Find Academic Supervisors

Derek AndersonView profile

Professor

• Artificial Intelligence
• Machine Learning
• Information Fusion
• +12 more

Derek Anderson is a Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri, within the College of Engineering. He is a core faculty member of the MU Institute for Data Science & Informatics and leads the Mizzou INformation and Data FUsion Laboratory (MINDFUL). His research spans artificial intelligence, machine learning, information fusion, computer vision, and remote sensing, with applications in defense, healthcare, and humanitarian technology. Education: PhD, University of Missouri MS, University of Missouri BS, Wichita State University Anderson's research focuses on foundational AI challenges, particularly explainable AI (XAI) and the use of simulated data to train robust models under uncertainty. He investigates how AI systems make decisions and how to communicate those decisions clearly to humans, especially in high-stakes environments like medical imaging and autonomous vehicles. His work leverages tools like Unreal Engine and Infinite Studio to generate photorealistic and multispectral synthetic data, enabling broader and more diverse training sets than real-world data alone can provide. His recent publications and projects highlight trends in generative AI limitations , AI ethics , infrared sensor innovation , and AI for humanitarian demining . These works collectively emphasize transparency, robustness, and societal benefit in AI development. Scientific Awards and Leadership: Program Co-Chair, three national AI conferences (2023) Member, IEEE USA AI Committee Member, IEEE CIS Industry & Government (I&GA) Committee Member, IEEE Vertical on Societal Implications of AI Anderson is deeply committed to mentoring the next generation of AI scientists. He advises undergraduate, master’s, and PhD students, many of whom engage in hands-on research from their first year. His lab has secured approximately $29 million in funding across 25 projects from agencies like the U.S. Army ERDC and the National Science Foundation. He has published over 190 articles and is a sought-after leader in the AI research community. His lab, MINDFUL, focuses on information fusion and AI under uncertainty, with active projects in drone-based sensing, material design, and geospatial analytics. The team uses simulation as a core methodology to advance AI trustworthiness and performance across diverse domains.

• Cognitive Psychology
• Human Factors
• Ergonomics
• +7 more

Dr. Kyung Hun Jung is a faculty member at Kennesaw State University, affiliated with the Department of Psychology. He teaches courses including Cognitive Psychology, Engineering Psychology, Experimental Design, and Research Methods and Statistics. Ph.D. in Experimental Psychology (University of New Mexico, 2013) M.S. in Experimental Psychology (Korea University, 2006) B.A. in Psychology (Korea University, 2004) His research spans cognitive psychology, human factors, and ergonomics, with recent work focusing on automated vehicle interaction and driver training using moving-base driving simulators and virtual-reality headsets. Earlier research includes studies on attentional mechanisms, facial attractiveness, color perception, and document similarity analysis. Recent publications highlight applications of human factors in transportation technology and usability engineering. Data and code from his studies are publicly shared for replication purposes.

Ali FarzamniaView profile

Lecturer

• Signal Processing
• Bio-Medical Signal Processing
• Machine Learning
• +3 more

Dr. Ali Farzamnia is a Lecturer in Electronic and Electrical Engineering at the Department of Engineering , School of Computing and Engineering , University of Huddersfield, United Kingdom. He is actively involved in research and supervises PhD students. Research focus areas: Signal Processing, Deep Learning, and Wireless Communications His recent work demonstrates interdisciplinary applications of machine learning in diverse domains including sign language recognition, financial trading, agricultural stress detection, and software testing automation. Dr. Farzamnia employs cutting-edge techniques like third-generation transformers and few-shot learning frameworks in his research projects.

Eirini NtoutsiView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +6 more

Eirini Ntoutsi is an Associate Professor at the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover and a member of the L3S Research Center. Her academic journey includes a post-doctoral position at LMU Munich with an Alexander von Humboldt Foundation fellowship, and she earned her PhD from the University of Piraeus, Athens under the supervision of Y. Theodoridis. She holds a diploma and M.Sc. in Computer Engineering & Informatics from the University of Patras, Greece. Her research lies at the intersection of Artificial Intelligence and Machine Learning, focusing on two main pillars: learning over complex data and data streams (covering adaptive learning, change detection, and model stability), and responsible Artificial Intelligence (covering fairness-aware learning, data quality, and proper evaluation of AI/ML methods). Her work addresses critical societal challenges related to bias in algorithmic decision-making systems. Dr. Ntoutsi's recent publications demonstrate a strong focus on drift-aware learning for imbalanced data streams and fairness in AI systems. Her research shows a clear progression from foundational work in pattern management during her PhD to cutting-edge applications addressing real-world challenges in social streams, sensor data, and recommendation systems. Alexander von Humboldt fellowship for postdocs Best-student paper award at ICBK 2018 1st place in the 2006 innovation competition in Greece Dr. Ntoutsi actively mentors PhD and master's students while leading major research initiatives including NoBIAS (EU-funded, as Network coordinator), BIAS (Volkswagen Stiftung-funded), and OSCAR (DFG-funded). Her interdisciplinary approach combines technical AI solutions with philosophical and legal considerations to develop more equitable algorithmic systems.

