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Giovanni Maria FarinellaView profile

Professor

• First Person (Egocentric) Vision
• Computer Vision
• Pattern Recognition
• +1 more

Giovanni Maria Farinella is a Full Professor at the Department of Mathematics and Computer Science, University of Catania, Italy. He is the Founder Member of the IPLAB Research Group at University of Catania since 2005, an Associate Member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006, and an Associate Member of the Italian National Research Council since 2018. He serves as Scientific Advisor of the NVIDIA AI Technology Centre and board member of the CINI Laboratory of Artificial Intelligence and Intelligent Systems. Dr. Farinella obtained his degree in Computer Science (summa cum laude) from the University of Catania in 2004 and was awarded a Doctor of Philosophy (Computer Vision) from the same institution in 2008. He has obtained the National Scientific Qualification to Associate Professor in 2013-2014 and to Full Professor in 2018. His primary research interests focus on First Person (Egocentric) Vision, Computer Vision, Pattern Recognition, and Machine Learning. Dr. Farinella has pioneered research in egocentric video analysis, object interaction detection, activity recognition, and scene understanding from a first-person perspective. His work bridges theoretical computer vision with practical applications in industrial settings, healthcare, and cultural heritage sites. His recent publications demonstrate a clear trajectory toward more sophisticated, real-time egocentric vision systems capable of understanding complex human activities, anticipating actions, and detecting procedural mistakes through integration of visual analysis, gaze tracking, and large language models. Dr. Farinella has published extensively in top-tier computer vision venues, with his most recent work focusing on egocentric action anticipation, mistake detection in procedural tasks, and the integration of multimodal large language models with visual understanding. His research shows strong emphasis on practical industrial applications while advancing fundamental computer vision techniques. PAMI Mark Everingham Prize 2017 Intel's 2022 Outstanding Researcher Award Dr. Farinella has served as Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition - Elsevier, and International Journal of Computer Vision. He has held leadership roles as Area Chair for CVPR 2020/21/22/23, ICCV 2017/19/21/23, ECCV 2020, and as Program Chair of ECCV 2022. He founded and directs the International Computer Vision Summer School (since 2006) and the Medical Imaging Summer School (since 2014). As PI of the EGO4D project, he helped create one of the largest egocentric video datasets with over 3,670 hours of footage from 923 participants across 74 locations worldwide. His IPLAB research group leads multiple significant projects including VALUE, ENIGMA, and FIPEVIS, with strong industry collaborations.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +6 more

Tengda Han is a Research Scientist at Google DeepMind , specializing in neural networks for video understanding. He earned his PhD from the University of Oxford under Andrew Zisserman and a BEng in Mechanical & Material Engineering from the Australian National University . Prior to his PhD, he studied business administration and law at Renmin University of China for one year. PhD in Computer Science, University of Oxford (2022) BEng in Mechanical & Material Engineering, Australian National University (2016) One year of business administration and law at Renmin University of China His research focuses on video representation learning , self-supervised methods , multimodal learning , and efficient prompting techniques . He has pioneered training-free frameworks for audio description, zero-shot object counting, and hyper-realistic movie generation. Recent publications demonstrate trends in temporal alignment , discriminative prompting , 5D video modeling , and open-world counting . His work often bridges computer vision with natural language processing and speech recognition . Scientific Awards : Best Paper Award at ACCV2024 Best Poster Award at BMVC2023 BMVA Sullivan Doctoral Thesis Prize Runner-up He has co-organized workshops at ICCV25 , NeurIPS23 , and BMVA Symposium on Vision & Language . Collaborators include Andrew Zisserman, Dima Damen, and Flamingo project members at DeepMind.

• Artificial Intelligence
• Remote Sensing
• Earth Observation
• +5 more

Annisa Puspa Kirana is a Ph.D. candidate and researcher at the Department of Geo-information Processing (ITC-GIP), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente. She is a Lecturer in the Department of Information Technology at State Polytechnic of Malang, currently on study leave to focus on her Ph.D. research. Her work integrates Artificial Intelligence , Computer Vision , and Geospatial Analytics to analyze satellite/aerial imagery for Climate Change Mitigation , Disaster Monitoring , and Resource Management . PhD in Geo-Information Science @ University of Twente (Netherlands) Master of Computer Science @ IPB University (Indonesia) Her research emphasizes Deep Learning applications in Earth Observation, including Vision-Language Models and Agentic AI for multimodal data analysis. She collaborates with interdisciplinary teams , government agencies , and industry partners . Selected Publications Trends: Focus on AI agents , LLMs , VLMs , and Vision Transformers for geospatial and environmental applications Technical tutorials on Streamlit , TalkToEBM , and LangChain integration Conceptual breakdowns of agentic vs. agent-based systems , interpretability in AI , and prompt engineering Scientific Awards: LPDP Awardee (Indonesian Endowment Fund for Education) Microsoft Certified Educator She actively mentors students in AI/geospatial fields and advocates for open-source science and ethical AI practices in environmental decision-making. Her work bridges academic research and practical policy tools .

