جست‌وجوی استاد و پژوهشگر

Edith Alice Kovácsمشاهده پروفایل

دانشیار

• Multivariate Statistics
• Probability Graph Models
• Artificial Intelligence
• +۸ مورد دیگر

Edith Alice Kovács is an Associate Professor at the Department of Differential Equations, Budapest University of Technology and Economics (BME). She holds a PhD from the West University of Timisoara (2005) and has taught at multiple institutions, including the General College of Entrepreneurship (ÁVF) and universities in Arad. Her research focuses on multivariate statistics, probability graph models, information theory, AI/ML, network reliability, and mathematical modeling in finance/medicine. She has received the BME Innovation Award 2020 for anomaly detection work. Kovács has presented at conferences worldwide, including the European Conference on Operational Research and the IEEE SACI conference. Her teaching spans subjects like economic modeling, operations research, and data science across BME and ÁVF programs. Education: BSc and MSc in mathematics from the West University of Timisoara (1994), PhD in Mathematics (2005). Professional roles include full-time employment at BME since 2015 and prior experience at Aurel Vlaicu and Vasile Goldis Universities (1994–2000). Research interests include decision-making under uncertainty, metaheuristic algorithms, neural halos, and social network modeling. Recent work explores copula-based anomaly detection and vine copula structures. Kovács has contributed to projects like stochastic inventory modeling for TESCO and VNF fingerprinting for Nokia.

Cynthia Matuszekمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Human-Robot Interaction
• +۵ مورد دیگر

Cynthia Matuszek is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She directs UMBC’s Interactive Robotics and Language lab, focusing on enabling robots to learn from human interaction using natural language. Her research spans robotics, machine learning, human-robot interaction (HRI), and natural language processing (NLP). Notably, she was recognized in IEEE's 2018 '10 to Watch in AI' list. Dr. Matuszek holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Washington (2014, 2010) and a B.S. in Computer Science from the University of Texas at Austin (2000). Her work bridges theoretical and applied domains, addressing challenges in grounded language learning, multimodal data fusion, and ethical AI deployment. Her recent publications emphasize cybersecurity (e.g., malware detection using compact features), neurosymbolic reasoning, and the integration of large language models into robotics. The lab’s work often explores VR-based human-robot collaboration scenarios, leveraging photogrammetry and simulation for reproducible studies. Key thematic trends in her articles include: Advancing grounded language learning through cross-modal alignment and interactive labeling Developing ethical AI systems capable of moral reasoning and command rejection Pioneering domain-specific cybersecurity solutions using tensor factorization and vector stores Expanding multilingual and multimodal capabilities for robots in unstructured environments Her research also addresses societal impacts, including studies on gender stereotypes in AI datasets and privacy implications of pervasive robotics. The lab’s work often intersects with NSF-funded initiatives, such as the CAREER award for inclusive robotic learning environments.

• Machine Learning
• Cyber-Physical Systems
• Intelligent Embedded Systems
• +۵ مورد دیگر

Cesare Alippi is a Professor at Politecnico di Milano, Italy, and Università della Svizzera italiana, Switzerland, with visiting appointments at the University of Kobe (Japan) and Guangzhou (China). His research focuses on machine learning in nonstationary environments, intelligent embedded systems, and cyber-physical systems. Alippi holds IEEE Fellow status and has received notable awards, including the 2016 Gabor Award and the 2018 IEEE CIS Outstanding Computational Intelligence Magazine Award. He has authored a monograph, 6 edited books, and over 200 papers. Alippi's academic roles include Vice-President for Education of the IEEE Computational Intelligence Society and Distinguished Lecturer (2014-2016). He has organized major conferences like IJCNN and IEEE SSCI and contributed to initiatives promoting education in computational intelligence. His research addresses challenges in adaptive systems, embedded intelligence, and fault diagnosis. Key projects include deploying wireless sensor networks for environmental monitoring and developing machine learning algorithms for real-world applications like landslide prediction and network security. Alippi's awards reflect his contributions to neural networks and adaptive learning, including the IBM Faculty Award (2013) and IEEE Young Engineer Award (2004).

