جست‌وجوی استاد و پژوهشگر

Bjoern Andresمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Graph Algorithms
• +۳ مورد دیگر

Prof. Dr. Bjoern Andres holds the position of Professor of Machine Learning for Computer Vision at Technische Universität Dresden . He is a Principal Investigator at the Center for Scalable Data Analytics and Artificial Intelligence and an Academic Fellow at the School of Embedded Composite Artificial Intelligence. His work bridges theoretical computer science, applied mathematics, and biomedical imaging through innovative graph algorithms. Education : PhD in Physics (Heidelberg University), Diploma in Physics (Heidelberg), Pre-Diploma in Physics and Computer Science (Technical University Dortmund) Research Interests : His research focuses on graph-based methods for computer vision and biomedical image analysis, including: Lifted multicut and correlation clustering algorithms Integer programming for anatomical network reconstruction 3D segmentation of neural tissue Optimal tracking of cellular lineages Partial ordering and quantum-inspired optimization Scientific Contributions : His recent publications emphasize: Advancing multicut polytope analysis (2023-2025) Quantum alternating operator ansatz for correlation clustering (2025) 4-approximation algorithms for Min Max correlation clustering Medical imaging applications for organoid segmentation Biomedical applications in vascular network reconstruction Development of scalable graph decomposition methods Awards & Recognitions : IEEE CVPR Outstanding Reviewer (2022) NIPS Best Reviewer (2017) MICCAI Best Paper (2015) DAGM Best Paper Runner-Up (2008) Heidelberg International Exchange Scholarship (2005) Studienstiftung Scholarship (2000-2007) Software Development : Maintains andres::graph , a C++ library for graph algorithms and multi-dimensional arrays, featuring: Efficient graph data structures with constant-time access Implementations of Prim's algorithm and max-flow methods Applications in medical imaging and computer vision

Michael Mahoneyمشاهده پروفایل

استاد

• Randomized Numerical Linear Algebra
• Machine Learning
• High Dimensional Statistics
• +۳ مورد دیگر

Michael Mahoney is Professor of Statistics at the University of California, Berkeley, with additional affiliations at the International Computer Science Institute (ICSI) where he is Vice President and Director of the Big Data Group, the Lawrence Berkeley National Laboratory (LBNL) where he leads the Machine Learning and Analytics Group, and the EECS department's RISELab. He is also an Amazon Scholar. Education: While specific degrees are not listed in the provided text, his extensive record of teaching, research leadership, and publications indicates doctoral-level training in Statistics and Computer Science. Research Interests: Mahoney's work centers on the applied mathematics of data , spanning algorithmic and statistical foundations of big data, randomized numerical linear algebra (RandNLA), high-dimensional statistics, machine learning, and scientific machine learning. He develops theory, scalable implementations, and real-world applications in internet analytics, social networks, genetics, astronomy, and climate science. Recent software contributions include the RandBLAS and RandLAPACK libraries (standardizing RandNLA routines), Landscaper for visualizing deep-learning loss landscapes, and packages such as FreeAlg , DetKit , Imate , and LeaderBot . Awards & Honors: NeurIPS 2020 Best Paper Award (co-authored work on column subset selection) Director, NSF TRIPODS UC Berkeley FODA Institute Key contributor to the BALLISTIC project for next-generation BLAS/LAPACK Grants & Leadership: Principal Investigator, NSF TRIPODS FODA Institute (Foundations of Data Analysis) Group Lead, Machine Learning and Analytics, LBNL Vice President & Director, Big Data Group, ICSI Advising & Mentoring: Mahoney has an extensive network of current and former PhD students, postdocs, and visiting researchers, including placements at MIT, Stanford, Waterloo, Stevens, Tsinghua, and Georgia Tech. Current advisees include Shengaho Yang, Zhichao Wang, Hyunsuk Kim, and Pu Ren, among many others. Labs & Teams: He directs research efforts across UC Berkeley Statistics, ICSI’s Big Data Group, LBNL’s Machine Learning and Analytics Group, and the RISELab (formerly AMPLab), fostering cross-disciplinary collaboration between statistics, computer science, and domain sciences.

