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Quan NguyenView profile

Research Fellow

• Bioinformatics
• Computational Biology
• Biomedical Engineering
• +7 more

Dr. Quan Nguyen is a Senior Research Fellow at the Institute for Molecular Bioscience and affiliate of the School of Biomedical Sciences at The University of Queensland . As a National Health and Medical Research Council leadership fellow (EL2) , he leads the Genomics and Machine Learning (GML) Lab , specializing in single-cell spatial omics and machine learning for cancer and neuroinflammatory disease research. Education : PhD in Bioengineering (UQ, 2013), Postdoctoral Bioinformatics (RIKEN, 2015) Expertise : Integrating single-cell spatiotemporal data with population genomics to reconstruct gene regulatory networks and cell-cell interactions in situ. His research focuses on cancer ecosystems (melanoma, brain cancer) and neuroinflammation , using spatial transcriptomics and deep learning to uncover cell-type specific biomarkers for precision medicine . Key contributions include improving histological cancer diagnosis and identifying novel regulators of cell differentiation. His 15 most recent publications (2024-2025) span spatial omics in colorectal cancer , HPV-associated cervical neoplasia , neuroinflammation in aging, and machine learning methods for multi-omics integration . These works emphasize single-cell resolution , tissue context , and clinical translation . Scientific Awards : Australian Research Council DECRA fellowship (2019-2021) CSIRO OCE Research Fellowship (2016) IMB Fellow (2018) Predictive modeling tools for digital pathology in GigaScience (prize-winning) Supervision : Actively mentoring 10+ PhD students on topics like spatial diagnostics , cellular heterogeneity , and multi-omics data fusion . His lab develops open-source software and databases for single-cell analysis , supported by US Congressionally Directed Medical Research Programs and National Health and Medical Research Council grants.

• Scientific Software Engineering
• Accessibility
• Reproducibility
• +5 more

Sebastian Franz is a researcher at the Chair for Bioinformatics (Informatics 12) of the Technische Universität München (TUM). He focuses on scientific software engineering and biomedical data analysis, with a strong emphasis on accessibility, reproducibility, and open-source solutions. Teaches Problembasiertes Lernen Bioinformatik (PBL) (course number IN2343) in the Sommersemester 2025. Develops biomedical software tools such as Biocentral and Biotrainer , available on GitHub and as web applications. Supervises students in projects related to protein language models, Bayesian optimization, and data/model evaluation. His research spans software engineering for biomedical laboratories, protein language model distillation, and protein-protein interaction analysis. Projects under his supervision include deploying sustainable protein prediction models and integrating Bayesian optimization into biocentral. Collaborations include partnerships with companies like iteratec GmbH , where students work alongside industry professionals such as Paula Schlötermann, Christian Rack, and Tehseen Rug.

Fabio CalefatoView profile

Associate Professor

• Collaborative Software Engineering
• Social Software Engineering
• Mining Software Repositories
• +7 more

