Find Academic Supervisors

Sikha BaguiView profile

Professor

• Big Data Analytics
• Machine Learning
• Data Mining
• +7 more

Dr. Sikha Bagui is a Distinguished University Professor in the Department of Computer Science at the University of West Florida , within the Hal Marcus College of Science and Engineering . She previously served as Chair of the department and Founding Director of the Center for Cybersecurity. Her research spans Big Data Analytics, Machine Learning, Data Mining, and Database Design, with extensive publications and funded projects from NSF and NSA. Ed.D. in Curriculum & Instruction: Math & Stat / Science / Computer Science, University of West Florida M.B.A., University of Toledo B.S., Cuttington University (Liberia) Dr. Bagui's research centers on data-intensive computing , focusing on scalable algorithms for Big Data analytics, optimization in distributed environments (Hadoop, Spark, Hive), and applications in cybersecurity such as intrusion detection and phishing classification. She is particularly known for her work in data preprocessing, association rule mining, and improving classifier performance on imbalanced datasets. Her recent publications reveal a strong trend toward cybersecurity applications of machine learning , leveraging frameworks like MapReduce and Spark for scalable solutions. Topics include network traffic classification, load balancing in FP-Growth, and resampling techniques for intrusion detection. Her work bridges theoretical algorithm development with practical implementation in real-world Big Data systems. Distinguished University Professor Askew Fellow NSF CSForALL Grant ($300,000) NSA NCAE Grant ($375,511) Dr. Bagui has successfully led multiple federally funded research projects and mentored numerous students through research and academic programs. While specific advisees are not listed, her leadership in research groups and outreach initiatives like Women in Computing demonstrates strong mentorship. She has also authored influential textbooks used internationally. Her lab and research efforts are aligned with the UWF Smart Home Research , AI Research Group , and High Performance Computing Research , contributing to interdisciplinary innovation.

Rohit ChadhaView profile

Associate Professor

• Computer Security
• Formal Methods
• Program Analysis
• +5 more

Rohit Chadha is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri and serves as the Director of the Mizzou Cybersecurity Center . His research focuses on formal methods and their application to cybersecurity , particularly in verifying differential privacy mechanisms and randomized security protocols . Education : PhD from the University of Pennsylvania, BTech from the Indian Institute of Technology, Delhi Previous Positions : INRIA Saclay (France), University of Illinois at Urbana-Champaign, Instituto Superior Tecnico (Portugal), University of Sussex (UK) Research Interests include automated verification of security hyperproperties , probabilistic automata , and quantitative information flow . His work addresses privacy risks in aggregate data and AI-driven battlefield communication systems. Publication Trends show a focus on differential privacy verification , zero-trust architectures , and model checking of probabilistic systems. He has contributed to security frameworks for military applications and educational initiatives using collegiate competitions. Scientific Awards : NSF CAREER Award (2016) NSF SHF: Medium Grant (2019) NSF TWC: Medium Grant (2013) Rohit leads the Mizzou Cybersecurity Center , collaborating with industry partners through its Industrial Advisory Board . His research integrates formal verification with practical cyber defense applications.

