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Xiaoquan WenView profile

Professor

• Bayesian Statistics
• Computational Statistics
• Genetics
• +5 more

Xiaoquan Wen is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health, where he joined the faculty in 2011 after earning his PhD in Statistics from the University of Chicago. His research focuses on developing advanced Bayesian and computational statistical methods for genetics and genomics applications. His educational background includes a PhD in Statistics (2011) from the University of Chicago, an MS in Computer Science (2002), and an MS in Mathematics (2001), both from the University of Illinois. His research spans Bayesian model comparison, multiple hypothesis testing, probabilistic graphical models, and molecular QTL analysis, with applications in genetics and functional genomics. Wen's publication trends reveal consistent contributions to statistical genetics methodology, particularly in false discovery rate control, eQTL discovery, and integrative genomic analyses. His work increasingly emphasizes multi-omics integration and causal inference for complex traits, as evidenced by recent publications on metabolome-wide Mendelian randomization and multi-resolution genomic clustering. He is an active participant in the NIH GTEx project and affiliated with the Center of Statistical Genetics at the University of Michigan. His software contributions include BLIMP for imputation, SLAT for gene-level testing, and STRUCTURE for population genetics analysis. Wen teaches advanced courses including BIOS 699 (Design and Analysis of Biostatistical Investigations), BIOS 680/MATH 627 (Applications of Stochastic Processes), and BIOS 830 (Methods and Applications of Statistical Learning), demonstrating his commitment to statistical education.

• Cognitive Psychology
• Communication Studies
• Visual Perception
• +4 more

Dr. Hans Westerbeek serves as an Assistant Professor in the Language and Communication group within the Department of Languages, Literature and Communication at Utrecht University's Faculty of Humanities. His academic work bridges cognitive psychology, communication studies, and visual design with a particular focus on data visualization and visual communication. Westerbeek completed his PhD at Tilburg University in 2016 with a dissertation titled 'Visual realism: Exploring effects on memory, language production, comprehension, and preference.' His research career demonstrates a consistent trajectory from cognitive aspects of visual perception toward practical applications in information design and data communication. His research interests encompass cognitive psychology, communication studies, visual perception, visual design, and information visualization. Westerbeek's work particularly examines how visual elements affect comprehension, memory, and decision-making processes. His current research includes a Netspar Topicality project on visual communication of expected pension benefits and supervision of a PhD project addressing challenges in communicating about changes in the Dutch pension system. Analysis of his publication history reveals a strong focus on the intersection of visual design principles and cognitive processing. His work spans from fundamental research on visual perception and language production to applied studies on data visualization in domains including sports statistics, urban tourism, and financial communication. The recent shift toward pension communication demonstrates his ability to apply visualization principles to complex real-world communication challenges. As an educator, Westerbeek teaches CIS bachelor-level courses including CIW-lab and Rhetorics of Data Visualization, as well as the master-level Data Analysis and Visualization course. He also supervises theses and internships, mentoring students in the practical application of visualization principles. His technical expertise includes quantitative and qualitative research methods, statistical analysis, and programming skills in Bash, JavaScript, HTML, and Python. These technical capabilities support his research in creating and evaluating visual communication solutions across various domains.

• Computational Biology
• Machine Learning
• Computer Vision
• +3 more

Yixin Chen serves as Chair and Professor in the Department of Computer and Information Science at the University of Mississippi, holding dual Ph.D. credentials in Electrical Engineering and Computer Science. His educational background includes: B.S. in Electrical Engineering from Beijing Polytechnic University (1995) M.S. in Electrical Engineering from Tsinghua University (1998) M.S. in Electrical Engineering from the University of Wyoming (1999) Ph.D. in Electrical Engineering from the University of Wyoming (2001) Ph.D. in Computer Science from Pennsylvania State University (2003) Professor Chen's research demonstrates exceptional interdisciplinary breadth, with Computational Biology forming the dominant theme in recent work—particularly protein structure analysis, antibody interactions, and cancer genomics. His Machine Learning contributions span feature selection, classification algorithms, and deep learning optimization, while Computer Vision applications focus on medical imaging and industrial defect detection. Methodologically, he integrates statistical learning, graph-based models, and spatial relationship analysis to solve complex biomedical problems. Analysis of his publication trajectory reveals a strategic pivot toward bioinformatics since 2020, with protein structure methods (TSR-based approaches) dominating his highest-impact recent work. This complements sustained contributions to statistical learning (Gini correlation/distance methods) and computer vision (Faster R-CNN applications), creating a cohesive research program bridging theoretical algorithms and biomedical applications. As department chair, Professor Chen provides academic leadership. While specific advising details and grant information aren't provided in source materials, his prolific publication record across top venues indicates an active research program with significant real-world impact in medical diagnostics and industrial automation.

