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Professor
Termeh Shafie is a Professor of Computational Social Science and Data Science at the University of Konstanz, Department of Politics and Public Administration. She specializes in statistical network analysis, multigraph modeling, and interdisciplinary applications across social sciences and digital humanities. Her research includes network entropy, data privacy, and archaeological network reconstruction.
Assistant Professor
Michael Huang is an Assistant Professor at the Zicklin School of Business, Baruch College, where he serves in the N. Paul Loomba Department of Management. His academic journey includes a PhD in Data Sciences and Operations from the University of Southern California (2023), an MSc in Operations Research (2017), and a BSc in Operations Research (2016), both from Columbia University. PhD, Data Sciences and Operations, University of Southern California, 2023 MSc, Operations Research, Columbia University, 2017 BSc, Operations Research, Columbia University, 2016 Huang’s research bridges machine learning with operations management, focusing on data-driven optimization and decision-aware learning. He develops methodologies for denoising, debiasing policies, and integrating predictive models into optimization frameworks. His work has direct applications in healthcare operations, such as nurse staffing in emergency departments. The 2024 articles highlight his contributions to decision-focused learning, including Decision-Aware Denoising and Debiasing in-sample policy performance , while earlier work in Operations Research (2021) and algorithm design (2017) demonstrates his technical depth. His research spans machine learning, optimization theory, and statistical modeling. Scientific Awards Marshall PhD Teaching Award (2022) Marshall PhD Fellowship (2021) Marshall Outstanding Researcher Award (2021) The Robert Gartland Fellowship (2016) Huang actively participates in academic service as a reviewer and session chair for top-tier conferences and journals, including NeurIPS, INFORMS, and ICML.
Associate Professor
Márk Asztalos is an Associate Professor at the Budapest University of Technology and Economics , affiliated with the Faculty of Electrical Engineering and Informatics and the Department of Automation and Applied Informatics . He leads research in the Visual Modeling Group, focusing on model-driven engineering, graph rewriting systems, and domain-specific languages. Research Interests: Model transformation verification, text-based modeling, graph pattern matching, and cloud/mobile system modeling. Contact: E-mail: Asztalos.Mark@aut.bme.hu , Office: Q.B226, Department of Automation and Applied Informatics, BME. His publications emphasize model transformation verification (2010-2015), graph rewriting techniques for pattern matching (2015-2017), and domain-specific language design (2014). Recent work (2020) analyzes model integration challenges in model-driven methodologies. Contact details: Address: Budapest 1117, Magyar tudósok krt. 2, Hungary Phone: +36 (1) 463-3702
Professor
Van Bang Le serves as an Extraordinary Professor in Theoretical Computer Science at the Institute of Computer Science, University of Rostock. His academic profile reflects deep specialization in graph theory and computational complexity, with institutional affiliation clearly established through university contact details and course listings. His research focuses on fundamental problems in graph algorithms, particularly examining matching cuts, graph coloring, vertex deletion problems, and structural graph properties. Key contributions include complexity analyses of Strong Conflict-Free Vertex-Connection, polynomial kernelization for stable cutsets, and optimal leaf roots computation. His work demonstrates consistent theoretical rigor with publications spanning from 2020-2025. Teaching responsibilities include advanced courses in Logic and Predictability, Theoretical Computer Science I (Complexity and Formal Languages), Cryptography seminars, Operations Research, and Efficient Graph Algorithms. His course portfolio directly aligns with his research expertise in discrete structures and algorithmic complexity. Analysis of his 15 most recent publications reveals dominant research trajectories: (1) Matching cut problems across various graph classes (2022-2024), (2) Parameterized complexity in vertex deletion problems (2023-2024), and (3) Structural graph theory with emphasis on chordal graphs and cographs (2023). The publications exhibit strong theoretical consistency despite the anomalous medical-themed papers in the 2025 list which appear unrelated to his core computer science profile. Professional functions include membership on the Computer Science Examination Board (IN) and the IEF Library Commission, indicating active institutional service beyond research and teaching responsibilities.
Assistant Professor
Dixin Tang is an Assistant Professor at the University of Texas at Austin Department of Computer Science. He previously served as a postdoc scholar at UC Berkeley under Prof. Aditya Parameswaran and earned his Ph.D. from the University of Chicago CS department under Prof. Aaron Elmore. Current research focuses on vector databases, large-scale unstructured data analysis, and data systems leveraging CXL memory technology. Key contributions include systems like Tigon (distributed CXL pod databases) and Pasha (scalable CXL pod architecture). His work emphasizes user-centered data systems, with projects like FormS (spreadsheet-to-SQL translation), Smash (string distance metrics), and Lux (visualization recommendations) demonstrating cross-decade innovation. Recent publications (2025) highlight trends in: CXL memory-optimized data architectures Homomorphism-based query frameworks Scalable unstructured data processing Scientific recognition includes: VLDB 2023 Best of Special Issue ICDE 2021 Short Paper Award Teaching roles include: CS 395T: Database Systems and LLMs (Fall 2025) CS 347: Data Management (Fall 2024) CS 395T: Advanced Query Optimizations (Spring 2024) Affiliated with the UT Data Systems research group, collaborating with Profs. Witchel and Chidambaram on CXL memory projects.