Stefan BorgwardtView profile

Researcher

• Knowledge Representation
• Description Logics
• Automated Reasoning
• +8 more

Stefan Borgwardt is a Researcher and Teaching Associate at the Chair of Automata Theory, Faculty of Computer Science, Technische Universität Dresden, working under Prof. Franz Baader. He leads key research projects such as Practical Planning with Ontologies (PPO) and contributes to the Center for Perspicuous Computing (CPEC), funded by DFG and SNF. His work bridges theoretical logic and practical AI systems. Research Interests: Knowledge Representation and Reasoning Description Logics and Ontology-Mediated Query Answering Explainable AI and Proof Visualization (Evee/ Evonne systems) Temporal, Fuzzy, and Probabilistic Logics Automated Planning with Ontologies Logic-Guided Natural Language Generation His recent publications (2021–2025) show a strong trend toward explainability in logical reasoning, with a focus on modular, interactive, and user-friendly proof systems. He investigates reasoning complexity in expressive description logics and develops practical tools for ontology explanation and planning. His work often combines symbolic AI with real-world applications such as sensor data interpretation and autonomous systems. Scientific Awards: Harold Boley Distinguished Paper Award (RuleML+RR'23) Best Paper Award (RuleML+RR'22, JELIA'19, AI'15) Quality Champion for ECAI'23 reviewing Commerzbank Award for PhD thesis (2014) Distinguished Student Paper Awards (DL'14, DL'13) N. J. Lehmann Award (2011) Stefan Borgwardt actively advises and collaborates with students and early-career researchers on projects involving explanation, planning, and reasoning. He has secured competitive funding through DFG and SNF grants and plays a leadership role in the KR community as PC co-chair, senior PC member, and steering committee member for major conferences like DL, KR, and JELIA. He is also a reviewer for top-tier journals including Artificial Intelligence and Journal of the ACM . Labs and Teams: He is a core member of the Chair of Automata Theory at TU Dresden and contributes to the Center for Perspicuous Computing (CPEC), where he works on projects A3 (Description Logic Explications) and E2 (Safe Handover in Mixed-Initiative Control) in collaboration with Vera Demberg and Antonio Krüger.

• Artificial Intelligence
• Biometrics
• Security Systems
• +5 more

Kingsley Nwosu is an Associate Professor in the Department of Computer Science at Norfolk State University's College of Science, Engineering and Technology. His research spans artificial intelligence, biometric systems, and security frameworks, with a focus on healthcare, campus safety, and low-resource environments. Email: kcnwosu@nsu.edu Research Themes: AI-driven emotion recognition and facial recognition systems Biometric authentication for security and privacy Blockchain applications in e-voting and data integrity Urban mobility optimization via embedded systems Public policy evaluation for social enterprises Data partitioning strategies for machine learning Publication Trends: His work since 2012 highlights interdisciplinary applications of AI and biometrics in healthcare, security, and economic development, with recent emphasis on scalable solutions for developing economies and proactive cybersecurity measures.

Yichuan ZhuView profile

Assistant Professor

• Machine Learning in Geotechnical Engineering
• Computational Geomechanics
• Uncertainty Quantification
• +9 more

Yichuan Zhu is an Assistant Professor in the Department of Civil and Environmental Engineering at Temple University's College of Engineering. He leads the Computational Geosystems Group (CGG), focusing on interdisciplinary research at the intersection of civil engineering, geosciences, and computer science. Research Interests: Dr. Zhu’s work centers on applying machine learning, uncertainty quantification, and computational modeling to geotechnical and geological challenges. Key areas include granular mechanics, landslide characterization, remote sensing applications, risk and reliability assessment, and AI-driven geosystem modeling. His research combines physical principles with data-driven techniques to enhance predictive accuracy and decision-making in complex earth systems. Publication Trends: His recent publications (2021–2025) reflect a strong emphasis on integrating machine learning (including transformers and Bayesian deep learning) with traditional geomechanical models. Topics span tunnel safety, jet grouting, seismic site response, and landslide susceptibility, demonstrating a consistent focus on uncertainty-aware, AI-enhanced solutions for geotechnical problems. Professional Recognition and Activities: Chair, Session MS 0706 (Natural Hazards), EMI/PMC 2024 Conference Presented at Geotechnical Frontiers 2025 and Geo-Congress 2024 Actively recruiting fully-funded Ph.D. students Advising and Teaching: Dr. Zhu advises Ph.D. students, including Dr. Khabiri, whose dissertation informed recent conference presentations. He teaches core undergraduate and graduate courses in soil mechanics, foundation engineering, and geotechnical earthquake engineering (CEE 2011, CEE 3331, CEE 4821–4823, CEE 5821–5823). His lab, the Computational Geosystems Group, supports ongoing research in AI and computational geosciences. Research Group: The Computational Geosystems Group (CGG) conducts cutting-edge research in probabilistic modeling, geosystem simulation, and data-driven geotechnics. The team is actively expanding, with funded positions available for graduate researchers.

