Find Academic Supervisors

Craig A. ChinView profile

Associate Professor

• Digital Signal Processing
• Machine Learning
• Biomedical Signal/Image Processing
• +7 more

Craig A. Chin is an Associate Professor in the Department of Electrical Engineering at Kennesaw State University. He holds a Ph.D. and M.S. in Electrical Engineering from Florida International University (2006, 2001) and a B.S. in Electrical and Computer Engineering from the University of the West Indies (1995). Research Interests: Digital Signal Processing for Biomedical Applications Machine Learning in Biomedical Signal/Image Analysis Wireless Body Area Networks (WBANs) for Healthcare Network Security in Resource-Constrained Environments Biometric Authentication for Wireless Sensor Networks Innovations in Engineering Education (Cooperative/Active Learning, Humanities Integration) Article Trends: His publications (2001–2019) focus on Biomedical Signal Processing (EMG/Eye-Gaze Integration, Stress Detection), WBANs for mHealth , and Engineering Education Pedagogy . Recent work (2013–2019) emphasizes Cooperative Learning Strategies and Wireshark-based Network Security Labs . Academic Contributions: Craig has taught courses such as Biometrics, Data Communications, Biomedical Instrumentation, and Medical Electronics. He actively explores future research on biometric-driven security for Body Area Sensor Networks and active learning strategies in online education.

Elizabeth LipkeView profile

Professor

• Tissue Engineering
• Cancer Research
• Cardiac Tissue Engineering
• +5 more

Dr. Elizabeth Lipke is the Mary and John H. Sanders Professor of Chemical Engineering at Auburn University’s Samuel Ginn College of Engineering. Her lab focuses on tissue engineering, particularly in cancer and cardiovascular systems, with a focus on 3D in vitro models for drug screening and disease mechanisms. She leads interdisciplinary research combining biomaterials, microfluidics, and stem cell technologies to model complex biological systems. Education & Affiliations: PhD in Chemical Engineering (assumed based on role) Director of the Lipke Lab at Auburn University Collaborator with Dr. Arnold (cancer biology) and Dr. Mistriotis (biomedical engineering) Research Interests: Engineered cancer models to study obesity-related colorectal cancer progression Cardiac tissue engineering using pluripotent stem cells Microfluidic platforms for scalable production of hydrogel microspheres Drug efficacy testing in vascularized tumor models Awards & Recognition: 2024 AIMBE College of Fellows Inductee (top 2% in medical/biological engineering) NSF Grant ($621,934) for cardiac tissue manufacturing research (2018) Alabama Launchpad $25,000 award for Vivosphere technology (2023) Lab Highlights: Developed tumor-mimetic microvascular platforms for anti-cancer drug screening Engineered insulin-sensitive/insulin-resistant colorectal cancer models Pioneered scalable production of cardiac tissue microspheres from hiPSCs Grants & Funding: NSF RECODE Grant (2021) LAUNCH Innovation Grant (2023) Labs/Teams: Lipke Lab at Auburn University Collaborations with Arnold Lab (cancer biology) and Mistriotis Lab (biomedical engineering)

Jianwen SuView profile

Professor

• Database Systems
• Formal Verification
• Web Services
• +5 more

Jianwen Su is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where he has been since 1990. He holds a Ph.D. in Computer Science from the University of Southern California and B.S./M.S. degrees from Fudan University in China. His research focuses on databases, formal verification, web services, business process management (BPM), and workflow systems. He has contributed to data-centric workflow modeling, artifact-based BPM frameworks, and tools like the Web Service Analysis Tool (WSAT). Adjunct professorships at Peking, Fudan, and Donghua Universities in China. Key roles: General co-chair of ICSOC 2013, PC chair of PODS 2009, and general chair of SIGMOD 2001. Recipient of the 2000 Outstanding Faculty Award (UCSB College of Engineering) and IBM Faculty Awards (2007, 2008). Research spans database query languages, incremental query evaluation, spatial databases, and formal verification techniques for software systems. Current emphasis is on data modeling for workflows and BPM systems.

