Find Academic Supervisors

• Artificial Intelligence in Medicine
• Cardiovascular Disease
• Machine Learning Applications
• +3 more

Sumukh Vasisht Shankar is a Researcher at Yale University's Yale School of Medicine, Department of Internal Medicine. His work focuses on advancing artificial intelligence applications in cardiovascular diagnostics and healthcare data analysis, particularly leveraging deep learning and large language models (LLMs) to improve clinical decision-making. He specializes in developing algorithms for ECG analysis, structured data transformation from unstructured clinical reports, and scalable phenotyping across non-interoperable health systems. Key research areas include AI-driven detection of structural heart diseases, myocardial infarction, and atrial fibrillation risk assessment. His projects often involve multinational validation and integration of AI tools into clinical workflows, such as the CarDS-Plus ECG platform and digital twin strategies for clinical trial adaptation. Shankar's contributions bridge machine learning innovation with real-world medical challenges, aiming to enhance diagnostic accuracy, streamline healthcare data management, and personalize patient care. Recent work includes developing ensemble learning models for heart disease screening, retrieval-augmented generation (RAG) systems for clinical risk scoring, and AI-based surrogate imaging techniques for aortic stenosis. His methodologies emphasize scalability, adaptability to portable devices, and ethical compliance with data localization regulations.

Elisabeth AndréView profile

Professor

• Tangible user interfaces
• Perceptive user interfaces
• Interactive multimodal user interfaces
• +10 more

Elisabeth André is a Full Professor of Computer Science and Chair of Multimedia Concepts and Applications at University of Augsburg, where she has been faculty since 2001. She previously served as Managing Director of the Institute for Computer Science at Augsburg University from 2004 to 2006. Professor André has received multiple prestigious professorship offers, including W3-Professorships in Human-Computer Interaction and Cognitive Systems from University of Stuttgart and Human-Machine Interaction from Otto-Friedrich-Universität Bamberg in 2009. Her academic journey began with a Diploma in Computer Science (1988) and Dr. rer. Nat. (1995) from Saarland University. Before joining Augsburg, she spent over a decade as a Scientific Researcher at DFKI GmbH (German Research Center for Artificial Intelligence), where she rose to Principal Researcher and was appointed a DFKI Research Fellow. Professor André's research focuses on designing and evaluating interactive multimodal user interfaces, experimental learning environments with animated characters, and affective computing. She is internationally recognized as a pioneer in embodied conversational agents, having organized one of the first international workshops on the topic in 1997. Her work stands out for its empirical foundation, including extensive corpus studies of human behaviors to inform virtual agent behavior modeling. Notably, she has conducted significant cross-cultural research with Japanese partners to develop culture-specific behaviors in virtual agents. Her publication record shows a consistent trajectory of innovation in human-computer interaction, with particular emphasis on multimodal analysis, gaze behavior simulation, and emotion recognition systems. The research trends in her recent work demonstrate increasing sophistication in input recognition methods and the development of practical toolboxes (AuBT, EmoVoice, SSI) that have been adopted by research institutions worldwide. 2007 Alcatel-Lucent Fellowship at Universität Stuttgart Best Paper Finalist at International Conference on Intelligent Virtual Agents (2007-2009) 2005 Convivio Best Demo Award 2000 Best Paper Award at International Conference on Intelligent User Interfaces 1998 RoboCup Scientific Award Multiple student projects winning international awards including GALA Awards and TEI conference awards Professor André has supervised 2 completed dissertations and is currently guiding 11 PhD students and 1 Habilitation candidate. Her leadership extends to major research projects including EU-funded initiatives (METABO, E-Circus, IRIS, DynaLearn, CALLAS) and DFG projects (CUBE-G, OC-Trust). She serves on numerous editorial boards and has held significant organizational roles in major conferences including IUI, IVA, and CASA. Her laboratory has developed multiple software toolkits that are used internationally in large-scale research projects, demonstrating the practical impact of her work. Current research directions include advanced emotion recognition systems, culture-adaptive virtual agents, and applications of her technology to educational and healthcare domains.

