMM (2013–2017). He earned his PhD in Theoretical Computer Science from TU Berlin (2012) and a Diploma in Computer Science from Friedrich Schiller University Jena (2009). His research focuses on Parameterized Algorithmics, Genome Scaffolding, Phylogenetic Networks, and Structural Parameterization. He has contributed extensively to algorithm design for computational biology, including preprocessing techniques and kernelization concepts. Weller’s work bridges theoretical computer science and applied bioinformatics, addressing challenges in phylogenetic analysis, genome assembly, and combinatorial optimization. His publications span algorithmic efficiency, network analysis, and complexity theory, with a focus on practical applications in genomics and phylogenetics.

Petteri Kaskiمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithm Design
• Exact and Parameterized Algorithms
• +۵ مورد دیگر

Petteri Kaski is an Associate Professor at the Department of Computer Science, School of Science, Aalto University , and a member of the Helsinki Institute for Information Technology (HIIT) . His research focuses on theoretical computer science, particularly in algorithm design, exact and parameterized algorithms, algebraic algorithms, and combinatorics. Doctoral Degree in Engineering and Technology, Helsinki University of Technology (2005) Licentiate Degree in Engineering and Technology, Helsinki University of Technology (2002) Master's Degree in Engineering and Technology, Helsinki University of Technology (2001) His recent work explores tensor scaling, Johnson-Lindenstrauss transforms, Hamiltonian cycles, and computational complexity, with contributions to polynomial-time algorithms, finite field computations, and combinatorial optimization. Notable awards include the Best Paper Award at ICALP 2017 , an ERC Starting Grant (2014) , and the Kirkman Medal (2007) . He has served on scientific committees for conferences like STACS 2025 and ICALP 2024 , and collaborated with institutions such as the IT University of Copenhagen and Universität Regensburg . Key Research Areas : Theoretical Computer Science, Algorithm Design, Exact Algorithms, Algebraic Computation, Graph Theory, Combinatorics

Mayank Chadhaمشاهده پروفایل

استادیار مدعو

• Structural Health Monitoring
• Applied Mechanics
• Machine Learning
• +۷ مورد دیگر

Mayank Chadha is an Assistant Adjunct Professor in the Department of Structural Engineering at the University of California, San Diego. He holds a Ph.D. and M.S. in Structural Engineering from UC San Diego and a B.E. in Civil Engineering from PSG College of Technology, Anna University. His research focuses on integrating applied mechanics, machine learning, and Bayesian methodologies to address challenges in structural health monitoring (SHM), risk analysis, and optimal sensor design. He also explores the application of physics-constrained machine learning in hydrology and battery degradation modeling. Education: 2019: Ph.D. in Structural Engineering, UC San Diego 2023: Postdoctoral Fellowship, UC San Diego 2014: B.E. in Civil Engineering, PSG College of Technology Dr. Chadha’s research interests include structural health monitoring, applied mechanics, uncertainty quantification, and the development of decision-making frameworks for engineering systems. He has pioneered work on Bayesian model optimization, sensor placement strategies, and value-of-information analysis. His interdisciplinary approach bridges theoretical mechanics with practical data-driven solutions for modern engineering problems. His recent work emphasizes hybrid physics-based machine learning models for streamflow forecasting and battery RUL prediction, demonstrating the fusion of computational efficiency and physical principles. He has authored numerous articles on optimal sensor design, VoI metrics, and risk-informed decision-making in SHM systems. Scientific Awards: Gold medal for CBSE 12th exams (2010) Central Government Fellowship (2014) Gold medal for Civil Engineering proficiency (2014) In addition to academia, Dr. Chadha co-founded LogSpi LP, a quantitative investment hedge fund. He teaches courses on mechanics and signal processing, emphasizing foundational engineering principles and their real-world applications. His research also extends to the development of higher-order geometrically exact beam theories, with applications in computer graphics and shape sensing. He actively collaborates with the U.S. Army Corps of Engineers on projects involving inland waterway infrastructure and risk-based sensor design.

Andrew McGregorمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Data Streams
• +۵ مورد دیگر

Andrew McGregor is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. He is a core member of the Theory Group and directs the TRIPODS Institute for Theoretical Foundations of Data Science. His research focuses on algorithms for massive data sets, data streams, and information theory, with applications in clustering, approximation algorithms, and coding theory. Education: PhD, Computer Science, University of Pennsylvania (2007) MEng, Computer Science, University of Pennsylvania (2002) BA, Mathematics, University of Cambridge (2000) Research Interests: Design and analysis of algorithms for data streams and massive datasets Clustering algorithms and approximation techniques Coding theory and information-theoretic approaches Algorithmic fairness and combinatorial optimization Recent Article Trends: Advances in streaming algorithms for dynamic graphs and parameterized complexity Graph reconstruction and allocation problems under noisy conditions Quantum communication and scheduling policies for satellite networks Entropy estimation and probabilistic methods in data analysis Awards: NSF CAREER Award (2010) ACM PODS Test-of-Time Award (2020) Lilly Teaching Fellowship (2012) Outstanding Teacher Award (2016) Grants & Labs: Director, TRIPODS Institute for Theoretical Foundations of Data Science Collaborations with Microsoft Research and UCSD's Information Theory & Applications Center Active in Center for Data Science and Theoretical Computer Science Group Future Work: Expanding research on quantum algorithms, scalable causal inference, and fair data diversification techniques.