• Natural Language Processing
• Sentiment Analysis
• Social Media Analytics
• +14 more

Dr Marco Palomino is a Senior Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen and holds a Visiting Associate Professor position at the University of Plymouth in the School of Engineering, Computing and Mathematics. He is actively involved in research, teaching, and PhD supervision, with a focus on data science and computing. His roles include Admissions Tutor, Programme Manager for the Digital and Technology Solutions Professional Degree Apprenticeship, and Athena Swan Lead at Aberdeen. His research interests span Natural Language Processing , Sentiment Analysis , Social Media Analytics , Spatiotemporal Databases , and Human-AI Interaction . He employs interdisciplinary methods to analyze public opinion, improve cybersecurity training, support elderly care through robotics, and develop decision-support systems. His work contributes to Sustainable Development Goals related to technology and societal well-being. His recent publications (2022–2025) reflect a strong trend in applying machine learning and data science to real-world problems, including sentiment analysis during the pandemic, adaptive cybersecurity training, human-AI collaborative decision-making, and assistive social robots for older adults. These works emphasize practical applications in healthcare, security, urban mobility, and policy. Scientific recognition includes: Highly Commended Paper award, Emerald Literati Network (2014) He has supervised numerous students on topics including sentiment analysis, malware detection, and cybersecurity education. His research is supported by affiliations with both the University of Aberdeen and the University of Plymouth, and he has contributed as a Guest Editor and reviewer for journals such as Applied Sciences , Big Data and Cognitive Computing , and Mathematics . He is a Fellow of the Higher Education Academy.

José Nuno OliveiraView profile

Professor

• Formal Methods
• Algebra of Programming
• Functional Programming
• +3 more

Roles and Affiliations: José Nuno Oliveira is a Full Professor at the University of Minho's School of Engineering, Department of Computer Science. He is a member of the HASLab (High-Assurance Software Laboratory) and the Formal Methods Europe (FME) Association. He has held roles such as General Chair of the 3rd World Congress on Formal Methods (FM'19) and served on numerous international conference committees. Education: Habilitation in Computing Foundations (2010), University of Minho Ph.D. in Computer Science (1984), University of Manchester, UK M.Sc. in Computer Science (1981), University of Manchester, UK Lic. Eng. Elect. (Digital Systems & Computers) (1978), University of Porto, Portugal Research Interests: Oliveira focuses on formal methods, program calculation, and algebraic programming. His work emphasizes improving software design through mathematical rigor, including applications of relational algebra, functional programming, and program transformation. He has contributed to frameworks like Alloy and QAlloy for software verification. Publications: His recent work spans topics like quantum programming (e.g., quantamorphisms), typed linear algebra for data analysis, and formal methods in education. Over 80 publications include influential papers on Galois connections, metaphorisms, and coalgebraic systems. Awards and Grants: Not explicitly listed, but his leadership roles and extensive conference involvement highlight his recognition in formal methods. Active in projects like IBEX (cyber-physical systems) and TRUST (Alloy-based design). Teaching and Service: Teaches courses on program calculation and formal methods. Serves on editorial boards (e.g., Formal Aspects of Computing ) and chairs international conferences. Active in promoting formal methods education through workshops and training initiatives. Labs and Teams: Co-leads the HASLab (INESC TEC & University of Minho), focusing on high-assurance software systems. Collaborates on projects like EVEREST (railway network verification) and LeanBigData (big data analytics).

Raquel Garrido AlhamaView profile

Assistant Professor

• Computational modelling of language learning
• Language acquisition
• Language evolution
• +7 more

