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Theodosiadou OuraniaView profile

Researcher

• Stochastic modeling
• Time series analysis
• Applied probabilities
• +3 more

Ourania Theodosiadou is a Researcher at the Department of Mathematics, Aristotle University of Thessaloniki (since Oct 2024). Previously, she served as a Postdoctoral Researcher at the Institute of Information and Communication Technologies (CERTH) from 2019 to 2024 and held multiple contracted lecturer roles at Aristotle University and the University of Macedonia. She earned a PhD in Mathematics (2019) and a Master's in Statistics and Modeling (2014), both from AUTH, with a focus on stochastic processes and financial applications. Her doctoral thesis explored latent stochastic processes with jumps in finance under Prof. Georgios Tsaklidis. Her research interests span stochastic modeling, time series analysis, computational statistics, and machine learning. Recent work includes real-time threat assessment using Hidden Markov Models (2023), cryptocurrency transaction analysis for illegal activity detection (2023), and centrality-based network node identification (2022). Methodological contributions include state space modeling with constraints (2021) and Kalman filter applications for jump detection in financial markets (2017-2019). Publications reflect interdisciplinary applications in finance, security, and computational methods. Current work extends AI-driven solutions against terrorist financing and explores blockchain forensics through time series analysis.

Daniele ZambonView profile

Researcher

• Graph Representation Learning
• Machine Learning
• Learning in Non-Stationary Environments
• +7 more

Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.

• Algorithms on strings
• Small-space algorithms
• Data structures
• +4 more

Tatiana Starikovskaya is an Assistant Professor in the Talgo team at the Computer Science Department of École normale supérieure (ENS Paris), France. Her work focuses on the design and analysis of algorithms for string processing, space-efficient computation, and complexity theory, with applications in bioinformatics and data security. Education : PhD in Mathematics (2013) and MSc in Mathematics (2009) from Lomonosov Moscow State University; MSc in Data Science (2009) from Moscow Institute of Physics and Technology and Yandex School of Data Analysis. Her research explores the intersection of algorithm theory and practical applications, particularly in streaming models , probabilistic text indexing , and clustering of sequence data . Key themes include handling wildcards , k-mismatch problems , and edit distance under space constraints. Current projects include PARSe (ANR-20-CE48-0001) , aiming to develop ultra-efficient algorithms for noisy string data, and AlgoriDAM (ANR-19-CE48-0016) , focusing on algorithmic theory for modern data models. She co-supervises PhD students Gabriel Bathie and T. El Ghazi, with collaborations spanning institutions like University of Wrocław and IRISA. She has served on program committees for major conferences (STACS, ESA, ICALP, CPM) and organized the CPM Summer School (2023) and New Horizons of Stringology (2024) workshop at Cirm. Teaching roles include courses at ENS Paris (Algorithmique, Programming for Non-CS Students) and Higher School of Economics (Moscow).