Shweta Jainمشاهده پروفایل

پژوهشگر ارشد

• Randomized Algorithms
• Approximation Algorithms
• Combinatorial Optimization
• +۲ مورد دیگر

Shweta Jain is a Research Fellow at the University of Utah, specializing in randomized and approximation algorithms, combinatorial optimization, graph mining, and algorithms for massive data. Previously, she held postdoctoral positions at the University of Illinois, Urbana-Champaign, and a PhD in Computer Science from the University of California, Santa Cruz, where she was advised by Prof. Seshadhri Comandur. SIGKDD Best Dissertation Runner-Up, 2021 Computing Innovation Fellowship, 2021 CSE Best Dissertation Award, UCSC, 2020 Rising Stars Workshop, Berkeley, 2020 Her research focuses on advancing algorithms for efficient graph analysis, particularly for maximal clique detection, cycle counting, and weighted clique decomposition. She develops practical frameworks like YACC and PEANUTS, with applications in large-scale network optimization and real-world data mining. Her work appears in top conferences including SIAM ACDA, WWW, ITCS, and WSDM. Shweta contributes to open-source algorithm implementations for clique counting and network analysis, with repositories hosted on platforms like Bitbucket. She has received multiple accolades, including Best Paper and Best Poster awards.

Serge Gaspersمشاهده پروفایل

استاد

• Algorithms and complexity
• Graph algorithms
• Quantum algorithms
• +۸ مورد دیگر

Professor Serge Gaspers is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), specializing in algorithms for computationally intractable problems. His research focuses on parameterized algorithms, quantum algorithms, and graph theory, with applications in computational social choice and constraint satisfaction. He joined UNSW in 2012 as an ARC DECRA Fellow and later held an ARC Future Fellowship. Gaspers obtained his PhD from the University of Bergen (Norway) in 2008, followed by postdoctoral positions in Montpellier, Santiago, and Vienna. His research interests include algorithms for NP-hard problems, quantum computing, and fair resource allocation. He has been awarded grants totaling over A$2 million, including an ARC Discovery Project (DP210103849) on improved algorithms via random sampling and collaborations with Data61/CSIRO and NICTA. Notable awards include the ARC Future Fellowship (2014), IJCAI 2013 Most Educational Video Award, and DECRA (2012). Teaching: Gaspers teaches COMP6741 - Algorithms for Intractable Problems . His advising spans parameterized algorithms, quantum algorithms, and graph algorithms. Research grants highlight his work in algorithms, with a focus on turbocharging heuristics and computational complexity of resource allocation problems.

Saket Saurabhمشاهده پروفایل

استاد

• Parameterized Complexity
• Exact Exponential Algorithms
• Graph Theory
• +۵ مورد دیگر

Saket Saurabh is a Professor at the Institute of Mathematical Sciences (IMSc), Chennai, India, and an Adjunct Faculty at the University of Bergen, Norway. He holds a PhD in Theoretical Computer Science (TCS) from IMSc (2008). His research focuses on Parameterized Complexity, Exact Exponential Algorithms, Graph Theory, Algorithmic Game Theory, and Theoretical Foundations of Machine Learning. Before joining IMSc, he held postdoctoral positions at the University of Bergen (2007–2009) and was a Research Assistant there (2006–2007). He teaches advanced courses such as Parameterized Complexity, Kernelization, and Algorithms for Big Data. His work emphasizes developing efficient algorithms for NP-hard problems through techniques like kernelization and fixed-parameter tractability. His publications primarily address graph algorithms, parameterized complexity, and algorithm design, with contributions to meta-kernelization frameworks, representative families, and lower bounds for clique-width parameterizations. Notable collaborations include seminal work on graph isomorphism for bounded treewidth graphs and the fixed-parameter tractability of minimum bisection.