Sotiris Nikoletseasمشاهده پروفایل

استاد

• Wireless Sensor Networks
• Internet of Things (IoT)
• Artificial Intelligence of Things (AIoT)
• +۶ مورد دیگر

Sotiris Nikoletseas is a Full Professor and Founding Director of the Internet of Things Laboratory (IoT-Lab) at the Computer Engineering and Informatics Department of the University of Patras, Greece. He also serves as a Senior Researcher of the Algorithms Group at the Computer Technology Institute and Press "Diophantus" (CTI), Greece. His academic career includes Visiting Professor positions at the Universities of Geneva, Ottawa and Southern California (USC). Professor Nikoletseas's research spans several cutting-edge domains in computer science, with primary focus on algorithmic aspects of wireless sensor networks and the Internet of Things (IoT). His work extends to Artificial Intelligence of Things (AIoT), wireless energy transfer protocols, probabilistic algorithms and random graphs, and innovative applications of psychoanalysis driven computing to social networks and interactive media. His interdisciplinary approach bridges theoretical computer science with practical applications in networked systems. His publication record is exceptional, with over 300 publications in international journals and refereed conferences, three influential books, and 30 invited chapters in publications by major publishers. His most recent book, 'Wireless Power Transfer Algorithms, Technologies and Applications in Ad Hoc Communication Networks' (Springer, 2016), represents a significant contribution to the field. Best Paper award at the 18th International Conference on Distributed Computing and Networking (ICDCN), Hyderabad, India, 2017 Best Paper Award at the 6th IEEE International Conference on Distributed Computing in Sensor Networks (DCOSS), Santa Barbara, USA, 2010 Inclusion in the list of highly cited computer scientists based on Google Scholar h-index As an academic leader, Professor Nikoletseas has supervised over 50 diploma theses and numerous graduate students, including 7 PhD candidates who have completed their degrees under his guidance. His research has been supported by substantial external funding, including multiple European Union projects where he served as site leader or coordinator. His work with industry includes direct contracts with organizations like Pfizer through their Center for Digital Innovation. The IoT-Lab he founded at the University of Patras serves as a hub for research in algorithms and systems for the Internet of Things, fostering collaboration between academia and industry. His current research directions include AI-enhanced IoT applications, wireless power transfer systems, and innovative approaches to cybersecurity and environmental monitoring through advanced sensor networks.

• Machine Learning
• Human-Computer Interaction
• Network Science
• +۳ مورد دیگر

Hasti Nariman Zadeh is a Researcher at the Department of Computer Science , Aalto University . Her work spans interdisciplinary domains, combining computational methods with applications in social sciences and agronomy. Research Interests Network science and community detection algorithms Crowdsourcing methodologies with bias mitigation Machine learning for high-dimensional data representation Data-driven approaches in agricultural informatics and phenology modeling Analysis of social media dynamics and cross-domain engagement patterns Publications Her research output demonstrates expertise in network science, crowdsourcing, and computational modeling. Key themes include: modularity optimization for graph partitioning, social behavior analysis during global crises, and uncertainty quantification in crop simulations. Collaborative Networks She has collaborated extensively with international research groups across agronomy, computational social sciences, and machine learning domains, as evidenced by co-authorships on interdisciplinary publications.

• Formal Methods
• Model Checking
• Satisfiability (SAT) Solving
• +۴ مورد دیگر

Tommi Junttila serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, Finland, where he conducts cutting-edge research at the intersection of formal methods and computational logic. His academic profile demonstrates sustained contributions to theoretical computer science with practical applications in system verification and blockchain technology. His research program centers on advancing formal verification techniques, with core expertise in: Symmetry reduction algorithms for state space explosion SAT/SMT solving with specialized parity and XOR reasoning Bounded model checking of timed and asynchronous systems Blockchain protocol verification (notably DeFi lending pools) Canonical labeling tools for graph automorphism detection Recent work shows increasing focus on decentralized finance applications while maintaining foundational contributions to solver technology. Analysis of his publication trajectory (2011-2022) reveals consistent innovation in SAT solving methodologies, with symmetry reduction and parity reasoning forming persistent research threads. His 2022 work on DeFi lending pools represents a strategic expansion into blockchain verification, leveraging established formal methods expertise for emerging financial technologies. Tool development (bliss, PySMT) demonstrates commitment to practical research impact. No scientific awards were documented in the source materials. Similarly, no information regarding student supervision, grant funding, or laboratory affiliations was present in the provided text. His independent tool development (including bliss for graph canonical labeling and PySMT for SMT solver interfaces) indicates significant technical leadership within the formal methods community.

