جست‌وجوی استاد و پژوهشگر

Don Kulasiriمشاهده پروفایل

استاد

• Computational Biology
• Systems Biology
• Mathematical Modeling
• +۵ مورد دیگر

Professor Don Kulasiri is a distinguished academic at Lincoln University in New Zealand, holding the position of Professor in the Department of Wine Food & Molecular Biosciences. He serves as the Head of the Centre for Advanced Computational Solutions (C-fACS) and has previously held leadership roles as Head of Department in both the Wine Food & Molecular Biosciences and Computing Group. Professor Kulasiri maintains numerous international affiliations including Visiting Professor positions at Oxford University's Mathematical Institute, Princeton University, and adjunct positions at the Korea Advanced Institute of Science and Technology. His research spans computational and mathematical systems biology, with a particular focus on understanding the mathematical basis for biological and environmental phenomena through physics-based modeling. Professor Kulasiri has developed models related to memory formation and impairment in the brain, focusing on computational modeling of biochemical pathways related to synaptic plasticity and Alzheimer's disease. He also works extensively on stochastic mathematics applied to environmental systems, having developed a mechanistic and stochastic theory for understanding solute transport in underground aquifers. Professor Kulasiri's recent publications reveal a strong trend toward computational neuroscience and Alzheimer's disease research, with over half of his most recent work focusing on synaptic plasticity, memory formation mechanisms, and calcium signaling dysregulation in neurodegenerative conditions. His work consistently integrates mathematical modeling with biological experimentation, demonstrating an interdisciplinary approach that bridges physics, mathematics, and life sciences. Fellow of Modelling and Simulation Society of Australia and New Zealand World Class Research rankings (A grade) by PBRF panel, New Zealand government (2012, 2018) Sigma Xi honorary membership for outstanding PhD thesis (1990) Lincoln Vice Chancellor commendations (2004-2008, 2013) Member of European Physical Society (EPS) With an impressive record of supervising 60 PhD students and 19 masters students over 33 years, Professor Kulasiri currently mentors 10 PhD students at Lincoln University. He has served as Principal Investigator for significant government-funded research programs including a NZ government (MBIE) funded national research program to improve wine quality and yield (2017-2022), and as Co-PI for the reimagine urban agriculture research programme funded by the National University of Singapore (2020-2023). Professor Kulasiri leads the Centre for Advanced Computational Solutions (C-fACS) and is instrumental in the Complex Systems, Big Data and Informatics Initiative at Lincoln University. His research team develops innovative computational approaches including WaphHound® technology for odor detection using wasps, and applies machine learning and AI to solve complex problems in agriculture, environmental science, and neuroscience.

• Electromagnetic fields with complex systems
• Neuromorphic computing
• Plasmonic resonators and materials
• +۷ مورد دیگر

Matthew Arnold is an Associate Professor in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS), where he has been employed since 2007, progressing from Lecturer to Senior Lecturer and currently Associate Professor. His academic career includes postdoctoral research at the University of Canterbury and visiting positions at the Fraunhofer Institute Jena (2001) and University of Southampton (2008). Arnold holds a PhD and BSc(Hons) from the University of Otago, New Zealand. His leadership roles at UTS include serving as Physics Discipline Leader, BSc(Physics) Program Director, and currently as HDR director and RAO for the Faculty of Science. He is also active in professional organizations, having served as Chair of the NSW Australian Institute of Physics and as a senior member of both Optica and SPIE. Arnold's research focuses on the interaction of electromagnetic fields with complex systems, spanning from modeling and design to fabrication and characterization. His primary research areas include neuromorphic computing using percolating networks of nanoparticles, plasmonic resonators and materials, self-assembled metamaterials, and opto-thermal coatings for energy applications. His work bridges fundamental physics with practical applications in building technologies, solar energy, and next-generation computing architectures. His recent publications demonstrate a strong trend toward neuromorphic and brain-inspired computing systems, particularly exploring the computational capabilities of self-assembled nanoscale networks. These works investigate how the intrinsic physical properties of nanomaterials can be harnessed for energy-efficient information processing, with applications in random number generation, Boolean operations, and image classification. His research also maintains a strong focus on practical optical applications, including high-temperature polarizers and spectrally selective solar absorbers. AIP NSW Branch Service Award (2024) UTS MAPS: Individual Teaching Award (2023) STANSW Dedicated Service Award (2019) Arnold is deeply committed to mentoring the next generation of scientists, having successfully guided research students at all levels from internship to PhD. His teaching philosophy emphasizes engaging students in experiences that develop practical skills and deep insight, which has been recognized with teaching awards. He has secured significant research funding through ARC Linkage Infrastructure grants and numerous industry collaborations, particularly with building industry partners on glazing and facade performance. His current funded projects include advanced deposition systems for superconducting circuits and solar-thermal performance evaluations. Arnold leads research activities centered around experimental and computational investigations of nanoscale systems, with particular expertise in physical vapor deposition, optical characterization techniques, and computational modeling of complex electromagnetic systems. His work connects fundamental physics with real-world applications through strong industry partnerships and interdisciplinary collaborations across physics, materials science, and engineering disciplines.