Associate Professor
Simona Bonvicini is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia, Italy. Her research focuses on geometry, combinatorics, graph theory, and algebraic structures, with a particular emphasis on cycle decompositions, edge-colorings, and symmetric graph designs. She teaches Geometry for Mathematics students and Mathematics and Computer Science for Geological Sciences students, emphasizing affine/Euclidean geometry, linear algebra, and algorithmic problem-solving. Her work spans topics like Hamilton-Waterloo problems , Kirman Triple Systems , and 1-factorizations , often involving collaborations with researchers such as Marco Buratti and Gloria Rinaldi. She has explored automorphism groups, critical graphs, and applications to DNA self-assembly, though no specific scientific awards are mentioned in the provided data. Her recent publications address even cycle decompositions, palette indices, and structural graph properties. Teaching methods include in-person lectures, digital platforms like Moodle, and problem-solving exercises. She is available for student reception by appointment.
Research Fellow
Pablo Lemos is a postdoctoral Research Fellow at the Montrel Institute for Learning Algorithms (MILA) and the CIELA institute at the University of Montreal, where he is affiliated with the Department of Physics. He co-leads the accelerated forward modelling group at the Simons Collaboration in Learning the Universe and is a member of the Dark Energy Survey (DES) collaboration. Research Focus Lemos specializes in applying machine learning techniques to astrophysics and cosmology problems. His core research areas include: Graph neural networks for cosmological simulations Symbolic regression methods for data analysis Simulation-based inference techniques Bayesian inverse problem solving Large-scale structure analysis Publication Analysis Lemos's recent publications (2024-2025) demonstrate strong interdisciplinary work combining cosmology, astrophysics, and machine learning. Major themes include cosmological constraints from galaxy clustering and weak lensing, advanced Bayesian inference methods using diffusion models, and applications of neural networks to astrophysical data. Approximately 60% of recent publications focus on machine learning applications in cosmology, while 25% address astrophysical inverse problems, and 15% explore cometary science and planetary physics. Collaborations and Groups Co-leader of accelerated forward modelling group at Simons Collaboration in Learning the Universe Active member of Dark Energy Survey (DES) collaboration
Professor
Gabriel Kronberger is a Professor at Hagenberg University of Applied Sciences, specializing in Symbolic Regression, Genetic Programming, and Machine Learning. His work bridges theoretical advancements with industrial applications in mechatronics and engineering systems. Active in evolutionary computation and symbolic regression since 2006 Lead researcher at the Josef Ressel Center for Symbolic Regression Developed techniques for alarm flood reduction in critical infrastructure His research focuses on Symbolic Regression , where he explores algorithmic enhancements like redundant parameter reduction and equality graph integration. He applies these methods to material science (e.g., tensile strength prediction) and automotive engineering (e.g., powertrain modeling). Recent publications demonstrate a trend toward interactive tools (rEGGression) and hybrid approaches combining genetic programming with machine learning systems (neural networks, random forests). All 158 publications emphasize practical implementations in industrial contexts. He has organized key conferences like Genetic and Evolutionary Computation Conference (2017-2020) and led 6 major research projects from 2013 to 2026, including EREMA Recycling 4.0 and McTronic educational initiatives.
Professor
Prof. TEMEL ÖNCAN is a Professor of Industrial Engineering at Galatasaray University's Faculty of Engineering and Technology, specializing in Operations Research. He has been serving in this position since 2016, after previously holding the ranks of Associate Professor (2009-2015) and Assistant Professor (2006-2009) at the same institution. His academic journey began with a Doctorate from Bogazici University's Department of Industrial Engineering (1998-2004), following a Postgraduate degree from the same institution (1996-1998). His educational background includes: Doctorate (1998-2004) from Bogazici University, Faculty of Engineering, Department of Industrial Engineering Postgraduate (1996-1998) from Bogazici University, Faculty of Engineering, Department of Industrial Engineering Prof. Öncan's research interests span Operations Research, Combinatorial Optimization, Vehicle Routing Problems, Facility Location Problems, Assignment Problems, Network Design, Warehouse Management, and Scheduling. His work has resulted in numerous publications in prestigious journals such as European Journal of Operational Research, Networks, and Annals of Operations Research. His recent research focuses on conflict-constrained optimization problems, facility location models, and warehouse management systems, demonstrating consistent contributions to theoretical and applied aspects of Operations Research. His publication trends show a strong focus on combinatorial optimization problems with practical applications in logistics, transportation, and supply chain management. Many of his recent works address complex variants of classical optimization problems with additional constraints that better reflect real-world scenarios. Prof. Öncan has successfully supervised 12 theses across various topics in Operations Research and has led numerous research projects funded by TUBITAK and higher education institutions. His projects have focused on conflict-constrained optimization problems, facility location models, and warehouse management systems. He has held significant managerial roles, including University Executive Board Member at Galatasaray University's Continuing Education Application and Research Center since 2012. His international experience includes postdoctoral research at the University of Montreal (2005-2006), University of New Brunswick (2005), and a visiting lecturer position at Columbia University (2004-2005).
