Find Academic Supervisors

Ashok AgrawalaView profile

Professor

• Computer Systems
• Distributed Algorithms
• Real-Time Systems
• +10 more

Ashok Agrawala is a Professor in the Department of Computer Science at the University of Maryland, with joint affiliations in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Department of Electrical Engineering. He directs the Maryland Information and Network Dynamics (MIND) Lab, a leading research group focused on real-time systems, network performance, and wireless localization technologies. Education: B.E. and M.E. in Electrical Engineering, Indian Institute of Science, Bangalore M.A. and Ph.D. in Applied Mathematics, Harvard University His research interests span computer systems, distributed algorithms, real-time operating systems, network performance modeling, traffic shaping, and indoor localization . He is renowned for pioneering the Ricart-Agrawala distributed mutual exclusion algorithm, developing the Maruti real-time operating system, and creating the Horus and Locus indoor positioning systems. His work has led to significant advances in temporal guarantees, jitter-free data delivery, and calibration-free localization. His recent publications show sustained innovation in wireless localization, context-aware computing, breath analytics, and energy-efficient IoT systems , reflecting an evolving research trajectory toward smart health and urban computing. He has authored over 200 papers and seven books, with consistent publication activity into 2024. Scientific Recognition: Fellow, IEEE Member, ACM Member, AAAS Member, Sigma Xi Agrawala has advised over thirty Ph.D. students, many of whom now lead research in academia and industry. His research has been funded by DARPA, NSF, and AFOSR, with corporate partnerships from IBM, Novell, and AT&T. He previously worked at Honeywell Information Systems, where he designed an Optical Character Reader. He has also served as a consultant to the UNDP and the Government of India, and as a technical expert in legal cases. He leads the MIND Lab, which focuses on mobile computing, wireless networks, and context-aware systems , continuing to innovate in real-time and networked systems.

Gao MinView profile

Professor

• Personalized Recommendation Systems
• Anomaly Detection
• Social Media Mining
• +7 more

Gao Min is a Professor and Doctoral Supervisor at the School of Big Data and Software, Chongqing University. He is a member of IEEE, CCF, and CAAI, and has held visiting scholar positions at Arizona State University and Reading University. His research focuses on personalized recommendation systems, anomaly detection, and social media mining, with strong emphasis on security aspects such as shilling attacks and fake news detection. His research interests include: Personalized Recommendation Systems Anomaly and Attack Detection in Recommender Systems Social Media Mining and Fake News Detection Graph-based and Contrastive Learning for Recommendations Domain Adaptation and Meta-Learning Time Series and Behavioral Forecasting The recent articles highlight a consistent trend in adversarial and robust learning for recommender systems and misinformation detection. His team leverages contrastive learning, graph neural networks, and meta-learning to enhance model robustness against poisoning and shilling attacks. There is a growing focus on simulating user behaviors, modeling fine-grained discrepancies, and applying domain adaptation techniques, particularly in detecting fake news and securing recommendation platforms. His scientific awards are primarily reflected through the recognition of his students, including multiple recipients of the National Graduate Scholarship, Huawei Scholarship, and Chongqing Outstanding Master’s Thesis Award. National Graduate Scholarship (awarded to students: Tian Renli, Yu Junliang, Song Yuqi, Zhao Zehua, Zhang Junwei, Wang Jia, Peng Lin, Ma Hao) Huawei Scholarship (awarded to students: Tan Kan, Wang Jia, Huang Yinqiu) Chongqing Outstanding Master's Thesis Award (awarded to students: Yu Junliang, Zhao Zehua, Zhang Junwei) Aerospace Scholarship (awarded to student: Zhang Junwei) Gao Min has secured significant research funding as principal investigator, including two National Natural Science Foundation projects, a sub-project of the National Key R&D Project, two Chongqing Natural Science Foundation projects, and one China Postdoctoral Fund project. He has also contributed as a main researcher in major national programs such as the 973 Program, National Key R&D Program, and National Science and Technology Support Program. He advises a vibrant research group that values autonomy, academic freedom, and practical research, with students regularly publishing in top venues and securing top-tier industry and academic positions. His team has developed key research platforms including QRec (Recommendation Algorithm Experiment Platform), Yue (Music Recommendation), ARLib (Data Pollution Attack Platform), and SDLib (Shill Attack Detection Platform). He serves as a reviewer for major journals and is a PC member of top conferences including CIKM, IJCAI, and AAAI.

