Find Academic Supervisors

• Entomology
• Molecular Diagnostics
• Genetic Variation
• +3 more

Allen L. Szalanski is a Professor in the Department of Entomology and Plant Pathology at the University of Arkansas. His research focuses on molecular diagnostics and genetic variation of invasive insect species, particularly honey bees, termites, ticks, and agricultural pests. He teaches ENTO 4043/5043 Honey Bee Biology and Beekeeping. Education : PhD in Entomology (University of Nebraska-Lincoln, 1995), MS in Entomology (Kansas State University, 1992), BS in Agriculture (University of Manitoba, 1989) His work includes phylogenetic studies of termites using mitochondrial markers, population genetics of Amblyomma americanum ticks in Arkansas, and development of multiplex PCR assays for bed bug and trypanosomatid diagnostics. Recent studies examine Lotmaria passim in honey bees and expanded host ranges of invasive pests like Acanthococcus lagerstroemiae. Key collaborative efforts involve taxonomy revisions (e.g., Nasutitermes synonymy) and applied research on biological control agents such as Metarhizium species. He has mentored over 10 graduate students, many now in prominent roles in academia, USDA, and industry.

• Biomedical Informatics
• Cancer Genomics
• Computational Biology
• +3 more

Dr. Yi Qiao is a researcher in the Department of Biomedical Informatics at the University of Utah School of Medicine. His work focuses on integrating computational methods with genomic data to study cancer evolution, tumor subclonal dynamics, and precision oncology. Key contributions include developing tools like SubcloneSeeker, quickBAM, and Gene.iobio for analyzing genomic variants and clinical data. Education : PhD in Biomedical Informatics from Boston College BS from Zhejiang University Research Interests : Dr. Qiao's research bridges computational biology and clinical oncology. He develops innovative methods to dissect tumor heterogeneity using single-cell and bulk sequencing, with applications in drug response prediction and therapy selection. His work emphasizes understanding how subclonal populations evolve in cancers like leukemia and lung cancer, and how this informs targeted treatments. Key Projects : Framework for combining bulk DNA and single-cell RNA sequencing to study tumor expression (Genome Res, 2024) quickBAM API for high-throughput sequence analysis (Bioinformatics, 2023) ScreenDL deep learning model for drug response prediction (2024) Grants & Collaborations : His research is supported by collaborations with oncology teams and computational biology groups, focusing on translational applications of genomic data. Tools like Taxonomer and bam.iobio have advanced clinical diagnostics through real-time genomic analysis. Labs & Teams : He leads the Computational Oncology Lab, specializing in developing AI-driven tools for precision medicine. Active in cross-disciplinary teams at the University of Utah Health's cancer research initiatives.

Tara Maria BolandView profile

Research Fellow

• Computational Materials Science
• Materials Discovery
• Database Integration
• +3 more

Tara Maria Boland is a Postdoc in the Department of Physics at the Technical University of Denmark, specializing in Computational Atomic-scale Materials Design. She maintains an active research profile with significant contributions to materials science infrastructure. Her research spans critical areas of modern computational science: Computational Materials Science and Atomic-scale Modeling Materials Discovery and Design Database Integration and Data Exchange Systems API Development for Scientific Applications 2D Materials and Heterostructures Machine Learning Applications in Materials Science Boland's publication record reveals a dual focus on fundamental materials research and practical computational tools. Her work on the OPTIMADE API has established new standards for materials data exchange, while her contributions to GPAW provide essential infrastructure for electronic structure calculations. The research demonstrates strong international collaboration across 10+ institutions with notable media coverage. As part of DTU Physics' Computational Atomic-scale Materials Design group, Boland collaborates extensively on projects advancing computational methodologies for materials science, with particular emphasis on creating accessible research infrastructure for the global community.