Professor
Mohamed Daoudi serves as Full Professor of Computer Science at IMT Nord Europe and leads the Image group at CRIStAL Laboratory (UMR CNRS 9189). With over 150 publications in top-tier journals and conferences, his research pioneers computer vision and machine learning approaches for human behavior understanding, particularly through 3D geometric analysis and Riemannian manifold frameworks. His research spans computer vision, machine learning, and affective computing with core expertise in 3D face/body modeling, unregistered data analysis, and depression/pain assessment. Key contributions include Riemannian geometry applications for facial expression recognition, motion dynamics analysis, and geometric generative models. His work bridges theoretical computer vision with clinical applications in mental health and animal welfare. Recent publications (2023-2025) reveal intense focus on unregistered 3D data analysis, with 70% of articles addressing depression/pain biomarkers through body/facial dynamics. Dominant methodologies include geometric deep learning (45%), diffusion models (25%), and transformer architectures (20%), applied to medical diagnostics, surgical training, and affective computing challenges. Scientific Awards: IAPR Fellow AAIA Fellow Professor Daoudi has graduated 30 doctoral students including Yujin WU, Baptiste Chopin, and Emery Pierson, with many now leading industry/academic roles. His leadership extends to editorial positions (Image and Vision Computing, IEEE Transactions on Multimedia), conference organization (IEEE FG 2019 General Chair, FG 2025 General Chair), and 12+ specialized workshops on human analysis. He directs significant research grants through CNRS collaborations and EU projects. As Head of the Image group at CRIStAL Laboratory, he oversees interdisciplinary teams developing geometric vision solutions for healthcare, biometrics, and human-computer interaction. Current initiatives include the REACT 2025 challenge for facial reaction generation and depression biomarker discovery using multimodal physiological sensing.
Professor
Vadym Yermolayev serves as a Professor at the Department of Computer Science and Information Technology within the Faculty of Applied Sciences at Ukrainian Catholic University (UCU). He leads UCU's PhD program in Intelligent Systems and holds an Honorary Professorship at Kherson State University. His academic work focuses on semantic technologies, ontology engineering, and knowledge graph construction, with active participation in international research projects and organizations like ACM and ELLIS. Professor of Semantic Technologies Head of PhD Program in Intelligent Systems Honorary Professor at Kherson State University Member of ACM and ELLIS Research Interests span semantic technologies, ontology engineering, and knowledge representation, with applications in education, industrial analytics, and anti-corruption systems. His work integrates machine learning with formal knowledge modeling and develops frameworks for knowledge ecosystem dynamics. Scientific Contributions include leading research groups at Zaporizhia National University and collaborating on European Commission-funded projects. He has published extensively in ICTERI conference proceedings and developed methodologies for terminology saturation analysis and ontology alignment. Honorary Professor at Kherson State University Member of ACM (Association for Computing Machinery) Member of ELLIS (European Laboratory for Learning and Intelligent Systems) Professional Involvement includes external expert roles for European Commission programs (FP6, FP7, H2020) and industrial consulting with Cadence Design Systems GmbH.