• Digital Marketing
• Consumer Behavior
• Influencer Marketing
• +5 more

Andreas Bayerl is an Assistant Professor of Marketing at the Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Quantitative Marketing from the University of Mannheim and conducts interdisciplinary research at the intersection of digital behavior, consumer psychology, and data science. His research focuses on how individuals generate, process, and are influenced by digital information, particularly in the context of online reviews and influencer marketing. Using a mixed-method approach—combining large-scale observational data, text and image analysis, and field and laboratory experiments—Andreas investigates behavioral patterns in digital ecosystems. His work has been published in leading journals including Journal of Marketing , Harvard Business Review , MIT Sloan Management Review , and Nature Human Behavior . The most recent publications reveal a consistent focus on digital influence, credibility, and consumer decision-making. Key themes include the effectiveness of micro-influencers, the psychological impact of fake reviews, multimodal analysis of visual and textual content, and experimental validations of marketing strategies across platforms. His methodological rigor and real-world relevance are evident across these works. Andreas has received significant recognition for his contributions, including: H. Paul Root Award for groundbreaking research in influencer marketing He actively teaches in the area of data science and marketing analytics, contributing to the next generation of analytically skilled marketers. While no formal advisees are listed, his role as an assistant professor suggests involvement in student supervision and academic mentorship. His research program appears to be supported by empirical rigor and industry relevance, though specific grants or lab affiliations are not mentioned in the provided text.

• Face Recognition
• Biometrics
• Synthetic Data Generation
• +4 more

Ivan DeAndres-Tame is an Assistant Professor in the Department of Signal Theory and Communications at the School of Engineering, Universidad Autónoma de Madrid. His research focuses on advancing biometric systems with particular emphasis on face recognition and behavioral biometrics using cutting-edge computer vision and machine learning techniques. His research interests span face recognition systems, synthetic data generation for biometric applications, explainable AI in biometric contexts, and keystroke verification systems. DeAndres-Tame has made significant contributions to the field through his leadership in major international challenges including the FRCSyn Challenge at CVPR and WACV, and the Keystroke Verification Challenge at IEEE BigData. His work bridges theoretical advances with practical applications in biometric security systems. Analysis of his recent publications reveals a strong focus on evaluating and improving face recognition systems using synthetic data, with a growing interest in the intersection of large language models and biometric systems. His research demonstrates consistent innovation in benchmarking methodologies and evaluation frameworks for biometric systems. Throughout his career, DeAndres-Tame has collaborated extensively with leading researchers in the biometrics community, including Ruben Tolosana, Rubén Vera-Rodríguez, Aythami Morales, Julian Fiérrez, and Javier Ortega-Garcia, contributing to the advancement of biometric technologies through both theoretical and applied research.

• Machine Learning
• Information Theory
• Deep Learning
• +4 more

Giulio Franzese is an Assistant Professor in the Data Science department at EURECOM. His research focuses on generative models, information theory, deep learning, and their applications in telecommunications and AI-driven networks. He has contributed to advancements in diffusion processes, multi-modal data alignment, and 6G network architectures. Notable projects include the ADROIT6G initiative for next-generation networks and methodologies like INFO-SEDD for scalable information metric estimation. His work spans theoretical foundations (e.g., infinite-dimensional generative models) and applied systems (e.g., GNSS for rail transportation). His publications reflect interdisciplinary strengths in machine learning, statistical theory, and engineering applications. He holds a robust publication record with over 30 articles since 2014, including impactful works on uncertainty quantification in deep learning and entropy-based optimization. Key Research Themes: Generative Diffusion Models Information-Theoretic Metrics AI for 6G Networks Selected Projects: ADROIT6G: Distributed AI-Driven 6G Architecture RFMI: Text-to-Image Alignment Framework

