Find Academic Supervisors

John-Paul OreView profile

Assistant Professor

• Robot Software Reliability & Testing
• Program Analysis for Cyber-Physical Systems
• Flying Robots for Environmental Monitoring
• +6 more

John-Paul Ore is an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering. His research bridges software engineering and field robotics, with a focus on program analysis, system testing, and high-resolution physical simulators for robotics systems, particularly those built with the Robot Operating System (ROS). He develops software engineering methods that improve the dependability of robotics systems through techniques for dimensional analysis without developer annotations, open-source tools like PHYS, and public datasets documenting dimensional inconsistencies in real-world systems. His educational background includes a Ph.D. from the University of Nebraska-Lincoln (2019) and a B.A. in Philosophy from the University of Chicago. His interdisciplinary background informs his approach to combining software engineering with robotics to address challenges in reasoning about full-system behavior across multiple layers of abstraction. Ore's research interests center on applying program analysis techniques to software that controls robots and interacts with the physical world. His work includes abstract type inference of physical unit types (like 'meters-per-second'), probabilistic techniques for combining semantic information in identifiers with code flow inference, and empirical measurements of how developers make decisions about robotic software. He focuses on program analysis and software testing that enhances system safety and reliability while remaining practical and economically efficient. His research has significant applications in environmental monitoring, addressing climate challenges, food production, and liberating people from dangerous, dirty, and dull work. His publication record shows a clear trajectory from foundational work on dimensional analysis in robotics software to increasingly complex applications in environmental monitoring and autonomous systems. The most recent publications demonstrate expansion into Large Language Models for code analysis while maintaining focus on practical robotics applications. His research consistently addresses the critical gap between theoretical program analysis and practical robotics system development. Best Tool Demonstration Award, ISSTA'17 for Phriky-Units ACM SIGSOFT Travel Award ($300) Othmer Fellowship 2014-2018 ($8K/year) UNL CSE Outstanding Master's Thesis Award 2015 RSS 2013 Travel Grant ($500) Ore actively mentors students, having served as research mentor for undergraduates Becca Horzewski (2016-17) and Lambros Karkazis (2018). His research is supported by significant grants including FARM BILL: NRI: INT ($1,018,596 from NSF), SHF: SMALL ($499,994 from NSF), and North Carolina Space Grant ($5,000 from NASA). He is currently recruiting PhD students for his lab focused on robotics and software engineering. His laboratory work combines robotics, software engineering, and environmental monitoring, with projects including autonomous aerial water sampling systems, UAV-based environmental sensing, and tools for improving robotics software reliability. His team develops both theoretical approaches and practical implementations, often creating open-source tools that bridge the gap between academic research and industry applications.

Nickvash KaniView profile

Academic

• Mathematical Language Processing
• Natural Language Processing
• Artificial Intelligence
• +4 more

