Find Academic Supervisors

Pavol FederlView profile

Assistant Professor

• Computational Biology
• Computer Graphics
• Biological Modeling
• +4 more

Pavol Federl is an Assistant Professor (Teaching) in the Department of Computer Science at the University of Calgary, Faculty of Science. His research focuses on computational modeling in biological systems, particularly plant growth, fracture mechanics, and interdisciplinary applications of L-systems. He holds a PhD (2002), MSc (1997), and BSc (1995) in Computer Science from the University of Calgary. Research Interests Biological modeling using L-systems Fracture formation in growing materials Computer graphics and interactive simulation environments Plant growth dynamics and pattern formation His work integrates mathematical modeling with computational techniques to study natural phenomena, such as leaf venation patterns and tree bark fracture. Recent research highlights include: Developmental models of multicellular structures Finite element analysis of fracture dynamics Interactive design tools for bonsai tree modeling Grants and Advising No specific grants or advisees are listed in the provided text. His teaching includes courses such as CPSC 457: Principles of Operating Systems. Professional Contributions He has contributed to software development in virtual laboratories and license plate recognition systems, demonstrating expertise in both theoretical and applied computing.

Peter JansenView profile

Associate Professor

• Natural Language Processing
• Artificial Intelligence
• Cognitive Science
• +5 more

Peter Jansen is an Associate Professor at the University of Arizona's College of Information Science with a joint appointment at the Allen Institute for Artificial Intelligence (Ai2). He specializes in natural language processing (NLP), cognitive artificial intelligence, and automated scientific discovery. His research focuses on virtual environments for scientific reasoning, such as ScienceWorld and DiscoveryWorld, and methods for explainable AI through projects like the Explanation Bank. Education: PhD in Psychology and Neuroscience from McMaster University, and a Bachelor of Independent Studies from the University of Waterloo. His interdisciplinary background combines NLP, computer science, physics, and electrical engineering. Key projects include open-source hardware initiatives like the science tricorder (featured in over 50 media outlets and exhibited at the German Museum of Technology in Berlin) and TextWorldExpress, a text-game simulator. His work emphasizes grounding science education through sensing and developing AI systems capable of systematic reasoning and explanation generation. Current roles include advising students in automated scientific discovery and contributing to cross-listed courses in Computer Science and Linguistics. He maintains an active presence in academic outreach through teaching, conferences, and publications.

• Requirements Engineering
• Human-Computer Interaction
• Digital Creativity Support
• +12 more

N. A. M. Maiden is a leading academic at City, University of London, with a prolific research output in human-centred artificial intelligence, digital creativity support, and requirements engineering. His work focuses on designing and evaluating digital tools that augment human creativity in professional domains such as journalism, elite sports coaching, and dementia care. His research interests are highly interdisciplinary, bridging computer science, human-computer interaction, and social applications. Key areas include co-creative AI systems, computational creativity, natural language processing, rule-based reasoning, and user-centred design. He investigates how technology can empower professionals by enhancing their creative thinking processes while maintaining high levels of human control. The trends in his recent publications demonstrate a consistent focus on developing and evaluating practical digital tools like 'Sport Sparks' for coaches and 'JECT.AI' for journalists. His work employs design science methodology, emphasizing co-creation with practitioners and empirical evaluation in real-world settings. The research spans from technical system architecture to human factors and societal impact, particularly in journalism innovation and healthcare technology. Automating science journalism tasks Designing digital tools for creative thinking in sports coaching Towards AI-enabled support for business model creativity Using computational tools to support journalists' creativity Creativity Triggers for requirements elicitation Dr. Maiden has made significant contributions to the field through numerous publications in top journals and conferences. His research often involves collaborative projects with industry partners and other academic institutions, securing funding for innovative work in human-computer collaboration. He mentors a research group focused on digital creativity and has been instrumental in developing novel methodologies for requirements engineering and creative problem solving. His work includes the development of serious games for forensic mental health settings (e.g., 'StreetWise') and digital solutions for improving dementia care. These projects highlight his commitment to applying technology for social good and improving quality of life through innovative information systems.