• Education Policy
• Artificial Intelligence in Education
• Race and Computation
• +4 more

Prof. Kalervo N. Gulson is a Professor of Education Policy at The University of Sydney, affiliated with the Sydney Policy Lab and Sydney Centre for Healthy Societies. His work bridges social, political, and cultural geography with education policy studies, focusing on AI's transformative impact on educational institutions. He explores AI-driven policy challenges, racial equity in automated systems, and interdisciplinary approaches to reimagining education futures. Previously held roles include positions at the University of New South Wales (2011-2019) and the University of British Columbia (2006-2010). Gulson leads the Education Futures Studio , a collaborative space for developing innovative concepts and methods in AI policy research. His current research emphasizes democratizing AI policy through participatory frameworks and addressing algorithmic inequality. Research interests include education policy governance, AI ethics, race and computation, human geography, and science and technology studies. Notable grants include an Australian Research Council Discovery Project (2024-2026) on AI in education policy and collaborations with the James Martin Institute for Public Policy. Publications span books like Algorithms of Education (2022) and over 100 peer-reviewed articles. He advises doctoral students in related areas and advocates for technical democracy in education decision-making.

Catherine FaronView profile

Professor

• Artificial Intelligence
• Knowledge Management
• Semantic Web
• +8 more

Catherine Faron is a Full Professor at Université Côte d'Azur , affiliated with the I3S laboratory and Inria center . She serves as vice-head of the Wimmics joint research team and leads the Artificial Intelligence and Data Engineering (IAID) program at Polytech Nice Sophia engineer school. Habilitation à diriger les recherches (HDR) in Computer Science, UCA (2017) PhD in Computer Science, Univ. Paris 6 (1997) Her research focuses on Artificial Intelligence , particularly in Knowledge Representation and Reasoning (KRR) and Semantic Web technologies. She develops hybrid intelligent systems combining KRR with machine learning for knowledge extraction, integration, and exploitation across education, health, and digital humanities. Recent publications highlight her work on: 2025 : Knowledge graphs for historical zoological data 2024 : Semantic annotation frameworks in agronomy 2023 : Agricultural data mapping and medical record enrichment 2022 : Visual exploration of big linked data Scientific recognitions include: 2023: Best Paper Award, ESWC 2017: Scientific Excellence Award, UCA 2016: Best Demo Award, ISWC 2015: Best Paper Award, IC 2008: Best PhD Paper Award, ECPPM She has supervised 19 PhD/Master's students and leads/has led projects like D2KAB , DEKALOG , and ZOOMATHIA , with partnerships across academic and industrial institutions.

• Declarative Process Modeling
• Process Mining
• Formal Verification
• +4 more

Paul Cosma is a Postdoctoral Researcher at the Department of Computer Science (DIKU), University of Copenhagen, specializing in the Software, Data, People & Society (SDPS) section. His work focuses on declarative process modeling, formal verification, and explainable AI systems, with strong connections to process mining and Petri net theory. He completed his PhD at the University of Copenhagen's Faculty of Science in 2024 with a thesis on declarative process models as verifiable AI. His research interests center on declarative process modeling and formal verification of complex systems. Cosma develops techniques for improving model simplicity through nested group discovery and creates frameworks like BERMUDA for participatory mapping of domain activities to event data. His work bridges theoretical computer science with practical applications in business process management and AI explainability, emphasizing human-centered design principles where software systems are developed with societal impact in mind. Cosma's publication record shows a clear trajectory in process modeling research, with recent work focusing on transforming Dynamic Condition Response Graphs to Safe Petri Nets (2023) and improving declarative model simplicity (2024). His research demonstrates strong interdisciplinary connections between formal methods, AI verification, and human-computer interaction, particularly in making complex process models accessible and verifiable. Cosma actively collaborates with researchers including Thomas Hildebrandt, Tijs Slaats, and Axel Christfort, primarily within the SDPS section at DIKU. His work receives consistent citations in process mining literature, with his 2023 PETRI NETS paper accumulating 1 citation and his CAiSE 2024 paper gaining 2 Scopus citations. He maintains an ORCID profile (0000-0001-8022-6402) and contributes to open-access research through the university's Pure repository. Based in Sigurdsgade 41, Copenhagen N, Cosma operates within DIKU's collaborative research environment that emphasizes industry partnerships and interdisciplinary work. His recent PhD defense (June 13, 2024) marks his transition from doctoral candidate to postdoctoral researcher, positioning him to expand his contributions to process-aware information systems and verifiable AI.