Nickvash Kani is a Teaching Assistant Professor in the Electrical and Computer Engineering department at the University of Illinois at Urbana-Champaign since August 2023. He is affiliated with the Coordinated Science Lab and leads research in Mathematical Language Processing, focusing on developing systems that can process mathematical data with the sophistication currently applied to natural language. His educational background includes: Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2017) M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2013) B.S. in Computer Engineering from Boston University (2010) Dr. Kani's research centers on Mathematical Language Processing, which he describes as essential for achieving true artificial intelligence since mathematics represents axiomatic knowledge. His work bridges natural language processing with mathematical reasoning systems, developing algorithms to extract knowledge from mathematical literature. He emphasizes that "true intelligence is axiomatic" and believes focusing on mathematical literature enables automated collection of axiomatic knowledge from unlabeled sources. His publication record shows a clear evolution from traditional electrical engineering topics like nanomagnetics and spintronics to cutting-edge AI research. Recent publications (2023-2025) focus on mathematical expression representation, formula search, and enhancing mathematical reasoning in language models. His work spans multiple venues including Transactions on Machine Learning Research, IEEE/ACM Transactions, and NeurIPS workshops, demonstrating interdisciplinary impact across computer science and engineering. His notable achievements include: George Anner Excellence in Undergrad Teaching Award (September 2023) 1st Place - NL4Opt-Task2 challenge at NIPS 2022 (December 2022) 2023 Engineering Council Outstanding Advisors award Dr. Kani actively mentors students and has built a research group dedicated to Mathematical Language Processing. His graduate teaching assistants, including Neeraj Gangwar and Suyuan Wang, have been instrumental in his research and teaching. He has taught ECE 374 (Introduction to Algorithms and Models of Computation) to 200-300 students and developed ECE 364 (Data Science and Engineering), focusing on implementation with auto-differentiation tools like PyTorch. He leads the Mathematical Language Processing Group, which is dedicated to developing algorithms that process mathematical data and literature, with the long-term goal of enabling AI systems to reason axiomatically. His teaching philosophy emphasizes "slow, iterative changes" to improve course accessibility and support student learning objectives in STEM education.

Franciele SpinelliView profile

Lecturer

• Applied Linguistics
• Second Language Acquisition
• Language Education
• +3 more

Dr. Franciele Spinelli is a Lecturer in Applied Linguistics at the School of Languages and Cultures, University of Queensland. Her research focuses on second language acquisition, educational technology integration, drama-based pedagogy, and student-staff partnership models in higher education. She actively collaborates with scholars from Australia, Chile, and international institutions. Key Research Areas: Generative AI applications in language pedagogy Proficiency-level grouping impacts on ESL learners Digital tools for multilingual education (Microsoft Flip, ChatGPT) Microtheatre for language revitalization Cross-cultural communication in L2 classrooms Her recent publications examine AI prompting in Australian schools, Latin American ESL learner interactions, and the use of TikTok-like platforms in academic settings. She engages in collaborative educational research with institutions across Australia and Chile. Spinelli’s work bridges digital innovation with traditional language teaching methods, emphasizing inclusive practices and student agency. She contributes to critical multilingualism studies and pedagogy-focused translations of educational theory into practice.

Matthew GriffithsView profile

Professor

• Physics
• Theoretical Physics
• Science Education
• +3 more

Matthew Griffiths, Ph.D. is a Professor in the Department of Mathematics and Physics - Physics at the University of New Haven, affiliated with the College of Arts and Sciences. His teaching philosophy emphasizes "Demand Understanding" and making physics engaging through exploration of the material world's puzzles. B.Sc. in Physics and Ph.D. in Physics from the University of Edinburgh Griffiths focuses on active student participation, using evidence-based concept development and small class sizes to foster trust in students' critical thinking. He highlights communication as vital for internalizing technical language and taking ownership of knowledge. Labs play a central role in his pedagogy, enabling: Exploration and hands-on theoretical modeling Measurement diagrams and error estimation Development of scale perception and graphical analysis His approach cultivates students' ability to apply physics concepts in future careers through problem-solving ownership and conceptual belief.

