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Devin SilviaView profile

Teaching Professor

• Computational Science
• Astrophysics
• Data Science Education
• +3 more

Devin Silvia is a Teaching Professor in the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University, affiliated with both the College of Engineering and College of Natural Science. His work bridges computational science, astrophysics, and education, focusing on integrating computational tools into STEM education and advancing astrophysical simulations. Research interests include computational pedagogy, data science curriculum design, and astrophysical phenomena like star formation and galaxy evolution. His contributions to software development include packages like yt (volumetric data analysis), ENZO (astrophysical simulations), and Trident/SALSA (synthetic spectra). Recent articles highlight efforts in computational essays for student agency, interdisciplinary education frameworks, and cosmological feedback mechanisms. No scientific awards are explicitly mentioned in the texts provided. Advising and grant details remain unreported, though involvement with ENZO and Trident projects suggests active collaborations in astrophysical simulation communities. Labs/teams: Core contributor to ENZO and Trident simulation frameworks, engaged in collaborative efforts to model galactic and intergalactic environments through computational tools.

Mingxia LiuView profile

Assistant Professor

• Machine Learning in Medical Imaging
• Neuroimaging Analysis
• Alzheimer’s Disease Research
• +5 more

Mingxia Liu, PhD, is an Assistant Professor in the Department of Radiology at the University of North Carolina School of Medicine and Co-Director of Radiological Sciences Faculty Development. She specializes in advancing machine learning technologies for automated brain disease analysis, particularly using MRI/PET modalities. Her work focuses on clinical applications of neuroimaging analysis, including Alzheimer’s disease, MCI, and autism. Dr. Liu leads the UNC Imaging Analysis Group and has developed groundbreaking frameworks for disease diagnosis and prognosis. Dr. Liu holds a PhD from Nanjing University of Aeronautics & Astronautics and completed a postdoctoral fellowship at UNC. Her research integrates computational methods with clinical neuroimaging, emphasizing multimodal data fusion and domain adaptation techniques to address challenges in heterogeneous datasets. Her research interests include: Machine learning for brain disorder classification Functional connectivity analysis using fMRI Domain adaptation in medical imaging Federated learning for multisite studies AI-driven clinical decision support systems Her recent publications highlight advancements in self-supervised learning, graph neural networks, and hybrid multimodal frameworks, with applications to cognitive decline prediction and neurodegenerative disease modeling. These innovations aim to improve early diagnosis and personalized treatment strategies. Dr. Liu has been recognized with prestigious awards including the MICCAI Young Scientist Award (Honorable Mention) and multiple doctoral dissertation honors. Her work bridges cutting-edge AI techniques with translational neuroimaging research, fostering interdisciplinary collaboration in healthcare technology.

• Molecular Dynamics
• Condensed Matter Physics
• Biophysics
• +3 more

Raffaello Potestio is an Associate Professor in the Department of Physics at the University of Trento and serves as Coordinator of the Doctoral Course in Physics. His research focuses on molecular dynamics, soft matter physics, and computational modeling of biological systems. He teaches courses such as Physics 2 (electromagnetism fundamentals), Multi-scale Methods in Soft Matter Physics, and Statistical Mechanics, emphasizing theoretical and computational approaches. His work integrates advanced simulation techniques, including adaptive resolution methods, to study complex systems like proteins, DNA, and viral capsids. He has contributed to tools like EXCOGITO for coarse-grained modeling and explored topics ranging from knot interactions in polymers to chromatin mechanics in diseases. His research bridges theoretical physics with applications in biophysics, materials science, and epidemiology. Education details are not explicitly provided in the text. His teaching activities span undergraduate and graduate levels, with a focus on equipping students with analytical and computational skills for modern physics challenges. His publications highlight contributions to multiscale modeling frameworks, protein dynamics, and data-driven analysis of mobility patterns during the pandemic. Collaborations involve interdisciplinary teams addressing both fundamental and applied questions in physics and biology. Labs and teams associated with his work include computational physics groups at the University of Trento, though specific lab names are not mentioned. His research emphasizes the development of novel algorithms and their application to diverse systems, reflecting a commitment to advancing both methodological and applied aspects of theoretical and computational physics.