Jan Friso GrooteView profile

Professor

• Formal Methods
• Verification Technology
• Software Engineering
• +12 more

Jan Friso Groote is a Full Professor and Chair of the Formal System Analysis group in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He also holds professorial roles in the EAISI Foundational and EAISI High Tech Systems institutes. Since 2016, he has been working part-time at ASML, contributing his expertise in formal verification to industrial applications. Education: Born in 1965, studied Computer Science at Twente University of Technology (now University of Twente), 1983–1988. PhD in 1991 from the University of Amsterdam with thesis 'Process algebra and structured operational semantics', based on research at CWI (Centrum Wiskunde en Informatica). Jan Friso Groote is a leading researcher in formal methods and software verification. His work focuses on enabling the development of flawless software through rigorous formal analysis. Key research areas include structural operational semantics, model checking, branching bisimulation, protocol verification, and the development of the mCRL2 toolset. His current goal is to integrate formal techniques into complete software system design, improving both development speed and quality. His research has demonstrated that formal methods can reduce development time by a factor of three and increase quality tenfold, with potential for zero-defect software. His recent publications demonstrate sustained contributions in formal verification, including work on mutual exclusion algorithms, industrial control system modeling, probabilistic systems, and efficient bisimulation algorithms. The articles span topics such as tunnel control systems, simulation lower bounds, and formal methods for critical systems, reflecting both theoretical depth and practical application. Scientific Awards: Best Paper Award FACS 2018 FMICS-AVoCS Best Paper Award (2017) Jan Friso Groote has held significant leadership roles in education, including Director of Education for Computer Science (2000–2010) and for multiple bachelor’s and master’s programs. He has advised numerous researchers and supervised a large body of research output (over 320 publications). He leads the Formal System Analysis group and has been involved in projects such as 'Composable Embedded Systems for Healthcare'. His work bridges academia and industry, particularly through collaborations with ASML and Rijkswaterstaat, and he has been a visiting researcher at institutions across Europe and China. He is a key contributor to the mCRL2 toolset, which supports modeling and verification of software behavior with data, time, and probabilities. His research fingerprints highlight strong expertise in model checking, transition systems, software design, and process algebra. He teaches courses such as System Validation, Embedded Software, and Capita Selecta in Formal System Analysis.

Hsin-Yi LinView profile

Assistant Professor

• Quantum Machine Learning
• Speech Enhancement
• Data Assimilation
• +3 more

Hsin-Yi Lin is an Assistant Professor in the Department of Mathematics and Computer Science at Seton Hall University. Her research focuses on quantum machine learning, speech enhancement, and data assimilation with applications in computational physics and neural network interpretability. She explores interdisciplinary topics including quantum neural networks, adversarial robustness in speech models, and multi-scale geophysical dynamics modeling. Recent work emphasizes quantum measurement optimization, transfer learning in quantum circuits, and novel diffusion models. Her publications address challenges in speech quality assessment, domain adaptation, and scalable state estimation using recurrent networks. She has contributed to foundational mathematical analysis of transport equations and developed extensions to signal processing frameworks like SEMamba. Key research themes include hybrid quantum-classical systems, explainable AI for quantum models, and robust machine learning under adversarial conditions. Collaborations bridge theoretical mathematics with applied computational methods in earth system prediction and periodic migration modeling.

Jiarui DingView profile

Assistant Professor

• Bioinformatics
• Computational Biology
• Machine Learning
• +5 more

Jiarui Ding is an Assistant Professor in the Department of Computer Science at the University of British Columbia , within the Faculty of Science. His research focuses on the intersection of bioinformatics, computational biology, and machine learning, with an emphasis on single-cell genomics and probabilistic deep learning. Key interests include computational immunology, cancer biology, and the application of AI to biomedical problems like food allergy neuroscience. He is affiliated with the CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action and the Data Science Institute , indicating strong interdisciplinary engagement. He actively recruits doctoral students for research projects in these areas, with desired start dates year-round. His work bridges computational methods with biological systems, exemplified by publications on single-cell data integration ( e.g. , CellUntangler), generative models for T-cell receptors, and mechanistic studies of immune responses in diseases like eosinophilic esophagitis. His research also addresses challenges in multiomics data analysis and the development of novel algorithms for genomic data interpretation. No awards or grants are explicitly listed in the provided materials, though his involvement in high-impact projects suggests potential external funding. He emphasizes collaboration, stating availability for interdisciplinary projects and undergraduate research mentorship.

• Formal Methods
• Probabilistic Systems
• Model Checking
• +3 more

Joost-Pieter Katoen is a Full Professor at the Digital Society Institute and Formal Methods and Tools department. His research focuses on formal methods, probabilistic systems, and model checking, with applications in automated verification and Markov models. He has been actively involved in international conferences like FM 2023 and has collaborated globally on safety-critical systems analysis. Research Interests: Katoen’s work bridges theoretical foundations with practical tools, including parameter synthesis in probabilistic systems, fault tree analysis, and Bayesian network optimization. His contributions span algorithm design for model checking and probabilistic program verification. Awards: Received the Beste paper award in 2018 for contributions to uncertainty management in probabilistic programming. Advising & Grants: Supervised 10 academic works and contributed datasets for tools like Prophesy and SAFEST. Active in organizing events such as the 2017 Symposium on Model-Ed, Test-Ed, and Trust-Ed.