• Veterinary Histology
• Embryology
• Cell Death Mechanisms
• +3 more

Assoc. Prof. Musa TATAR serves at Kastamonu University's Faculty of Veterinary Medicine within the Department of Basic Sciences of Veterinary Medicine, specializing in Veterinary Histology and Embryology. His academic journey includes a Doctorate in Veterinary Histology and Embryology from Aydın Adnan Menderes University (2015-2019) and a Bachelor's in Veterinary Medicine from Yüzüncü Yıl University (2008-2013). Doctorate: Aydın Adnan Menderes University, Institute of Health Sciences (2015-2019) Bachelor's: Yüzüncü Yıl University, Faculty of Veterinary Medicine (2008-2013) Language Proficiency: English (YDS, 65/100, 2014) His research focuses on cellular death mechanisms and stress responses in veterinary contexts. Key interests include ferroptosis , necroptosis , endoplasmic reticulum stress , and reproductive toxicology , particularly examining how environmental toxins (bisphenol-A, acrylamide) and physiological stressors (heat, obesity) impact reproductive organs and fetal development in rodent models. His work integrates molecular pathology with therapeutic interventions using antioxidants like melatonin and oleuropein. Recent publications (2022-2025) reveal a strong emphasis on testicular tissue pathology (42% of articles), maternal-fetal interface dynamics (28%), and toxicant-induced organ damage (30%). Methodologically, his team employs advanced histological techniques, immunohistochemistry, and molecular assays to quantify oxidative stress markers and cell death mediators. Collaborations span 36 researchers across Turkish institutions, with frequent co-authorship with Kıymet Kübra Tüfekci (10 papers). No scientific awards or formal student advisement are documented in available records. His teaching portfolio includes core courses Histology-I/II and Embryology since 2019. Current research projects (2023-2024) investigate bisphenol-A's hepatic ferroptosis mechanisms, electromagnetic radiation effects on neurotoxicity, and broiler chicken muscle degeneration pathways. As Department Head since 2023, he leads curriculum development for veterinary histology education.

• Machine Learning
• Deep Learning
• Computer Science
• +12 more

Dr. José SantaMaría López is a Professor at the Department of Computer Science , University of Jaén, specializing in Machine Learning , Medical Imaging , and Evolutionary Computation . His work bridges Artificial Intelligence with practical applications in Healthcare and Security . Current research focuses on Trustworthy AI , Explainable AI , and 3D Modeling for forensic identification. His Google Scholar profile highlights publications on adversarial attacks, data fusion, and real-time diagnostics using Deep Learning . He employs Evolutionary Algorithms and Fuzzy Logic for optimization in medical imaging and vehicular networks. Key trends in his recent publications include Decentralized Learning , Security in AI , and Parallel Computing for healthcare applications.

Jeffrey P. YaegerView profile

Associate Professor

• Pediatric Hospital Medicine
• Health Equity
• Clinical Decision-Making
• +5 more

Dr. Jeffrey P. Yaeger is an Associate Professor in both the Department of Pediatrics and the Department of Public Health Sciences at the University of Rochester School of Medicine and Dentistry. He specializes in Pediatric Hospital Medicine and is a board-certified pediatrician through the American Board of Pediatrics. Dr. Yaeger serves as the Principal Investigator of the Child Health Equity and Decision-making Research (CHEDR) team, focusing on addressing health disparities through innovative research approaches. Dr. Yaeger received his MD from the State University of New York at Buffalo in 2007 and completed his Pediatrics residency and research fellowship at the University of Wisconsin Hospital and Clinics. He also earned an MPH from the University of Wisconsin School of Medicine & Public Health in 2013. His educational background reflects his dual commitment to clinical medicine and population health. Dr. Yaeger's research centers on integrating social determinants of health with clinical decision-making to improve outcomes and mitigate healthcare disparities, particularly for vulnerable pediatric populations. His work employs advanced methodologies including decision science, community-based participatory research, and data mining of electronic medical records. He has developed predictive models aimed at optimizing disease risk assessment and resource utilization in pediatric care. Analysis of Dr. Yaeger's recent publications reveals a strong focus on febrile infants and pediatric sepsis, with particular attention to racial disparities and the impact of social determinants of health. His research spans clinical prediction modeling, implementation of clinical practice guidelines, and the development of natural language processing algorithms for electronic health records. A consistent theme across his work is the pursuit of health equity through evidence-based clinical decision support. Richard Sarkin/Gold Humanism Honor Society (2007) Phi Beta Kappa Honor Society (2003) Dr. Yaeger mentors numerous students through the CHEDR lab, including undergraduate researchers, graduate students, and medical students. His research is supported by significant funding, including a K23 grant from the National Institute of Child Health and Human Development and funding from the Gordon and Betty Moore Foundation for his Pediatric Severe Sepsis Project. These grants support his work investigating practice variation in febrile infant care and racial disparities in sepsis outcomes. The Child Health Equity and Decision-making Research (CHEDR) team, led by Dr. Yaeger, employs a multidisciplinary approach combining decision science, community-based participatory research, and advanced data analytics. Current projects include the Practice Variation Project examining clinical decisions for febrile infants and the Pediatric Severe Sepsis Project investigating racial disparities in sepsis outcomes. The lab utilizes mixed methods, multi-level modeling, small area variation analysis, causal inference, decision analysis, and machine learning to address healthcare disparities.