Panagiota BirmpaView profile

Assistant Professor

• Generative modeling
• Scientific Machine learning
• Uncertainty Quantification
• +4 more

Panagiota Birmpa is an Assistant Professor in the Department of Actuarial Mathematics & Statistics at Heriot-Watt University's School of Mathematical and Computer Sciences, Edinburgh. Her research bridges advanced mathematical theory with cutting-edge machine learning applications, focusing on uncertainty-aware methodologies for complex data systems. She actively supervises PhD students and maintains strong collaborative ties with international research groups in applied mathematics and computational science. Her academic credentials include: BSc+MSc (integrated master) in Applied Mathematics and Physical Sciences (majors in Analysis and Statistics) from National Technical University of Athens (NTUA), 2011 MSc in Pure Mathematics from National and Kapodistrian University of Athens (NKUA), 2014 PhD in Mathematics from University of Sussex, UK, 2018 (Thesis: Quantification of Mesoscopic and Macroscopic Fluctuations in Interacting Particle Systems) Dr. Birmpa's research program integrates theoretical mathematics with modern computational challenges through seven core domains: Generative modeling, Scientific Machine learning, Uncertainty Quantification, Probabilistic Graphical models, Interacting Particle Systems, Optimal transport Theory, and Partial Differential Equations. Her interdisciplinary approach enables innovative solutions for complex data analysis problems across scientific domains, particularly where traditional statistical methods face limitations in high-dimensional spaces. Analysis of her publication trajectory reveals a progression from foundational statistical physics (2017-2018 interface dynamics research) toward contemporary machine learning applications (2021-2024). Her recent work demonstrates increasing sophistication in merging deep learning architectures with uncertainty quantification frameworks, especially for scarce high-dimensional data scenarios where conventional approaches fail. This evolution reflects broader trends in mathematical data science toward robust, interpretable AI systems. No scientific awards or fellowships are currently listed in her professional profile. Dr. Birmpa accepts PhD candidates for projects exploring deep learning-graphical model interfaces with uncertainty quantification, building on her prior AFOSR-funded postdoctoral research at UMass Amherst (2021-2022). Her grant history includes significant support from the Air Force Office of Scientific Research for developing particle-based generative algorithms. She maintains active supervision of graduate researchers while pursuing methodological innovations in probabilistic modeling. Her collaborative research network spans multiple institutions including University of Massachusetts Amherst, with interdisciplinary teams developing novel mathematical frameworks for scientific machine learning. Current projects focus on Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional data, extending her earlier work on non-equilibrium fluctuations in particle systems.

• Social Data Science
• Digital Humanities
• Quantitative and Computational Social Science
• +4 more

Panagiotis Michailidis is a Professor at the Department of Balkan, Slavic and Oriental Studies, School of Economic and Regional Studies, University of Macedonia. His academic work focuses on Computational Methods and Informatics, integrating technological expertise with regional studies. BSc (1998) and PhD (2004) in Applied Informatics from University of Macedonia Academic career progression: Lecturer (2004-2013), Assistant Professor (2014-2019), Associate Professor (2019-2023), now Professor Research spans computational approaches to social science and humanities, with significant contributions to: Social Data Science Digital Humanities Quantitative Social Science Parallel/Distributed Computing String Matching Algorithms Scientific Computing Recent publications reveal interdisciplinary focus combining: Augmented Reality applications for cultural heritage Machine Learning for Greek language sentiment analysis High-performance computing implementations Metaheuristic optimization techniques Scientometric analyses of research fields Active in academic service through: IEEE memberships Conference program committee roles Guest editor for Algorithms special issue Research program participation Reviewing for multiple journals Teaching portfolio includes: DIGITAL SOCIAL RESEARCH METHODS INFORMATION, TECHNOLOGY AND SOCIETY QUANTITATIVE METHODS OF SOCIAL SCIENCES SPACE AND CULTURAL MANAGEMENT COMPUTER SCIENCE TOPICS