Armin LechlerView profile

Researcher

• Automation Technology
• Industrial Robotics
• Control Systems
• +4 more

Armin Lechler , holding the title of Dr.-Ing. , is a Senior Researcher at the Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) at the University of Stuttgart. He is also a key member of the Cluster of Excellence IntCDC (Integrative Computational Design and Construction for Architecture). His work focuses on control engineering, automation technology, and robotics, with a particular emphasis on data-driven manufacturing systems and cyber-physical platforms.

Yuming SunView profile

Assistant Professor

• Biostatistics
• Statistical Learning
• Survival Analysis
• +7 more

Yuming Sun is an Assistant Professor in the Department of Mathematics at the College of William & Mary. His work bridges statistical methodology and applications in biomedical and public health domains, with a strong emphasis on developing robust models for complex health data. Dr. Sun's research focuses on statistical learning, survival analysis, causal inference, and non-/semi-parametric modeling . He applies these methods to clinical datasets, electronic health records, and medical images, contributing to predictive analytics in healthcare. His work often involves high-dimensional and longitudinal data, requiring innovative integration of traditional statistics with modern machine learning. The recent publications reflect a consistent trend toward integrating deep learning with classical survival models , especially in medical imaging and environmental health contexts. His research spans prognostic modeling for diseases like COVID-19 and assessing population-level responses to environmental stressors such as wildfires and air pollution. While no formal scientific awards are listed in the provided text, his active research output and software development indicate strong scholarly engagement. Dr. Sun advises graduate students in statistics and biostatistics, though specific names are not listed. His research is likely supported by grants related to health data science, though specific funding sources are not mentioned. He maintains an active presence in open science through GitHub, sharing code and analyses. He is involved in research teams or labs focused on biomedical data science, possibly within or affiliated with the Department of Mathematics, where he develops computational tools for health applications.

Thanh Thai NguyenView profile

Assistant Professor

• Commutative Algebra
• Algebraic Geometry
• Computational Algebra
• +7 more

Thanh Thai Nguyen is an Assistant Professor in the Department of Mathematics at the University of Dayton, part of the College of Arts and Sciences. He joined the faculty in 2025 after serving as a Britton Postdoctoral Fellow at McMaster University and completing his Ph.D. at Tulane University in 2022 under Dr. Tài Huy Hà. Institution: University of Dayton School: College of Arts and Sciences Department: Department of Mathematics Position: Assistant Professor Education Ph.D., Tulane University, 2022 Thanh Thai Nguyen specializes in commutative algebra with a focus on computational and combinatorial aspects, and its interactions with algebraic geometry and combinatorics. His research centers on asymptotic behavior of algebraic invariants, symbolic powers of ideals, polynomial interpolation (including Chudnovsky’s and Demailly’s conjectures), and the algebra and combinatorics of monomial and binomial ideals. He has made significant contributions to understanding resurgence numbers, Waldschmidt constants, and the structure of Rees algebras. The most recent articles demonstrate a strong trend in linking combinatorial structures (such as simplicial complexes and matroids) with algebraic invariants, exploring duality in asymptotic behavior, and developing computational tools for algebraic geometry. His work often involves deep interplay between convex geometry, commutative algebra, and combinatorics, particularly in the context of graded families of ideals and their symbolic powers. Scientific Awards No scientific awards or honors were mentioned in the provided text. Advising and Grants There is no information available about students advised by Thanh Thai Nguyen or any grants he has received. However, given his active publication record and postdoctoral background, it is likely that he is involved in research mentoring and grant-funded projects, though specifics are not disclosed in the source material. Labs and Research Groups Nguyen was previously affiliated with the Combinatorial Algebraic Geometry group at McMaster University. At the University of Dayton, no specific lab or research group is mentioned, but his research profile suggests he is building a program in computational and combinatorial commutative algebra.

• Human Activity Recognition (HAR)
• Wearable Computing
• Sensor-based Systems
• +3 more

Paul Lukowicz is a distinguished researcher affiliated with the University of Kaiserslautern and DFKI, Kaiserslautern, Germany. His work focuses on Human Activity Recognition (HAR) using wearable sensors, Augmented Reality for surgical navigation, and Quantum-Inspired Computing for AI efficiency. He leads interdisciplinary projects involving capacitive sensing , bio-impedance , and large language models (LLMs) in real-world applications. Research Trends from his recent publications (2024-2025) reveal a focus on Energy-efficient AI for wearables Context-aware AR systems Physics-informed neural networks Quantum machine learning Human-in-the-loop training frameworks Medical device innovation His work spans health applications , industrial IoT , and human-AI collaboration , often involving partnerships with institutions like University of Passau and international collaborators. Key technical approaches include sensor fusion , cross-modal learning , and edge computing for wearable systems.