Cameron BrownView profile

Associate Professor

• Pharmaceutical Manufacturing
• Crystallization Processes
• Digital Design Tools
• +4 more

Dr. Cameron Brown is a Reader (equivalent to Associate Professor) at the Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow. He specializes in developing digital design tools and strategies for pharmaceutical manufacturing. Brown joined Strathclyde in 2014 and has progressed through research associate, research fellow, and Chancellor's fellow positions. He currently coordinates the Drug Substance Manufacturing module for the Advanced Pharmaceuticals Manufacturing MSc program. Education: Brown holds a PhD in crystallization process characterization and a Chemical Engineering degree, both from Heriot-Watt University. Research Focus: His work centers on three primary areas: Hybrid modeling approaches : Integrating physics-based and data-driven models to enhance drug substance manufacturing efficiency Self-driving labs : Developing automated systems for drug substance process development with model-based experimental design Digital decision-making : Implementing coupled models through GenAI and LLMs for rapid pharmaceutical process development His research contributes to UN Sustainable Development Goals through improved medicine manufacturing sustainability. Publication Trends: Brown's recent articles focus on pharmaceutical crystallization, digital design methodologies, AI applications in manufacturing, and process optimization. His work demonstrates strong emphasis on translating computational models into industrial practice, particularly in continuous manufacturing and quality-by-design frameworks. Honors: Elected staff officer of British Association of Crystal Growth (2024) Research Leadership: Brown serves as Principal Investigator for PharmaCrystNet and co-investigator on multiple major initiatives including Digital Design and Manufacturing of Amorphous Pharmaceuticals, Future CMAC Manufacturing Hub, Accelerated Discovery and Development of New Medicines Prosperity Partnership, and ARTICULAR. He leads knowledge exchange projects with pharmaceutical companies and manages knowledge transfer partnerships. Professional Engagement: Brown is active in the Acceleration Consortium and serves on the committee of the British Association of Crystal Growth.

Evan BiederstedtView profile

Researcher

• Computational Biology
• Cancer Genomics
• Bioinformatics
• +3 more

Evan Biederstedt is an Associate in Biomedical Informatics at Harvard Medical School, affiliated with the Gehlenborg Lab. He holds a Master of Science (MSc) and specializes in computational biology with a focus on cancer genomics. His work includes developing the Cell Annotation Platform (CAP) to enhance cellular state interpretation through metadata and molecular signature analysis. Biederstedt’s research spans single-cell analysis, spatial transcriptomics, and genomic tools for structural variant discovery. His contributions include scaling cross-tissue annotation models and haplotype-aware somatic variation analysis. Key areas of focus include bioinformatics tool development, cancer genomics, and genomic data integration. He has collaborated on projects like the Virus Discovery Codeathon and international hackathons for genomic analysis. Biederstedt’s GitHub profile showcases his open-source contributions. His publications highlight advancements in tensor decomposition for patient cohort analysis, spatial transcriptomics, and pangenome strategies. Biederstedt’s work bridges computational methods with biomedical applications, emphasizing scalable solutions for complex genomic data challenges.

