Find Academic Supervisors

• Autonomous Industrial Systems
• Model Predictive Control
• Optimization
• +7 more

Mehmet Mercangoz is an Associate Professor in Autonomous Industrial Systems at the Department of Chemical Engineering, Faculty of Engineering, Imperial College London. His research focuses on developing intelligent systems for industrial processes and energy systems, emphasizing safety, reliability, and sustainability through process modeling, model predictive control, optimization, and machine learning. His recent publications highlight applications of large language models , reinforcement learning , and hybrid mechanistic-machine learning models to address challenges in industrial automation, fault handling, energy storage, and gas compression systems. Key themes include decarbonization, electrification of process industries, and adversarially robust control frameworks. Current affiliations include Imperial College London and ABB, with prior roles at ABB Corporate Research Switzerland and ABB Future Labs. His work bridges chemical engineering , control theory , and artificial intelligence , targeting zero-emission industrial operations.

Ruben MartinsView profile

Research Professor

• Constraint Programming
• Program Synthesis
• Software Analysis
• +3 more

Ruben Martins is an Assistant Research Professor and Master’s Program Director at the School of Computer Science, Carnegie Mellon University. He holds a Ph.D. from the Technical University of Lisbon, followed by postdoctoral research at the University of Oxford and UT Austin. His work focuses on constraint programming, program synthesis, and formal verification, with applications in improving programmer productivity and automating data science tasks. Education: Ph.D. in Computer Science, Technical University of Lisbon (2013) Postdoctoral Researcher, University of Oxford (2014-2015) Postdoctoral Researcher, UT Austin (2015-2017) Ruben's research bridges constraint programming with program synthesis, aiming to automate tasks such as vulnerability detection, code repair, and SQL synthesis. He developed Open-WBO , a MaxSAT solver that won multiple gold medals and is used in real-world optimization scenarios like seating arrangements for events. His work integrates large language models (LLMs) with traditional methods to enhance software analysis and debugging. Awards: Distinguished Paper Award at PLDI 2018 Gold Medal in MaxSAT Competitions for Open-WBO Advising & Grants: Ruben advises Master’s students and directs courses such as 15639, 15604, and others. His research has been supported by grants focusing on program synthesis and cybersecurity. Labs/Teams: Leads development of Open-WBO and contributes to projects like Crabtree (Rust API testing) and Pryde (evasion attack analysis).

• Machine Learning
• Generative Modeling
• Scientific Computing
• +3 more

Tanya Marwah is a Research Fellow at the Simons Foundation , collaborating with Polymathic AI . She earned her PhD from Carnegie Mellon University's Machine Learning Department, co-advised by Prof. Andrej Risteski and Prof. Zachary Lipton, and holds a master's degree from CMU's Robotics Institute. Her research bridges Machine Learning and Scientific Computing , focusing on generative modeling , inverse problems , and building scientific agents . Her work explores theoretical and empirical foundations for applying ML to differential equations, with key contributions in neural operators , memory mechanisms , and edge embeddings in GNNs . Recent publications highlight trends in PDE solvers via LLMs , cross-modal adaptation , and implicit regularization in SGD . She has received the prestigious Siebel Scholar award and actively contributes to top ML venues (NeurIPS, ICML, ICLR, TMLR). Her collaborations span institutions including Carnegie Mellon University, Polymathic AI, and CMU's Robotics Institute.