Ruhul AminView profile

Assistant Professor

• Artificial Intelligence
• Bioinformatics
• Data Science
• +5 more

Ruhul Amin is an Assistant Professor at Fordham University's Department of Computer and Information Science, where he explores the intersection of Artificial Intelligence , Data Science , and Public Health . His work spans multiple domains including Bioinformatics , Natural Language Processing , and Computational Social Science . PhD in Computer Science (Stony Brook University, 2019) MS in Computer Science (Stony Brook University, 2015) BSc in Computer Science (Shahjalal University, 2007) His research focuses on developing deep learning algorithms for genome annotation, anomaly detection in high-cardinality spaces, and assistive technologies for visually impaired users. Notably, he designed the Mongol Dip bilingual screen reading software and the Pipilika Bengali text search engine. Recent publications (2025-2024) demonstrate his expanding interests in large language model alignment , sentiment analysis across languages, and distributed reinforcement learning for cybersecurity applications. His work frequently appears at IEEE conferences and in journals like PLOS One. Scientific Recognition BASIS National ICT Award (Bangladesh) Best Poster Awards at IEEE R10 HTC and CEWIT conferences Bangladesh Prime Minister’s Award (2012) mBillionth South Asia Award (2010) He maintains active collaborations with research teams at University of Toronto , University of British Columbia , and Stony Brook University . Contact: moamin@cs.stonybrook.edu

Ken SatohView profile

Professor

• Artificial Intelligence (AI) and Law
• Logical Foundations of AI
• Juris-Informatics
• +6 more

Ken Satoh is a full Professor at the National Institute of Informatics (NII) and Sokendai (The Graduate University of Advanced Studies), Japan. He leads the Center for Juris-Informatics within the Joint Support-Center for Data Science Research (ROIS-DS). Previously, he worked at Fujitsu (1981-1995) and was an Associate Professor at Hokkaido University until 2001. He holds a law degree from the University of Tokyo (2006-2009) and passed the Japanese bar exam in 2017. Roles: Director of Center for Juris-Informatics, Principal Investigator in multiple AI/Law projects Research Focus: Juris-informatics (merging informatics and law), logical foundations of AI, legal debugging, and compliance mechanisms for AI systems His work bridges AI and legal systems, including developing the PROLEG framework for legal reasoning and organizing international workshops like JURISIN. Key contributions include applying logical inference to detect legal conflicts in algorithmic governance systems and advancing AI ethics through compliance checks with regulations like GDPR. He has authored over 130 publications, including works on legal norm reasoning, multi-agent systems, and formal methods in law. His research group collaborates globally, hosting competitions like COLIEE to advance legal AI technologies.

• Natural Language Processing
• Machine Translation
• Speech Processing
• +3 more

Popescu-Belis Andrei serves as an Associate Professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO), specifically within the Institute of Information and Communication Technologies (IICT) at HEIG-VD campus in Yverdon-les-Bains. His academic work spans teaching computer science fundamentals and advanced natural language processing concepts across both undergraduate and graduate programs. His research interests focus on Natural Language Processing , Machine Translation , and Human-Machine Dialogue systems, with particular emphasis on low-resource language scenarios. His work addresses challenges in speech-to-speech translation, subword tokenization, neural machine translation optimization, and creative applications like poem generation. Recent publications through 2025 demonstrate his ongoing contributions to the field, particularly in multilingual NMT training schedules and low-resource language processing pipelines. Analysis of his 15 most recent publications reveals consistent focus on practical solutions for language barriers, with increasing attention to reinforcement learning applications and creative language generation. His work on GPoeT demonstrates the intersection of traditional literary forms with modern language models, while his research on low-resource translation pipelines addresses real-world community interpreting needs. He has led significant research projects including Digital Lyric (2019-2020), a collaboration with UNIL (University of Lausanne) to develop systems for aiding poem generation for a public exhibition at Morges Castle. This project demonstrates his interdisciplinary approach bridging computational linguistics with creative applications. His academic contributions include: Development of specialized training pipelines for low-resource neural machine translation Innovative approaches to speech-to-speech translation for community interpreting Novel methods for constrained language model applications in creative writing Significant contributions to understanding subword tokenization mechanics Popescu-Belis maintains active research collaborations across institutions, as evidenced by his work with UNIL and various international conference publications. His teaching portfolio includes courses in natural language processing, data preparation, unsupervised machine learning, and computer science fundamentals, demonstrating his commitment to both theoretical and practical aspects of the field.