Terry Jo Leitermanمشاهده پروفایل

دانشیار

• Applied Mathematics
• Fluid Dynamics
• Mathematical Modeling
• +۳ مورد دیگر

Terry Jo Leiterman is an Associate Professor of Mathematics at St. Norbert College. She holds a B.S. from the University of Wisconsin-Oshkosh and a Ph.D. from the University of North Carolina. Her research focuses on applied mathematics with emphasis on fluid dynamics, mathematical modeling in biomedical contexts (e.g., Virtual Lung Project), and population dynamics. She directs the SNC Fluid Mechanics Laboratory and established the Poss-Wroble Summer Undergraduate Fellowship in Mathematics, supporting student research leading to national presentations. Education: B.S., University of Wisconsin-Oshkosh Ph.D., University of North Carolina Research Interests: Dr. Leiterman investigates fluid dynamics phenomena at micro- and macro-scales, including cilia motion modeling and applications to Cystic Fibrosis therapies. She also develops mathematical frameworks for phytoplankton population dynamics and fluid-structure interactions. Her work bridges theoretical models with experimental validation in collaborative environments. Awards: Recognized as a Project NExT Fellow (2006) by the Mathematical Association of America. Advising & Grants: Spearheaded the SNC summer undergraduate research program since 2008, fostering over 40 student presentations at national conferences. Created the Poss-Wroble Fellowship supporting full-time summer research culminating in MathFest presentations. Labs & Teams: Leads the SNC Fluid Mechanics Laboratory in the Gehl-Mulva Science Center, focusing on experimental fluid dynamics and computational modeling.

Petra Mutzelمشاهده پروفایل

استاد

• Graph Algorithmics
• Graph Learning
• Combinatorial Optimization
• +۳ مورد دیگر

Petra Mutzel is a Professor of Computer Science at the University of Bonn. Her research focuses on graph algorithmics, temporal networks, and combinatorial optimization with applications in data science and bioinformatics. She holds a PhD in Computer Science from the University of Cologne (1994) and a Diplom in Mathematics from the University of Augsburg (1990). Her work emphasizes algorithmic solutions for complex graph problems, including temporal graph analysis, graph learning, and optimization frameworks for real-world networks. She has contributed to open-source libraries like Tglib for temporal graph processing and frameworks like Scaffold Hunter for medicinal chemistry. Her research spans theoretical foundations and practical implementations, with notable contributions to graph drawing, vehicle routing optimization, and protein complex analysis. Mutzel’s recent publications (2022-2025) explore temporal network dynamics, robust combinatorial optimization, and scalable graph kernel methods. She actively engages in academic leadership, organizing conferences such as WALCOM 2022, and serves on editorial boards for journals in algorithms and computational geometry.

Sándor Kisfaludi-Bakمشاهده پروفایل

استادیار

• Algorithms
• Computational Geometry
• Geometric Optimization
• +۴ مورد دیگر

Sándor Kisfaludi-Bak is an Assistant Professor in the Department of Computer Science at Aalto University, specializing in theoretical computer science with a focus on computational geometry. He develops algorithms for geometric problems involving points, curves, shapes, and spatial networks. His research interests include Algorithm design for geometric optimization Computational geometry fundamentals Spatial network analysis Hyperbolic and planar graph algorithms Parameterized complexity in geometric contexts Recent publications demonstrate expertise in Traveling Salesman Problem optimization, Steiner network construction, and hyperbolic graph analysis. Key research trends span computational geometry, graph theory, and algorithmic complexity in spatial domains. He has no listed scientific awards in the provided materials. No information about student advising or organizational affiliations beyond Aalto University was found.

Stefan Kratschمشاهده پروفایل

استاد

• Parameterized complexity
• Efficient preprocessing
• Computational complexity

Stefan Kratsch is a Professor of Theoretical Computer Science at the Institute of Computer Science, Humboldt University of Berlin. He holds this position since September 2017 and has previously held academic roles at the University of Bonn (2015–2017) and Technical University Berlin (2012–2014). His research focuses on parameterized complexity, efficient preprocessing, and computational complexity, with a strong emphasis on theoretical foundations and algorithm design. University: Humboldt University of Berlin Department: Institute of Computer Science (Algorithm Engineering group) Academic Rank: Professor Email: stefan.kratsch@hu-berlin.de, kratsch@informatik.hu-berlin.de Kratsch’s recent publications highlight his work on advanced algorithmic techniques for graph problems. Key areas include kernelization methods, flow-augmentation for connectivity problems, and tight complexity bounds for classical problems parameterized by structural measures like clique-width and cutwidth. His theoretical contributions aim to bridge preprocessing efficiency with computational hardness. Stefan actively engages in academic service, including organizing workshops and serving on program committees for leading conferences such as IPEC, LATIN, and SWAT. He has reviewed for numerous journals and grant panels, including the European Research Council and German Research Foundation. Advising: He supervised PhD candidate Michael Piechotta, whose defense is scheduled for September 2025.