Sylwia Cichaczمشاهده پروفایل

استادیار

• Graph Theory
• Combinatorics
• Zero-Sum Problems
• +۳ مورد دیگر

Dr. Sylwia Cichacz is an Assistant Professor in the Department of Discrete Mathematics within the Faculty of Applied Mathematics at AGH University of Science and Technology in Kraków, Poland. Her primary research focuses on Graph Theory , with expertise in combinatorial structures, algebraic methods, and labeling techniques. Research Interests: Her work spans zero-sum problems in Abelian groups, magic and antimagic graph labelings, irregular graph decompositions, and applications of group theory to combinatorial optimization. Key themes include distance magic labeling, acyclic coloring, fault-tolerant graph structures, and the interplay between algebraic invariants and graph properties. Publications: Recent articles (2022–2025) explore graph labeling innovations (e.g., cordial, antimagic, and distance magic techniques), zero-sum partitions of Abelian groups, and combinatorial designs. Her research consistently integrates algebraic frameworks with discrete optimization, demonstrating applications in spectral graph theory and extremal combinatorics. Grants & Affiliations: She is supported by the NCN grant 2011/01/D/ST1/04104. No student advisees, team structures, or additional awards are documented in the provided materials.

Guan Yuمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• High-Dimensional Statistics
• Multi-Modality Data Integration
• +۳ مورد دیگر

Guan Yu is an Associate Professor in the Department of Biostatistics at the University of Pittsburgh's Graduate School of Public Health. His methodological research focuses on statistical machine learning, high-dimensional statistics, and non-parametric methods, with applications to biomedical problems such as Alzheimer’s disease, cancer, autoimmune diseases, and environmental health. He received his PhD in Statistics from the University of North Carolina at Chapel Hill (2016), an MS in Statistics from Nankai University (2011), and a BS in Mathematics from Nankai University (2008). His teaching includes courses like Biostatistics Seminar (BIOST2025) and Introduction to Statistical Theory 2 (BIOST2044) . Research interests include: Multi-modality data integration High-dimensional inference Transfer learning Uncertainty quantification for predictions Penalized regression techniques Robust classification methods His recent publications address challenges in block-missing data, sparse modeling, and cost-constrained regression. Awards include the 2020 ICSA Outstanding Young Researcher Award and multiple ASA recognitions for student papers and teaching excellence (2015-2017). He actively collaborates on interdisciplinary projects and mentors graduate students in biostatistical research.

Anusch Tarazمشاهده پروفایل

استاد

• Discrete Mathematics
• Graph Theory
• Combinatorics
• +۵ مورد دیگر

Anusch Taraz is a Professor at the Institute of Mathematics , Hamburg University of Technology. His research focuses on Discrete Mathematics , particularly in Graph Theory , Hypergraphs , and Combinatorics . He holds the Chair of Discrete Mathematics and has contributed extensively to topics like Ramsey Theory , Random Graphs , and Algorithmic Complexity . Education : International Baccalaureate (United World College of the Atlantic, UK), MSc in Mathematics (University of Bonn), PhD in Theoretical Computer Science (Humboldt-University of Berlin, supervisor: Hans Jürgen Prömel) His research explores Size-Ramsey Numbers , Graph Embeddings , and Resilience in random graphs. Recent publications emphasize Random Graphs , Hypergraph Theory , and Extremal Graph Problems . No specific scientific awards are highlighted in the provided texts. Email: taraz@tuhh.de .

• Soft Condensed Matter
• Atomic and Molecular Clusters
• Percolation
• +۴ مورد دیگر

Dr Mark Miller is an Associate Professor in the Department of Chemistry at Durham University. He serves as Chair of the Board of Examiners in Chemistry (2022-2025) and previously held the Presidential role at the Senior Common Room of St Cuthbert's Society (2015-2024). Education : Ph.D. in Natural Sciences (Cambridge University); Postdoctoral work at University of Washington and AMOLF Amsterdam Research Interests focus on computational methods for molecular and colloidal systems, including: Coarse-grained modeling (Martini force field optimization) Percolation theory in active matter and nanoparticle suspensions Energy landscapes of finite clusters and self-assembly Simulation methodology development Biomolecular condensate modeling Recent Publications (2021-2025) examine percolation thresholds, membrane partitioning predictions, and self-assembly control. Collaborations include industry and regulatory partners for high-throughput compound screening.