• Formal Verification
• Model Checking
• Automated Reasoning
• +9 more

Georg Weissenbacher is a Full Professor of Computer Science at Vienna University of Technology (TU Wien), working in the Institute of Logic and Computation within the Faculty of Informatics. He leads the Formal Methods in Systems Engineering research group and has established himself as a leading researcher in formal verification and automated reasoning. His educational background includes a DPhil from Oxford University (2008-2010), research at ETH Zürich (2005-2010), and a Master's degree from TU Graz (completed by 2003). He completed his Habilitation at TU Wien in 2016 with a thesis on Logical Methods in Automated Hardware and Software Verification. Weissenbacher's research focuses on developing automated tools for software and hardware verification, with particular emphasis on detecting and explaining bugs in complex systems. He is renowned for his work on heisenbugs - bugs that disappear when analyzed, which are particularly challenging in concurrent and multi-core systems. His research bridges theoretical foundations in logic with practical applications in software engineering, with significant contributions to interpolation-based verification techniques and SAT/SMT solving applications. His recent publications show a clear progression from traditional software verification toward emerging challenges in AI security, neural network verification, and sophisticated concurrency models. The research trajectory demonstrates increasing sophistication in handling complex systems properties, with a growing emphasis on probabilistic guarantees and formal methods applied to modern computing challenges. OOPSLA'18 Distinguished Paper Award for 'Randomized Testing of Distributed Systems with Probabilistic Guarantees' Weissenbacher has advised numerous PhD students and postdocs, including current researchers Mai AL-Zu'bi, Katalin Fazekas, and Sarah Sallinger. His research has been supported by significant grants including the Vienna Research Groups for Young Investigators (2011), the National Research Network 'Rigorous Systems Engineering' funded by FWF, and a Microsoft Research PhD Scholarship (2016). He actively contributes to the academic community through his service as co-chair for major conferences including CAV 2018 and FMCAD 2017. As leader of the FORSYTE research group at TU Wien, Weissenbacher oversees a vibrant research team working at the intersection of logic, verification, and practical software engineering challenges. The group is currently involved in the Doctoral College on Automated Reasoning, funded by FWF, which aims to train the next generation of researchers in this critical field.

Burcu Can BuglalilarView profile

Lecturer

• Natural Language Processing
• Unsupervised Learning
• Statistical Methods
• +6 more

Dr. Burcu Can Buglalilar is a Lecturer in Computing Science at the Department of Computing Science and Mathematics, University of Stirling, where she is a member of the Data Science and Intelligent Systems Research Group. She previously held academic positions at Hacettepe University (2015-2020) and University of Wolverhampton (2020-2022). Her research focuses on Natural Language Processing with particular emphasis on unsupervised learning techniques for morphology, syntax, and semantics in agglutinative languages like Turkish. She applies both statistical methods (including nonparametric Bayesian learning) and deep learning approaches to language representation problems. Dr. Can Buglalilar's recent work shows a clear trajectory toward Large Language Models and their applications, with upcoming lectures scheduled for 2025 on both Large and Small Language Models. Her publications consistently address fundamental challenges in representing and processing morphologically rich languages. Scientific Recognition: Best Paper Award at RepL4NLP, ACL 2018 TUBITAK Project Performance Award (2022) As an active member of the NLP community, Dr. Can Buglalilar serves as Senior Associate Editor for ACM TALLIP and Associate Editor for Journal of Natural Language Engineering. She has organized workshops including the 8th Representation Learning for NLP (Repl4NLP) at ACL 2023. She is currently developing the Turkish Neural NLP Toolkit and welcomes MSc and PhD students interested in natural language processing, computational linguistics, and machine learning for language.