Madi BabaiaslView profile

Assistant Professor

• Human intention detection
• Human-robot interaction
• Rehabilitation robotics
• +7 more

Madi Babaiasl is a Clare Booth Luce Assistant Professor in the Department of Aerospace and Mechanical Engineering at Saint Louis University's School of Science and Engineering. Her research focuses on developing innovative robotic solutions for assistance, rehabilitation, agriculture, and education applications. Education: Ph.D. in Mechanical Engineering (Robotics) from Washington State University M.S. in Mechatronics Engineering from Tabriz University, Iran B.S. in Electrical Engineering from Tabriz University, Iran Dr. Babaiasl's research spans multiple domains at the intersection of robotics, machine learning, control theory, and artificial intelligence. Her primary focus centers on developing safe and efficient human-robot interaction systems, particularly for assistive and rehabilitation applications. She investigates human intention detection, agricultural automation, and novel approaches to robotics education, integrating knowledge from psychology, physical therapy, agriculture, and education to create comprehensive robotic solutions. Her work addresses real-world problems such as developing devices to suppress hand tremors for Parkinson's patients and creating steerable needles for surgical applications. Analysis of her recent publications (2022-2025) reveals a clear evolution from medical robotics (particularly steerable needles) toward more diverse applications including assistive and agricultural robotics. The newer publications increasingly incorporate advanced AI techniques like transformer models, large language models, and multimodal signal processing for human-robot interaction, with strong emphasis on practical implementation using ROS2, edge computing, and real-time control systems for applications where cloud connectivity may be limited. Scientific Recognition: Featured in RSIP Vision Magazine as a Woman in Science (January 2022) Featured in SWE's 'A Day in The Life of Robotics Engineer' blog (April 2022) Multiple features in university publications for steerable needle research (2018-2019) Wanda Munn Scholarship for outstanding academic achievement (2019) Top 0.2% in national university entrance exams in Iran (2006) Recent lab achievements including Midwest Robotics Workshop 2025 Travel Grant (2025) Dr. Babaiasl actively mentors students through her Mecharithm Lab at SLU, seeking those with robotics foundations, programming skills (particularly Python), and familiarity with ROS. She has successfully blended academic and entrepreneurial pursuits by founding Mecharithm in 2021, which generated $73,000 in its first year. Her mentorship philosophy emphasizes project-based learning, self-directed growth, and developing as an independent researcher. Leading the Mecharithm Lab, Dr. Babaiasl oversees research projects including WheelArm (enhancing independence in assistive robotic systems), multi-modal human intent recognition, and deploying large language models for intuitive human-robot interaction. Her lab comprises graduate and undergraduate students primarily from Mechanical Engineering and Computer Science backgrounds, working on meaningful projects that aim to leave a lasting legacy for society through robotics innovation.

• AI for Healthcare
• Large Vision-Language Models
• 3D Computer Vision
• +3 more

Zhenyue Qin is a Postdoctoral Associate at the Yale School of Medicine , affiliated with the Biomedical Informatics & Data Science department. His research focuses on AI for healthcare , large vision-language models , and 3D computer vision . Prior to Yale, he worked as a Senior Researcher at Tencent XR Vision Labs , following his PhD and undergraduate studies at the Australian National University . Education: PhD in Computer Vision (2022) BS (Hon) in Computer Science (2017) His research interests span AI applications in healthcare , with specific emphasis on medical imaging , emotion recognition , and skeleton-based action analysis . He also explores graph neural networks , vision-language models , and modularity-inducing systems in evolutionary computation. Zhenyue has published in top-tier conferences and journals including CVPR , AAAI , EMNLP , and TNNLS . His recent work addresses challenges in medical dataset benchmarking , diffusion model applications , and privacy-preserving action recognition . Scientific Honors Awarded 1st Honors in Computer Science at Australian National University