Ugo Finnendahlمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Mesh Parameterization
• Geometric Acoustics
• +۳ مورد دیگر

Ugo Finnendahl is a Researcher in the Department of Computer Graphics at Technische Universität Berlin. He holds an M.Sc. in Computer Science and is affiliated with the Computer Graphics group led by Prof. Marc Alexa. His research focuses on advanced computer graphics topics including mesh parameterization, geometric acoustics, physically-based simulation, and exact arithmetic algorithms. Key contributions include methods for differentiable rendering, intrinsic triangulation optimization, and geometric stylization techniques. Recent work emphasizes integrating geometric modeling with machine learning frameworks (e.g., path tracing backpropagation), improving mesh deformation techniques (ARAP revisited), and developing efficient exact arithmetic systems for planar embeddings. No scientific awards are explicitly mentioned in the provided materials. His research spans collaboration with institutions like TU Berlin's Computer Engineering and Microelectronics Institute, contributing to both theoretical advancements and practical applications in computer graphics.

Saurabh Saketمشاهده پروفایل

استاد

• Parameterized Algorithms
• Kernelization
• Exact Algorithms
• +۶ مورد دیگر

Saurabh Saket is a Professor in Algorithms at the Department of Informatics, University of Bergen, Norway (since 2013), and concurrently a Professor at the Institute of Mathematical Sciences, India (since 2009). Previously, he was a Postdoctoral Fellow at the Department of Informatics, University of Bergen (2007–2009). His research focuses on parameterized algorithms, kernelization, exact algorithms, matroid algorithms, algorithmic graph minors, treewidth, and approximation algorithms. He has made breakthrough contributions to complexity theory, kernelization preprocessing, and exact exponential-time algorithms, with key results published in top venues like Journal of the ACM, SODA, FOCS, and STOC. Awards and Grants 2020: Fellow of Indian Academy of Sciences 2019: Outstanding Young Researcher Meltzer Award (Norway) 2017–2018: Swarnajayanti Fellowship (India) 2019–2024: ERC Consolidator Grant 'LOPRE' 2013–2017: ERC Starting Grant 'PARAPPROX' His work bridges parameterized complexity with approximation algorithms and has led to foundational results in algorithmic lower bounds, combinatorial optimization, and logic applications.

Adolfo R. Escobedoمشاهده پروفایل

دانشیار

• Mathematical Programming
• Power Systems Operations
• Circular Economy
• +۴ مورد دیگر

Adolfo R. Escobedo is an Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research focuses on mathematical programming, power systems operations, circular economy, computational social choice, and computational linear algebra. He has received prestigious awards including the NSF CAREER Award (2024) and the INFORMS MIF Early Career Award (2023). His work bridges theoretical advancements in optimization algorithms with practical applications in energy systems, crowd intelligence, and STEM education equity. Education: PhD in Industrial Engineering from Texas A&M University (2016), BA in Mathematics from California State University-Los Angeles (2009). Affiliations: Member of the College of Engineering, actively contributing to interdisciplinary research and education initiatives. His research emphasizes robust numerical guarantees in optimization solvers, with a focus on overcoming computational bottlenecks in linear and mixed-integer programming. Recent projects include developing open-source tools for reliable optimization software and launching inclusive STEM education programs targeting underrepresented minorities. Escobedo’s work also addresses societal challenges such as sustainable power systems and waste management through data-driven models. His publications highlight contributions to rank aggregation, crowd wisdom enhancement, and exact matrix factorization techniques. Beyond academia, he advocates for diversity in STEM through recruitment programs and research internships. Key Grants: NSF CAREER Award (2024), CRII: III Grant (2019). Labs/Teams: Leads research groups focused on optimization algorithms and computational social choice, collaborating with interdisciplinary teams in energy and environmental systems.

• Computer Science
• Theoretical Computer Science
• Parameterized Complexity
• +۴ مورد دیگر

Ivan Bliznets is an Assistant Professor at the University of Groningen, affiliated with the Fundamental Computing Science department within the Bernoulli Institute, part of the Faculty of Science and Engineering. His research focuses on parameterized complexity, FPT-algorithms, and exact exponential algorithms. He holds a PhD and has published extensively in top-tier conferences and journals. Education: PhD in Computer Science (specific details not provided) His research interests span theoretical computer science, with an emphasis on developing efficient algorithms for computationally hard problems. Recent work includes studies on algorithm design for choosability, fair division mechanisms, and parameterized complexity of satisfiability and domination problems. Bliznets' publications (2023–2024) reflect a focus on algorithmic innovation in discrete mathematics, graph theory, and computational complexity. Notable areas include improving exact exponential algorithms, exploring fair division under constraints, and analyzing parameterized problems in social networks. Labs/Teams: Part of the Bernoulli Institute, a hub for interdisciplinary research in mathematics, computer science, and systems.