Gonzalo NápolesView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Cognitive Science
• +5 more

Gonzalo Nápoles is an Assistant Professor at Tilburg University's School of Humanities and Digital Sciences, Department of Cognitive Science and AI. He holds a Doctoral Degree in Computer Science from Rough Cognitive Networks (2014–2017). His research focuses on AI applications in cognitive modeling, pattern classification, and neural networks with interdisciplinary applications in healthcare, finance, and social sciences. Key research areas include Fuzzy Cognitive Maps, data augmentation techniques for neuroimaging, and interpretable machine learning systems. He actively contributes to UN Sustainable Development Goals related to education and innovation. Recent work explores AI ethics, financial risk assessment using dynamic networks, and sensory processing disorder analysis through neural networks. Education: Doctoral Degree in Computer Science, 2017 (Thesis: Rough Cognitive Networks) Prize-winning research includes Best Paper Awards at CIARP 2021 and IWAIPR 2023. He collaborates internationally, hosting academic visitors and serving on multiple PhD committees. Current projects involve stock prediction using graph neural networks and fMRI data augmentation methodologies. Awards: Best Paper Award - CIARP 2021 Best Paper Award - IWAIPR 2023 Nápoles advises on PhD theses in cognitive science and AI applications. His work bridges theoretical advancements with practical implementations in healthcare, finance, and urban systems.

• Human-AI Interaction
• Explainable AI
• Ethical AI
• +5 more

Chiara Natali is a PhD Student in Computer Science at University of Milan-Bicocca (2022-present) and a Visiting Research Fellow at the Dalle Molle Institute for Artificial Intelligence USI-SUPSI, supported by a Swiss Government Excellence Research Fellowship. She serves as a Lecturer for Interaction Design Lab and Human-Computer Interaction courses at University of Milano-Bicocca, and as a Tutor for Advanced Data Management and Decision Support Systems and Human-System Interaction courses across multiple Italian universities. Her educational background includes: MA in Politics, Philosophy and Public Affairs at University of Milan (2020-2022) Master's in Digital Communication Strategy at IED, Milan (2019-2020) BSc in International Politics and Government at Bocconi University, Milan (2016-2019) Natali's research centers on the complex relationship between humans and AI systems, with particular focus on Human-AI Interaction, Explainable AI (XAI), Ethical AI, and her signature concept of Frictional AI. She investigates the multidirectional effects of AI on human cognitive faculties, examining the tension between Augmentation and Deskilling. Her work explores how intentional design friction can serve as a debiasing strategy against Automation Bias, promoting more thoughtful human-AI collaboration while preserving human agency and critical thinking. Her publication record reveals a strong emphasis on practical applications of XAI in high-stakes domains like healthcare, with particular attention to medical decision-making processes. Her research consistently addresses the challenge of designing AI systems that support rather than replace human expertise, exploring how explanations impact accuracy in hybrid decision-making and how to measure technology dominance in AI-supported environments. Her significant contributions have been recognized with: Best Paper Award at the World Conference on Explainable Artificial Intelligence (2024) Best Doctoral Consortium Award at CEUR Workshop Proceedings (2023) Natali actively shapes her field through academic service, serving as PUBLICITY & PROCEEDINGS CHAIR for HHAI 2025 and organizing multiple workshops on Human-Centred Machine Learning, Algorithmic Authority, and Frictional AI. She also contributes to gender equality in STEM as a Science Ambassador for her institution's Gender Equality Plan and previously served as PhD co-representative at the Department Board. Her interdisciplinary approach extends into creative domains, where she is developing an Interactive AI Opera on 'The Garden of (Un)Earthly AI's' funded by the University of Edinburgh's Generative AI Laboratory, and has curated projects exploring Human-AI Music Co-Creation and live-coding music performances.

Liu HongyanView profile

Professor

• Big data management and analysis
• machine learning
• data mining
• +7 more

Professor Liu Hongyan is a full-time Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management, where he has served since 1994, achieving the rank of Professor in 2011 after previously holding positions as Associate Professor (2003-2011) and Teacher. His research bridges theoretical data science with practical applications across e-commerce, healthcare, and social media platforms. Education: PhD in Management, School of Economics and Management, Tsinghua University (2001) His research focuses on big data management , machine learning , and business intelligence with specialized expertise in personalized recommendation systems , medical/financial data analysis , and computer vision applications . Recent work integrates large language models and causal inference to solve complex problems in short video platforms, live streaming, and healthcare analytics, emphasizing real-world impact through industry collaborations. Analysis of his 15 most recent publications (2023-2025) reveals a strong trajectory toward multimodal AI systems combining recommendation engines with computer vision, particularly in 3D animation for advertising and healthcare. Key trends include LLM-enhanced display advertising, emotion-aware facial animation, and medical image annotation using adversarial learning, while maintaining core contributions to behavioral data mining in social networks. Scientific recognition includes: National Archives Administration's Outstanding Scientific and Technological Achievement Award Multiple Best Paper Awards at international conferences Outstanding Doctoral Dissertation Supervisor designation from the Society for Management Science and Engineering Special Award for National Natural Science Foundation project on user behavior pattern discovery Professor Liu has secured leadership roles in major National Natural Science Foundation projects including Innovation Research Groups and international cooperation initiatives. His industry impact is demonstrated through patented recommendation systems adopted by multiple companies, particularly in personalized content delivery for live streaming and short video platforms. As an Outstanding Doctoral Dissertation Supervisor, he mentors the next generation of data science researchers. He serves as Deputy Director of Tsinghua University's Center for Artificial Intelligence and Management Research and holds key positions in national academic societies including the E-Commerce and Cyberspace Management Committee (China Management Modernization Research Association) and the Information Systems Engineering Committee (Chinese Society for Systems Engineering).