• Quantitative Analysis
• Business Environment
• Market Research
• +5 more

Miroslava Barkóciová serves as an Assistant Professor in the Department of Quantitative Methods at the Faculty of Economics and Finance, University of Economics in Bratislava. Based in Michalovce, she teaches a comprehensive range of courses including Quantitative Analysis and Forecasts, Human Resource Management, Market Research, Statistics, Management, Quality Management in IS, Management Informatics, Corporate Planning, and Corporate Finance. Her research focuses on the intersection of quantitative business analysis, economic environments, and computational methods. Dr. Barkóciová has established expertise in analyzing business environments across Central European contexts, particularly within the Visegrád Group countries, while also exploring the application of programming languages like Python in business evaluation frameworks. Her work bridges traditional economic analysis with modern computational approaches, creating valuable interdisciplinary insights. Dr. Barkóciová's recent publications demonstrate a consistent research trajectory examining business environments through both theoretical and practical lenses. Her work on V4 countries, hybrid threats to business performance, SME dynamics in the EU, and computational business evaluation reveals a cohesive research agenda focused on enhancing business decision-making through quantitative methods. She actively participates in research projects including 'Analysis of Tourist Service Consumers' Attitudes to Waste and Their Consequences for Sustainable Tourism Development in V4 Countries' (I-22-106-00) and 'Achieving the Goals of the 2030 Sustainable Development Agenda under the Influence of the Global COVID-19 Pandemic' (KEGA 035EU-4/2022), demonstrating her commitment to addressing contemporary economic challenges with scholarly rigor.

• Control Systems
• Wireless Communications
• Network Optimization
• +3 more

Dr. Michael Reyer is a Professor and holds the Chair of Intelligent Control Systems at the Faculty of Electrical Engineering and Information Technology, RWTH Aachen University. He serves as Deputy Chair and is based at the ICT Cube 1 in the Electrical Engineering building (room 302) at Kopernikusstr. 16, Aachen. His primary research areas include: Control Systems and Intelligent Automation Wireless Communications (LTE, OFDM, MIMO) Network Optimization and Resource Allocation Machine Learning for Communication Networks Natural Language Processing for Social Media His publication record from 2011 to 2021 demonstrates a dual focus: advancing wireless network technologies (including cognitive radio, LTE planning, and MIMO channel modeling) and developing NLP techniques for social media analysis. This interdisciplinary work bridges electrical engineering and computer science, with applications in 4G/5G networks and user-generated content processing. As head of the Chair of Intelligent Control Systems, he leads research efforts in intelligent control and communication systems at RWTH Aachen University.

Bin ChenView profile

Associate Professor

• Computational Biology
• Bioinformatics
• Machine Learning
• +6 more

Bin Chen is an Associate Professor at Michigan State University with joint appointments in the Genetics & Genome Sciences Program and Department of Computer Science and Engineering . His research combines multi-omics data , electronic medical records , and advanced machine learning to develop novel therapeutic approaches. Key affiliations : Genetics & Genome Sciences Program Computer Science and Engineering Department NIH BDMA Study Section Research focus : OMICS-driven drug discovery Biomarker identification Translational bioinformatics Virtual screening platforms Single-cell & spatial transcriptomics His lab actively develops machine learning models like transfer learning and deep reinforcement learning to analyze bulk/single-cell transcriptomics and EMR/EHR data. The team has made significant contributions to COVID-19 research, diffuse midline glioma , and Alzheimer's disease through computational drug repurposing and biomarker discovery . Selected scientific awards include: NIH R01 Diversity Supplement (2021) Early Career Research Excellence Award (2021) Best Paper Award at KDD Health Day (2022) NIH BDMA Study Section Membership (2025) The lab has trained numerous students and postdocs who have transitioned to academic and industry positions. Current funding includes NIH R01 grants , NCATS Translator program , and MSU-Spectrum Health Alliance collaborations.