Fabio Calefato is an Associate Professor at the Department of Computer Science, University of Bari "A. Moro" since 2022, holding national habilitation as Full Professor in academic disciplines 01/B1 (since Dec. 2023) and 09/H1 (since Mar. 2025). He serves as CEO & co-founder of PeoplewareAI academic spin-off (2021-present), Rector's delegate of GARR network (2023-present), and Director's delegate for student traineeships (2023-present). Previously, he was Assistant Professor at the Department of Computer Science (2019-22) and at the Jonian Department, Taranto (2015-19), and conducted post-doctoral research at the Department of Computer Science (2007-08; 2010-14). Dr. Calefato received his Ph.D. in Computer Science in May 2007 from the University of Bari with a thesis titled "Supporting Synchronous Communication in Distributed Software Teams," which was listed on SIGSOFT Selected Ph.D. Dissertations in Software Engineering and published by VDM Publishing. He also holds an MSc in Computer Science from the University of Bari (2002). His research focuses on collaborative and social software engineering, mining software repositories and technical Q&A sites, SE4AI, psychometric and sentiment analyses from communication channels in the social programmer ecosystem, collaboration in online creative communities, community-driven knowledge sharing, and computer-mediated communication theories and tools. His work bridges software engineering with social psychology and AI, particularly examining how developers interact, collaborate, and express emotions in online platforms. Analysis of his recent publications reveals a strong trajectory toward AI integration in software engineering practices, with increasing focus on AutoML applications, sentiment analysis in developer communities, and the impact of generative AI on open-source development. His research spans both theoretical foundations and practical tools, with several software implementations including EMTk (Emotion Mining Toolkit), Apache-Big5 (developer personality profiler), and Pynblint (Jupyter notebook quality assurance). Best Paper Award at ICGSE'18 SIGSOFT Selected Ph.D. Dissertations in Software Engineering Dr. Calefato actively advises doctoral students, with Kelly Azevedo Borges Leal (current, researching AutoML for Software Engineering), Luigi Quaranta (graduated 2022, thesis on collaboration around computational notebooks), and Giuseppe Iaffaldano (graduated 2020, thesis on dynamics of online collaborative communities). His research has been supported through various grants enabling international collaborations with institutions including Carnegie Mellon University, University of Victoria, and TU Wien. He leads the COLLAB research group at the University of Bari, which develops tools and conducts empirical studies on collaborative software development. The group maintains several active GitHub projects including Apache-Big5, mini-IPIP test, EMTk, and pySOreputation, alongside experimental datasets used in multiple published studies. His academic spin-off PeoplewareAI focuses on applying AI to human aspects of software development.

• Neuroscience
• Digital Phenotyping
• Functional Brain Connectivity
• +4 more

Ana Triana Hoyos is a Researcher in the Department of Computer Science at Aalto University, Finland, holding a Contingent Worker position (T313) while serving as a Visitor (Faculty) within Professor Jari Saramäki's research group. Her work bridges computational methods with clinical neuroscience through advanced digital phenotyping and neuroimaging techniques. Her primary research investigates functional brain connectivity using fMRI combined with real-world behavioral monitoring, focusing on mental health disorders including depression, ADHD, and psychosis. She pioneers digital phenotyping methodologies through mobile sensing and develops analytical tools like the Niimpy behavioral data analysis toolbox, enabling longitudinal assessment of environmental and lifestyle impacts on neural function. Analysis of her 2019-2025 publications reveals consistent innovation in integrating neuroimaging with real-world behavioral data, particularly through multimodal longitudinal designs. Key contributions include establishing digital biomarkers for mood disorders, mapping connectivity alterations in psychiatric conditions, and developing rigorous methodological frameworks for fMRI preprocessing and behavioral data analysis. Scientific Awards: No awards were documented in the source materials. Advising and Grants: No student advisement records or grant funding details were specified in the available information. She actively contributes to Professor Saramäki's research ecosystem at Aalto University, collaborating with multidisciplinary teams spanning computer science, clinical psychiatry, and neuroscience to advance computational approaches in mental health research.

• Fog Computing
• Edge Computing
• Distributed Systems
• +5 more

Claudia Canali is an Associate Professor at the Department of Engineering "Enzo Ferrari" within the University of Modena and Reggio Emilia. Her work focuses on distributed systems, edge and fog computing, and gender equality in academia. She has extensively researched optimization algorithms for micro-service placement, load balancing, and resource allocation in fog and edge computing environments. Her publications address challenges such as latency reduction, energy efficiency, and network congestion in smart city applications. Recent studies include the development of next-generation collaborative research platforms like NextPyter, containerized Jupyter environments, and simulation tools such as ConSUMO. She also contributes to initiatives like the "Digital Girls" summer camp to reduce gender gaps in computer science education. Her peer-reviewed articles span theoretical models validated through simulation, practical implementations, and interdisciplinary studies on organizational behavior. Her work integrates technical solutions with social impact, particularly in multidisciplinary contexts.