• Software Engineering
• Software Testing
• Program Analysis
• +4 more

Alessandro Orso is the Dean and Professor of the University of Georgia College of Engineering. Previously, he served as a professor at Georgia Institute of Technology, where he directed the Scientific Software Engineering Center and held leadership roles, including interim dean of the College of Computing (2023–2024). He earned his Ph.D. and master’s in computer science and electrical engineering from Politecnico di Milano, Italy. Orso’s research focuses on software testing, program analysis, and debugging, with applications in improving software reliability and security. His work includes developing automated testing tools (e.g., AutoRestTest), methodologies for scientific software engineering, and techniques for program debloating. His research has secured funding from agencies and industry partners like Google, IBM, and Microsoft, including a $11M grant from Schmidt Futures. His scientific awards include being a Distinguished Member of the ACM and IEEE Fellow. He advocates for innovative education, such as Georgia Tech’s Online Master of Science in Computer Science and his virtual software engineering course. His publications span automated testing, API analysis, and security, reflecting his commitment to advancing both academic research and practical software solutions. Orso’s leadership includes directing the Scientific Software Engineering Center, which pioneers methodologies for scientific software improvement and trains a new generation of engineers. His career blends academic excellence with impactful industry collaborations, driving advancements in software engineering practices.

Marco KuhrmannView profile

Professor

• Software Engineering
• Agile Software Development
• Process Improvement
• +3 more

Marco Kuhrmann is a Professor of Agile System Development at the Faculty of Informatics at Reutlingen University. He holds positions including Chair of the Examination Board for the Medical Technology Informatics program. His research focuses on software engineering methodologies, particularly agile development, process improvement, and hybrid methods. He has held academic roles at institutions like the Technical University of Munich, University of Southern Denmark, and the University of Passau. Education: PhD (Dr. rer. nat.) in Computer Science, Technical University of Munich (2008) Habilitation (Dr. rer. nat. habil.), Technical University of Munich (2014) Diploma in Computer Science, University of Potsdam (2004) His research interests include software process improvement, hybrid development approaches combining agile and traditional methods, and software engineering education. He leads projects like ANUKI and contributes to initiatives such as the HELENA survey on hybrid methodologies. His work addresses challenges in regulated domains, global software engineering, and the application of AI techniques in requirements engineering. Publications highlight themes like process transparency, compliance automation, and data augmentation. He is active in academic communities through workshops (e.g., PROFES, ICSSP) and has authored textbooks like Einführung in die Softwaretechnik (Springer, 2021). His teaching emphasizes hands-on experiments, such as simulating distributed teams and process adaptation in stressful scenarios. He collaborates with labs like the Virtual Reality Lab (VRehago) and contributes to research infrastructure like the IPSSE (Goslar) and software process ecosystems. Current projects explore AI-driven requirements evaluation and measurable artifact models in regulated industries.

Yasir ZakiView profile

Assistant Professor

• Communication Networks
• Wireless Networks
• Cellular Communication
• +9 more

Yasir Zaki is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Courant Institute of Mathematical Sciences, NYU. He leads the Communication Networks Lab, focusing on next-generation communication systems, performance optimization, and digital equity. University: New York University Abu Dhabi School: Courant Institute of Mathematical Sciences Department: Department of Computer Science Academic Rank: Assistant Professor Email: yz48@nyu.edu Dr. Zaki holds an MSc and PhD in Communication and Information Technology from the University of Bremen, graduating with honors. His research centers on communication and wireless networks, cellular systems, congestion control, and enhancing internet access in developing regions. His work bridges theory and real-world impact, especially in digital inclusion and AI's role in education. His recent publications span top venues like PNAS, IEEE TCSS, and ACM IMC, covering topics such as satellite network performance, digital inequality (Lite-Web), AI in education, and Big Tech's global influence. These works reveal a strong trend toward socially impactful computing, network measurement at scale, and algorithmic transparency. Big Tech Dominance Despite Global Mistrust Perception of AI in Education YouTube's Political Bias Lite-Web for Digital Equity Satellite Network Analysis His research has been recognized through high-profile media coverage in Nature and The National , and his PhD student Hazem Ibrahim received the MIT Technology Review Arabia’s Innovators Under 35 MENA 2023 award. This reflects the lab's excellence in computational social science and AI policy. Dr. Zaki mentors students in the Capstone and Research Seminar courses and actively advises PhD and research assistants. He has secured research funding through NYUAD and collaborative projects, enabling field deployments in 56 countries. His lab, the Communication Networks Lab, fosters interdisciplinary work, involving researchers from computer science, social sciences, and policy.