Lidia PokrzyckaView profile

Professor

• Media and Communication Studies
• E-learning and Digital Education
• International Political Relations
• +5 more

Lidia Pokrzycka is a professor at the Department of International Political Relations , Faculty of Political Science and Journalism , Maria Curie-Skłodowska University (UMCS). She specializes in media studies, e-learning, international political relations, and educational innovation. Education: Harvard University (Leaders of Learning), University of British Columbia (online teaching strategies) Research: Focuses on media systems, digital education, and political communication with comparative studies across Baltic/Nordic countries Recent publications analyze e-learning trends in Belgium, Norway, and Poland, comparing digital education frameworks and pedagogical innovations. Her work also examines media systems in Iceland and Eastern Europe. Scientific Awards : Eduinspirator 2019, Microsoft Innovative Educator Expert (multiple years), Key Mind Trainer certification International Collaborations : Partnerships with OsloMet, University of Iceland, Ventspils University of Applied Sciences, Yasar University (Turkey) She leads the postgraduate program in Public Relations and Media Marketing and directs the project "Cared for in Stress - Nature as a Source of Mental Health" (2025, National Institute of Freedom).

Dirk BalfanzView profile

Researcher

• Human-Robot Interaction
• Explainable AI
• Cognitive Science
• +3 more

Dr. Dirk Balfanz serves as Scientific Managing Director at the Centre for Cognitive Science, Technical University of Darmstadt since 2016. With a background in computer science and electrical engineering, he bridges cognitive science with interactive AI systems , focusing on human-robot interaction , explainable AI , and geospatial metadata . His leadership spans strategic development of cognitive science programs and third-party funding acquisition across multiple projects. Doctorate in Computer Science (2002), TU Darmstadt Diplom in Electrical Engineering (1992), University of Dortmund His research explores the intersection of cognitive science and AI , particularly in situational autonomy , multimodal interaction , and human uncertainty detection . Current projects like WhiteBox and IKIDA investigate how probabilistic models and cognitive explanations can enhance AI transparency. Earlier work on geospatial metadata laid foundations for ISO standardization. His recent publications in human-AI collaboration (15 articles) demonstrate expertise in interactive reinforcement learning , probabilistic movement primitives , and explanation generation for complex systems. Notable contributions include Bayesian fusion of human feedback and explainable linear programming in energy systems.

Temitayo Matthew FagbolaView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Medical Image Processing
• +4 more

Temitayo Matthew Fagbola is a Teaching Fellow at the University of Hull's Data Science, AI and Modelling Centre (DAIM) within the Faculty of Science and Engineering. He holds a BTech (2008), MSc (2011), and PhD (2015) in Computer Science, along with a PGCert in Academic Practice (2023-2024). His research focuses on AI-driven pedagogy, including virtual/augmented reality simulations for education, and biomedical AI applications such as medical image processing and ethical AI frameworks. Key qualifications include memberships in the British Computer Society (MBCS) and Fellowship of the Higher Education Academy (FHEA). His recent work addresses renewable energy adoption in developing countries, generative AI in healthcare, and brain tumor classification using deep learning. He actively contributes to academic conferences and has supervised projects on medical report generation and privacy-preserving medical data systems. Awards include the 'Excellence through Feedback' award (2023) and runner-up in 'Excellence in Teaching'. His research spans ethics in AI, small-language models, and domain-specific AI solutions, reflecting a commitment to both technical innovation and equitable educational practices.