• Medical Image Analysis
• Statistical Signal Processing
• Machine Learning
• +۸ مورد دیگر

Paul Salama is a Professor of Electrical and Computer Engineering at the Elmore Family School of Electrical and Computer Engineering, Purdue University, Indianapolis. He holds a Ph.D. in Electrical Engineering from Purdue University (1999), an M.S. from the same institution (1993), and a B.S. from the University of Khartoum (1991). His research focuses on Medical Image Analysis, Statistical Signal Processing, Machine Learning, and Biomedical Data, with a particular emphasis on applications in neuroscience, neurodegenerative diseases, and medical diagnostics. Key research interests include imaging security, sensor fusion, and pattern recognition, with cross-disciplinary work in communications and networking. Recent publications highlight advancements in Alzheimer’s disease diagnosis, generative models for medical imaging, and distributed image analysis systems. Salama’s work often integrates multi-omic data and deep learning techniques to address complex biomedical challenges. His contributions span algorithm development for image segmentation, 3D microscopy analysis, and systems biology, with applications in precision medicine and disease biomarker discovery.

• Probability Theory
• Stochastic Processes
• Spatial Population Models
• +۳ مورد دیگر

Marcel Ortgiese is a Reader in the Department of Mathematical Sciences at the University of Bath, affiliated with the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) and the Probability Laboratory at Bath. His research focuses on probability theory, including spatial population models, stochastic processes in random environments, and evolving random graphs. He investigates large-scale behaviors of interfaces in evolutionary biology contexts, branching processes in inhomogeneous environments, and dynamic processes on random graph structures. His work integrates theoretical analysis with applications to real-world systems, emphasizing interdisciplinary connections between probability and complex networks. Current projects include studying the interplay between geometry and randomness in fitness landscapes for expanding populations (EPSRC-funded) and advancing methods for cumulants and superconcentration phenomena. Ortgiese has contributed to foundational research on contact processes, voter models, preferential attachment networks, and symbiotic branching systems. His recent studies explore adaptive network dynamics, subcritical random graph properties, and asymptotic behaviors in weighted recursive trees. He holds a PhD from the University of Bath (2009) under Prof. Peter Morters and has supervised numerous graduate students through SAMBa's training programs. His academic contributions are reflected in over 20 peer-reviewed articles in top journals such as Stochastic Processes and their Applications and Annals of Applied Probability . Research themes consistently emphasize stochastic analysis of complex systems, with applications ranging from epidemiological modeling to evolutionary dynamics.

Mathew Penroseمشاهده پروفایل

استاد

• Stochastic Geometry
• Probability Theory
• Percolation Theory
• +۳ مورد دیگر

Mathew Penrose is a Professor in the Department of Mathematical Sciences at the University of Bath. He is affiliated with the Probability Laboratory at Bath, the Centre for Networks and Collective Behaviour, and the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa). His research focuses on stochastic geometry, pure and applied probability, and spatial networks, with applications to percolation, random graphs, and limit theorems. Key research interests include discrete and continuous percolation, interacting particle systems, random packing, stochastic networks, and stochastic analysis on Poisson spaces. He has authored the influential monograph Random Geometric Graphs (2003). Recent work explores connectivity thresholds in random geometric graphs, coverage models, and asymptotic behaviors in stochastic systems. His projects include EPSRC-funded research on stochastic geometry coverage and connectivity, and he has led collaborations on parameter estimation in adsorption models and spatial networks. Penrose has advised on multiple doctoral training programs and contributed to the SAMBa initiative. His research outputs span over 60 peer-reviewed articles, with a focus on advancing theoretical probability through geometric and combinatorial approaches.

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.

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.

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.