• Computer Science
• Machine Learning
• Artificial Intelligence
• +3 more

Yongtao Wu is a Doctoral Assistant and student in the doctoral program in Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the Laboratory for Information and Inference Systems (LIONS), part of the School of Engineering (STI) and the Institute of Electrical Engineering (IEM). His research focuses on machine learning, adversarial learning, computer vision, and deep learning, with applications in vision-language models, robustness analysis, and optimization algorithms. His work explores topics such as diffusion-based purification for vision-language models, adversarial attacks on large models, and parameter-efficient fine-tuning techniques. He contributes to advancing foundational AI models and their robustness in real-world scenarios. His research also intersects with cybersecurity, structural engineering predictions, and quantum computing methodologies. Yongtao is actively involved in the LIONS lab, collaborating on projects that bridge theoretical advancements with practical applications in computer science and engineering. His doctoral studies emphasize interdisciplinary approaches to solving complex problems in AI and machine learning.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +3 more

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

• Digital Photogrammetry
• Computer Vision
• Geospatial Imaging
• +10 more

Dr. Jackson David Cothren is a Professor in the Department of Geosciences at the University of Arkansas, where he also serves as the Leica Geosystems Chair in Geospatial Imaging. He holds dual leadership roles as Director of the Center for Advanced Spatial Technologies (CAST) and the Arkansas High Performance Computing Center (HPCC). His academic affiliations are deeply rooted in geospatial science, computer vision, and high-performance computing, bridging engineering and environmental applications. Ph.D. in Geodetic Science and Surveying, The Ohio State University M.S. in Geodetic Science and Surveying, The Ohio State University B.S. in Applied Mathematics, United States Air Force Academy Dr. Cothren's research spans digital photogrammetry, computer vision, UAV-based geospatial monitoring, and spatial archaeometry. He investigates non-traditional sensor modeling, feature extraction, surface generation, and integration with enterprise geospatial systems. His work increasingly incorporates deep learning, transformer models, and AI-driven analytics for applications in renewable energy, autonomous systems, and environmental sustainability. His recent publications highlight innovations in solar PV profiling, aerial image segmentation, and fairness-aware domain adaptation. The trends in his recent scholarly output reflect a strong shift toward machine learning and AI in geospatial analysis, particularly using transformer architectures for high-resolution imaging and cross-domain adaptation. His work integrates Lidar, GPS, and InSAR for deformation monitoring and leverages HPC for large-scale data processing. Applications span archaeology, agriculture, transportation, and energy infrastructure. Dr. Cothren has received numerous competitive grants from NSF, NEH, and USDA, supporting interdisciplinary research in geospatial analytics, smart transportation, and cultural heritage. His projects emphasize data-driven decision-making, community engagement, and workforce development in geospatial technologies. Principal Investigator, NSF E-RISE Rll: Arkansas Smart Transportation Research Incubator (2025–2029) Lead, RII Track-1: DART – Data Analytics that are Robust and Trusted (NSF, 2020–2025) Director, OPEN-GATE: Expanding Geospatial Education (NSF, 2016–2020) He mentors a broad interdisciplinary team and leads collaborative research initiatives involving computer vision, environmental science, and archaeology. His labs and research centers—CAST and HPCC—serve as hubs for innovation in spatial technologies, high-performance computing, and data-intensive research across the university and beyond. These centers support large-scale projects in archaeo-geophysics, UAV monitoring, and enterprise GIS integration.