Drahomira CuparView profile

Associate Professor

• Information Organization
• Information Retrieval
• Reading Habits Research
• +4 more

Assoc. Prof. Drahomira Cupar is an Associate Professor at the Department of Information Sciences and Technologies, University of Zadar. She specializes in information organization, digital humanities, and reading habits research. Her work focuses on classification systems, open access publishing, and cultural heritage preservation. She actively participates in projects like TRIPLE, OPERAS-PLUS, and CRAFT-OA. Consultation hours are Wednesdays 10:00–12:00 via email. Her research spans over 60 publications covering digital transformation of historical texts, metadata strategies, and literacy promotion. Key projects include digitization of Glagolitic texts and development of multilingual taxonomies. She teaches courses on information organization, research methodology, and library science. Awards section is currently unspecified. Grants involve EU-funded initiatives focusing on open science infrastructure and digital preservation. She leads the Thesaurus.hr initiative and contributes to the OPERAS network.

• Visual Analytics
• Temporal Data Analysis
• Data Cleansing
• +3 more

Theresia Gschwandtner is a Researcher at TU Wien's Research Division of Visual Analytics (E193-07). Her work focuses on advancing visual analytics methodologies for temporal data, fraud detection, and uncertainty visualization. She leads the Network Lab and contributes to tools like TimeCleanser for data cleansing and NEVA for fraudulent network identification. Her research emphasizes interactive systems for guidance in data analysis, provenance tracking, and enhancing user-centric visualization frameworks. Key research interests include temporal data preprocessing, multivariate time series analysis, and the integration of automated guidance systems into visual analytics platforms. She has collaborated on projects such as Hermes (economic network exploration) and TBSSvis (temporal blind source separation), which combine algorithmic innovation with intuitive user interfaces. Guidance frameworks and user studies are central to her work, exploring how automated support impacts performance and mental state during complex data analysis tasks. She has advised students on theses addressing data quality, cyclical pattern detection, and lighting design visualization. Notable contributions include the Quantifying Uncertainty in Time Series Processing framework and the LightGuider system for interactive lighting design guidance. Her work bridges theoretical advancements with practical applications in healthcare, finance, and engineering domains.

Padmini SrinivasanView profile

Professor

• Information Retrieval
• Natural Language Processing
• Text Mining
• +12 more

Padmini Srinivasan is a Professor in the Department of Computer Science at the University of Iowa, affiliated with the College of Engineering. Her work bridges computer science, informatics, and social applications through advanced research in information retrieval, natural language processing, and data mining. Research Interests: Her research focuses on Information Retrieval & NLP , Text and Web Mining , Biomedical Text Mining , Privacy/Security & Censorship , Social Media Analytics (particularly in political and health belief contexts), and Crowdsourcing & Games . She leads the Text Retrieval & Text Mining Group , fostering interdisciplinary research involving machine learning, human computation, and real-world data challenges. Publication Trends: Her recent work appears in top-tier venues such as SIG-IR, KDD, WSDM, ICWSM, EMNLP, JASIST, and PLOS One, reflecting sustained contributions to both foundational and applied aspects of data science. These publications span topics from ranking optimization and query modeling to social dynamics, health informatics, and ethical AI. Scientific Awards: No specific awards were mentioned in the provided text. Advising and Grants: She has advised numerous graduate students including Osama Khalid, Ingroj Shrestha, Asad Mahmood, Jonathan Rusert, and others. While grant details are not listed, her publication record in premier venues suggests consistent external funding and collaborative research activity. Labs and Teams: She leads the Text Retrieval & Text Mining Group , which conducts cutting-edge research in search technologies, text analysis, and social media understanding, often integrating crowdsourcing and game-based methods for data collection and evaluation.