Ana DjurdjevacView profile

Assistant Professor

• Numerical Analysis
• Stochastic Processes
• Partial Differential Equations
• +3 more

Ana Djurdjevac is an Assistant Professor in the Department of Numerical Analysis and Stochastics at the Freie Universität Berlin , within the Department of Mathematics and Computer Science. Her research focuses on numerical analysis, stochastic processes, and partial differential equations, with particular emphasis on uncertainty quantification and mathematical modeling in evolving domains. She teaches advanced courses such as Numerical Methods for Stochastic Differential Equations and Stochastik I , reflecting her expertise in computational methods and probabilistic frameworks. Her work integrates theoretical analysis with practical numerical techniques, addressing challenges in domains such as fluid dynamics, quantum systems, and biological surface fluctuations. Recent contributions include studies on hybrid algorithms for particle systems, rough homogenization in stochastic dynamics, and synchronization mechanisms in conservation laws. Djurdjevac actively participates in academic events, including the 2025 SIAM Conference on Computational Science and Engineering, and collaborates on projects involving quasi-Monte Carlo methods for Bayesian inversion and domain decomposition techniques. Professional activities highlight her role in shaping emerging fields like stochastic PDEs on evolving domains and feedback loops in agent-based models. While no specific awards are listed, her prolific publication record and teaching roles underscore her contributions to computational science and applied mathematics.

Felix HöflingView profile

Professor

• Statistical Physics
• Biological Physics
• Molecular Dynamics
• +3 more

Prof. Felix Höfling holds a W2 non-tenure professorship for 'High-performance computing in molecular dynamics' at Freie Universität Berlin (since 2015). He leads the Computational Statistical & Biological Physics group within the Department of Mathematics and Computer Science. Prior roles include research associate positions at the Max Planck Institute for Intelligent Systems (2010–2015), postdoctoral research at the University of Oxford (2009), and doctoral work at Ludwig-Maximilians-Universität München (2006, summa cum laude). His research focuses on computational methods in statistical and biological physics, including molecular dynamics simulations, anomalous transport in crowded environments, and high-performance computing. Notable contributions include studies on liquid-vapor interfaces, colloidal dynamics, and the interplay between microscopic and macroscopic transport phenomena. His work integrates theoretical modeling, numerical simulations, and interdisciplinary applications in soft matter and biophysics. Editorial Board Member of Communications Physics (since 2023) Director of the CECAM node 'Mathematics and Computation in Molecular Simulation' (since 2022) Head of the MSc Examination Board for Computational Sciences (since 2019) His research articles explore topics such as surface tension modulation, confined fluids, and active matter systems. Key achievements include a highly cited review on anomalous transport in biological cells (2013) and contributions to the development of the H5MD molecular data format. Awarded 'Distinguished Referee' by the European Physical Journal (2013) Recipient of grants for projects on open systems and parallel computing Prof. Höfling oversees the Computational Statistical & Biological Physics lab, emphasizing collaborative research in computational physics and interdisciplinary applications.

Jerry TaltonView profile

Assistant Professor

• Computer Graphics
• Machine Learning
• Human-Computer Interaction
• +3 more

Jerry Talton is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign (UIUC). He holds a PhD in Computer Science from Stanford University (2011), and BS and MS degrees from UIUC (2003 and 2006, respectively). His research focuses on computer graphics, machine learning, and human-computer interaction, with notable work on emoji-first communication, rating systems analysis, and data-driven design tools. Key contributions include the Opico study on emoji communication in social apps and the Bricolage algorithm for web design transfer. He received the Siebel School Young Alumni Achievement Award (2022). His work spans academic collaborations with institutions like Stanford and industry applications in social media and design. Research interests emphasize user interaction, algorithmic ranking, and leveraging big data for design automation. His publications address challenges in social networks, fluid simulation, and educational outreach through initiatives like Scavenger Hunt, enhancing student retention in computer science.