Bing LiuView profile

Adjunct Professor

• Natural Language Processing
• Large Language Models
• Dialogue Systems
• +4 more

Bing Liu serves as Director of Applied Research at Scale AI and Adjunct Professor in the Computer Science and Engineering department at the University of California, Santa Cruz. Previously, he held leadership roles at Meta (GenAI and Reality Labs), Google Research, and Capio.ai (acquired by Twilio), with expertise spanning generative AI, NLP, and spoken dialogue systems. His educational background includes: Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University B.Eng in Electrical and Electronic Engineering (First Class Honors) from Nanyang Technological University, Singapore Exchange studies at KTH Royal Institute of Technology, Sweden Liu's research focuses on advancing large language models, dialogue systems, and reinforcement learning for conversational AI. His work bridges theoretical innovation with industrial-scale applications, particularly in zero-shot learning, multilingual capabilities, and evaluation frameworks for generative AI. He has pioneered techniques in dialogue state tracking, knowledge-enriched task-oriented systems, and continual learning to address catastrophic forgetting in neural dialogue models. Analysis of his 15 most recent publications reveals a clear trajectory toward industrial-scale generative AI: early work (2018-2021) established foundational methods in task-oriented dialogue systems, while recent publications (2022-2024) focus on LLM post-training, multimodal integration, and evaluation benchmarks like Humanity's Last Exam. Key thematic clusters include cross-lingual transfer, knowledge grounding in dialogue, and robust evaluation frameworks for frontier models. As an educator, Liu guides graduate research at UC Santa Cruz in LLM and multimodal AI while serving in critical conference roles including Publication Chair for ACL 2024 and Area Chair for ACL 2023's Large Language Models track. His industry leadership includes building Scale AI's 40+ member research team and driving Meta's Llama3 development, managing multimillion-dollar data roadmaps that scaled the Meta AI Assistant to 700M MAU. Liu directs Scale AI's applied research lab focused on GenAI data and evaluation, having previously built Meta's NLU team for AR/VR voice assistants deployed across Portal, Oculus, and Ray-Ban Smart Glasses. His current work centers on creating evaluation leaderboards adopted by top AI labs and developing the data engine for next-generation generative models.

• Data Mining
• Social Network Analysis
• Machine Learning
• +3 more

Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.

• Time Series Analysis
• Machine Learning
• Solar Physics
• +3 more

Shah Hamdi serves as an Assistant Professor in the Computer Science Department at Utah State University's College of Engineering. His research bridges machine learning and space physics, with emphasis on time series analysis for solar phenomena prediction and explainable AI systems. His primary research focuses include Time Series Analysis for space weather forecasting, Solar Physics applications in flare and particle event prediction, Explainable AI through counterfactual methods, and Natural Language Processing for social media analysis. He develops novel frameworks for multivariate time series classification, data augmentation of imbalanced datasets, and interpretable model architectures that handle complex spatio-temporal patterns. Analysis of his recent publications reveals three dominant trends: (1) Application of graph neural networks and multimodal fusion to solar flare prediction using photospheric magnetic field data, (2) Development of shapelet-based and saliency-guided counterfactual explanation techniques for time series classification, and (3) Creation of generative models like ChronoGAN and AVATAR for synthetic time series data augmentation. His work consistently addresses challenges in imbalanced data and space weather forecasting accuracy. Dr. Hamdi leads collaborative research initiatives including the CAIG project for synthetic data generation in solar energetic particle events. His grant portfolio demonstrates expertise in securing funding for interdisciplinary space weather and machine learning projects, while his advising focuses on training graduate students in time series analysis and explainable AI methodologies for real-world applications.

• Theoretical Linguistics
• Formal Semantics
• Computational Linguistics
• +3 more