Zheng ZhangView profile

Researcher

• Human-AI Interaction
• Human-Computer Interaction
• Machine Learning
• +3 more

Zheng Zhang is a Ph.D. candidate in the Department of Computer Science and Engineering at the University of Notre Dame, where he focuses on human-AI interaction. He also works as an Applied Scientist in Adobe's GenAI team, developing interactive AI systems for user experience enhancement. He has held internships at Apple, AWS AI, and Meta Reality Labs. Education: Ph.D. (in progress) at University of Notre Dame M.S. in Computer Science from University of Rochester and University of Minnesota B.Eng. in Software Engineering from Shaanxi Normal University His research explores the intersection of human-computer interaction (HCI) and machine learning (ML), emphasizing systems that provide adaptive, context-sensitive support for complex cognitive tasks. Key areas include collaborative tools for team ideation, incremental learning from user demonstrations, and multimodal context-aware interfaces. Recent publications demonstrate a focus on AI-augmented collaboration through large shared displays (LADICA), audio-visual annotation (PEANUT), and interactive qualitative coding (PaTAT). His work increasingly integrates generative AI with real-time human input, particularly for co-located teams and educational applications. He serves as a Program Committee member and reviewer for top HCI/ML conferences including CHI, UIST, IUI, and ACL. Teaching experience spans courses in human-AI systems, algorithms, and data structures.

Sandaru SeneviratneView profile

Research Fellow

• Natural Language Processing
• Machine Learning
• Text Simplification and Generation
• +1 more

Sandaru Seneviratne is a Research Fellow at the School of Computing , The Australian National University , focusing on Natural Language Processing (NLP), Machine Learning, Text Simplification, and Health Informatics. His work bridges advanced language technologies with healthcare applications. PhD in Computer Science, The Australian National University Bachelor of Computer Science and Engineering, University of Moratuwa Research Interests His NLP research emphasizes text simplification frameworks like TextSimplifier and Prompt-based methods for multilingual contexts (e.g., English-Sinhala translation). He investigates factuality error detection in simplified text and lexical substitution techniques for accessibility. In Health Informatics, he contributes to medical term identification using neural networks (CNNs/Transformers), CLEF eHealth evaluations, and diabetes management technologies for teens. Earlier work includes clustering word embeddings for knowledge graphs and restaurant domain information extraction. Publication Trends His publications span 2018–2024, with recent focus on multilingual text simplification, factuality in AI-generated text, and healthcare information retrieval. Technical methods include transformers, triplet networks, and hierarchical clustering, often applied to medical/health domains.