Zachary NealView profile

Professor

• Network Science
• Social Networks
• Computational Social Science
• +7 more

Zachary Neal is a Professor in the Department of Psychology at Michigan State University , affiliated with the Social-Personality program. He is an active researcher in network science, with interdisciplinary work spanning psychology, sociology, education, and geography. He serves as editor of Global Networks and promotes open science through initiatives like the Recommendations for Sharing Network Data and Materials. Position: Professor Institution: Michigan State University Department: Psychology School: College of Social Science Email: zpneal@msu.edu Website: www.zacharyneal.com Bluesky: @zpneal.bsky.social His research focuses on networks , particularly ego networks, two-mode networks, and cognitive social structures. He emphasizes methodological rigor, open data practices, and the use of R for network analysis. He is also known for his work on childfree identity and public scholarship. The most recent publications reflect a strong commitment to open science, data sharing standards, network pedagogy, and interdisciplinary collaboration . Themes include network visualization (e.g., 'nutrition labels'), higher-order networks, and the social dynamics of research communities. His work bridges theoretical network science with practical applications in health, education, and policy. Scientific Recognition: SIPS 2025 Commendation for Recommendations for Sharing Network Data and Materials Editor, Global Networks Organizer of teaching initiatives in network science Zachary Neal is actively involved in mentoring students, promoting best practices in research, and securing collaborative grants related to network science and open scholarship. He contributes to the academic community through conference organization (e.g., Sunbelt), editorial work, and public engagement on social media. He leads research initiatives that often involve cross-institutional teams and open-source tools. He is associated with research groups and labs focused on computational social science, network analysis, and open research practices . His work on the Moral Minds Lab (via affiliation) and independent projects indicates leadership in teams that study social cognition and behavior through network lenses. Future work is likely to expand on data sharing frameworks, network teaching tools, and the social implications of network structures in diverse contexts.

• Empirical Software Engineering
• Code Quality Analysis
• Software Maintenance
• +7 more

Rocco Oliveto is a Professor in the Department of Computer Science at the University of Salerno, Italy, with a distinguished research career spanning over two decades in empirical software engineering. His work bridges theoretical software engineering principles with practical applications, with recent expansion into healthcare informatics and machine learning applications. His research interests focus on code quality assessment, software maintenance practices, developer behavior analysis, and empirical studies of software engineering phenomena. He has made significant contributions to understanding code smells, bug prediction, API compatibility issues, and more recently, container technologies and smart contract analysis. His recent work demonstrates a strategic expansion into healthcare applications, leveraging software engineering techniques for medical diagnostics and rehabilitation systems. Oliveto's publication pattern shows consistent productivity with multiple high-impact publications each year across top venues including IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, and Empirical Software Engineering journal. His recent articles (2023-2025) reveal a growing interest in applying software engineering techniques to healthcare domains while maintaining strong contributions to core software engineering topics. The research demonstrates sophisticated methodological approaches combining empirical studies with machine learning techniques. His collaborative network includes prominent researchers such as Simone Scalabrino, Gabriele Bavota, and Andrea De Lucia, with whom he has co-authored numerous high-impact publications. This collaboration spans both traditional software engineering topics and emerging interdisciplinary applications in healthcare.

• High-Performance Computing
• Energy Efficiency in Computing
• Parallel Algorithms
• +4 more

Dr. Lennart Johnsson is a Professor of Computer Science at the University of Houston, with affiliations at the Royal Institute of Technology (KTH) in Sweden. He has held faculty positions at Caltech, Yale University, Harvard University, and KTH, and industry roles at ABB Research and Thinking Machines Corp. His research focuses on High-Performance Computing (HPC), energy-efficient systems, parallel algorithms, and grid computing. He has collaborated with institutions like PRACE, and companies including AMD, Intel, and Texas Instruments, leading to innovations such as energy-efficient HPC servers and DSP-based architectures. Research interests include optimizing HPC for energy efficiency, embedded processors (e.g., DSPs), and novel interconnection networks. He has pioneered software libraries like CMSSL and contributed to standards such as MPI and High-Performance Fortran. Awards include the Machtey Best Student Paper Award and recognition in the Gordon Bell Prize competition. Dr. Johnsson has supervised numerous students, including those working on adaptive scheduling, grid computing, and bioinformatics. He founded the Texas Learning and Computation Center and led initiatives like the Texas GigaPoP and RENoH network. Current projects explore energy-efficient HPC using DSP architectures. Key contributions include the first No. 1 system on the Top500 list (1993), grid computing frameworks, and infrastructure for distributed applications. He has served on boards for PRACE, NSF, and Swedish research councils, and advises on national HPC strategies.