• Mathematics Education
• Interactive Multimedia in Teaching
• Educational Technology
• +5 more

Marina Milovanović is a Professor at the University of Singidunum, Faculty of Informatics and Computing, Department of Mathematics. She holds dual doctoral degrees from the Faculty of Science, University of Kragujevac (Department of Mathematics, 2014) and Faculty of Entrepreneurial Business, Union University (2008), along with Master's and Bachelor's degrees from the Faculty of Mathematics, University of Belgrade (2000-2005 and 1995-2000 respectively). Faculty of Science, University of Kragujevac, Department of Mathematics (PhD, 2014) Faculty of Entrepreneurial Business, Union University (PhD, 2008) Faculty of Mathematics, University of Belgrade (Master's, 2000-2005) Faculty of Mathematics, University of Belgrade (Bachelor's, 1995-2000) Svetozar Marković High School, science and mathematics major (1991-1995) Professor Milovanović specializes in Mathematics Education and Educational Technology, with particular expertise in interactive multimedia applications for teaching mathematics. Her research consistently bridges theoretical mathematics with practical educational technology solutions, evolving from traditional multimedia approaches to incorporating cutting-edge AI and machine learning techniques. She has authored multiple books including 'Interactive multimedia in mathematics teaching' (2015) and collections of solved mathematics problems for entrance exams. Her recent publication record through 2025 demonstrates active engagement in interdisciplinary research, particularly at the intersection of educational technology, artificial intelligence, and practical applications in fields ranging from software engineering to medical diagnostics. Her work shows a clear trajectory from foundational educational technology research toward more sophisticated AI-enhanced learning systems. Professor Milovanović has made significant contributions to semantic web applications in education, particularly through Moodle LMS enhancements, and has explored SCADA applications in industrial contexts. Her collaborative research spans multiple countries and institutions, reflecting an international scholarly network. She has extensive experience developing computer tools for engineering education and has published on diverse topics including petroleum industry processes, environmental management, and financial mathematics. Her work demonstrates consistent application of computational approaches to solve domain-specific problems across multiple disciplines.

• Probabilistic Machine Learning
• Physics-Informed Machine Learning
• Industrial Applications of Data Science
• +4 more

Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.

Leonid GoubergritsView profile

Professor

• Cardiovascular Modeling
• Computational Fluid Dynamics (CFD) in Medicine
• Hemodynamics
• +10 more

Leonid Goubergrits is a Professor of Cardiovascular Modeling and Simulation at the Einstein Center Digital Future and Charité – Universitätsmedizin Berlin . With a background in applied mathematics and physics from the Moscow Institute of Physics and Technology, he has dedicated his career to applying computational fluid dynamics (CFD) to cardiovascular medicine since immigrating to Germany in 1995. His work bridges fundamental research and clinical applications, aiming to integrate numerical models into everyday medical practice to enhance diagnostics and reduce invasiveness. Doctorate at Technische Universität Berlin (2000) Habilitation at Technische Universität Berlin (2016) His research spans blood flow modeling in coronary vessels, cerebral aneurysms, heart valves, and the aorta, alongside artificial organ development and blood damage modeling . He leads a research group at Charité and the German Heart Center Berlin, focusing on patient-specific simulations and their translation to clinical settings. Recent publications highlight his work on deep learning integration for hemodynamic analysis, 4D Flow MRI validation , and medical device optimization using computational models. His team’s research includes virtual therapy planning for aortic valve replacements, hemolysis modeling , and pulmonary artery pressure sensors . Leonid actively contributes to education, redesigning TU Berlin’s Fluid Mechanics in Medicine curriculum and fostering interdisciplinary collaboration between engineers, physicians, and computer scientists. His vision emphasizes the digital transformation of medicine through computational modeling and simulation.