Dr Raquel Garrido Alhama is an Assistant Professor of Artificial Intelligence for text and human interaction at the Institute for Logic, Language and Computation (ILLC) , University of Amsterdam. She also co-directs the /k/omputation and Language Lab and previously served as Assistant Professor at the Cognitive Science and Artificial Intelligence department of Tilburg University. She earned her PhD from the ILLC under the supervision of Jelle Zuidema, Remko Scha and Carel Ten Cate, with a dissertation entitled Computational Modelling of Artificial Language Learning . Prior post-doctoral positions include the Max Planck Institute for Psycholinguistics and the Basque Center on Cognition, Brain and Language. Research Interests: Computational modelling of language learning and evolution Language acquisition, word learning and sentence processing Neural-symbolic integration and connectionist architectures Visual and auditory word recognition, syntactic parsing and part-of-speech tagging In recent years her work has converged on validating developmental milestones—such as the onset of productive determiner–noun combinations—using large-scale child corpora and neural models. She also investigates how multi-agent referential games can lead to emergent compositional languages, moving from image to richer graph representations. Scientific Awards: Best Article Award (2019) from Psychonomic Bulletin & Review for the review on computational models of rule learning Best Poster Award (2015) at the International Conference on Cognitive Modeling Teaching & Supervision: Dr Alhama has designed and lectured courses on advanced programming for cognitive science, computational linguistics, linear algebra and language acquisition at Tilburg University, University of Amsterdam, Radboud University and the University of the Basque Country. She currently supervises eleven MSc and BSc students in AI, Logic and Cognitive Science. Labs & Teams: She is the founding co-director of the /k/omputation and Language Lab together with Phong Le, where they use Language Emergence frameworks to explore the cognitive prerequisites for displacement and compositionality in human language.

• Algorithms
• Data Structures
• Combinatorics
• +9 more

Conrado Martinez Parra is a Professor in the Department of Computer Science at the Faculty of Computer Science, Universitat Politècnica de Catalunya (UPC). He is a core member of the ALBCOM research group, which focuses on Algorithmics, Bioinformatics, Complexity, and Formal Methods. Affiliation : Department of Computer Science, Faculty of Computer Science (FIB), UPC Research Group : ALBCOM - Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals Email : conrado@cs.upc.edu ORCID : 0000-0003-1302-9067 Researcher ID : G-4629-2015 His research spans theoretical computer science with a strong emphasis on the design and analysis of algorithms and data structures. His work includes average-case analysis of algorithms, combinatorial generation, probabilistic methods in algorithmics, and applications in information retrieval and data stream processing. He has extensively studied multidimensional data structures such as quadtrees, K-d trees, and skip lists, analyzing their performance under various query models including partial match and orthogonal range searches. His recent publications reveal a sustained focus on algorithmic efficiency, sampling techniques, and probabilistic modeling in data structures. Trends indicate a deep engagement with randomized algorithms, unbiased estimation, and cache-efficient selection methods, reflecting both theoretical rigor and practical applicability in modern computing environments. Scientific Contributions Extensive publication record spanning over three decades, from 1989 to 2024. Active in major algorithmic conferences such as ANALCO, AofA, and AAAI. Contributions to foundational algorithm analysis including Hoare’s FIND, Quickselect variants, and deletion in binary search trees. Collaborative research with prominent figures in theoretical computer science across Europe. Professor Martinez Parra has advised or collaborated with several doctoral students, including Gustavo Lau, whose thesis on partial match queries he supervised. He has participated in numerous competitive R&D projects funded by national and regional programs, focusing on large-scale information processing and graph-based computing models. His work is supported by long-standing grants from Spanish and Catalan research councils. He is affiliated with the ALBCOM research group, a leading team in algorithmic research at UPC, contributing to both theoretical advances and practical implementations in combinatorics and data structure optimization.

• Geotechnical Engineering
• Underground Construction in Soft Soil
• Soil Stabilisation
• +4 more

Stefan Ritter serves as an Adjunct Associate Professor at Oslo Metropolitan University (OsloMet) within the Faculty of Technology, Art and Design , specifically in the Department of Built Environment . In parallel, he is a Senior Specialist at the Norwegian Geotechnical Institute (NGI) , where he contributes to geotechnical engineering research and practice. Education: BSc and MSc in Geotechnics and Tunnelling, University of Leoben , Austria Marshall Plan Fellow at MIT , USA (Master’s research) PhD in Geotechnical Engineering, University of Cambridge , UK (awarded Philip Turner Prize) Research Interests: Ritter's expertise lies in underground construction in soft soil , soil stabilisation , and the interaction between ground and built environment . He focuses on sustainable urban geotechnics, integrating underground space into urban planning, and advancing instrumentation and monitoring techniques to enhance geotechnical performance. Scientific Awards: Philip Turner Prize for excellence in geotechnical centrifuge testing Teaching and Mentorship: Since February 2023, Ritter has been teaching the 10 ECTS MSc course Urban Geotechnics at OsloMet. While no specific student advisees are listed, his role involves direct engagement with master's students in geotechnical engineering. Affiliations and Labs: He is affiliated with NGI , Norway’s leading geotechnical research institute, and collaborates extensively on both national and international projects. His work bridges academic research and practical engineering solutions.