• Business Analytics
• Computer Information Systems
• Predictive Analytics
• +5 more

Richard V. McCarthy is a Professor of Business Analytics and Information Systems and Associate Dean for the School of Business at Quinnipiac University. His work bridges academia and industry through predictive analytics, data management, and virtual team pedagogy. BS, Central Connecticut State University MBA, Western New England College DBA, Nova Southeastern University Dr. McCarthy's research focuses on healthcare analytics, financial fraud detection, and data science pedagogy. He applies machine learning to prevent post-surgical falls, combat money laundering, and enhance educational strategies. His publications emphasize predictive modeling techniques, including decision trees, neural networks, and regression analysis, across healthcare, finance, and information systems domains. Computer Educator of the Year, International Organization for Computer Information Systems (2019)

• Computer Science
• Software Engineering
• Artificial Intelligence
• +2 more

Maria Eckes-Kondak is an Assistant Professor at AGH University of Science and Technology in Krakow, Poland. Her academic affiliations are structured as follows: University: AGH University of Science and Technology School: Faculty of Computer Science Department: Institute of Computer Science Her research spans core domains of Computer Science with emphasis on applied methodologies. Key interests include: Software Engineering principles and practices Artificial Intelligence algorithm development Data Science analytics frameworks Information Systems architecture Computer Science theoretical foundations No scientific awards or honors were documented in the source material. Available information does not specify student supervision activities, research grants, or collaborative funding initiatives. Similarly, laboratory affiliations, research teams, and future project directions remain unmentioned in the provided text.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +10 more

Dr. Tolga Berber is an Assistant Professor at the Faculty of Science, Karadeniz Technical University . With a PhD in Computer Engineering from Dokuz Eylül University, his academic career spans over 20 years, including roles as Deputy Head of Department (2013–2023) and extensive research in Computer Sciences, Artificial Intelligence, and Medical Informatics .

• Scientific Visualization
• Data Visualization
• Computer Graphics
• +4 more

Pere Pau Vázquez Alcocer is an Associate Professor in the Department of Computer Science at Universitat Politècnica de Catalunya (UPC), affiliated with the ViRVIG research group and the School of Informatics (FIB). With a PhD in Software from UPC (2003), he specializes in Scientific Visualization , Data Visualization , and Virtual Reality applications. His work bridges Computer Graphics with Medical Data Visualization and Molecular Visualization , emphasizing AI applications to visualization . His research spans two decades, focusing on Interactive volume rendering VR-based biomedical analysis Perceptual optimization in visualizations Urban mobility data modeling with recent publications in Computer Graphics Forum , IEEE CG&A , and arXiv on AI-driven visualization frameworks and molecular interaction techniques. His 15 most recent publications (2025-2023) demonstrate trends in AI integration for data visualization, immersive analytics for biomedical applications, and optimization of rendering techniques on mobile and VR platforms, with keywords spanning Computer Science , Neuroscience , and Urban Planning . Awarded the Best PhD thesis (2003) and Best Paper at international conferences, he has supervised 8 PhD students including Pedro Hermosilla (now Assistant Professor at TU Wien) and Jesús Díaz (now Associate Professor at Universitat de Vic). Teaching for over 20 years at UPC across five schools, he currently leads courses in Data Visualization , Scientific Visualization , and Virtual Reality .

Agron ÇaushiView profile

Associate Professor

• Economic Sciences
• Information Systems
• E-Government
• +6 more

Agron Çaushi is an Associate Professor at the Faculty of Business and Economics, South East European University, Tetovo, North Macedonia. With a PhD in Economic Sciences (2017) and an MSc in Information Systems (2007), his work bridges economic theory, information systems, and digital transformation. His career has focused on e-Government, business analytics, and AI applications in education and organizational efficiency. Education: PhD in Economic Sciences (2017), Master of Science in Information Systems (2007), Bachelor of Arts in Business Administration (2005) Research interests include the socio-economic impact of digital technologies, ERP systems, AI ethics in education, and sustainable development through digitalization. Recent publications analyze AI's role in higher education, robotics in renewable energy, and e-Government maturity models. His work combines empirical studies with comprehensive reviews, often collaborating with regional institutions. Publications span topics like ERP evolution , AI in education , cloud healthcare , and robotics , reflecting his interdisciplinary approach. Co-authored works frequently address policy implications and technological integration in public and private sectors.