• 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.

Varghese JacobView profile

Professor

• Information Systems
• Management Information Systems
• Operations Research
• +11 more

Varghese Jacob is a Professor of Management Information Systems and holds the Ashbel Smith Professor and Lars Magnus Ericsson Chair titles at the Naveen Jindal School of Management, University of Texas at Dallas. He has served in key administrative roles including Senior Associate Dean since 2002 and previously as Associate Dean (1999–2002). Prior to joining UTD, he was an Associate Professor at The Ohio State University's College of Business. Ph.D. in Management, Purdue University (1986) M.S. in Physics, Indian Institute of Technology (Delhi) (1980) His research spans Information Systems, Operations Research, and Artificial Intelligence , focusing on data mining, dynamic pricing, software requirements, and group decision support. He explores how data reduction techniques, neural networks, and incentive structures impact business systems. His work integrates technical modeling with behavioral and strategic considerations in IT adoption and e-commerce. His recent publications highlight consistent contributions to top-tier journals such as Management Science , Information Systems Research , and European Journal of Operational Research . The research trends show a strong focus on data quality, transaction analysis, IT strategy, and open-source collaboration , with methodological diversity spanning empirical studies, optimization models, and experimental designs. Ashbel Smith Professor Lars Magnus Ericsson Chair Dr. Jacob has co-chaired four Ph.D. dissertations and served on multiple dissertation committees. He has secured research funding from organizations such as JETRO, Ameritech Foundation, and internal university grants, supporting work in e-commerce, genetic algorithms, and telecommunications. His leadership in accreditation (AACSB) and academic policy at UTD underscores his institutional impact. He has been actively involved in executive education, organizing and teaching programs on object-oriented systems, internet business, and client-server computing. His professional affiliations include the Association for Information Systems (AIS), INFORMS, IEEE Computer Society, and ACM, reflecting his interdisciplinary engagement.

Ian LaneView profile

Associate Professor

• Natural Language Processing
• Speech Recognition
• Machine Translation
• +5 more

Ian Lane is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin Engineering school, serving as Program Director for the Natural Language Processing Professional Master's Degree Program. He joined UCSC in Fall 2022 after an extensive career spanning academia and industry. His research centers on computational systems that understand spoken human language, spanning speech recognition, transcription, meaning interpretation, and contextually appropriate responses. Key research areas include: Natural Language Processing for real-world applications Conversational AI systems development Speech-to-speech translation technologies Multimodal interaction (audio-visual integration) Language technologies that learn through real-world interaction His recent publications demonstrate strong focus on hallucination detection in LLMs, tabular data understanding, explainable AI, and robust speech recognition systems. Current work emphasizes "in the wild" language technologies that adapt through user interaction. Dr. Lane has received recognition through impactful industry applications including Jibbigo (the first mobile speech translation app) and military translation systems deployed in Iraq and Afghanistan. He actively mentors students and collaborates across UCSC's Silicon Valley Campus programs including Games and Playable Media and Human-Computer Interaction. His vision includes integrating NLP with virtual environments for language learning and skill acquisition.

• Data Mining
• Machine Learning
• Natural Language Processing
• +5 more

Luca Cagliero is an Associate Professor in the Department of Control and Computer Engineering at Politecnico di Torino (Polytechnic University of Turin), Italy. His research spans multiple domains within computer science, with particular expertise in data mining, machine learning, natural language processing, and multimodal analysis. He has established a prolific research career with over 150 publications spanning from 2009 to the present, demonstrating consistent scholarly productivity. Dr. Cagliero's research interests focus on the intersection of artificial intelligence and practical applications. His work addresses fundamental challenges in data mining, information retrieval, and educational technology, with recent publications showing increasing emphasis on large language models, multimodal analysis, and explainable AI. He has made significant contributions to text summarization techniques, database systems, and applying machine learning to educational contexts. His recent publications (2023-2025) demonstrate a clear research trajectory toward multimodal AI systems, with particular attention to the integration of vision and language processing. His work spans theoretical contributions in machine learning methods as well as practical applications in educational technology, social media analysis, and document understanding. The breadth of his collaborations across different application domains indicates a versatile research profile that bridges theoretical and applied computer science. Dr. Cagliero has mentored numerous researchers who have become his frequent collaborators, including Lorenzo Vaiani, Moreno La Quatra, and Davide Napolitano. His work has appeared in top-tier venues including ACL, IEEE Transactions on Knowledge and Data Engineering, and Expert Systems with Applications, reflecting the high quality and impact of his research contributions.