• Healthcare Informatics
• Artificial Intelligence
• Machine Learning
• +3 more

Maqbool Hussain is a Senior Lecturer in Computer Science at the College of Science and Engineering. His research focuses on healthcare informatics, artificial intelligence (AI) applications in medical decision-making, and the development of knowledge-based systems for aerospace and healthcare sectors. He has contributed significantly to areas such as digital twin frameworks for critical care workflows, clinical decision support systems, and semantic interoperability in healthcare. Research Interests Hussain’s work spans multiple disciplines, including: Healthcare Informatics and AI-driven medical solutions Knowledge Graphs and Large Language Models (LLMs) in healthcare and aerospace Digital twins for optimizing critical care and aerospace maintenance Data-driven clinical decision support systems Medical data security and interoperability standards Machine learning for text annotation and classification Recent Research Trends Recent publications highlight his focus on integrating advanced technologies like LLMs and digital twins into healthcare and aerospace contexts. His work addresses challenges in critical care workflow optimization, kidney disease diagnosis, and aerospace product maintenance through innovative knowledge graph approaches. He also explores the application of transformer-based models for text analysis and the secure exchange of medical data using biometric methods. Labs and Teams Hussain is affiliated with the College of Science and Engineering, contributing to interdisciplinary research teams focused on healthcare technology, aerospace systems, and AI applications. His collaborations span academic and industry partners, emphasizing practical solutions for healthcare informatics and aerospace engineering challenges.

• Data Science
• Speech Processing
• Natural Language Processing
• +3 more

Dr. Yunting Yin is an Assistant Professor of Computer Science at Earlham College, located in Richmond, Indiana. She holds a Ph.D. from Stony Brook University (2024) and a B.S. from Pace University (2019). Her research focuses on speech processing, natural language processing, and machine learning, with applications in aging analysis, facial expression recognition, and large language model forecasting. She actively collaborates with students on innovative projects and contributes to interdisciplinary fields such as computational linguistics and data science. Education: Ph.D., Stony Brook University, 2024 B.S., Pace University, 2019 Research Interests: Speech Processing : Analyzing vocal attributes to estimate aging and mortality risk in veterans. Natural Language Processing : Exploring alignment between computational language models and classical dictionaries. Machine Learning : Developing forecasting systems using large language models (LLMs) for real-world event prediction. Publications reflect a blend of interdisciplinary work, spanning computational linguistics, multimedia analysis, and biochemistry. Recent trends emphasize aging studies and LLM applications. Her 2024 dissertation underscores the intersection of speech and language in gerontology. Professional memberships include IEEE and IEEE WIE. She advises students on research projects and fosters collaborative environments for academic growth. Labs/Teams: Her research projects engage students in cutting-edge areas like facial expression bias analysis, LLM-driven forecasting, and audio-linguistic studies of aging.

Thilo DieingView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Thilo Dieing is a social data scientist at the Chair of Artificial Intelligence led by Prof. Dr. Heiner Stuckenschmidt at the University of Mannheim's Mannheim Center for Data Science. His research focuses on AI-supported study orientation, including developing chatbots and machine learning models to improve student advising. He contributes to projects such as NEST-BW, aiming to reduce dropout rates by creating personalized degree recommendations via generative AI chatbots, and the TransforMA project, which produced the multilingual recipe chatbot Foodle. His work emphasizes ethical AI use and explainable methods (XAI). Thilo holds a bachelor’s degree in political science and a master’s in data science from Mannheim. His technical expertise spans NLP, large language model optimization, and classification models. Future interests include integrating speech/image recognition and combining LLMs with knowledge graphs for advanced reasoning. His research underscores the critical role of data science in analyzing student data to create tailored educational pathways, ensuring chatbots like Foodle and the NEST-BW advisor deliver safe, effective guidance. Collaboration with student teams (e.g., a six-person TransforMA group) highlights his commitment to mentoring and practical application of AI in education.

• Microchannel Engineering
• Proton Exchange Membrane Fuel Cell
• Two-phase Flow
• +7 more