Stephen RamseyView profile

Associate Professor

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +6 more

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

• Software Engineering
• Probabilistic Programming
• Security and Penetration Testing
• +5 more

Jürgen Cito is an Associate Professor in the Department of Software Engineering at the Faculty of Informatics, TU Wien, where he leads research in probabilistic programming, security, and configuration management. His work is supported by major grants from the Austrian Science Fund (FWF), European Commission, and Meta Platforms, Inc., with active projects spanning 2022-2027. His research focuses on the intersection of software engineering and machine learning, particularly in static analysis of probabilistic programs, AI-driven penetration testing, and infrastructure security. Key contributions include identifying secret exposure in configuration files, grammar inference for ad hoc parsers, and performance prediction from source code, often combining empirical studies with tool development. Analysis of his 15 most recent publications (2020-2024) reveals three dominant trends: (1) Security vulnerabilities in configuration management systems, especially secret leakage in dotfiles; (2) Application of large language models to offensive security testing; and (3) Machine learning techniques for performance prediction and AutoML optimization in software contexts. Cito has supervised 22 Master's students on cutting-edge topics including AI security, infrastructure as code, and program analysis. His current research portfolio includes: Types4Strings (FWF, 2024-2027): Type systems for string processing Cloud Open Source Research Mobility Network (EU, 2023-2026): Open-source cloud infrastructure Software Assistants for Probabilistic Programming (Meta, 2022-2026): AI tools for probabilistic code He is embedded in TU Wien's Institute of Software Technology and Interactive Systems (E194), collaborating on cross-institutional projects focused on software security and developer tooling, with particular emphasis on empirical validation of security practices and configuration management systems.

Jianliang WuView profile

Assistant Professor

• Systems Security and Privacy
• Formal Analysis
• Machine Learning Security and Privacy
• +4 more

Jianliang Wu is an Assistant Professor in the School of Computing Science at Simon Fraser University. His research focuses on systems security and privacy, formal analysis, and machine learning security. He holds a PhD from Purdue University (2022), and MSc and BSc from Shandong University (2015 and 2012). His research interests include Bluetooth security, IoT privacy, and formal methods for protocol analysis. He teaches courses such as Network Security, Software Security, and Networks, including CMPT 403 (System Security and Privacy) and CMPT 479 (Special Topics in Computing Systems). His work spans both theoretical and applied cybersecurity, with a strong emphasis on real-world system vulnerabilities and mitigation strategies. Recent research highlights include systematic reviews of Bluetooth protection strategies, analyses of IoT data exposure via companion apps, and formal model-driven discovery of protocol design flaws. His contributions address critical challenges in wireless communication security and IoT ecosystem vulnerabilities. No scientific awards are explicitly mentioned in the provided text. Advising and grants details are not available here. He is affiliated with the School of Computing Science, which hosts labs like the Tangent Lab, though direct lab affiliations for Wu are not specified.

Massimo MecellaView profile

Professor

• Service-Oriented Computing
• Business Process Management
• Process Mining
• +12 more

Massimo Mecella serves as Full Professor at Sapienza University of Rome's Faculty of Information Engineering, Computer Science and Statistics, where he leads the Processes, Services and Software Management research group and participates in the CINI National Cyber Security Lab. His academic foundation stems from a PhD in Computer Engineering earned at Sapienza in 2002. His research expertise spans service-oriented computing, business process management, and cyber-physical systems with emphasis on service composition, process mining, and adaptive distributed architectures. Key technical domains include IoT integration, digital twin development, and security frameworks for complex software ecosystems. Recent publications (2024-2025) demonstrate a pronounced shift toward Large Language Model (LLM) integration in business processes and smart manufacturing. Dominant themes include LLM-driven process modeling, multimodal quality control systems, and digital twin implementations for energy management and production optimization. Methodological innovations focus on retrieval-augmented generation (RAG) for service discovery and fuzzy cognitive maturity assessments. His distinguished recognition includes: ICSOC 2013 Most Influential Paper Award (2003-2012 decade) 2017 Best Paper Award As research group leader, Mecella directs projects in smart manufacturing, process mining, and cyber-physical security, with funding typically sourced from EU initiatives and industry partnerships in aerospace, healthcare, and energy sectors. His team maintains active collaborations with aerospace manufacturers through the MICS SPOKE8 project and develops frameworks like SAMBA for human-in-the-loop manufacturing systems. The Processes, Services and Software Management group operates within Sapienza's engineering faculty infrastructure, utilizing specialized labs for CPS/IoT testing and digital twin simulation, with strong ties to the CINI Cyber Security Lab for security validation.