Jennifer ListgartenView profile

Professor

• Machine learning
• Computational biology
• Protein design/optimization/engineering
• +5 more

Jennifer Listgarten is a Professor in the Electrical Engineering and Computer Science (EECS) Department, the Center for Computational Biology, and the Bioengineering program at the University of California, Berkeley. She serves as a member of the steering committee for the Berkeley AI Research (BAIR) Lab and holds the Jeffrey Huber and Angel Vossough Chancellor's Chair in Computational Biomedicine. Her multidisciplinary appointments reflect her work at the intersection of computer science, statistics, and biological sciences. Professor Listgarten completed her Ph.D. in Computer Science from the University of Toronto in 2007, following undergraduate degrees in Physics and Computer Science from Queen's University in Canada. Prior to joining UC Berkeley, she spent a decade (2007-2017) at Microsoft Research with positions in Cambridge, MA, Los Angeles, and Redmond, WA. Her research focuses on developing and applying machine learning methods to solve problems in biology and medicine, with current emphasis on protein design, optimization, and engineering for properties such as expression, fluorescence, binding, and stability. She also works on computational chemistry methods, drug repositioning, and machine learning methodology at the intersection of graphical models, neural networks, and variational inference. Her earlier work addressed statistical genetics methods for correcting confounding factors in GWAS, epigenome-WAS, and eQTL studies, as well as immunoinformatics problems including HLA class I epitope prediction. Her recent publications demonstrate a strong trajectory in applying machine learning to protein engineering, with numerous high-impact papers in Nature Biotechnology, Science Advances, and top machine learning conferences. Her work shows increasing integration of computational methods with experimental validation, particularly in CRISPR technology and protein design applications. Bakar Fellows Spark Award (2024) Professor Listgarten actively mentors PhD students through EECS, the Center for Computational Biology, and Bioengineering programs. She maintains scientific advisory roles with several biotechnology companies including Dayzero Diagnostics, Deep Apple Therapeutics, Inscripta, and Fable Therapeutics (where she serves as Academic co-founder). Her research group collaborates with prominent scientists including Chris Garcia (Stanford), Phil Romero (U Wisconsin), David Savage (UC Berkeley), and David Schaffer (UC Berkeley). The Listgarten lab operates within the Berkeley Artificial Intelligence Research Lab (BAIR) and the Center for Computational Biology (CCB), focusing on developing computational methods that enable new biological insights and therapeutic applications, particularly in the areas of protein engineering and CRISPR technology.

Patrick van der SmagtView profile

Professor

• Machine Learning
• Probabilistic Inference
• Neural Networks
• +1 more

Patrick van der Smagt is a Professor at the Technical University of Munich in the Department of Computer Science. He serves as the Director of the Foundation Robotics Labs and AI Research Group, focusing on machine learning and robotics research. His work spans probabilistic inference, neural networks, and sim-to-real transfer for robotics. Research Interests : Machine Learning Probabilistic Inference Neural Networks Robotics Publications : His recent work includes advancements in robotic action space design, sim-to-real transfer, and human reflex adaptation studies, alongside foundational contributions to convolutional networks for optical flow and neural-controlled prosthetics. Labs & Teams : Leads the Foundation Robotics Labs and AI Research Group, fostering interdisciplinary research at the intersection of machine learning and robotics.

• Artificial Intelligence
• Biometrics
• Security Systems
• +5 more

Kingsley Nwosu is an Associate Professor in the Department of Computer Science at Norfolk State University's College of Science, Engineering and Technology. His research spans artificial intelligence, biometric systems, and security frameworks, with a focus on healthcare, campus safety, and low-resource environments. Email: kcnwosu@nsu.edu Research Themes: AI-driven emotion recognition and facial recognition systems Biometric authentication for security and privacy Blockchain applications in e-voting and data integrity Urban mobility optimization via embedded systems Public policy evaluation for social enterprises Data partitioning strategies for machine learning Publication Trends: His work since 2012 highlights interdisciplinary applications of AI and biometrics in healthcare, security, and economic development, with recent emphasis on scalable solutions for developing economies and proactive cybersecurity measures.