• Embedded Systems
• Software Verification and Validation
• Model Checking
• +5 more

Stefan Kowalewski serves as Professor of Embedded Software at RWTH Aachen University, leading the Chair of Embedded Software (Informatik 11) within the Department of Computer Science. His research spans critical domains including medical cyber-physical systems, automotive software, and industrial automation, with over 150 publications demonstrating sustained scholarly impact. Professor Kowalewski's work focuses on three interconnected research pillars: Embedded Systems Verification: Pioneering model checking techniques for PLC code, particularly addressing state space challenges in GRAFCET-based specifications Medical Cyber-Physical Systems: Developing safety-critical software for mechanical ventilation, extracorporeal membrane oxygenation, and ARDS diagnosis systems with strong clinical collaborations Automotive Software: Creating verification frameworks and safety architectures for automated vehicles through projects like UNICARagil Recent publications reveal an increasing integration of AI techniques with traditional verification methods, particularly for medical applications involving neonatal care and critical respiratory support. His 2024-2025 work shows particular emphasis on timing isolation in vehicle communication systems, middleware performance evaluation, and robust AI models for medical diagnosis. Professor Kowalewski maintains active collaborations with RWTH Aachen University Hospital's medical departments and automotive industry partners. His laboratory operates specialized facilities including the Cyber-Physical Mobility Lab for vehicle research and in-vivo testing setups for medical device validation. He has supervised numerous doctoral candidates, with recent students focusing on topics like ARDS classification algorithms, GRAFCET verification techniques, and safety architectures for software-defined vehicles. His educational contributions include developing remote teaching platforms for cyber-physical systems education.

Michael OberstView profile

Assistant Professor

• Causal inference
• Machine learning
• Healthcare decision-making
• +3 more

Michael Oberst is an Assistant Professor of Computer Science at Johns Hopkins University's Whiting School of Engineering, affiliated with the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on developing reliable machine learning systems for healthcare decision-making, emphasizing causal inference and robust performance across diverse clinical settings. Key research themes include: Ensuring ML system reliability comparable to FDA-approved medical tools Causal reasoning in observational healthcare data Robustness to dataset shifts across hospitals Algorithmic fairness under unobserved confounding Medical adaptation of large language models Recent publications (2025-2024) demonstrate trends in prediction-powered inference, clinical validation frameworks, and robustness evaluation methods. His work appears in top ML venues (NeurIPS, ICML, UAI, EMNLP) and translational medicine journals. Michael holds a BS in Statistics from Harvard University and a PhD in Computer Science from MIT, with postdoctoral training at Carnegie Mellon University's Machine Learning Department. His group actively seeks PhD students and postdocs for developing trustworthy AI solutions in healthcare.

• Haptics
• Assistive Technologies
• Rehabilitation Engineering
• +3 more

Troy McDaniel is an Assistant Professor at Arizona State University's School of Manufacturing Systems and Networks, specializing in haptic interfaces and assistive technologies for people with disabilities. With over 50 peer-reviewed publications and two authored books, his work bridges engineering, computer science, and healthcare to develop innovative rehabilitation solutions. Ph.D. from Arizona State University His research focuses on haptic perception and human augmentation through wearable technologies, with emphasis on assistive devices for motor and cognitive rehabilitation. Key areas include vibrotactile communication systems, social robotics for elderly care, and machine learning applications for activity recognition. His work prioritizes user-centered design for real-world disability challenges. Recent publications (2023-2025) demonstrate strong trends in haptic neuro-spatial rehabilitation, executive function therapy apps, and social robot companionship systems. His research increasingly integrates privacy-preserving AI for smart city health applications while maintaining clinical validity through partnerships with institutions like Mayo Clinic. Multiple Top 5% teaching awards for faculty at the Ira A. Fulton Schools of Engineering Dr. McDaniel advises graduate students in manufacturing systems and robotics through dissertation committees (MFG 799, CSE 799), with recent projects spanning haptic training simulations to PERACTIV activity monitoring systems. His research funding includes significant NSF grants like the IGERT program on person-centered technologies for disabilities and collaborations with Intel Corp on smart stadium applications. He contributes to ASU's Smart Living Research initiative, developing haptic neuro-spatial rehabilitation devices and social robotics frameworks within interdisciplinary teams focused on translating lab innovations to community health solutions.