• Digitalization
• Multi-Agent Systems
• Semantic Web
• +5 more

Prof. Dr. Thomas Farrenkopf is a full-time Professor in the Department of Business Informatics at the Technische Hochschule Mittelhessen , serving as Dean of Studies . He leads the Business Informatics Bachelor program and specializes in digitalization, multi-agent systems, and AI-driven decision support. Expertise Areas : Industry 4.0, Smart Factory, Semantic Web, full-stack software development Contact : thomas.farrenkopf@mnd.thm.de Research Focus : His work bridges knowledge-based methods with practical applications in artificial intelligence, particularly through multi-agent systems for traffic modeling and business simulations. He developed AGADE Traffic (a knowledge-based traffic simulator) and ATHOS (a domain-specific language for multi-agent simulations), focusing on scalability and complexity reduction. Recent studies explore individual preferences' impact on traffic policies and agent-based feedback mechanisms in business contexts. Academic Contributions : With over 15 publications since 2013, his research spans agent-based modeling, semantic web applications, and smart manufacturing - notably improving throughput times in toolmaking by 90% through Industry 4.0 implementations. Teaching & Supervision : Actively supervises Bachelor's Practical Projects (BPP), providing guidance for internship-related academic work through structured evaluation processes and electronic reporting systems. Collaborations : Works with international researchers like Johannes Nguyen, Simon T. Powers, and Michael Guckert, publishing in venues such as SN Computer Science , Lecture Notes in Computer Science , and Journal of Artificial Societies and Social Simulation .

Qiuyu LuView profile

Assistant Professor

• Ecological HCI
• Computational Design
• Bio-inspired & Biomimetic Design
• +3 more

Dr. Qiuyu (Luca) Lu is an Assistant Professor (Presidential Young Scholar) at the School of Design of The Hong Kong Polytechnic University and Director of the Interbeing Lab , which focuses on designing intelligence for mutualism between humans, nature, and machines. He holds a Ph.D. in Design and a B.Eng. in Mechanical Engineering from Tsinghua University. Ph.D. in Design, Tsinghua University B.Eng. in Mechanical Engineering, Tsinghua University His research bridges Ecological HCI, Computational Design , and Bio-inspired Engineering , creating sustainable interactive systems that leverage natural processes. Recent projects include: RoboMoRe (2025): LLM-driven robotic morphology optimization Degrade to Function (2024): Programmable eco-degradable interfaces Sustainflatable (2023): Ambient energy harvesting for morphing devices His work has been exhibited internationally at Ars Electronica and the Milan Triennale . Scientific recognition includes a Best Paper Honorable Mention at UIST 2023. Dr. Lu is actively recruiting PhD students and postdocs across disciplines, including design, computer science, mechanical engineering, and materials science, with research tracks in: Experimental Track: Interactive hardware prototyping Computational Track: AI-aided design tools Design Track: User experience frameworks

Alessandro Lameiras KoerichView profile

Professor

• Machine Learning
• Computer Vision
• Pattern Recognition
• +6 more

Alessandro Lameiras Koerich is a Professor in the Department of Software Engineering and IT at École de technologie supérieure (ÉTS), part of the Université du Québec network in Montreal, Canada. He is affiliated with the LIVIA (Imaging, Vision and Artificial Intelligence Laboratory), where he conducts research in machine learning, computer vision, and pattern recognition. His work focuses on developing AI solutions for real-world applications in healthcare, security, and multimedia analysis. His research interests span multiple domains of artificial intelligence, with particular emphasis on multimodal machine learning, affective computing, and trusted machine learning systems. Dr. Koerich's work explores how AI can interpret human emotions through facial expressions and audio analysis, develop robust models resistant to adversarial attacks, and create efficient learning systems that can adapt to changing data streams. His research bridges theoretical advances with practical applications in healthcare monitoring, security systems, and multimedia content analysis. Analysis of his recent publications reveals a strong focus on multimodal emotion recognition, with increasing emphasis on transformer architectures, knowledge distillation techniques, and addressing challenges in real-world settings (in-the-wild recognition). His work consistently combines computer vision with audio processing, demonstrating expertise in both visual and auditory modalities for affective computing applications. Recent trends show growing interest in large language models for audio classification and addressing challenges of concept drift in text stream mining. Dr. Koerich actively supervises numerous graduate students across various research projects, with a particular focus on multimodal expression recognition, music genre classification, and robust machine learning systems. His collaborations span multiple institutions and research groups, reflecting the interdisciplinary nature of his work in artificial intelligence. He is a key member of the LIVIA laboratory, which has established ÉTS as a leading institution in computer vision research in Canada (ranked 6th according to CSRankings). The laboratory focuses on solving real-life problems through AI engineering, particularly in developing complex deep learning models with massive amounts of data that have incomplete annotations.