• Human-Robot Interaction
• Autonomous Vehicles
• Driver Behavior Modeling
• +3 more

O. Siebinga is a Research Fellow at Delft University of Technology specializing in human-automation interaction within transportation systems. Their work bridges Industrial Design Engineering and Mechanical Engineering to model human driver behavior in autonomous vehicle contexts, with emphasis on communication-enabled traffic frameworks and safety-critical scenarios like highway merging. Education: Ir. (Engineering Degree) in Industrial Design Engineering PhD completed in 2024 with dissertation: Communication-Enabled Interactions in Highway Traffic: A joint driver model for merging Research focuses on decoding human-vehicle interaction dynamics through driving simulators and computational modeling. Key interests include conflict resolution in traffic, AI-driven behavior assessment, and human factors in automated systems. This interdisciplinary work integrates transportation engineering, cognitive science, and human-computer interaction to develop cooperative vehicle controllers that anticipate human behavior. Recent publications reveal a concentrated effort on highway merging behavior using the Communication-Enabled Interaction Framework. Studies combine experimental methods (coupled driving simulators) with computational models to analyze dyadic interactions, control inputs, and decision-making processes. A notable trend involves translating human communication patterns into formalized models for automated vehicle controllers. No scientific awards are documented in the available information. Collaborative research patterns indicate active participation in multi-investigator projects, though specific grants or student supervision details are unreported. Work consistently involves cross-disciplinary teams from TU Delft’s engineering faculties. Siebinga contributes to the JOAN framework—a virtual reality platform for human-automated vehicle interaction experiments—and develops open-source software for modeling traffic interactions, supporting reproducible research in intelligent transportation systems.

Dimitrije D. ČvokićView profile

Assistant Professor

• Bioinformatics
• Software Development
• Operations Research
• +5 more

Dimitrije D. Čvokić serves as Assistant Professor and Head of the Department of Computer and Information Sciences within the Faculty of Science and Mathematics at the University of Banja Luka. His academic leadership spans curriculum development across computer science and mathematics disciplines, with teaching responsibilities including Data Structures, Algorithms, Software Engineering, and Mobile Application Development. His research focuses on competitive hub location problems, bioinformatics tool development, and blockchain applications. Čvokić pioneered the integration of Linux-based bioinformatics tools like BioLinux and UGene into biology curricula, while developing novel optimization algorithms for Stackelberg games in logistics networks. His recent work explores large language model terminology standardization and blockchain-based property investment platforms. Analysis of his publication trends reveals a strong trajectory from theoretical optimization (2015-2020) toward applied computational solutions (2021-2024), with increasing interdisciplinary collaboration spanning public health, real estate, and linguistic computing. His work demonstrates consistent methodological rigor in operations research while adapting to emerging technological domains. As an academic advisor, Čvokić has mentored multiple thesis students including Aleksandar Vrhovac (Interval Arithmetic, 2023), Danijela Kovačević (Automatic Control Systems, 2023), and Nemanja Marjanović (CAPTCHA systems, 2022). His teaching innovations include introducing Python programming, RDKit cheminformatics tools, and BASH scripting into diverse science curricula across chemistry, biology, and geodesy programs. Čvokić established computational laboratories featuring specialized software environments including Chemistry Add-in for Word, ChemSketch, UGene, and NET Bio Extension for Excel. His cross-departmental collaborations have modernized computer science instruction across the university's technical and natural science programs through systematic integration of open-source tools and modern development frameworks.