Jiří Fialaمشاهده پروفایل

دانشیار

• Graph algorithms
• Computational complexity
• Restricted graph classes
• +۴ مورد دیگر

Jiří Fiala is an Associate Professor at the Department of Applied Mathematics (KAM), Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His academic career spans over two decades with significant contributions to graph theory and computational complexity. He has held positions at prestigious institutions including University of Kiel (Germany), University of Bergen (Norway), and University of Oregon (USA) as a Fulbright fellow. Dr. Fiala completed his Master's degree (Mgr.) at Charles University in 1997 followed by his PhD in 2000. His academic journey progressed from research and teaching assistant (2000-2008) to his current position as Associate Professor since 2008. His research focuses on graph algorithms and their computational complexity , with particular emphasis on restricted graph classes including bounded width structures, topological restrictions, and geometric intersection graphs. He has made significant contributions to algebraic graph theory , especially in homomorphisms and covering projections, as well as graph coloring and distance labeling problems. His work on Hamiltonian properties of graphs represents another important research direction. Analysis of his recent publications (2021-2025) reveals a continued focus on computational complexity of graph covering problems across various graph structures including regular trees, multigraphs with semi-edges, and disconnected multigraphs. His work frequently intersects with computational geometry, particularly in graph drawing problems related to outerplanar graphs and forest structures. The consistent publication record across top venues demonstrates sustained research productivity in theoretical computer science and discrete mathematics. Scientific recognition includes being awarded a Fulbright fellowship for research at the University of Oregon (2012-2013). His habilitation thesis Structure and Complexity of Locally Constrained Graph Homomorphisms (2007) represents a significant scholarly achievement. Dr. Fiala maintains active research collaborations, particularly with Jan Kratochvíl (45 joint publications), Daniël Paulusma (19), and Jan Bok (12), among others. His teaching responsibilities include advanced courses such as Introduction to Parameterized Algorithms for the winter term 2025-2026. His work is well-documented through comprehensive publication records available via DBLP, ResearcherID, ORCID, and Scopus.

Petr Kolmanمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Combinatorial Optimization
• Graph Theory
• +۷ مورد دیگر

Assoc. Prof. Petr Kolman is an academic at the Department of Applied Mathematics , Charles University , where he serves in advisory bodies of the faculty management. His research focuses on theoretical computer science , combinatorial optimization , and graph algorithms , particularly in approximation techniques and network flow problems . He has taught courses such as Linear Algebra 1/2 and Mathematical Programming and Polyhedral Combinatorics . Recent publications highlight his work on spanning tree congestion (2025), length-bounded cuts (2020), and treewidth-based extended formulations (2020). His research spans algorithms , flow theory , and graph modification problems . He maintains contact through kolman@kam.mff.cuni.cz and office hours in Prague Malostranské nám. 2/25, 2nd floor, room S225 .

• Mathematical Optimization
• Network Optimization
• Railway Operations
• +۳ مورد دیگر

Ralf Borndörfer is a Professor and Head of the Network Optimization Department at the Zuse Institute Berlin (ZIB) , a leading research institution in mathematical algorithmic intelligence. His work focuses on optimizing complex transportation systems, particularly in railway operations, public transit, and air cargo logistics. He leads projects like Timetabling with Duality and Zonotopes, Symmetric Line Planning, and WILSON-LEARN, which address challenges in train scheduling, electric vehicle integration, and predictive maintenance. Key Research Areas : Mathematical optimization, railway timetabling, public transport planning, game theory for toll enforcement, and electric vehicle scheduling. Notable Collaborations : Projects with Deutsche Bahn, BIFOLD, and MATH+ Cluster of Excellence. His recent publications (2023-2025) explore: Non-linear battery modeling in electric bus scheduling Predictive maintenance integration in rolling stock rotations Logic-constrained shortest paths for flight planning Price-sensitive routing in public transport He has contributed to algorithmic frameworks like the Restricted Modulo Network Simplex Method and Bayesian rolling horizon approaches, emphasizing computational efficiency and real-world applicability.