Eli Ben-MichaelView profile

Assistant Professor

• Causal Inference
• Statistical Methods
• Computational Methods
• +5 more

Eli Ben-Michael is an Assistant Professor in the Department of Statistics & Data Science and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He is also affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC). Previously, he was a Post-Doctoral Fellow at Harvard University in the Institute for Quantitative Social Science and the Department of Statistics. Ben-Michael's research focuses on developing statistical and computational methods to solve practical issues in public policy and social science research. His work brings together ideas from statistics, optimization, and machine learning to create methods for credible and robust causal inference and data-driven decision making. His research spans multiple domains including healthcare policy, criminal justice reform, reproductive health, and education. His scholarly work shows a strong emphasis on causal inference methodology, with particular attention to synthetic control methods, balancing weights, policy learning, and sensitivity analysis. His recent publications demonstrate applications across diverse fields including healthcare, economics, social policy, and criminal justice. Ben-Michael completed his PhD in Statistics from UC Berkeley and earned his undergraduate degree in Computer Science and Statistics from Columbia University. His technical expertise includes developing open-source software, with contributions to the R packages 'augsynth' and 'multical' for synthetic control methods and multilevel calibration.

Christos OuzounisView profile

Professor

• Bioinformatics
• Digital Biology
• Biological Computation
• +5 more

Christos Ouzounis is a Professor of Bioinformatics at the Department of Informatics, Aristotle University of Thessaloniki , with a career spanning institutions including the European Bioinformatics Institute , King's College London , and University of Toronto . His work bridges Computational Biology , Digital Biology , and Metagenomics , focusing on large-scale data analysis, machine learning applications, and functional annotation of proteins. Education : BSc in Biological Sciences (1986), MSc in Biological Computation (1987), and DPhil in Computational Chemistry (1993) Key Roles : Director of the Bioinformatics Centre at King's College London (2007-2010), Research Director at IDEP-EKETA (2014-2020) His research interests include low-complexity protein sequences , Covid-19 seasonality patterns linked to UV radiation, and metagenomic analysis of urban microbiomes in cultural heritage sites. Current projects involve machine learning models for microbial coexistence networks, ontological classification of biomedical literature, and bioinformatics tool development . Publications highlight trends in archaeal genomics , functional dark matter in metagenomics, and epidemiological modelling . Notable collaborations include work on BioTextQuest v2.0 for concept discovery and MjCyc for metabolic pathway analysis.

Gal ElidanView profile

Associate Professor

• Probabilistic Graphical Models
• Machine Learning
• Copula Models
• +3 more

Gal Elidan is an associate professor in the Department of Statistics at the Hebrew University of Jerusalem, Faculty of Social Sciences. His work bridges statistical theory with practical applications in machine learning and artificial intelligence. Dr. Elidan's research focuses on Probabilistic Graphical Models , with emphasis on fundamental representations and methods for inference and learning in large-scale domains. His work addresses structure learning, discovery of hidden variables, transfer of knowledge between related tasks, and nonlinear high-dimensional representations of continuous or hybrid distributions. He also applies these techniques to real-life applications including computational biology, machine vision, and medical informatics, with recent focus on principled techniques for medical diagnosis. His publication record shows consistent contributions to top machine learning venues with a recent emphasis on copula models for high-dimensional data. His work demonstrates a progression from foundational graphical model research to specialized applications in medical informatics and time series analysis. Scientific recognition includes: Runner-up for Best Student Paper Award at UAI 2004 Best Paper Award at ISMB 2001 Dr. Elidan serves on the editorial board of the Journal of Artificial Intelligence Research (JAIR) and has contributed to the development of important software tools including FastInf, a library for large-scale inference of graphical models, and LibB, a package for Bayesian network inference and learning. He has organized significant academic events including the NIPS 2011 Workshop on Copulas in Machine Learning and co-organized the PASCAL2 Probabilistic Inference Challenge and UAI 2010 Approximate Inference Challenge.

Wayne WobckeView profile

Associate Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +7 more

Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.