Roxanne TaylorView profile

Lecturer

• Historical Linguistics
• Corpus Linguistics
• Holocaust Studies
• +4 more

Roxanne Taylor is a Lecturer in English at the Department of Media, Humanities and the Arts within the School of Arts and Humanities at the University of Huddersfield. She holds a PhD in Linguistics from the University of Manchester (2019-2023) and a Master's in Ancient Greek Linguistics from the University of Oxford (2017-2019). Her academic profile includes external examiner roles at the University of Essex (2024-2028) and visiting research fellowships with Pennine Heritage Limited. Education: PhD (University of Manchester), MA (University of Oxford) External Roles: University of Essex Examiner (2024-2028), Pennine Heritage Visiting Fellow Taylor's research spans historical syntax, corpus linguistics, and Holocaust studies with focus on language use. Her work examines grammatical functions in Old English noun phrases, adposition evolution in Present-Day English, and syntactic/semantic patterns in ancient Greek texts. Current supervision includes projects on bilingualism and pragmatic developments in English. Recent research output includes analyses of argument realization, grammaticalization processes, and valency patterns across diachronic and synchronic frameworks. While no formal awards are listed, her contributions intersect with UN Sustainable Development Goals for education. Supervision: English-Punjabi bilingualism, Pragmatics PhD projects Methodologies: Corpus-based analysis, Lexical Functional Grammar, Historical syntax

• Machine Learning
• Case-Based Reasoning
• Natural Language Processing
• +3 more

Nirmalie Wiratunga is a Professor in Intelligent Systems at the School of Computing , Robert Gordon University, and serves as the Associate Dean for Research . She is also an Adjunct Professor at the Norwegian University of Science and Technology (IDUN program). Her academic excellence spans over two decades in Artificial Intelligence and Machine Learning , with a focus on Explainable AI (XAI) , Case-Based Reasoning (CBR) , and Natural Language Processing (NLP) . Her research explores innovative methodologies for knowledge-rich representations to automate decision-making through CBR for Retrieval-Augmented Q&A systems and human-centered AI platforms . She co-founded Attendr.app , a spinout for student and conference attendance tracking, and leads the Artificial Intelligence & Reasoning Research Group at RGU. Recent publications (2024–2025) highlight her work on LLM hallucination detection , counterfactual explanations in finance , cross-lingual biomedical review automation , and multi-query resolution in legal domains . Themes span AI explainability , NLP , CBR , and domain-specific knowledge integration across healthcare, law, and education. Her leadership extends to organizing international workshops on XAI , digital health , and Deep Learning , and co-chairing the ICCBR 2021 and 2022 conferences. She actively contributes to program committees for ECCBR , ECML/PKDD , and IJCAI .

Mingjun ZhongView profile

Lecturer

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +12 more

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.

Anna SidorovaView profile

Professor

• Artificial Intelligence in Business
• AI Ethics and Governance
• Digital Transformation
• +7 more

Anna Sidorova is a Professor and Chair of the Department of Information Technology and Decision Sciences at the G. Brint Ryan College of Business, University of North Texas. She holds a Ph.D. in Information Systems from Washington State University and has extensive experience in both academia and industry. Ph.D., Information Systems, Washington State University MBA, Washington State University B.A., Washington State University Her research focuses on Artificial Intelligence in Business , AI Ethics and Governance , Business Intelligence and Analytics , Text Mining , and Digital Transformation . She explores how organizations adopt and benefit from advanced technologies, particularly in decision-making and process improvement. The analysis of her publications reveals a strong trajectory in AI and machine learning applications in business, with a growing emphasis on ethical and governance aspects. Her work spans technical implementation and strategic implications, often published in premier journals like MIS Quarterly and Decision Support Systems . Dr. Sidorova has held significant leadership roles, including Academic Associate Dean for Undergraduate Programs at RCOB. She previously served as an Assistant Professor at the University at Albany, SUNY, and worked as a business consultant at PricewaterhouseCoopers, advising on business strategy and IT systems. She teaches graduate courses in Artificial Intelligence in Business, Information System Development, and Information Systems Theory and Research, contributing to the development of future business and technology leaders.