Günter Lastمشاهده پروفایل

استاد

• Stochastic Processes
• Stochastic Geometry
• Poisson Processes
• +۶ مورد دیگر

Prof. Dr. Günter Last is a Professor at the Institute of Stochastics within the Faculty of Mathematics at the Karlsruhe Institute of Technology (KIT) . He has held this position since 2000 (C4/W3 Professorship). Last's research focuses on stochastic processes , stochastic geometry , and their applications in mathematics, physics, and finance. Education: Diploma in Mathematics, Humboldt University Berlin (1984) PhD in Mathematics, Humboldt University Berlin (1987) Dr. Sc. in Mathematics, Technical University Braunschweig (1995) Research Interests: Last leads research in Poisson processes, Boolean models, Gibbs processes, and spatial random systems. His work explores Palm calculus, hyperuniformity, normal approximation, and percolation phenomena. He co-authored the textbook Lectures on the Poisson Process with Mathew Penrose. Publications & Trends: His recent work includes Normal approximation of Kabanov-Skorohod integrals , Hyperuniform stable matchings , and Poisson hulls , reflecting a focus on stochastic analysis and geometric probability. His research bridges theoretical insights with applications in digital microstructures and wireless network modeling. Scientific Leadership: Co-Editor for Electronic Journal of Probability (2018-2023) Speaker of DFG Research Unit Geometry and Physics of Spatial Random Systems (2011-2018) Associate Editor for Applied Probability Journals (2005-2019) Advising: Last has supervised 15 doctoral theses and numerous diploma/master's theses, including topics on neural networks, random tessellations, and stochastic financial models.

• Computational Biology
• Bioinformatics
• RNA Sequencing (RNA-seq)
• +۳ مورد دیگر

Mingfu Shao is an Associate Professor at the Department of Computer Science and Engineering, Pennsylvania State University, and affiliated with the Huck Institutes of the Life Sciences. His research focuses on computational biology and bioinformatics, specifically on RNA-seq data analysis, transcriptome assembly, and genome rearrangement algorithms. Research interests include developing exact and heuristic algorithms for genomic problems such as breakpoint distance computation, double-cut-and-join (DCJ) operations, and accurate assembly of circular RNAs and synthetic long reads. His work integrates machine learning techniques with traditional algorithmic approaches to enhance sequence analysis efficiency. Recent publications highlight advancements in RNA-seq data processing (e.g., Aletsch, TERRACE, Anchorage) and theoretical contributions to edit distance and k-mer analysis. His projects, funded by the National Science Foundation and the National Human Genome Research Institute, aim to improve isoform-level regulatory network inference and allele-specific transcript assembly.

Peter Moertersمشاهده پروفایل

استاد

• Probability Theory
• Random Graphs
• Large Deviations
• +۳ مورد دیگر

Peter Moerters is a Professor of Applied Mathematics at the University of Cologne , specializing in probability theory and its applications. His research spans random graphs, large deviations, Brownian motion, stochastic processes in random media, and geometric measure theory. He has extensive collaborations and editorial service, including contributions to journals like Stochastic Processes and their Applications and Journal of Theoretical Probability . Research Interests : Probability theory, random graphs, large deviations, Brownian motion, stochastic processes in random media, and geometric measure theory. Notable Coauthors : Yuval Peres, Jochen Blath, Wolfgang König, and others. Publications : Recent works include studies on percolation phase transitions, competing growth processes, tangent graphs, and branching with selection and mutation. Editorial Service : Serves on the editorial boards of Journal of Theoretical Probability and Stochastic Processes and Applications .

• Distributed Systems
• Network Coding
• Information Theory
• +۳ مورد دیگر

MALAK Derya is an Assistant Professor at EURECOM's Communication Systems department, specializing in networked distributed computing systems. Her research focuses on optimizing communication and computation trade-offs in networks, with a strong emphasis on network coding, information theory, and wireless communication systems. She holds a dual role in advancing theoretical foundations and practical implementations of distributed computing frameworks. Dr. Malak has received prestigious recognitions, including the Best Paper Awards at WiOpt 2023 and WIOPT 2022 for her work on content distribution optimization and random access protocols. Her research explores cutting-edge topics such as non-linear function computation, structured coding for matrix operations, and congestion-aware job scheduling in real-time systems. Key Research Areas: Distributed Algorithms, Network Coding, Machine Learning Systems, Wireless HetNets, and Multi-Server Architectures Awards: WiOpt 2023 Best Paper, WIOPT 2022 Best Paper Professional Activities: Invited speaker at major conferences and workshops, including the National Center on Networks and Systems for Digital Transformation Her work bridges theoretical insights with practical engineering solutions, addressing critical challenges in modern distributed and networked systems.