Xiaoyi LuView profile

Associate Professor

• Parallel and Distributed Computing
• High-Performance Communication and I/O Technologies
• Big Data Analytics
• +4 more

Dr. Xiaoyi Lu is an Associate Professor in the Department of Computer Science & Engineering at the University of California, Merced (UC Merced), where he founded and directs the Parallel and Distributed Systems Laboratory (PADSYS Lab). He is affiliated with the AgAID Institute since 2023 and has authored over 170 publications, including ten Best Paper Awards or Nominations (e.g., SC 2019, IPDPS 2024). His research outcomes like OpenDOTA and MVAPICH2-Virt are used by hundreds of organizations globally. Research Interests: He focuses on scalable parallel systems for HPC, Big Data, AI, Cloud, and Edge Computing, leveraging advanced technologies like RDMA/PMEM/NVMe/GPU/DPU. His work bridges high-performance computing with applications in precision agriculture, biostatistics, and digital twin technology. Article Trends: Recent publications emphasize DPU offloading, compression-optimized collective communication, error detection in HPC, and scalable Bayesian group testing. Topics span GPU clusters, NVMe-over-Fabrics, and adaptive networks for LLM training, reflecting his expertise in heterogeneous architectures and distributed systems. Scientific Awards: NSF CAREER Award (2024) Amazon Research Award (2023) Google Research Award (2022) Meta Faculty Research Award (2022) Multiple Best Paper Nominations Professional Activities: He serves as Associate Editor for Frontiers in High Performance Computing and organizes tracks at SCAsia and HiPC. His leadership in PADSYS Lab drives innovation in systems for social good.

Jabir Alshehabi Al-AniView profile

Lecturer

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

Jabir Alshehabi Al-Ani serves as a Lecturer in the Data Science department at York St John University's York Business School. With over 10 years of expertise in Machine Learning, AI, NLP, Data Retrieval, and Information Extraction across academic, research, and industry settings, he contributes significantly to the university's technical education programs. Dr. Alshehabi Al-Ani earned his PhD in computer science from the Department of Computer Science and Electronic Engineering at the University of Essex, UK. His academic foundation supports his current teaching responsibilities which include delivering lectures in Machine Learning and Practice in Interdisciplinary Problem Solving (PIIPS), building on seven years of prior teaching experience across numerous technical subjects. His research interests focus on Natural Language Processing, Image Processing, and interdisciplinary applications of AI in healthcare and sociology studies. This dual focus on technical AI development and social applications demonstrates his commitment to creating technology with real-world impact. His work bridges computer science with social sciences, particularly in analyzing gender bias in employment contexts and developing classification systems for diverse applications. Analysis of his publication record from 2017-2022 reveals a clear research trajectory evolving from technical computer vision and NLP methods toward more socially conscious applications of AI. His recent work emphasizes gender bias detection in job advertisements and interdisciplinary research management, reflecting growing interest in AI ethics and the societal implications of algorithmic systems. The publications span multiple venues including Frontiers Big Data, SocArXiv, and IEEE conferences, demonstrating both academic rigor and practical relevance. As a postgraduate research supervisor, Dr. Alshehabi Al-Ani contributes to the development of emerging scholars in data science and AI fields. His teaching spans foundational programming and database concepts to advanced machine learning and natural language processing topics, creating a comprehensive educational pathway for students. His research activities demonstrate strong interdisciplinary collaboration, particularly evident in his work on gender bias in job advertisements which involved researchers from multiple institutions across various disciplines. This collaborative approach extends to his contributions to parliamentary evidence submissions regarding diversity in STEM fields.