Eon Soo Lee is an Associate Professor in the Department of Mechanical and Industrial Engineering at the New Jersey Institute of Technology (NJIT). His primary research focuses on advanced materials engineering, biomedical microfluidics, and assistive technologies for individuals with disabilities. He has led federally funded projects including 'I-Corps: Multiplex Diagnostic Assay Using Interdigitated Nano-Sensing Technology' (NSF, 2023-2025) and 'Innovative Nano Catalysts for Automobile and Fuel Cell Applications' (NSF, 2018). Research Interests: Lee's work spans interdisciplinary areas including: Development of N-doped graphene/MOF composites for energy applications Microfluidic systems for blood plasma separation and antigen detection Design of accessible technologies for visually impaired users, including VR audio descriptions and remote sighted assistance systems Grants and Projects (select): National Science Foundation (2023): $500K for multiplex diagnostic assays National Science Foundation (2018): $300K for nano-catalysts in fuel cells Multiyear collaborations with industry partners on biosensor integration Innovation Highlights: Developed AIGuide: AR hand-guidance system for visual impairments Pioneered omnidirectional audio descriptions for VR music performances Published extensively in Carbon , Biomicrofluidics , and ACM/IEEE accessibility venues

Thaleia Dimitra DoudaliView profile

Assistant Professor

• Operating Systems
• Machine Learning
• Computer Vision
• +4 more

Thaleia Dimitra Doudali is an Assistant Professor at the IMDEA Software Institute in Madrid, Spain, leading the Muse research lab. She holds a PhD in Computer Science from Georgia Institute of Technology (2021), advised by Ada Gavrilovska, focusing on hybrid memory management with machine learning. Prior to her PhD, she earned a Diploma in Electrical and Computer Engineering from the National Technical University of Athens (2015). Her research interests span systems for machine learning, machine learning for systems, and computer vision applications in resource management. Her work has been recognized with awards including the 'César Nombela' (2024), 'Juan de la Cierva' (2021), and Rising Star in EECS (2020). She serves on program committees for top conferences (ASPLOS, EuroSys) and organizes workshops. Her lab's projects address cloud resource management, carbon efficiency, and sustainable computing. She advises multiple PhD and intern researchers, contributing to impactful publications in SIGMOD, EuroSys, and other venues. Notable contributions include Cronus (CV-based memory management), CaRE (carbon-efficient cloud-edge orchestration), and foundational work on ML-driven resource forecasting. Her research bridges systems and AI, aiming to optimize data center efficiency and sustainability. She actively promotes diversity in STEM through mentorship roles in ACM-W Greece and Women in HPC initiatives.

Saleh AshkboosView profile

Researcher

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +5 more

Saleh Ashkboos is a Ph.D. student in the Computer Science Department at ETH Zurich, advised by Professors Torsten Hoefler and Dan Alistarh. He is also a Research Assistant at the Scalable Parallel Computing Lab and an affiliated doctoral student of the ETH AI Center. His research focuses on accelerating deep neural network training and developing systems for large-scale graph processing. Prior to ETH Zurich, he earned his Master's degree in Computer Science from Sharif University of Technology, advised by Professor Amir Daneshgar. His work has led to notable contributions, including the best paper award at SC22 for 'ProbGraph.' Recent research emphasizes efficient LLM training and quantization techniques, with publications on topics like 4-bit inference, quantization-aware training frameworks, and scalable meteorological modeling. He has interned at Apple and Microsoft, and his work is accessible via Google Scholar and GitHub. Key projects include GPTQ (post-training quantization for transformers), SliceGPT (LLM compression), and ProbGraph (high-performance graph mining). His technical contributions span distributed systems, neural network optimization, and climate-related machine learning.

• Artificial Intelligence
• Data Science
• Knowledge Graphs
• +4 more

Yang Lei is an academic affiliated with the University of Melbourne's Department of Computing and Information Systems. Their research focuses on knowledge graphs, FAIR data principles, large language models (LLMs), and ontology engineering. They contribute to initiatives like the Open Research Knowledge Graph (ORKG) and NFDI4DataScience, emphasizing reproducibility, scholarly metadata, and systematic literature reviews. Recent work includes applications of LLMs for abstract summarization, leaderboard extraction, and entity recognition in scholarly documents. Yang also explores challenges in FAIR Digital Objects, machine-actionable workflows, and interoperability in research data management.