Eduardo G. Altmannمشاهده پروفایل

استاد

• Complex Systems
• Data Science
• Network Analysis
• +۵ مورد دیگر

Eduardo G. Altmann is a Professor in the School of Mathematics and Statistics at the University of Sydney. His research focuses on mathematical models and computational methods applied to complex systems, data science, and statistical laws. He is part of the Complex Systems and Data Science group and the Computational Social Science Lab, and contributes to interdisciplinary collaborations in areas like urban scaling and language dynamics. Altmann teaches courses such as MATH3076/3976/4076 (Mathematical Computing) and DATA5441 (Networks and High-Dimensional Inference). He serves on editorial boards for journals including the Journal of Statistical Mechanics and New Journal of Physics. His recent work includes studies on Monte Carlo methods for manifold triangulations, generative models for network communities, and statistical laws in complex systems. He has authored a monograph, 'Statistical Laws in Complex Systems,' published by Springer Nature, and frequently engages in academic outreach via platforms like Bluesky.

Nicolas Boumalمشاهده پروفایل

استادیار

• Optimization
• Non-convex Optimization
• Riemannian Manifolds
• +۵ مورد دیگر

Nicolas Boumal is an Assistant Professor in Mathematics at EPFL, Switzerland, holding dual affiliations in the Continuous Optimization Chair (OPTIM) within the Institute of Mathematics (MATH) and the SMA Education Group. He leads research in optimization on Riemannian manifolds, non-convex optimization, and statistical estimation. His work bridges geometry, numerical analysis, and applications in cryo-electron microscopy and synchronization problems. Education: PhD in Mathematical Engineering from UCLouvain (2014), postdoctoral research at Inria Paris and Princeton University. Prior to EPFL, he was an Instructor and Assistant Professor at Princeton’s Mathematics Department. Research Interests : Non-convex optimization landscapes, optimization on manifolds, low-rank matrix optimization, synchronization, phase retrieval, and computational methods in cryo-EM. His ERC Starting Grant (GEOSYM, 2022–2027) focuses on geometric and symmetric optimization techniques. Grants & Awards : ERC Starting Grant (2021), SIAM Optimization Prize (2018 for student co-author), and several conference best paper awards. Teaching : Courses include Continuous Optimization (MATH-329), Algebra Linéaire (MATH-111), and graduate-level Optimization on Manifolds (MATH-512). He authored the textbook An Introduction to Optimization on Smooth Manifolds (Cambridge University Press, 2023). Labs & Teams : Heads the OPTIM lab at EPFL, collaborating with global researchers in optimization and applications. His group develops the Manopt toolbox for manifold optimization.

Chang Suمشاهده پروفایل

استادیار

• Bioinformatics
• Biostatistics
• Genomics
• +۶ مورد دیگر

Chang Su serves as Assistant Professor in the Department of Biostatistics and Bioinformatics at Emory University, with a secondary appointment in the Department of Human Genetics, School of Medicine. His research develops statistical methodologies for single-cell genomics and genetics data analysis. His educational background includes: Ph.D. from Yale University B.S. from Sun Yat-sen University Chang Su's research focuses on gene regulation in specific contexts and the genetics basis of single-cell genomics, with applications to neurodegenerative diseases, lung diseases, and immune disorders. His work bridges biostatistics and biological discovery through computational innovation. His recent publications (2022-2025) demonstrate consistent advancement in single-cell multimodal data analysis, including cell-type-specific enhancer mapping, co-expression network inference, and applications to Parkinson's and Alzheimer's disease. Methodological contributions span microbial network analysis and functional connectome metrics related to aging and cognition. Chang Su teaches BIOS 555 - High Throughput Data Analysis R&B and maintains active research collaborations, though specific grant details or student mentorship information are not provided in available materials.

Philip Matchett Woodمشاهده پروفایل

استاد

• Probability Theory
• Combinatorics
• Random Matrices
• +۳ مورد دیگر

Philip Matchett Wood is a Professor in the Department of Mathematics at Harvard University. His research focuses on probability theory, combinatorics, and their applications, with particular emphasis on random matrices, Markov chains, structural behavior in sumsets, and computational methods. He has contributed to foundational work in random matrix theory, including spectral analysis of non-backtracking matrices and universality principles for sparse matrices. His work often bridges theoretical insights with computational exploration, as seen in his development of Maple packages for combinatorial bijections and probabilistic algorithms. Research Interests: Probability Theory, Combinatorics, Random Matrices, Markov Chains, Computational Mathematics, Geometry Grants: Supported by NSA grants (H98230-16-1-0301 and H98230-14-1-0149) from 2014–2018, focusing on random matrix theory and spectral analysis. Publications: Over 20 peer-reviewed articles, including work on outlier detection in random matrix products, computational number theory, and universality in spectral distributions. Contributions: Developed Maple tools for combinatorial bijections (e.g., Fibonacci, ZeckFibBijections), advancing algorithmic approaches to enumerative combinatorics. He is actively involved in teaching, such as Math 22A: Vector Calculus and Linear Algebra I in Fall 2024. His interdisciplinary interests include applying probability to clinical studies (e.g., heart rate variability under anesthesia) and recreational mathematics, exploring topics like the mathematics of Rubik's Cube and origami constructions for angle trisection.