Shahram RahimiView profile

Professor

• Artificial Intelligence
• Generative AI & LLMs
• Machine Learning
• +7 more

Shahram Rahimi is a Professor and Head of the Department of Computer Science at The University of Alabama, College of Engineering. He also serves as Chief Advisor to the Alabama Cyber Institute, demonstrating leadership in both academic administration and cybersecurity research. His career includes prior roles as the Gloria & Douglas Marchant Endowed Chair Professor and department head at Mississippi State University. Education: Ph.D., Computational Sciences, The University of Southern Mississippi, NASA John C. Stennis Space Center M.S., Computer Science, The University of Southern Mississippi M.S., Computer Engineering Technology, The University of Southern Mississippi M.S., Computer Architecture, Sharif University of Technology B.S., Computer Engineering, Shahid Beheshti University Dr. Rahimi's research is centered on Artificial Intelligence , with deep expertise in Generative AI & LLMs, Machine Learning, Neural Networks, and Quantum Computing . His work bridges theoretical innovation and real-world applications in healthcare, public safety, and policy. He has developed influential systems such as Foresight , a game theory-based decision support tool for foreign policy, and EmTime/Symphony , an AI algorithm optimizing emergency room staffing used in over 1,000 hospitals. His research integrates computational intelligence, multi-agent systems, and predictive analytics to solve complex societal challenges. The analysis of his recent publications reveals a consistent focus on hybrid AI models, uncertainty reasoning (e.g., Z-numbers), predictive modeling in healthcare and transportation, and the integration of game theory with machine learning. His work increasingly explores quantum computing and generative AI, reflecting cutting-edge trends in computer science. These publications span top journals such as IEEE Transactions on Cybernetics and Expert Systems and Applications. Scientific Recognition: EmTime/Symphony recognized as one of the top 10 AI healthcare technologies by HealthTech magazine (2018) Editor-in-Chief of two major AI journals Member of editorial boards for several AI and computing journals Advisor to federal task forces on predictive analytics for policy and defense Dr. Rahimi has been principal investigator on over $25 million in federally and industry-funded research projects, reflecting strong grant acquisition and project leadership. He has advised numerous students and mentored researchers in AI and computational intelligence. He has organized 15 international conferences on computational intelligence and multi-agent systems, fostering global academic collaboration. He leads key initiatives including the Alabama Center for the Advancement of AI and collaborates with multiple research centers such as the Center for Advanced Public Safety and the Center for Sustainable Infrastructure. His lab work integrates interdisciplinary teams focused on intelligent systems, cybersecurity, and AI for social good.

Filippo BergamascoView profile

Associate Professor

• Computer Vision
• Oceanography
• Image Processing
• +3 more

Filippo Bergamasco is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. He is a member of the Scientific Committee of the Center for Studies on Port Economics and Management (DAIS). His work bridges computer vision, environmental monitoring, and oceanography, with a focus on developing advanced imaging techniques for ocean wave analysis and stereo reconstruction. He leads projects like the WASS pipeline, an open-source tool for 3D stereo reconstruction of ocean waves, and collaborates on studies involving rogue wave dynamics, low-cost sensors, and Arctic sea-state analysis. His research leverages machine learning, deep learning, and real-time systems to address challenges in environmental and marine sciences. Research interests include computer vision applications in oceanography, stereo imaging systems, wave dynamics modeling, and environmental monitoring technologies. He has contributed to over 80 peer-reviewed publications, with recent works focusing on real-time wave tracking, 3D reconstruction, and the integration of physics-driven models with neural networks. His expertise spans interdisciplinary collaborations, including projects on fish oil's impact on wind waves and the development of robust camera calibration methods. He actively publishes in top journals like IEEE Transactions on Image Processing, Ocean Modelling, and Physical Review Letters, and engages in international conferences such as ECCV and ICPRAM.