Christian MuiseView profile

Assistant Professor

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +6 more

Christian Muise is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. He holds a PhD (2014) in Artificial Intelligence from the University of Toronto, where he was advised by Sheila McIlraith and J. Christopher Beck. His research focuses on automated planning under uncertainty, combining planning with learning for applications like goal-oriented dialogue systems and multi-agent coordination. He previously held postdoctoral roles at the University of Melbourne's Agentlab and MIT's CSAIL, and was a Research Staff Member at the MIT-IBM Watson AI Lab. Education: PhD in Artificial Intelligence, University of Toronto (2014) MSc in Computer Science, University of Toronto (2009) BSc in Computer Science, Carleton University (2007) Research Interests: His work bridges automated planning and machine learning, emphasizing robustness in uncertain environments. Key areas include non-deterministic planning, model acquisition with large language models (LLMs), and human-aware planning. He explores applications in healthcare (e.g., treatment response prediction), robotics (autonomous navigation), and dialogue systems for safety-critical domains. Current projects include developing explainable planning systems and mitigating bias in AI decision-making. Awards: Scotiabank Scholar (Scotiabank Centre for Customer Analytics) Advising & Labs: Leads the Mu Lab, supervising PhD and Master's students in topics like model acquisition, dialogue systems, and planning bias. Active in open-source tools (e.g., L2P, MACQ library) to democratize planning research. Collaborates on projects like PRP Rebooted and FixMyPlan to advance FOND planning and LLM integration. Labs/Teams: Mu Lab at Queen’s University, focusing on planning under uncertainty, AI safety, and neuro-symbolic systems.

Els LefeverView profile

Associate Professor

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +5 more

Els Lefever is an Associate Professor at Ghent University, where she works with the LT3 (Language and Translation Technology) research team. Her position focuses on computational linguistics and natural language processing research, with strong ties to both theoretical and applied aspects of language technology. Dr. Lefever earned her PhD in Computer Science from Ghent University in 2012 with her dissertation titled "ParaSense: Parallel Corpora for Word Sense Disambiguation." Her academic journey began as a computational linguist at the R&D department of Lernout & Hauspie Speech Products before transitioning to academia. Els Lefever's research spans multiple areas within computational linguistics with particular expertise in multilingual natural language processing. Her work focuses on computational semantics, cross-lingual word sense disambiguation, and multilingual terminology extraction. Recent research directions include automatic detection of irony in online text, argumentation mining in social media, sentiment analysis of financial news, language modeling for low-resourced languages, and computational approaches to Byzantine Greek epigrams. Her research demonstrates a consistent pattern of bridging theoretical computational linguistics with practical applications across diverse language domains and historical periods. Professor Lefever actively supervises PhD research on several cutting-edge topics including terminology extraction from comparable corpora, event extraction and sentiment mining of financial news, language modeling for low-resourced languages, argumentation mining in social media, and the automatic detection of links between Byzantine Greek epigrams. Her supervision portfolio demonstrates her commitment to advancing multiple frontiers of computational linguistics simultaneously. As an educator, Professor Lefever teaches courses in Terminology and Translation Technology, Language Technology, Localisation, Digital Text Analysis, and Digital Humanities. Her teaching reflects her research interests, providing students with both theoretical foundations and practical skills in language technology applications. The LT3 research group, where Professor Lefever is a key member, maintains strong connections with both academic and industry partners. The group has participated in numerous international conferences and shared tasks including SemEval competitions across multiple years, demonstrating consistent contributions to benchmark datasets and evaluation methodologies in natural language processing.