• Facial Expression Analysis
• Machine Learning
• Image Processing
• +4 more

Academic Overview Kristin Surpuhi Benli currently serves as an Assistant Professor in the Department of Software Engineering at Uskudar University's College of Engineering. With a research focus spanning facial expression analysis, machine learning applications, and data mining in medical contexts, her work bridges computational methods with interdisciplinary applications. Education Undergraduate Degree: Computer Engineering, Işık University (2005) Master Degree: Computer Engineering, Işık University (2007) Doctorate: Computer Engineering, Işık University (2013) Research Interests Her research spans multiple dimensions of computational science: Developing anatomically-informed facial expression recognition systems Applying machine learning to medical prognosis through blood test analysis Investigating programming self-efficacy in computer science education Exploring AI adoption in neuroscience and clinical decision-making Implementing image processing techniques for brain tumor detection Academic Contributions Dr. Benli has contributed to diverse research areas through her publications, including: Classification algorithms in stroke mortality prediction BERT-based stance detection in social media analysis Neuroscience applications of artificial intelligence Anatomical modeling for facial expression recognition Biometric data analysis through nearest neighbor methods Administrative Roles Deputy Head of Research Center (YAZAMER), since 2025 Member of Quality Commission (Faculty level), since 2023 Board Member of Faculty Administrative Council, since 2020 Deputy Head of Software Engineering Department, since 2019 She also maintains active student advisory roles and participates in various academic commissions.

Jacob O. WobbrockView profile

Professor

• Human-Computer Interaction
• Accessible Computing
• Mobile Computing
• +5 more

Professor Jacob O. Wobbrock is affiliated with the University of Washington , where he holds a position in the Information School and a courtesy role in the Paul G. Allen School of Computer Science & Engineering . He co-founded the DUB Group and MHCI+D degree , and serves as Interim Director of the MHCI+D program. As director of the ACE Lab and Founding Co-Director Emeritus of the CREATE Center , he leads research in human-computer interaction (HCI) and accessible computing . Education : B.S. in Symbolic Systems (1998), M.S. in Computer Science (2000), Ph.D. in Human-Computer Interaction (2006) Research Focus : Input techniques, performance modeling, mobile and accessible computing, VR, and gesture recognition Awards : ACM Fellow (2021), CHI Academy (2019), NSF CAREER (2010), ICMI/ASSETS/UIST Lasting Impact Awards His 15 most recent articles (2023-2025) explore AI-powered accessibility solutions, tactile 3D modeling, mobile reading adaptations, gesture recognition for motor impairments, and screen reader integration. These works span human-computer interaction , artificial intelligence , biomedical data analysis , and inclusive design in both academic and industry contexts. Scientific honors include the SIGCHI Social Impact Award and AMiner Most Influential Scholar rankings. As an entrepreneur , he co-founded AnswerDash (acquired by CloudEngage) and has contributed to patents and expert witness cases. His teaching and advising have produced doctoral graduates now at institutions like Harvard and CMU, though specific student names are not listed in this profile.

Diarmuid O'DonoghueView profile

Assistant Professor

• Computational Creativity
• Analogical Reasoning
• Natural Language Processing
• +4 more

Diarmuid O'Donoghue is an Assistant Professor in the Faculty of Science & Engineering at Maynooth University, specializing in Computational Creativity and Analogical Reasoning . His research explores topological similarities between text and source code to develop cognitively inspired systems for problem-solving and bias detection. Co-PI of the Modelling implicit bias project (€21/FFP-P/10118) Senior Scientific Coordinator for the €2.6M EU-funded Dr Inventor project Key research areas include: Latent homomorphism detection in lexical data Comparative analysis of LLMs and analogical systems Formal specification generation from code/text His work has shaped undergraduate project frameworks with ethical GenAI integration . Publications span ICCC , GECCO , and journals like Artificial Intelligence Review .