• Software Technology
• Artificial Intelligence
• Educational Software
• +5 more

Maria Virvou serves as Professor and Chair of the Department of Informatics at the University of Piraeus, where she also directs the Graduate Program in Informatics and leads the Research Laboratory 'Software Technology'. She holds significant institutional leadership roles including membership in the University Senate and has chaired the Department of Informatics for multiple terms. As Editor-in-Chief of Springer book series 'Learning and Analytics in Intelligent Systems' and 'Artificial Intelligence-Enhanced Software and Systems Engineering', she maintains substantial academic influence across international scholarly platforms. Dr. Virvou earned her PhD in Computer Science and Artificial Intelligence from the University of Sussex with a scholarship from the State Scholarships Foundation, a Master of Science in Computer Science from University College London, and her undergraduate degree from the Department of Mathematics at the National and Kapodistrian University of Athens. Her educational background in both mathematics and computer science has provided a strong foundation for her interdisciplinary research approach. Professor Virvou's research spans Software Technology, Artificial Intelligence, Educational Software and Games, User Modeling, and Human-Computer Interaction. She has pioneered work in personalized interactive software systems, applying fuzzy logic and machine learning techniques to create adaptive educational environments. Her recent work demonstrates a strategic expansion into AI applications for healthcare, with significant contributions to medical diagnostics using large language models and multimodal AI systems. She has also made notable advances in smart tourism applications through personalization techniques. With over 400 publications to her name, Professor Virvou's scholarly output shows a clear progression from foundational work in user modeling toward increasingly sophisticated AI applications across multiple domains. Her publication trends reveal a strategic focus on explainable AI, multimodal systems, and practical implementations that bridge theoretical advances with real-world applications, particularly in healthcare and education sectors. Ranked #1 worldwide in 'User Modelling' publications (147,450 total publications) according to Scopus Ranked #1 worldwide in 'Educational Software' publications according to both Scopus and Microsoft Academic Search Recognized among the top 2% of most influential Artificial Intelligence scientists worldwide by Stanford University General Co-Chair at the 14th IISA Conference 2023 Invited Keynote Speaker at the 35th IEEE International Conference on Software Engineering Education and Training (CSEE&T 2023) As Director of the Research Laboratory 'Software Technology', Professor Virvou has built a robust research team focused on AI applications across multiple domains. She co-founded and co-chairs the IEEE Intelligent Information Systems and Applications international conference series, creating a significant platform for scholarly exchange. Her leadership extends to editorial roles with major academic publishers and active participation in international research collaborations that have secured substantial funding for innovative projects in AI and software engineering.

• Computer Vision
• Machine Learning
• Human Pose Estimation
• +3 more

Robert Collins is an Associate Professor in Computer Science and Engineering, with a focus on computer vision and human motion analysis. His research spans topics such as human pose estimation, crowd dynamics, stability quantification, and 3D scene understanding. He has contributed extensively to tracking algorithms, motion modeling, and applications in biomechanics and surveillance. Notable projects include NSF-funded work on crowd analysis, partial body model fitting, and sensor network overlays. His recent publications address stability from video, alignment of pose sequences, and vanishing point detection. Research interests emphasize vision-based human motion analysis , tracking in complex scenarios , and 3D reconstruction . Trends in his work include leveraging mean-shift belief propagation , spatiotemporal models , and deep learning for applications in biomechanics, urban scenes, and rehabilitation engineering.

• Machine Learning
• Time Series Analysis
• Text Mining
• +7 more

Dr. Georgiana Ifrim is an Associate Professor at the School of Computer Science, University College Dublin , where she serves as Director of Graduate Research and Co-Lead of the SFI Centre for Research Training in Machine Learning (ML-Labs). She holds concurrent appointments as an SFI Funded Investigator at the Insight Centre for Data Analytics and VistaMilk SFI Research Centre . Her academic journey includes postdoctoral research at Insight Centre, Cork Constraint Computation Centre (4C), and Aarhus University's Bioinformatics Research Centre (BiRC). Education: BSc in Computer Science, University of Bucharest, Romania MSc and PhD in Informatics, Max-Planck Institute for Informatics, Germany Dr. Ifrim specializes in scalable predictive modeling for diverse applications including: Sequence learning (DNA analysis, time series) Real-time prediction for streaming data (news/social media, energy) Interpretable machine learning models Knowledge graph exploitation (WordNet/Yago, Naga) Wearable sensor data analysis (sports science, health monitoring) Energy price forecasting for sustainable systems Her recent publications focus on time series explainability (TSHAP, tsCaptum), multivariate analysis (scalable channel selection), and healthcare applications (fall detection, walking speed estimation). Key contributions include open-source tools like SEQL (sequence learner) and Twitter-Topics (event detection). Scientific Awards: Winner of SNOW@WWW14 Data Challenge As Director of Graduate Research, she oversees advanced academic training while leading funded projects at the intersection of machine learning , real-time analytics , and domain-specific applications in agriculture, healthcare, and digital journalism. Her research group maintains active GitHub repositories with open-source implementations.