Roberto Zamparelli is an Associate Professor at the University of Trento in both the Department of Psychology and Cognitive Science and the Center for Mind/Brain Sciences (CIMeC) . His research spans theoretical and computational linguistics with a focus on syntax-semantics interface phenomena. His research interests include formal semantics , distributional semantics , computational models of language , and linguistic education . He has supervised multiple PhD theses and contributed to interdisciplinary projects combining linguistics with cognitive science. The 15 most recent articles (2013-2024) demonstrate consistent focus on partitive structures , multimodal language models , semantic knowledge evaluation , and multilingualism effects . Key subfields include ellipsis mechanisms, cross-linguistic analysis, and neural network linguistic testing. Marie Curie Post-Doctoral Fellowship (1997-1999) British Academy 'Joint Activity Grant' (2000) Patent: Dispositivo didattico per l’apprendimento o l’esercizio nell’uso di strutture linguistiche (2016) Students supervised include Eva Vecchi , Rossella Varvara , and Sara Zanellini . His work integrates computational methods with theoretical linguistics , supported by grants like the PRIN REPLAI project (2023-25) and EU Atheme collaboration (2014-19).

Alan Wee-Chung LiewView profile

Professor

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +3 more

Professor Alan Wee-Chung Liew serves as Head of School at Griffith University's School of Information and Communication Technology, Australia. He joined Griffith University in 2007 after holding positions as Assistant Professor at Chinese University of Hong Kong and Senior Research Fellow at City University of Hong Kong. Professor Liew's research spans Artificial Intelligence, Machine Learning, Medical Imaging, Computer Vision, and Bioinformatics . His work focuses on developing AI solutions for healthcare applications, image analysis, and pattern recognition problems. He leads methodological innovations in machine learning algorithms while maintaining strong connections to real-world applications. His recent publications demonstrate a clear trend toward interdisciplinary AI applications, particularly in medical imaging, healthcare analytics, and trustworthy AI systems. The research shows increasing focus on explainability, privacy preservation, and practical deployment of AI solutions in critical domains. Professor Liew has received significant recognition including: Fellow of the Queensland Academy of Arts and Sciences Fellow of the Australia Computer Society Senior member of IEEE (USA) Stanford University's World's Top 2% Scientists (Computer Science: AI & Image Processing) He actively supervises numerous PhD students across diverse AI topics including medical imaging, graph neural networks, and trustworthy AI. His research is supported by substantial funding from government agencies including ARC, NHMRC, and international collaborations. Professor Liew co-leads the AI4Health lab and the TrustAGI lab , which focus on developing ethical, reliable, and safe AI technologies with strong industry and hospital partnerships.

Haohan WangView profile

Assistant Professor

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +5 more

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

• Computational Social Science
• Social Media Studies
• Mediated Publicness
• +12 more

Chamil Rathnayake is a Senior Lecturer in the Department of Journalism, Media and Communication at the University of Strathclyde, Faculty of Humanities & Social Sciences. He is actively engaged in research and teaching with a strong focus on new media, digital communication, and computational social science methodologies. His research interests lie at the intersection of communication, computer science, and sociology, particularly in understanding how social media affordances shape public discourse. Key areas include mediated publicness , issue-response networks , momentary connectedness , and the dynamics of transactive vs. non-transactive utterances . He applies advanced methods such as Social Network Analysis (SNA) , Natural Language Processing (NLP) , topic modeling , and text mining to analyze large-scale digital interactions. Chamil's recent publications explore multimodal discourse on social media, affective mobilization in climate communication, and the psychological implications of AI-generated self-images. His work consistently addresses the structural and emotional dimensions of online communities, with a strong emphasis on interdisciplinary, data-driven approaches. He has been recognized for excellence in teaching, having received the Frances Davis Award and the Dan Wedemeyer Award from the University of Hawaii. He also serves as a reviewer for prominent journals such as Convergence , Bulletin of Science, Technology and Society , and The Social Science Journal . Chamil is the Principal Investigator on research projects including Topical Diversity, Discursive Continuity, and the Engagement in Online Communities in Twitter Reactions to Acts of Extremist Violence , funded by the Carnegie Trust. He is also a co-investigator on collaborative knowledge exchange initiatives focused on equality in research partnerships. He welcomes PhD students interested in quantitative and computational approaches to social media, political communication, and digital culture. His work contributes to UN Sustainable Development Goals related to inclusive knowledge societies, quality education, and responsible digital engagement.