Soo Wooi KingView profile

Senior Lecturer

• Machine Learning
• Network Security
• Wireless Networks
• +3 more

Soo Wooi King serves as a Senior Teaching Fellow at Monash University Malaysia's School of Information Technology, contributing to both academic instruction and research in information technology systems. His professional profile centers on practical applications of computational methods in security and network optimization. His research spans Machine Learning, Network Security, Wireless Networks, and Natural Language Processing, with emphasis on implementing Naive Bayes classifiers for real-world problem solving. Key application areas include malware detection systems, news credibility assessment tools, and telecommunications network management solutions where algorithmic precision directly impacts operational reliability. Publication trends from 2017-2022 reveal consistent specialization in machine learning deployment across cybersecurity and networking domains. His highly cited 2018 survey on wireless load-balancing (18 Scopus citations) demonstrates significant field impact, while recent malware classification research shows evolving technical sophistication in feature engineering approaches. Work consistently bridges theoretical algorithms with industry-specific implementation challenges.

Oscar Romero MoralView profile

Professor

• Data Management
• Data Science
• Data Governance
• +7 more

Oscar Romero Moral is a Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics (FIB). He leads research in the inSSIDE, inLab FIB, and DTIM groups, focusing on data management, data science, and big data technologies. His work emphasizes knowledge graphs, data governance, and machine learning integration with data systems. Affiliations: UPC, inSSIDE, inLab FIB, DTIM Group Research Interests: Data Management, Data Engineering, Big Data, Knowledge Graphs, Data Governance, Machine Learning Integration He has authored over 276 academic contributions, including peer-reviewed articles on federated healthcare data systems, GPU-accelerated workflows, and graph-driven data integration. His recent work addresses challenges in heterogeneous computing, automated data governance, and scalable data architectures. Romero has served on the program committees of major conferences like VLDB, ICDE, and EDBT, and led competitive research projects in data systems and analytics. He collaborates extensively with industry partners and academic institutions, driving innovations in distributed data management and edge computing.

Shlomo DubnovView profile

Professor

• Music Technology
• Audio Signal Processing
• Machine Learning for Audio
• +3 more

Shlomo Dubnov is a Professor in the Department of Music at the University of California, San Diego. With an h-index of 36 and over 4,803 citations, his research spans the intersection of music, audio processing, and machine learning, making significant contributions to music information retrieval, sound synthesis, and speech recognition technologies. Dr. Dubnov's primary research interests focus on Music Technology and Audio Signal Processing , with particular expertise in: Machine learning applications for music and audio analysis Sound texture synthesis and modeling Music improvisation systems and computational creativity Audio adversarial examples and security Pitch tracking in noisy environments Multimodal learning for audio-text alignment His recent work demonstrates a strong trend toward applying transformer architectures and contrastive learning techniques to audio processing tasks, with publications in top conferences like IEEE International Conference on Acoustics, Speech, and Language Processing. Dr. Dubnov's research bridges theoretical music concepts with practical audio engineering applications, creating innovative solutions for music generation, analysis, and security. Dr. Dubnov has established productive collaborations with researchers across disciplines, particularly with: G. Assayag (102 Publications • 2,475 Citations) - music technology and improvisation systems Taylor Berg-Kirkpatrick (31 Publications • 1,459 Citations) - machine learning applications K. Chen (18 Publications • 1,150 Citations) - audio transformer models Cheng-i Wang (21 Publications • 305 Citations) - music information retrieval His work is supported by grants focused on advancing the state-of-the-art in music technology, audio processing, and machine learning applications for creative domains. Dr. Dubnov leads research in the Music Technology laboratory at UC San Diego, where his team develops innovative systems for music analysis, synthesis, and human-computer interaction in musical contexts. The lab focuses on creating intelligent systems that can understand, generate, and interact with musical content in real-time applications.