Jason PachecoView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Statistical Machine Learning
• +4 more

Jason Pacheco serves as an Assistant Professor in the Department of Computer Science at the University of Arizona, maintaining an office in GS 724. He earned his Ph.D. from Brown University in 2016 and specializes in theoretical and applied machine learning. His educational background includes: Ph.D. in Computer Science, Brown University (2016) Dr. Pacheco's research centers on statistical machine learning, probabilistic graphical models, and approximate inference algorithms, with emphasis on information-theoretic decision making. He bridges theoretical foundations with practical applications in cybersecurity, privacy-preserving AI, and environmental monitoring systems, developing novel approaches for robust sequential decision making under uncertainty. Analysis of his 15 most recent publications (2021-2025) reveals three dominant research thrusts: (1) Privacy-preserving machine learning, particularly federated learning and differential privacy for large language models; (2) Adversarial reinforcement learning for cyber defense and malware detection; and (3) Variational information-theoretic methods for mutual information estimation and sequential decision making. His work consistently integrates theoretical rigor with real-world applications in security and environmental science.

Lisovenko Iryna DmytrivnaView profile

Assistant Professor

• Parallel and Distributed Computing
• GPU Computing
• Automatic Program Parallelization
• +3 more

Lisovenko Iryna Dmytrivna serves as Assistant Professor in the Department of Computer Systems and Networks at Yuriy Fedkovych Chernivtsi National University since 2002, following her 2000 diploma in Computer Systems and Networks from the same institution. Her professional trajectory includes software engineering at a municipal medical institution (2000–2002) and ongoing contractual collaboration with Elogic Commerce since 2018. Her academic credentials include: Yuriy Fedkovych Chernivtsi State University, Faculty of Physics, Department of Computer Science, specialty "Computer Systems and Networks" (2000) Dr. Lisovenko's research centers on parallel and distributed computing, emphasizing automatic parallelization techniques, GPU-accelerated non-graphical computing, and structural analysis of program intermediate representations. She concurrently investigates specialized computer systems for data stream processing and develops methodologies for enterprise accounting automation, bridging theoretical computer science with business process optimization. Her scholarly output demonstrates sustained innovation in high-performance computing architectures. Analysis of her 15 most recent publications (2012–2021) reveals three dominant research thrusts: GPU-based parallelization (40% of works, including Kalyna encryption and bitonic sorting implementations), algorithmic optimization for diverse hardware (33%, covering sorting networks and structural matrices), and applied systems for real-world problems (27%, spanning smart home control, social media analysis, and enterprise automation). This evolution reflects increasing focus on practical GPU applications while maintaining core expertise in parallel algorithm design. She actively contributes to academic development through teaching Parallel and Distributed Computing, Economic and Legal Aspects of Enterprise Automation, and DevOps/DevNet courses, while authoring 12+ methodological manuals including "Parallel and Distributed Computing: Laboratory Workshop" (2022) and "System Administration of Linux OS" (2021). Her professional development includes certifications in Constitutional Law, Finance and Investment, DevNet Associate, and All Digital Week initiatives (2020–2021).