• Theoretical Computer Science
• Computational Complexity
• Approximation Algorithms
• +۶ مورد دیگر

Per Austrin is a Lecturer at the Royal Institute of Technology (KTH) in the Department of Theoretical Computer Science. He has been actively involved in teaching courses such as Algorithms and Complexity (DD2352) , Advanced Algorithms (DD2440) , and Problem Solving and Programming Under Pressure (DD2458) , serving as examiner and course coordinator for several programs. His research focuses on the foundations of computational complexity, approximation algorithms, and cryptographic barriers. Key areas include hardness of approximation for constraint satisfaction problems (CSPs), combinatorial optimization, and theoretical limits in cryptography and coding theory. His work often intersects with probabilistic methods and quantum computing challenges. Recent publications highlight his contributions to understanding lower bounds in optimization problems, inapproximability thresholds, and complexity in structured combinatorial problems. Detailed information about his work can be found on his personal website: Per Austrin's Website .

Seshadhri Comandurمشاهده پروفایل

استاد

• Theoretical Computer Science
• Graph Algorithms
• Data Mining
• +۴ مورد دیگر

C. Seshadhri (Seshadhri Comandur) is a Professor of Computer Science at the University of California, Santa Cruz's Baskin School of Engineering. He earned his Ph.D. from Princeton University and completed postdoctoral research at IBM Almaden Labs. His primary research spans theoretical computer science, graph algorithms, data mining, and sublinear algorithms, with applications in network analysis and scalable computation. His work focuses on graph property testing, monotonicity testing, subgraph counting, and combinatorial algorithms. Research integrates randomization, high-dimensional analysis, and optimization techniques to solve problems in large-scale network datasets. Key interests include developing efficient algorithms for complex graph structures and exploring limitations of low-dimensional embeddings. Publications demonstrate consistent focus on algorithmic efficiency in graph analysis, with recent work advancing sublinear-time methods, spectral decompositions, and combinatorial dichotomies. Trends show strong emphasis on theoretical foundations of data mining and rigorous complexity analysis. Invited Lecture at ICDT 2023 Best Paper at WSDM 2020 2019 SDM/IBM Early Career Data Mining Research Award Best Paper at WWW 2017 Best Paper at ICDM 2015 Best Paper Finalist at WWW 2015 Best Student Paper at KDD 2013 Best Research Paper at SDM 2013 2013 Sandia Employee Recognition Award Advises PhD/MS students and postdocs including Sabyasachi Basu, Daniel Paul Pena, Shweta Jain, and Andrew Stolman. Leads the PTReview blog on property testing. Current group includes postdocs and graduate students working on algorithms and complexity.

Benedikt Jahnelمشاهده پروفایل

استاد

• Statistical Mechanics
• Stochastic Geometry
• Spatial Epidemiology
• +۳ مورد دیگر

Prof. Dr. Benedikt Jahnel is a Professor at the Institute of Mathematical Stochastics, Carl Friedrich Gauss Faculty, Technical University of Braunschweig, and Head of the Leibniz Junior Research Group on Probabilistic Methods for Dynamic Communication Networks. His research focuses on the modeling and analysis of spatially embedded systems with interacting random components, with applications in physics, epidemiology, and telecommunications. Education: PhD in Organismic and Evolutionary Biology from Ruhr University Bochum (2011), Diploma from Technical University of Berlin. Research interests: Uses tools from statistical mechanics and stochastic geometry to study phase transitions, percolation theory, and spatial random processes. Current projects investigate continuum percolation, interacting particle systems, and probabilistic methods for communication networks. Recent publications demonstrate strong emphasis on spatial stochastic processes and percolation theory, with applications to network connectivity and epidemic modeling. Trends include mathematical analysis of phase transitions in random environments and dynamics of communication networks. Scientific awards include leadership of Leibniz Junior Research Group, EURANDOM Ambassador appointment, and election to the board of the Probability and Statistics Group of the German Mathematical Society. Leads a research team including 3 postdocs and 3 predoctoral researchers. Current projects funded by DFG, ERC, and Math+ Cluster of Excellence. Heads the DYCOMNET research group at Weierstrass Institute Berlin, focusing on dynamic spatial random systems.