• Statistical Genetics
• Graphical Models
• Genomics
• +۵ مورد دیگر

Pariya Behrouzi is an Assistant Professor in the Department of Mathematical and Statistical Methods - Biometris at Wageningen University & Research. She specializes in developing statistical methodologies with applications in genetics, genomics, and nutrition science. Her research focuses on graphical models, copula theory, and Bayesian approaches to analyze complex biological systems. Behrouzi leads or collaborates on projects such as SignAphy (plant signaling dynamics) and Sensing Age (epigenetic aging studies). She co-developed the netgwas R package for network-based genome-wide association studies. Her work bridges statistical theory with practical applications in agriculture, nutrition, and computational biology. Education: PhD in Statistics (as indicated by title) Key Research Tools: Copula graphical models, spatial statistics, and Bayesian networks Current Projects: Multi-omics network reconstruction, resilience in lettuce crops, and microbiota systems analysis Her recent publications explore topics like meat analogue texture optimization, telomere length dietary correlations, and plant disease resistance mechanisms. Behrouzi advises four PhD candidates and collaborates with international teams on interdisciplinary research.

Alessandro Fedrizziمشاهده پروفایل

استاد

• Quantum Technology
• Photonics
• Quantum Communication
• +۳ مورد دیگر

Alessandro Fedrizzi is a Professor at Heriot-Watt University, affiliated with the School of Engineering & Physical Sciences and the Institute of Photonics and Quantum Sciences. He holds an EPSRC Early Career Quantum Technology Fellowship. His research focuses on photonic quantum technology, including quantum communication, computation, and metrology, with an emphasis on scalable hybrid quantum systems. Fedrizzi completed his PhD in 2008 under Anton Zeilinger at the Institute for Quantum Optics and Quantum Information in Vienna. Prior to joining Heriot-Watt in 2015, he worked at the University of Queensland, Australia, on an ARC Discovery Early Career Award. Education: PhD in Quantum Optics (2008, Vienna) Research Interests: Fedrizzi develops photonic quantum technologies for experimental quantum information processing. His work bridges foundational research and applied areas like quantum networks, satellite communication, and hybrid systems. Key themes include scalable architectures, quantum entanglement, and photonics-based quantum devices. Recent projects explore programmable quantum networks, quantum-dot light purification, and anonymous quantum conferencing. Articles Overview: His recent publications address quantum networking protocols, photonics-based entanglement, and experimental validations of quantum communication systems. Notable works include programmable photonic networks and entanglement-induced many-body interference studies. Awards: EPSRC Early Career Fellowship (2015). Advising & Grants: Fedrizzi accepts PhD students in Experimental Quantum Networking and Satellite Quantum Communication. His fellowship supports quantum technology commercialization efforts. Labs & Teams: Active in the Institute of Photonics and Quantum Sciences, collaborating internationally on quantum network architectures and photonics systems.

Ahmet Sinan CEVIKمشاهده پروفایل

استاد

• Combinatorial Group Theory
• Graph Theory
• Algebraic Combinatorics
• +۵ مورد دیگر

Prof. Dr. Ahmet Sinan CEVIK is a Professor of Mathematics at the Department of Mathematics, Faculty of Science, Selcuk University, Konya, Turkey. He holds editorial roles in several journals including Guest Editor for special issues in Symmetry, Computer Modeling in Engineering & Sciences , and serves on the Advisory Board of TWMS Journal of Applied and Engineering Mathematics . His primary research interests span Combinatorial Group Theory, Graph Theory, Semigroup Theory, and Topological Indices, with a focus on algebraic structures, graph invariants, and their applications. Prof. CEVIK has authored numerous textbooks including Introduction to Algebra (multiple editions) and Lecture Notes - I: Some Topics of Combinatorial Group Theory . He has published over 100 peer-reviewed articles in high-impact journals like Proceedings of the Edinburgh Mathematical Society , Communications in Algebra , and Applied Mathematics and Computation . His research emphasizes Gröbner-Shirshov bases, group presentations, and topological indices of graphs. He has collaborated extensively with global researchers such as Prof. Kinkar Ch. Das, Ivan Gutman, and Ismail Naci Cangul. His work bridges algebraic structures with graph theory, contributing to both theoretical advancements and applied mathematical problems.