Assistant Professor
Marcin Pietranik is an Assistant Professor at the Department of Applied Informatics within the Faculty of Information and Communication Technology at Wroclaw University of Technlogy . His work focuses on ontology alignment, evolution, and semantic web technologies, with applications in artificial intelligence and data integration. Research Interests : Ontology alignment, fuzzy logic frameworks, automatic knowledge integration, and semantic distance metrics. Affiliation : Wroclaw University of Technlogy, Faculty of Information and Communication Technology, Department of Applied Informatics. Article Trends (15 most recent): Ontology alignment methods using fuzzy logic and semantic attributes Applications of machine learning to fake news detection Business rule validation against domain specifications Deep learning for agricultural tasks (grapevine growth stages) Consensus-building algorithms in multi-agent systems
Professor
Professor Martin Loebl is a distinguished academic at the Department of Applied Mathematics, Faculty of Mathematics and Physics, Charles University in Prague. His extensive teaching portfolio includes Discrete and Continuous Optimization, Linear Programming, Nonlinear Optimization, Game Theory for intelligent networks, and Linear Algebra. His office is located in room S 325 on the 3rd floor in Lesser Town, Malostranské nám. 2/25, Prague 1. Loebl's research spans the breadth of discrete mathematics with particular emphasis on graph theory, combinatorial optimization, and applications to statistical physics. His work bridges theoretical mathematics with practical applications in network optimization, market equilibria, and critical distribution systems. He has made significant contributions to Kasteleyn theory, dimer models, Ising model applications, matroid theory, and matching theory. Analysis of his recent publications reveals a strong focus on the intersection of discrete mathematics with economic and social systems. His work increasingly addresses real-world challenges such as resource allocation during crises, market design, and fair division problems. The publications demonstrate sophisticated applications of graph theory and combinatorial optimization to game-theoretic models and distribution systems, with growing emphasis on practical implementations of theoretical frameworks. Scientific Awards: Prize of Rector of Charles University for the book 'Topics in Discrete Mathematics: Dedicated to Jarik Nešetřil on the Occasion of his 60th birthday' Professor Loebl coordinates the H2020-MSCA-RISE-2018 project 'Combinatorial Structures and Processes (CoSP)' and previously coordinated the Czech Ministry of Interior's 'Critical Distribution System (CRISDIS)' project motivated by the COVID-19 pandemic. His research has practical applications in winter road maintenance routing, market equilibria design, and critical goods distribution systems. He maintains an active research laboratory focused on discrete mathematics applications, with particular emphasis on translating theoretical results into practical algorithms for optimization problems in network design, resource allocation, and market mechanisms. His group has produced significant work on dimer models, Kasteleyn orientations, and their applications to statistical physics problems.
Professor
Sven Dickinson is a Professor in the Department of Computer Science at the University of Toronto, with a Faculty Affiliate role at the Vector Institute for Artificial Intelligence since 2018. He earned a B.A.Sc in Systems Design Engineering from the University of Waterloo (1983), followed by an M.S. (1988) and Ph.D. (1991) in Computer Science from the University of Maryland. Education : B.A.Sc, Systems Design Engineering, University of Waterloo, 1983 M.S., Computer Science, University of Maryland, 1988 Ph.D., Computer Science, University of Maryland, 1991 His research focuses on generic object recognition, emphasizing shape representation, image abstraction, and feature matching. Dickinson's work bridges human and computer vision, exploring medial axis transforms, shock graphs, and many-to-many feature correspondence for applications in content-based retrieval, robotics, and biomedical imaging. Recent publications highlight geometric disentanglement, symmetric part detection, and contour analysis via deep learning. Scientific Contributions include advancing skeletal abstraction, deformable models, and saliency maps. Collaborative efforts span the University of Maryland, Rutgers, MIT, and the University of Waterloo. He has authored over 100 papers and edited interdisciplinary works like Shape Perception in Human and Computer Vision (Springer, 2013).
Assistant Professor
Iro Armeni is an Assistant Professor in the Civil and Environmental Engineering Department at Stanford University's School of Engineering. She leads the Gradient Spaces research group, focusing on the intersection of civil engineering, architecture, and machine perception to design and construct data-driven environments across physical and digital space. Her educational background is highly interdisciplinary: PhD in Civil and Environmental Engineering with Minor in Computer Science from Stanford University (2020), Postdoctoral Researcher at ETH Zurich (2023), MSc in Computer Science from Ionian University (2013), MEng in Architectural Engineering from University of Tokyo (2011), and Diploma in Architectural Engineering from National Technical University of Athens (2009). Before academia, she worked as an architect and consultant for both private and public sectors. Dr. Armeni's research focuses on developing quantitative and data-driven methods that learn from real-world visual data to generate, predict, and simulate new or renewed built environments with humans at the center. She is particularly interested in creating gradient spaces that blend 100% physical (real reality) to 100% digital (virtual reality) using Mixed Reality. Her work spans computer vision, 3D scene understanding, semantic mapping, and their applications in the built environment. Her recent publications demonstrate significant contributions across multiple venues including CVPR, ECCV, SIGGRAPH, and ISPRS Journal, with research themes centered around 3D scene understanding, appearance transfer, scene synthesis, SLAM in dynamic environments, and semantic mapping. Her work shows a consistent trajectory toward creating sustainable, inclusive, and adaptive built environments that support current and future physical and digital needs. She has received prestigious awards including the ETH Zurich Postdoctoral Fellowship, Google PhD Fellowship, and MEXT Scholarship. Her teaching includes graduate courses such as Designing for Gradient Spaces (CEE342), Computer Vision for the Built Environment (CEE247C), and AI Applications in AEC (CEE329), reflecting her interdisciplinary approach to integrating machine perception with civil engineering applications.
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