• Database Systems
• Data Management
• Recommender Systems
• +4 more

Sihem Amer-Yahia is a distinguished Research Professor at the University of Grenoble Alpes (affiliated with Grenoble Informatics Laboratory ), with significant contributions to database systems , data exploration , and fairness in AI . Her work bridges human-computer interaction and machine learning to create systems that enhance data-driven decision-making. Research Pillars : Algorithmic fairness, interactive data mining, recommender systems, and human-AI collaboration Recent Advances : 2023-2025 publications focus on statistically sound hypothesis testing , multi-objective recommendation , and conversational analytics Leadership : Co-organized major conferences (DASFAA 2024) and led DEI initiatives in database communities Her 15 most recent articles (2020-2025) span topics like producer fairness in recommendation , statistical hypothesis frameworks , and AI-powered education systems , with keywords covering database optimization , reinforcement learning , and ethical data mining . She actively contributes to ACM/IEEE journals and VLDB/SIGMOD conferences.

Mihai BoicuView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Structured Analytical Methods
• +9 more

Mihai Boicu is an Associate Professor at George Mason University's Volgenau School of Engineering , Department of Applied Information Technology, and Associate Director of the Learning Agents Center (LAC) . His research spans Artificial Intelligence , structured analytical methods, personalized education, and collective intelligence. He is the architect of the Disciple agent development platform and has secured grants from the US Department of Defense , VentureWell , and Oculus Info Inc. . His work includes over 100 publications, with three defense-focused textbooks and contributions to journals like AI Magazine and International Journal of Intelligence and CounterIntelligence . He has co-organized premier AI conferences such as ICMLA and AAAI Symposia . His research focuses on AI in defense , education technology , and metacognition in STEM , published in venues ranging from AAAI to IEEE and Hawaii International Conference on System Sciences . Scientific Awards : Innovative Application Award from AAAI Certificates of Appreciation from U.S. Army War College and Air War College GMU Teacher of Distinction (2015) His recent articles (2023–2025) analyze machine learning in education, quantum computing , LLM feedback systems , and metacognition , with projects like SugarNet (diabetes management) and FlightList (Air Force compliance). He advises PhD students in applied AI, education technology, and innovation.

Ayan SenguptaView profile

Assistant Professor

• Neuroimaging
• Functional magnetic resonance imaging (fMRI)
• Ultra-high-field 7 T MRI
• +5 more

Ayan Sengupta, Ph.D. is an Assistant Professor in the Department of Health Physics and Diagnostic Sciences at the University of Nevada, Las Vegas , where he began in Fall 2024. He teaches Comprehensive Medical Imaging , MRI Physics , Radiation Science , and Dosimetry for both undergraduate and graduate programs. Educational Background Bachelor’s degree in Engineering (India) Master’s degree in Computer Science, specializing in medical image processing, University of Nebraska Ph.D. in Neuroimaging, Otto-von-Guericke University, Germany (supported by DFG Research Scholarship) Postdoctoral research, University of Cambridge & Sir Peter Mansfield Imaging Centre, United Kingdom Lifetime Honorary Affiliate, Hughes Hall, University of Cambridge Research Fellow, Royal Holloway, University of London Research Focus Dr. Sengupta’s work centers on cutting-edge neuroimaging, particularly ultra-high-field 7 T human fMRI . He leverages machine learning and artificial intelligence to decode how the brain represents visual and tactile information. At UNLV he is establishing a program on accessible low-field MRI systems for primary care and is pioneering novel imaging and AI techniques for dementia biomarkers . Publication Landscape Across 20 peer-reviewed works, a clear arc emerges: pioneering 7 T fMRI methodologies (motion correction, resolution optimization), constructing probabilistic atlases of somatosensory digit maps, and integrating computational approaches (decoding orientation, musical genres). Recent efforts extend to low-field MRI accessibility and AI-driven dementia biomarkers , reflecting a translational trajectory from high-field neuroscience to clinical impact. Scientific Honors DFG (Deutsche Forschungsgemeinschaft) Research Scholarship Lifetime Honorary Affiliate, Hughes Hall, University of Cambridge Current Activities & Future Directions Dr. Sengupta is actively building his laboratory at UNLV, acquiring funding and collaborators to advance low-field MRI systems and AI-based dementia diagnostics. While no specific students are named in the provided text, his program is expected to recruit graduate researchers as infrastructure develops.