• Machine Learning
• Data Science
• Artificial Intelligence
• +۱۴ مورد دیگر

Jesse Read is a Professor (HDR) in the ORAILIX team of the Data Analytics and Machine Learning pôle at École Polytechnique, part of Institut Polytechnique de Paris. He leads research in machine learning with a focus on multi-label classification, data streams, and reinforcement learning, with applications spanning medicine, energy, and transportation systems. His research interests center around two major themes: multi-label and multi-target prediction (modeling multiple interconnected tasks), and learning from sequential data and data streams. He has particular interest in explainability, uncertainty analysis, robustness, domain shift, transfer learning, and continual learning. His methodological toolkit includes deep neural networks, probabilistic graphical models, Monte Carlo methods, and classical machine learning algorithms. Professor Read has built a strong research program with significant funding, including an ANR grant for Dynamic Graph Signal Processing in 2024. His work on Classifier Chains for Multi-label Classification received the prestigious Test of Time Award at ECML-PKDD 2019. His research bridges theoretical advances with practical applications across diverse domains. Test of Time Award at ECML-PKDD 2019 for Classifier Chains for Multi-label Classification As an educator, Read coordinates multiple courses including Deep Reinforcement Learning, Reinforcement Learning and Autonomous Agents, and Machine Learning. He co-coordinates the Trustworthy and Responsible AI (TRAI) Masters program and the Artificial Intelligence and Data Science Track. He has supervised numerous PhD students whose work spans predictive maintenance, reinforcement learning for routing problems, rail network modeling, and medical applications. His River library has become a standard tool for stream learning in Python, resulting from the merger of Creme and scikit-multiflow.

• Computational Biology
• Bioinformatics
• Cancer Biology
• +۷ مورد دیگر

Paul Stewart is a Research Professor in Nutrition & Integrative Physiology at the University of Utah, with appointments at the Huntsman Cancer Institute (HCI). His work focuses on integrating multi-omics data (genomics, proteomics, metabolomics) to uncover cancer biology mechanisms and improve patient outcomes. He holds expertise in computational mass spectrometry, tumor microenvironment dynamics, and translational oncology research. Education: Ph.D. in Biomedical Sciences (Florida State University, 2014) M.S. in Biomedical Sciences (Florida State University, 2008) B.S. in Biology (Florida State University, 2006) Research Interests: Cancer-associated cachexia and metabolic dysregulation Machine learning for omics data integration Proteogenomic characterization of tumors Exosome-based biomarker discovery Articles & Trends: Recent work emphasizes spatial transcriptomics in ccRCC, machine learning for interactome prediction, and clinical implementation of multi-omics panels. His research bridges computational methods with clinical translation, particularly in immunotherapy resistance and metastasis. Awards & Honors: National Cancer Institute Loan Repayment Program (2021, 2023) Spirit of Moffitt Award (2022) Best Poster Presentation (SUO Annual Meeting, 2021) Grants & Leadership: Leads projects funded by NIH, American Cancer Society, and the Florida Biomedical Research Program. Serves on editorial boards (e.g., Frontiers in Molecular Biosciences) and professional committees (US HUPO). Labs & Collaborations: Collaborates with teams at Moffitt Cancer Center and University of South Florida. Active in the Colocare Consortium and Bio-Data Club hackathons.