• Natural Language Processing
• Text Mining
• Machine Learning
• +3 more

Yuping Wu is a researcher specializing in Natural Language Processing , Text Mining , and Machine Learning . Her work focuses on enhancing large language models , data augmentation , and attention mechanisms in computational linguistics and clinical domains. She actively contributes to peer-reviewed research outputs in conferences like EACL , ProbSum , and journals such as Entropy . Research areas: Natural Language Processing , Machine Learning , Data Augmentation , Clinical Informatics , Large Language Models , and Text Mining . Key publications: Extractive summarization, clinical named entity recognition, data augmentation with black-box LLMs, attention networks in computer vision, and cost index prediction models. Collaborations: Active with teams like PULSAR and researchers including X. Zeng , G. Nenadic , and V. Schlegel . Her research spans artificial intelligence , computer vision , and healthcare text mining , often leveraging transformer architectures and neural network innovations . She explores applications in clinical data summarization and construction cost forecasting, demonstrating interdisciplinary impact through UN Sustainable Development Goals (SDGs) related to technological advancement and healthcare.

Fahrettin HorasanView profile

Associate Professor

• Machine Learning
• Data Security
• Recommender Systems
• +3 more

Dr. Fahrettin Horasan is an Associate Professor in the Department of Computer Engineering at the Faculty of Engineering and Natural Sciences . His research focuses on applied computational methods in data security, machine learning, and information retrieval systems. Specialized in medical image watermarking and encryption using matrix decomposition techniques (SVD, ULV) Developed novel collaborative filtering recommender systems with hybrid approaches and matrix approximation Contributions in sentiment analysis for healthcare and e-commerce domains His recent publications highlight interdisciplinary work combining cryptology, biomedical imaging, and large-scale data processing. Notable methods include chaotic system-based encryption, latent semantic indexing, and gradient boosting for darknet traffic analysis. While specific awards, educational background, and student advisement details are not publicly detailed in the provided texts, his technical output reflects active engagement in computational science advancements.

Mitsunori OgiharaView profile

Professor

• Bioinformatics
• Computer Science
• Computer Theory
• +4 more

Mitsunori Ogihara is a Professor of Computer Science at the University of Miami's College of Arts and Sciences, with secondary appointments in Electrical and Computer Engineering (ECE), Molecular and Microbiology (MMI), College of Arts (CoA), and Human Genetics and Genomics (HGG). He serves as Director of Workforce Development and Education at IDSC and Director of Graduate Studies for the Computer Science department. Education Ph.D. in Computer Science, Tokyo Institute of Technology (1993) Research Interests Professor Ogihara maintains a diverse research portfolio spanning multiple domains of computer science. His work demonstrates significant contributions to: Theoretical Computer Science - with publications on computational complexity and dynamical systems Bioinformatics and Medical Data Science - pioneering work on multi-omics data integration for Type 1 Diabetes biomarker discovery Data Mining and Machine Learning - developing novel algorithms for knowledge extraction from complex datasets Music Information Retrieval - editing the seminal book "Music Data Mining" and applying computational methods to music analysis Digital Humanities - advancing NLP techniques for historical Japanese text processing Publication Trends Professor Ogihara's recent publications reveal a strong interdisciplinary trajectory , increasingly connecting computer science with biomedical applications and cultural analytics. His work on computational data augmentation for small biomedical datasets represents a significant methodological innovation with potential clinical impact. Simultaneously, he continues to advance foundational work in computational complexity while expanding applications in entertainment analytics and historical text processing, demonstrating remarkable intellectual breadth. Teaching and Mentorship Professor Ogihara has supervised numerous PhD students who have secured positions at leading technology companies (Google, Amazon, IBM) and academic institutions worldwide. His commitment to education extends to textbook authorship, including "Exploring Data Science with R and the Tidyverse" and his upcoming "An Introduction to Theory of Computation," which aim to make complex computational concepts accessible to undergraduate students.

Nadeen FathallahView profile

Researcher

• Web Accessibility
• Natural Language Processing
• Computer Vision
• +3 more

Nadeen Fathallah is a researcher at the University of Stuttgart, affiliated with the Analytic Computing group at KI. Her work spans AI applications for accessibility, computer vision, and knowledge engineering. Research Focus: Web accessibility, ontology learning, and LLM-based solutions for Deaf/Hard of Hearing communities Projects: Key contributor to the IKILeUS project (Integrated AI in Teaching) at the University of Stuttgart Teaching: Has served as teaching assistant and assistant lecturer at German International University, German University in Cairo, and The Knowledge Hub Her research explores: Automated detection/correction of web accessibility violations (e.g., AccessGuru platform) Improving video captions using large language models Accessibility tools for tabular data (EchoTables) Ontology learning pipelines (NeOn-GPT, LLMs4Life) Recent work shows a focus on combining LLMs with domain-specific challenges across multiple fields, particularly emphasizing inclusive design principles. Contact details: Office at Universitätsstraße 32, Stuttgart, Germany (Room: 2.312b). Available via +49 711 685 88130.