Romeil Singh SandhuView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Network Science
• +4 more

Romeil Singh Sandhu serves as Assistant Professor in Biomedical Informatics at Stony Brook University with adjunct appointments in Computer Science and Applied Mathematics & Statistics, directing the Laboratory for Imaging, Networks, and Control (LINC) from the Health Sciences Center. His academic credentials include: B.S. from Georgia Institute of Technology (2006) M.S. from Georgia Institute of Technology (2009) Ph.D. from Georgia Institute of Technology (2010) Dr. Sandhu's research integrates geometry, statistics, and control theory to advance computer vision (3D reconstruction, satellite pose estimation), network science (hypergraph dynamics, Ricci curvature), and systems biology (protein interaction networks, cellular robustness). His methodological innovations span level-set methods, variational techniques, and curvature-based network analysis with applications in medical imaging and aerospace systems. Analysis of his 2019-2023 publications reveals three dominant trajectories: (1) geometric network analysis using Ricci curvature to quantify biological network fragility; (2) distributed reinforcement learning with communication-efficient multi-agent actor-critic frameworks; and (3) medical/satellite image reconstruction via radar-based variational methods and active surfaces. These threads consistently leverage differential geometry to solve inverse problems in complex systems. The Laboratory for Imaging, Networks, and Control (LINC) develops computational frameworks bridging theoretical mathematics with healthcare and aerospace applications, particularly focusing on shape analysis, network dynamics, and control systems for medical diagnostics and satellite imaging.

• Data Visualization
• Visual Analytics
• Big Data Analysis
• +10 more

Yun Jang is a Professor in the Department of Computer Engineering at Sejong University , South Korea. His research spans Data Visualization , Visual Analytics , and Artificial Intelligence applications, with a focus on Spatiotemporal Analysis , Volume Rendering , and Human-Computer Interaction . Ph.D. in Electrical and Computer Engineering, Purdue University (2007) M.S. in Electrical and Computer Engineering, Purdue University (2002) B.S. in Electrical Engineering, Seoul National University (2000) His research interests include: Visual Analytics for Big Data Causal Analysis in Urban Traffic Virtual Reality and Cybersickness Measurement Interactive Volume Rendering EEG and Gaze Data Analysis Recent publications emphasize causal modeling in traffic analysis, volume rendering techniques with CNNs, and AI-driven applications for smart cities. He holds multiple Korean and US patents in traffic analytics, VR systems, and data visualization technologies. Notable scientific awards include the Daeyang Distinguished Professor Award and a Best Poster Award at PacificVis 2020 . He leads the Data Visualization Lab at Sejong University, supported by grants from Korean government agencies and industry partners.

Sang Il ParkView profile

Professor

• Computer Graphics
• Character Animation
• 3D Geometry Processing
• +4 more

Sang Il Park is a Professor in the Department of Software at Sejong University, South Korea, where he has been a faculty member since 2007 following postdoctoral work at Carnegie Mellon University's Robotics Institute (2005-2007) and a brief research position at Japan's Digital Human Research Center. Ph.D. in Computer Science, KAIST (2004) M.S. in Computer Science, KAIST (1999) B.S. in Computer Science, Yonsei University (1997) Professor Park's research centers on Computer Graphics , with deep expertise in Character Animation , 3D Geometry Processing , and Image Processing . His research fingerprint shows dominant activity in Motion Capture (100%), Transition Graphs (72%), and Motion Synthesis (72%). His work has evolved from foundational research in human motion capture to incorporating deep learning techniques for image restoration, demonstrating both consistency in core interests and adaptation to emerging technologies. His publication pattern reveals a sustained research program with significant outputs in 2008, 2010, 2014, 2018, and continuing through 2023. The research spans from character animation and motion synthesis to practical applications like document processing and weathering simulation, showing remarkable breadth within his specialized domain. h-index: 12 Total citations: 933 Presented pioneering research at ACM SIGGRAPH Referenced in 4 patents and 2 Wikipedia pages Professor Park leads the Computer Graphics Lab at Sejong University, where he mentors graduate students and conducts research in animation, modeling, rendering, and computational photography. His patent portfolio includes innovations in facial expression restoration and motion synchronization, demonstrating the practical impact of his research beyond academia.