• Semantic Web
• Data Integration
• Ontology Matching
• +4 more

Dominique Ritze is a Research Fellow at the Data and Web Science Group of the University of Mannheim. Her research focuses on ontology alignment, semantic web technologies, linked open data integration, and knowledge organization systems. She collaborates with Prof. Dr. Christian Bizer and Prof. Dr. Kai Eckert on projects like InFoLiS II, aiming to advance data integration and semantic web applications. Education: MSc Computer Science (Diplom-Informatikerin) Research Interests: Dominique’s work bridges theoretical and applied aspects of semantic web technologies. Key areas include ontology evaluation frameworks, cross-domain data integration, and the development of tools for provenance tracking and data reuse. She has contributed to methodologies for aligning knowledge organization systems (KOS) and enhancing discovery systems with linked data. Publications Trends: Her articles from 2010-2015 emphasize ontology alignment (e.g., OAEI evaluations), semantic web applications, and data integration techniques. Notable contributions include the ICE-Map visualization for KOS evaluation and the Mannheim Search Join Engine for cross-website table integration. Awards: No scientific awards explicitly listed in the provided texts. Projects & Teams: Active in the Data and Web Science Group, leading projects on web table matching and semantic data integration. Collaborates with global research networks through initiatives like the Ontology Alignment Evaluation Initiative.

Jinqiu YangView profile

Associate Professor

• Software Engineering
• Automated Program Repair
• Software Testing
• +4 more

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on software reliability, automated program repair, software testing, and quality assurance of machine learning systems, particularly in autonomous vehicles. He holds a PhD and MASc from the University of Waterloo and a B.Eng. from Nanjing University. Yang has been a tenure-track faculty member at Concordia since 2018, following research roles at IBM Watson and IBM CAS. Research Interests: Automated Program Repair Software Testing Machine Learning Systems Text Analytics of Software Artifacts Mining Software Repositories Autonomous Systems Quality Assurance Key Contributions: His recent work includes detecting concept drifts in ML systems (ICSE-25) and investigating social bias in LLM-generated code (AAAI-25). He leads the O-RISA Lab and has authored over 50 peer-reviewed publications, including distinguished papers at MSR-2018. Yang currently holds grants such as the NOVA FRQNT-NSERC Program (2024-2027) and the NSERC Discovery Grant (2019-2025). Service & Awards: Editorial Board Member of the Empirical Software Engineering Journal (EMSE) and active PC member in top venues like ICSE and FSE. Recipient of the IBM CAS Fellowship and ACM SIGSOFT Distinguished Paper Award. Teaching & Students: He mentors students in Master's and PhD programs, with funding available. His lab focuses on cutting-edge topics like secure code generation and autonomous vehicle reliability.

Chengnian SunView profile

Associate Professor

• Software Engineering
• Programming Languages
• Compiler Testing
• +4 more

Chengnian Sun is an Associate Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. His research focuses on software engineering and programming languages with an emphasis on software reliability and programming productivity. Education : Ph.D. in Computer Science from National University of Singapore (2013) His work spans compiler testing (EMI, Dfusor, Kitten), program reduction (Perses, Vulcan, PPR), Android testing, and DNN testing. He has received multiple grants including Google Research Scholar Program (2025) and NSERC Discovery Grants (2024-2029). His recent publications focus on LLM-based compiler testing, weighted delta debugging, and ransomware resilience. Scientific Awards : Most Influential Paper Award at SANER (2022) NUS Research Scholarship (2008-2012) ACM SIGSOFT Distinguished Paper Award at ASE (2012) IBM Cup Campus Innovation Contest First Prize (2005) He advises Ph.D. and MMath students in software engineering, compiler testing, and program analysis, including several who have contributed to top-tier conferences like ICSE, ISSTA, and ASPLOS. His service includes program committee roles in ICSE, OOPSLA, and ISSTA.