Wim BouwmanView profile

Associate Professor

• Neutron scattering methods
• Spin-echo small-angle neutron scattering (SESANS)
• Soft condensed matter
• +4 more

Dr. Wim Bouwman is Associate Professor in the Neutron & Positron Methods for Materials section, Department of Radiation Science and Technology, Faculty of Applied Sciences, Delft University of Technology . Since 1997 he has led an interdisciplinary team that develops and applies novel neutron-scattering techniques—most notably spin-echo small-angle neutron scattering (SESANS)—to soft-condensed-matter and food-science challenges. He earned his PhD in 1993 at AMOLF/FOM, followed by post-doctoral stays at the Royal Institution of Great Britain, Risø National Laboratory and SRON, before joining TU Delft. His research portfolio spans: instrumentation & neutron optics (SESANS, SEMSANS, Larmor precession devices) method development for data acquisition and analysis (SasView integration) soft-matter physics of colloids, emulsions, gels and food proteins multiscale structure–function relationships in meat analogues, dairy and plant proteins Recent work (2020-2025) focuses on real-time SESANS monitoring of emulsion ripening , anisotropic structuring in soy-based meat alternatives , and chromatin organization in biological cells . He has co-authored >200 peer-reviewed papers since 2020 alone, and key publications appear in Langmuir , Food Hydrocolloids , J. Appl. Cryst. , Polymer and ACS AMI . Honours & awards J.B. Westerdijk Prize 2024 – exceptional contributions to education TU Delft Education Fellowship 2024 – curriculum reform in engineering education Teacher of the Year 2021, Faculty of Applied Sciences Education & outreach : He co-developed an open interactive textbook on electricity and magnetism (in Dutch), created extensive YouTube lecture series during the COVID-19 pandemic, and regularly delivers invited talks and workshops worldwide (e.g., PNCMI, Neutrons and Food, LENS). Research group & facilities : The team operates the SESANS instrument at the Reactor Institute Delft, collaborates with neutron facilities such as ISIS and Oak Ridge, and partners with industry (Shell, Unilever, food-tech start-ups) to translate fundamental insights into sustainable food-processing technologies.

Olivier FlückigerView profile

Researcher

• Programming Languages
• Compilers
• Just-In-Time Compilation
• +6 more

Olivier Flückiger is a Researcher affiliated with Google , with past involvement at Northeastern University. He actively contributes to programming language design, compiler optimization, and runtime system verification through publications in venues like PLDI, POPL, and SPLASH. Current affiliation: Google Past affiliation: Northeastern University Committee roles in ECOOP, PLDI, VMIL, and SPLASH Research Interests : Just-In-Time Compilation and Speculative Optimization Function Specialization and Runtime Monitoring Lazy Evaluation and Intermediate Representations Security Implications of Compilation Techniques Language Implementation for Dynamic Languages Compiler Verification and Deoptimization Mechanisms Trends in Publications : His work focuses on hybrid dynamic verification, on-stack replacement, and type feedback mechanisms, emphasizing correctness in speculative optimizations and language migration challenges (e.g., R to strict semantics). Conference Leadership : Co-chair for ICOOOLPS 2021 Program Committee for VMIL, MPLR, and SPLASH tracks Artifact Evaluation Committee for ECOOP Volunteer and session chair roles in academic conferences

Andreas KochView profile

Researcher

• Computational Modeling
• Compressible Multiphase Flows
• CutDG Methods
• +3 more

Andreas Koch is a Research Associate at the Professorship of Simulation for Additive Manufacturing at the Technical University of Munich (TUM), Germany. His research focuses on computational modeling of compressible multiphase flows, CutDG methods, and high-performance computing with software development. He holds a Master of Science (M.Sc.) in Mechanical Engineering from TUM (2024). Research Interests Koch's work bridges computational mechanics and machine learning, with emphasis on cut discontinuous Galerkin methods for simulating complex flows and high-performance computing systems. His publications demonstrate expertise in machine learning applications for aerospace systems, including anomaly detection in spacecraft telemetry and deep learning acceleration for spaceborne hardware. Scientific Awards ERC Starting Grant Publications (2024-2013) His research portfolio includes 15 recent publications spanning edge AI solutions for spacecraft , RF synchronization systems, and machine learning frameworks for aerospace applications. Earlier work explores robot perception and geoinformatics implementations.