Simone LentiView profile

Assistant Professor

• Cybersecurity
• Visual Analytics
• Human-Computer Interaction

Simone Lenti is an Assistant Professor (Ricercatore RTDa) at the Department of Computer, Control, and Management Engineering of Sapienza University of Rome. He is an active member of the A.WA.RE research group , which specializes in visual analytics. Lenti obtained his Ph.D. in 2021 with a dissertation on visual analytics techniques for cybersecurity. His research bridges cybersecurity , visual analytics , and human-computer interaction , focusing on: Developing computational methods for vulnerability analysis (e.g., NLP for CVE relevance, smart contract taxonomies) Designing visual tools for threat detection (e.g., attack graphs, firmware fuzzing) Enhancing interpretability in data-driven systems (e.g., partial dependence analysis, process mining) Lenti's publications (2019–2025) demonstrate a consistent focus on applying visual analytics to cybersecurity challenges , with recent expansions into bioinformatics and education. Key trends include automated vulnerability management, human-centered explainability, and scalable threat modeling. Awards: IEEE VizSec 2018 Best Paper for contributions to cybersecurity visualization. He contributes to academic infrastructure through tools like easyDeclare (declarative process modeling) and BUCEPHALUS (business-centric cybersecurity analysis), emphasizing practical applications of his research.

• Data Mining
• Data Science
• Data Analytics
• +4 more

Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.

• Cognitive Neuroscience
• Electroencephalography (EEG)
• Biosignal Processing
• +6 more

Clemens Brunner is a Researcher at the Institute of Psychology within the Faculty of Natural Sciences at the University of Graz. His work bridges electrical/biomedical engineering and cognitive neuroscience, specializing in the neural mechanisms of arithmetic processing and numerical cognition. He actively develops open-source neuroimaging tools used globally in EEG research and sleep analysis. His research focuses on EEG oscillations, biosignal processing, and machine learning applications in cognitive neuroscience. Key interests include arithmetic fact learning, neural correlates of numerical order processing, and non-invasive brain stimulation effects on mathematical cognition. His expertise spans Python programming, statistical analysis, and contributions to major scientific libraries like scikit-learn and SciPy. Analysis of his recent publications reveals consistent emphasis on electrophysiological signatures of arithmetic processing, with growing integration of computational methods and neuromodulation techniques. His work demonstrates strong methodological innovation through open-source software development for EEG analysis and sleep staging. Brunner contributes to the University of Graz's "Brain and behavior" research network, developing tools like MNELAB, SleepECG, and XDF.jl that enhance reproducibility in neuroscience. His collaborations extend to major projects including MNE-Python and BNCI Horizon 2020, advancing brain-computer interface methodologies and neuroimaging standards.

Benedikt WirthView profile

Professor

• Optimisation and calculus of variations
• image processing
• scientific computing
• +9 more

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

Simone Diniz Junqueira BarbosaView profile

Associate Professor

• Human-Computer Interaction
• Information Visualization
• Model-based Interactive Systems Design
• +7 more

Simone Diniz Junqueira Barbosa is an Associate Professor at the Informatics Department of Pontifical Catholic University of Rio de Janeiro (PUC-Rio). She leads the IDEIAS-SERG lab, focusing on Human-Computer Interaction (HCI) and Information Visualization research. Her work spans model-based interactive systems design, visual analytics, digital storytelling, and AI techniques for improving system usability and accessibility. Her research explores: Model-based approaches for interactive systems Data science and visual analytics for complex datasets Digital storytelling techniques Accessibility and usability enhancement through AI Semiotic engineering foundations for HCI Publication analysis reveals strong emphasis on HCI methodologies, data visualization literacy, ethical AI development, and applications in legal informatics/industrial automation. Recent works focus increasingly on explainable AI, visualization cognition, and ethical ML practices. Awards & Honors: IFIP TC13 Pioneer Award (2019) Outstanding HCI Career Award (Brazilian Computer Society, 2020) Selo de Inovação SBC Innovation Award (2022) IFIP Fellow Award (2024) She has advised over 45 graduate students (PhD/MSc) and coordinates multiple labs including DasLab, ExACTa, and Americanas Futuro Lab. Funded projects include CNPq, FAPERJ, Microsoft Research, and Petrobras collaborations focusing on HCI innovations and data visualization tools. Leads the IDEIAS-SERG research group merging semiotic engineering with interaction design. Serves on steering committees for ACM CHI and IFIP TC13, previously co-edited ACM Interactions Magazine (2016-2019).