Yousra AaferView profile

Assistant Professor

• Mobile and Smart Device Security
• System and Software Security
• Android Security
• +4 more

Yousra Aafer is an Assistant Professor in the Department of Computer Science at the University of Waterloo. Her research focuses on mobile and smart device security, system security, and software security, particularly in the context of Android and cyber-physical systems. She holds a Ph.D. and M.Eng. from Syracuse University. Education: Ph.D., Syracuse University, United States (2016) M.Eng., Syracuse University, United States (2012) Her research interests include analyzing vulnerabilities in Android systems, binary analysis, IoT security, and fuzzing techniques. She explores methods to enhance security through formalized protocols, probabilistic protection recommendations, and leveraging large language models for vulnerability detection. Her work addresses critical areas such as cross-language buffer overflow detection, residual API audits in custom ROMs, and cyber-physical inconsistency in robotic vehicles. Her recent publications span topics like Android security, binary disassembly (e.g., D-ARM), and IoT security protocols (e.g., ProFactory). She has also contributed to frameworks like Poirot for probabilistic protection recommendations and StochFuzz for efficient binary fuzzing. While no awards are explicitly mentioned, her extensive publication record indicates active recognition in the security research community. She advises on multiple projects but no student names are listed here. Her work often involves collaboration on tools and frameworks for practical security applications.

• Artificial Intelligence in Healthcare
• Health Informatics
• Digital Health
• +4 more

Professor Sandeep Reddy is a leading academic in healthcare management and digital health at Queensland University of Technology (QUT), where he heads disciplines in Healthcare Management, Health Information Management, and Health, Safety, and Environment. He co-leads the 'Health, Human Biology and MedTech' theme at QUT's Centre for Data Science. With a PhD in Healthcare Management and a medical degree, Reddy holds multiple professional fellowships, including the Australasian Institute of Digital Health and the UK Higher Education Academy. Roles: Professor, Academic Leader, Researcher, Editor Affiliations: QUT, WHO Digital Health Roster, European Academy of Translational Medicine Professionals His research focuses on AI integration in healthcare delivery, governance frameworks, and translational methodologies. Key areas include AI in chronic disease management, medical imaging, and facial recognition technology. Reddy has authored over 80 publications, including a 2024 textbook on translational AI applications. Reddy's work spans global health policy, having advised the WHO and participated in international panels. He teaches courses on health informatics, implementation science, and digital health, with over 20 years of academic and industry experience. Scientific awards and fellowships reflect his expertise: Fellow of AIDH, ACHSM, and Senior Fellow of the UK Higher Education Academy. His editorial roles include PLOS ONE, Computational and Structural Biotechnology Journal, and Frontiers in AI. Current research supervision focuses on AI in medical education, healthcare access, and explainability in AI systems. He advocates for equitable digital health solutions, addressing the rural-urban digital divide through empirical studies.

Stephan SchlöglView profile

Professor

• Human-Computer Interaction (HCI)
• Artificial Intelligence
• Interaction Design
• +6 more

Stephan Schlögl is a Full Professor at MCI - The Entrepreneurial School in Austria, leading research and teaching in Human-Computer Interaction (HCI), Artificial Intelligence (AI), and Information Systems. He holds editorial roles at journals like the Springer Discover Artificial Intelligence Journal and MDPI Multimodal Technologies and Interaction Journal. His work spans over two decades, with key roles including Postdoctoral Research Fellow at Télécom ParisTech (2012–2013) and PhD Researcher at Trinity College Dublin (2008–2012). Schlögl’s research focuses on HCI, AI-driven conversational systems, and assistive technologies for aging populations. Education includes a PhD in Computer Science from Trinity College Dublin, an MSc in Human-Computer Interaction from University College London, and a Mag.(FH) in Applied Informatics & Management from MCI. His teaching spans Software Engineering, Business Intelligence, and Research Methods. Research interests emphasize natural language interfaces, AI ethics, and technology’s societal impact. Notable projects include the EU-funded EMPATHIC initiative (2017–2021), developing an empathic virtual coach for elderly care, and the CRYSTAL project (2024–present) on conversational systems for emotional support. Schlögl has supervised numerous theses on AI applications, chatbots, and UX design. He co-organized major conferences like CUI (Conversational User Interfaces) and received awards for best papers in AI-HCI and CHIRA. His work bridges academia and industry, addressing challenges in AI adoption, digital well-being, and ethical technology design.