Emilio Crucianiمشاهده پروفایل

استادیار

• Algorithms
• Data Mining
• Social Networks
• +۹ مورد دیگر

Emilio Cruciani is a Tenure Track Assistant Professor in Computer Science at the European University of Rome, affiliated with the Department of Human Sciences. He holds a PhD in Computer Science from the Gran Sasso Science Institute (2019) and has conducted research at universities in Germany, France, and Austria. His research focuses on algorithms, data mining, and social network dynamics. Current work investigates local network interactions for graph clustering, stochastic processes on networks, and scalable algorithms for large datasets. He has contributed to Green AI through dynamic model selection for energy efficiency. Co-PI on two Facebook Research-funded projects Active in program committees for algorithms and artificial intelligence conferences Over 30 publications in international conferences and journals Publications span network dynamics, election control, software testing, and hypergraph convergence. His work bridges theoretical computer science with applied problems in social influence and biomedical informatics.

Daniel Andersonمشاهده پروفایل

استاد آموزشی

• Parallel Computing
• Algorithm Design
• Concurrent Memory Management
• +۴ مورد دیگر

Daniel Anderson is an Assistant Teaching Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He teaches Algorithm Design and Analysis (15-451) and previously taught at Monash University in Australia. His academic journey includes a PhD from CMU under Professor Guy Blelloch and a Bachelor of Science (Honours) in Applied Mathematics from Monash University. His research spans multiple areas of computer science with a strong focus on parallel computing. Anderson develops practical libraries like ParlayLib to make parallel programming more accessible, works on concurrent memory management techniques including "smarter atomic smart pointers," and explores parallel graph algorithms. His work bridges theoretical algorithm design with practical implementation challenges, particularly in making high-performance parallel computing more approachable for programmers. Anderson's publications reveal a strong trajectory in parallel algorithms and memory management, with significant contributions to wait-free algorithms, reference counting techniques, and batch-dynamic data structures. His research consistently appears in top venues like PLDI, SPAA, and EuroPar, demonstrating both theoretical rigor and practical relevance. Recent work focuses on making reference counting as efficient as manual memory reclamation while maintaining usability. Scientific Awards: Herbert A. Simon Award for Teaching Excellence in Computer Science (2024) Alan J. Perlis Graduate Student Teaching Award (2022) While Anderson currently focuses primarily on teaching, he remains active in research supervision, particularly for undergraduate and master's projects related to parallel computing. His work with the ParlayLib research group continues to advance the state of parallel programming tools. Anderson also has experience coaching the Monash University Programming Team for the International Collegiate Programming Contest (ICPC) and serving as a judge for South Pacific Programming Contests.

• Machine Learning
• Probabilistic Models
• Invertible Deep Learning
• +۷ مورد دیگر

David Inouye is an Assistant Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. He received a B.S. in Natural Sciences from Covenant College, Georgia, and a B.S. in Electrical Engineering from Georgia Institute of Technology. He earned his Ph.D. in Computer Science from the University of Texas at Austin and completed a postdoctoral fellowship in machine learning at Carnegie Mellon University. B.S. in Natural Sciences, Covenant College, Georgia B.S. in Electrical Engineering, Georgia Institute of Technology Ph.D. in Computer Science, University of Texas at Austin Postdoc in Machine Learning, Carnegie Mellon University Dr. Inouye's research focuses on the theoretical foundations of machine learning, particularly probabilistic models, invertible deep learning, and explainable artificial intelligence. He is pioneering work in destructive deep learning, with emphasis on generative models and density estimation. His research explores causal inference through invertible latent models, robustness in federated learning environments, and novel approaches to distribution matching and counterfactual analysis. Recent publications highlight advancements in diffusion models, generative adversarial frameworks, and vertical federated learning architectures. Key trends include explainable AI techniques, robust learning under distribution shifts, and geometrically-aware probabilistic modeling. Notable contributions include PO-Flow for counterfactual sampling and Att-Adapter for attribute-controlled text-to-image generation. NSF Graduate Research Fellowship Dr. Inouye's work bridges theoretical developments with practical implementations in dynamic computing environments, focusing on fault-tolerant systems and domain generalization challenges in distributed learning scenarios.