• Software Engineering
• Artificial Intelligence
• Data Mining
• +3 more

Timothy Menzies is a full Professor in the Department of Computer Science at North Carolina State University and director of the RAISE (Real World AI for SE) lab. His work bridges software engineering (SE) and artificial intelligence (AI), emphasizing the application of SE principles to enhance AI systems. Research Focus: Software engineering for AI, data mining, search-based SE, and open access science. Leadership: Co-founder of the PROMISE conference series on reproducible experiments in SE, and has held editorial roles in top-tier journals including IEEE Transactions on Software Engineering. Funding: Led projects funded by NSF, NIJ, DoD, NASA, USDA (totaling over $12 million), and collaborated with private companies. Recognition: IEEE Fellow, reflecting his contributions to SE and AI. Previous Roles: Served as Software Engineering Research Chair at NASA's Independent Verification and Validation Facility (2002-2004).

Radim HladíkView profile

Researcher

• Quantitative Science Studies
• Digital Humanities
• Text Analytics
• +9 more

Radim Hladík is a Researcher at the Centre for Science, Technology, and Society Studies within the Institute of Philosophy of the Czech Academy of Sciences in Prague. He serves as the coordinator of the LINDAT/CLARIAH-CZ working group , an editor of the Theory of Science journal , and the chair of the Czech Association for Digital Humanities . PhD in Sociology (Charles University, 2011) MA in Media Studies (Charles University, 2006) Fulbright Student Fellow (Columbia University, 2009–2010) JSPS Postdoctoral Fellow (National Institute of Informatics, 2017–2019) His research focuses on quantitative science studies , text analytics , digital humanities , and scholarly communication , with a particular interest in how science funding and open science principles shape research practices. He develops open-source software Requal for qualitative data analysis and advocates for computational methods in social sciences. The recent publications highlight his work in topic modeling , scientometric analysis , and reproducibility frameworks . He also organizes workshops on enhancing qualitative analysis reproducibility and contributes to software development for academic research. Scientific Awards : Fulbright Student Fellow (2009–2010) JSPS Postdoctoral Fellow (2017–2019) Hladík has previously held roles as scientific secretary at IP CAS and executive editor of the Theory of Science journal. He has taught at Charles University , Film and TV School of Academy of Performing Arts , Czech Technical University , and University of Finance and Administration , covering topics like post-socialism , collective memory , and social media studies .

• Machine Learning
• Computer Vision
• Bioinformatics
• +4 more

Adriana Birlutiu is a Lecturer in the Computer Science Department at 1 December 1918 University of Alba Iulia , Romania. Her expertise lies in machine learning, computer vision, bioinformatics, and transfer learning, with a recent focus on porcelain-industry optimisation. Education Ph.D., Radboud University Nijmegen, Netherlands (2011) M.Sc., Babeș-Bolyai University of Cluj-Napoca & University of Lorraine (Erasmus), 2005 B.Sc., Babeș-Bolyai University of Cluj-Napoca, 2004 Research Interests Adriana's research spans machine learning , deep learning , computer vision , and bioinformatics . She has contributed to preference learning, domain adaptation, protein–protein interaction prediction, and automated quality control in porcelain manufacturing. Her recent projects integrate deep neural networks with industrial computer-vision systems to detect defects and recognise characters on ceramic surfaces. Publication Trends Across 15 recent publications (2010-2019), Adriana has consistently explored transfer learning , multi-task learning , and Bayesian methods . Articles cluster around two major axes: biomedical applications (protein networks, cancer relapse prediction, respiratory-motion modelling for radiotherapy) and industrial AI (porcelain defect detection, character recognition). The work shows a clear evolution from theoretical machine-learning foundations to practical, domain-specific implementations. Grants & Projects SIVAP (2016-2018): Intelligent ML & computer-vision system for porcelain manufacturing optimisation, UEFISCDI PN-III-P2-2.1-BG-2016-0333. CMRCC (2017-2018): Computational Models for Reproducing Ceramics Colors, UEFISCDI PN-III-P2-2.1-PED-2016-1835. Student Supervision & Mentoring Adriana has supervised more than 25 undergraduate and master’s theses. Her students have won multiple awards at national conferences such as In-Extenso and SCCSS-IEECC , covering topics from automated defect detection to web applications for academic scheduling. Teaching Responsibilities She teaches courses including Machine Learning , Mathematical Software , Fundamental Algorithms , Object-Oriented Databases , and Modelling and Simulation at both undergraduate and master levels.

