Prof. Rosario Nunzio Mantegna is a Full Professor in the Department of Physics and Chemistry - Emilio Segrè at the University of Palermo (Unipa), Italy. He has held office hours in Building 18, Viale delle Scienze, focusing on appointments via email at rosario.mantegna@unipa.it. Research Interests: Econophysics, Complex Networks, Financial Market Dynamics, Air Traffic Systems, and Statistical Physics Applications. Methodological Expertise: Network Validation, Correlation Filtering, Hierarchical Clustering, and Stochastic Modeling. His work bridges physics, finance, and data science through network-based approaches to complex systems. Key contributions include analyzing financial indices, market lead-lag relationships, and air traffic networks. Publications span interdisciplinary topics from autism spectrum disorders to volcanic impact on ATM systems.
Dr. Joachim Spoerhase is a Lecturer at the Department of Computer Science , University of Liverpool , specializing in algorithm design and combinatorial optimization. His research focuses on approximation algorithms for clustering, network design, and geometric optimization problems. PhD in Computer Science (2010) and Habilitation (2017) from the University of Würzburg Former Research Associate at the Max Planck Institute for Informatics Research positions at Aalto University and University of Wroclaw His recent work explores high-dimensional data structures, polyline bundle simplification, and robust clustering frameworks. He also investigates hardness of approximation and theoretical limits in algorithm design. The University of Liverpool serves as his primary affiliation, with contributions to both computational geometry and machine learning. Notable research trends include interdisciplinary applications in network design, geometric optimization, and interpretable AI frameworks. Dr. Spoerhase teaches modules like Computer Networks (COMP211) and contributes to algorithmic theory development.
Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.
Daniel Kráľ is an Alexander von Humboldt Professor for Discrete Mathematics at Leipzig University and an affiliated member of the Max Planck Institute for Mathematics in the Sciences (MPI MiS). Previously, he held the Donald Ervin Knuth Professorship at Masaryk University in Brno and is also an honorary professor at the University of Warwick, where he was a professor of mathematics and computer science and a member of the Centre for Discrete Mathematics and its Applications (DIMAP). His research addresses various topics at the interface of mathematics and computer science, with primary focus on structural and extremal graph theory, discrete algorithms, and combinatorial limits. The theory of combinatorial limits is an emerging area that provides analytic methods to study large graphs such as social networks, establishing new links between analysis, combinatorics, ergodic theory, group theory and probability theory. His work has been supported by prestigious ERC grants including the CCOSA Starting grant and LADIST Consolidator grant. Dr. Kráľ's scholarly output includes over 150 journal research papers and numerous conference contributions. His recent publications demonstrate continued leadership in extremal combinatorics, graph limits, and structural graph theory, with significant contributions to understanding quasirandomness, Turán densities, and the coloring of complex graph structures. Scientific Recognition: SIAM Fellow (2024) Fellow of the American Mathematical Society (2020) Philip Leverhulme Prize in Mathematics and Statistics (2014) European Prize in Combinatorics (2011) ERC Consolidator grant LADIST (2015-21) ERC Starting grant CCOSA (2010-15) Professor Kráľ has supervised numerous PhD students and postdoctoral fellows throughout his career. His editorial service includes Editor-in-Chief of SIAM Journal on Discrete Mathematics (2017-2022), Co-Editor-in-Chief of Journal of Combinatorial Theory (since 2025), and Managing editor of Advances in Combinatorics (since 2018). He has organized multiple international workshops and conferences including Oberwolfach workshops on Graph Theory and the European Conference on Combinatorics, Graph Theory and Applications (EUROCOMB'23).
Rodrigo Santamaría Vicente is an Associate Professor in the Department of Computer Science and Automation within the Faculty of Sciences at the University of Salamanca. His academic career centers on bridging computer science with biological research through advanced data analysis and visualization techniques. He serves as director of the Diploma of Specialization in Bioinformatics and Computational Genomics, demonstrating leadership in specialized academic programming. Dr. Santamaría earned his PhD in Computer Science from the University of Salamanca in 2009 with his thesis "Visual analysis of gene expression data by means of biclustering," supervised by Dr. Luis Antonio Miguel Quintales and Dr. Roberto Therón Sánchez. His academic journey began with an M.S. degree in Computer Science from the same institution. His research primarily focuses on Bioinformatics and Information Visualization, with particular emphasis on integrating diverse data sources, analysis algorithms, and visual representations to enhance understanding of complex biological problems. His work spans multiple subdomains including gene expression analysis, biclustering algorithms, phylogenetic tree visualization, and collaborative tagging systems. Santamaría's research group affiliations include both the BIOINFORMÁTICA research group and the CaUSAL (Cultura Académica, Patrimonio y Memoria Social) research group at the Institute of Functional Biology and Genomics. His publication record reveals a consistent trajectory of scholarly output since 2006, with recent work showing increased focus on biological applications of computational methods. His most significant contributions appear in the integration of visualization techniques with bioinformatics analysis, particularly through tools like BicOverlapper and Voronto that facilitate interactive exploration of complex biological data. The evolution of his work demonstrates progression from foundational algorithm development to increasingly sophisticated applications in systems biology. Author of the book "How Machines Organize: An Introduction to Distributed Systems" Director of the Diploma of Specialization in Bioinformatics and Computational Genomics Active contributor to R programming ecosystem with the biclust package As an educator, Santamaría teaches across multiple programs including the Master's Degree in Computer Engineering and previously taught Bioinformatics for the Biotechnology degree program. His teaching portfolio spans Bioinformatics (from both computer science and biology perspectives), Distributed Systems, and Service-Oriented Architectures. He maintains an active research laboratory focused on developing visualization tools for biological data analysis, with current projects emphasizing integration of gene expression data with biological ontologies and advanced biclustering techniques.
Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.
Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.