Longxiu Huangمشاهده پروفایل

استادیار

• Data/Signal Processing
• Applied Harmonic Analysis
• Machine Learning
• +۴ مورد دیگر

Longxiu Huang is an Assistant Professor at Michigan State University (MSU), holding dual appointments in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics. Prior to this, she was an Assistant Adjunct Professor at UCLA's Department of Mathematics. Her research focuses on Data/Signal Processing, Applied Harmonic Analysis, Machine Learning, Random Matrix/Tensor Analysis, Dynamical Sampling, and Optimization. Education : Ph.D. in Mathematics, Vanderbilt University (2014-2019) M.S. in Mathematics, Fudan University (2012-2014) B.S. in Mathematics, Sun Yat-Sen University (2009-2012) B.S. in Software Engineering, Sun Yat-Sen University (2008-2009) Research Interests : Huang's work bridges theoretical mathematics and applied computational methods. She explores signal reconstruction in dynamical systems, tensor decompositions for high-dimensional data, robust matrix/tensor completion, and optimization algorithms for ill-conditioned problems. Her contributions include CUR-based methods for low-rank approximations and sampling strategies for sparse signals on graphs. Professional Activities : She co-organizes the One World MINDS Seminar, a global initiative in mathematical data science. Her research has been published in top journals like SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis, with a focus on theoretical guarantees and practical applications in imaging and machine learning. Lab/Teams : Active collaborator in interdisciplinary projects at MSU, particularly in computational mathematics and machine learning applications.

Hongsong Fengمشاهده پروفایل

استادیار مهمان

• Machine Learning
• Topological Data Analysis
• Computational Biology
• +۳ مورد دیگر

Hongsong Feng is a Visiting Assistant Professor in the Department of Mathematics at Michigan State University (MSU), located in C309 Wells Hall. His research focuses on interdisciplinary applications of mathematics and machine learning to problems in computational biology, drug discovery, and topological data analysis. He specializes in combining advanced mathematical frameworks like persistent homology, algebraic topology, and differential geometry with machine learning techniques to analyze complex biological systems and molecular interactions. Key research interests include protein flexibility analysis, drug-target interaction networks, and the development of AI-driven methods for predicting protein-ligand binding affinities. His work often bridges theoretical mathematics with practical biomedical challenges, such as opioid use disorder treatment and cocaine addiction drug development. Recent projects involve leveraging transformers, knot theory, and generative network models to advance drug discovery and systems pharmacology. Notable contributions include methodologies like Persistent Sheaf Laplacian analysis, CAML (Commutative Algebra Machine Learning), and TIDAL (Topology-Inferred Drug Addiction Learning). These approaches integrate multiscale geometric learning and topological signal processing to model molecular dynamics and network complexities. His research also extends to numerical methods for solving elliptic interface problems and parabolic partial differential equations, reflecting a strong foundation in applied mathematics. Despite his extensive publication record, no specific academic awards or grants are explicitly listed in the provided materials. No formal advisees are mentioned, suggesting he may be early in his academic career or focused primarily on research collaborations.

Hawlader Al-Mamunمشاهده پروفایل

پژوهشگر ارشد

• Bioinformatics
• Quantitative Genetics
• Machine Learning
• +۴ مورد دیگر

Hawlader Al-Mamun is an Adjunct Research Fellow at the School of Biological Sciences, University of Western Australia, where his work focuses on the intersection of computer science, bioinformatics, and quantitative genetics. His research centers on improving prediction accuracy of commercially important phenotypes in plants and animals, and unraveling complex biological factors through machine learning integration with multi-omic data. Education PhD in Quantitative Genetics from University of New England (2015) MSc in Bioinformatics from Chalmers University of Technology (2007) BSc in Computer Science and Engineering from Khulna University (2022) Research Focus Dr. Al-Mamun's expertise spans computational biology and agricultural genomics with emphasis on: Applying AI/ML to optimize biological challenges Developing computational methods for genomic data analysis Integrating GWAS with gene expression data Leveraging functional knowledge in genomic selection Improving phenotype prediction accuracy in plants/animals His work significantly contributes to Sustainable Development Goals related to food security through advanced genomic approaches. Publication Trends His 2024-2025 publications demonstrate strong focus on plant genomics (particularly Brassica species and legumes), integrating machine learning with genomic data to address agricultural challenges including disease resistance and yield prediction. Key thematic areas include pangenome construction, feature selection in GWAS, genotype-environment interactions, and multi-omics integration. Professional Activities Dr. Al-Mamun serves as a Visiting Scientist at CSIRO - Agriculture and Food since April 2023. His collaborative network spans multiple international institutions as evidenced by extensive co-authorship on recent publications. His research fingerprint shows significant contributions to Genomics (100%), Disease Resistance (66%), Genome-Wide Association Study (48%), and Brassica research (44%).