Pasquale Domenico ColaianniView profile

Researcher

• Mass Spectrometry
• Metabolomics
• Lipidomics
• +5 more

Pasquale Domenico Colaianni serves as Head of Research Software Engineering at the Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark (DTU), where he leads computational infrastructure development for biosustainability research. His work bridges software engineering and life sciences through advanced data processing solutions. His research specializes in mass spectrometry data engineering, with core expertise in metabolomics, lipidomics, and fluxomics. He develops reproducible computational workflows for LC-HRMS and LC-MS/MS data analysis, emphasizing automated processing systems that enhance scalability in large-scale biological studies. Key focus areas include data reproducibility frameworks and high-resolution mass spectrometry applications in life sciences. Recent publications demonstrate a clear trajectory toward open-source, scalable software tools for mass spectrometry data. The 2024 Nature Methods paper on OpenMS 3 establishes new standards for reproducible large-scale analysis, while the 2020 Analytical Chemistry article on SmartPeak advances automated metabolomics processing. Both works reflect his commitment to solving computational bottlenecks in quantitative biological data interpretation. He directs the Research Software Engineering group at DTU's Center for Biosustainability, managing cross-disciplinary teams that develop critical infrastructure for metabolomics and proteomics research. This includes maintaining OpenMS and SmartPeak ecosystems used globally for chromatography-mass spectrometry data analysis.

• Statistical Computing
• Machine Learning
• Biostatistics
• +4 more

Michael Kane is an Assistant Professor Adjunct in the Biostatistics Department at Yale University's School of Public Health and an associate research scientist at the Yale Center for Analytical Sciences. His work focuses on statistical and machine learning methods applied to biomedical problems, particularly in clinical trials and human mobility analysis. PhD in Biostatistics, Yale University (2010) MA in Biostatistics, Yale University (2006) MS in Electrical Engineering, Rochester Institute of Technology (2003) MS, Rochester Institute of Technology (2003) BS in Computer Engineering, Rochester Institute of Technology (2000) Dr. Kane's research centers on high-performance statistical computing and graphical models with applications in biomedicine. He develops methods to understand patient-level heterogeneity in clinical trials and to analyze patterns of human mobility. His work spans computational statistics, machine learning, and their applications in healthcare data analysis, with particular emphasis on reproducibility and innovative trial designs. His recent publications demonstrate a strong focus on innovative statistical methodologies for clinical trials, including Bayesian approaches, mobility network analysis, and reproducible research practices. Kane frequently collaborates with researchers in gerontology and clinical epidemiology, particularly on fall injury prevention studies. His work bridges traditional biostatistics with modern computational approaches, including applications of large language models to healthcare data. John M. Chamber's Statistical Software Award from the American Statistical Association Dr. Kane actively collaborates with numerous researchers at Yale, including Denise Esserman, Wei Wei, David Ganz, Daniel Zelterman, and Erich J Greene, among others. His methodological work supports various clinical studies, particularly those involving complex trial designs and innovative data collection approaches. As part of the Yale Center for Analytical Sciences, Dr. Kane contributes to the development of analytical approaches for clinical research, working within a team that provides statistical support for various biomedical investigations.