Andrew S. GordonView profile

Academic

• Interactive Narrative
• Commonsense Reasoning
• Computational Models of Narrative
• +6 more

Andrew S. Gordon is a Research Associate Professor of Computer Science at the University of Southern California and Director of Interactive Narrative Research at the Institute for Creative Technologies. His work integrates artificial intelligence, cognitive science, and interactive storytelling to create systems that automatically interpret and generate narrative structures, with a special focus on commonsense reasoning and abductive inference. Education Ph.D. in Computer Science, Northwestern University, 1999 Research Interests Gordon’s research converges on computational narrative intelligence . He develops formal models of commonsense psychology that enable machines to reason about human intentions, beliefs, and emotions. His group designs interactive narrative systems for training and education, builds large-scale story corpora, and pioneers abductive reasoning techniques that combine symbolic logic and statistical learning to interpret temporal data. Recent work explores how large language models can be guided to co-create interactive fiction and how vision–language models can be benchmarked for causal understanding. Publication Trends Across more than two decades, his publications reveal consistent themes: (1) foundational theories of commonsense psychology and strategy representation, (2) narrative technologies that blend AI planning with human creativity, and (3) practical training simulations for defense and education. The 2024-2025 papers highlight a pivot toward evaluating and steering large language models for narrative tasks, while earlier work established abductive reasoning frameworks such as “Etcetera Abduction.” Awards & Honors Best Paper Award, System Lifecycle and Technologies Track, Simulation Interoperability Standards Organization (SIW 2021) Advising & Grants He has successfully mentored three PhD students—Reid Swanson (2010), Christopher Wienberg (2017), and Melissa Roemmele (2018)—whose dissertations span computational narrative, commonsense reasoning, and interactive fiction. His research has been continuously supported by agencies including the U.S. Army, DARPA, and NSF for projects on virtual training environments, narrative-centered learning, and large-scale commonsense knowledge acquisition. Labs & Teams Gordon directs the Interactive Narrative Research Group at USC’s Institute for Creative Technologies, where interdisciplinary teams of computer scientists, cognitive psychologists, and interactive media designers collaborate on systems such as the Rapid Integration & Development Environment (RIDE) for embodied conversational agents and story-driven training simulations.

Anton RagniView profile

Senior Lecturer

• Core automatic speech recognition
• Efficient and expressive speech synthesis
• Spoken Language Translation
• +2 more

Dr. Anton Ragni is a Senior Lecturer in Speech and Language Technologies at the University of Sheffield's School of Computer Science, where he serves as Assessments Lead and contributes to the Speech and Hearing (SpandH) research group. His educational background includes: BEng in Information Technology from the University of Tartu (2005) MEng in Information Technology from the University of Tartu (2007) PhD from the University of Cambridge (2013) Ragni's research centers on machine learning approaches for speech and language processing, with core expertise in automatic speech recognition (ASR), expressive speech synthesis, spoken language translation, information retrieval, and conversation modeling. His work increasingly integrates self-supervised learning and foundation models to address challenges in speech technology and cross-domain applications like music processing. Analysis of his recent publications reveals a strong trend toward applying speech processing techniques to music understanding and developing robust ASR systems for specialized populations, including hearing-impaired users and children. His work demonstrates consistent innovation in leveraging contextual information and novel architectures like energy-based models. His scientific recognition includes: Best Student Paper Award at IEEE ASRU 2011 for 'Generative kernels for noise robust ASR' Ragni has secured significant research funding as Principal Investigator and Co-Principal Investigator: EPSRC grant 'Exemplar-based Expressive Speech Synthesis' (2021-2023, £218,290) as PI Innovate UK grant 'Automatic voice conversion for transforming professional adult voice actors to artificial child voice actors' (2021-2023, £173,605) as Co-PI He actively contributes to the Speech and Hearing research group, focusing on advancing speech technology through interdisciplinary collaboration and real-world applications.

Jie LuView profile

Professor

• Artificial intelligence
• Machine learning
• Computational intelligence
• +4 more

Distinguished Professor Jie Lu AO is an internationally renowned scientist in computational intelligence at the University of Technology Sydney, where she serves as Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology and Director of the Australian Artificial Intelligence Institute (AAII), the largest AI hub in Australia with 35 researchers and 230 PhD students. She has been a Professor at UTS since 2007 after serving as Associate Professor from 2004-2006. Professor Lu earned her PhD from Curtin University, Perth, Australia. Her research focuses on computational intelligence with significant contributions to fuzzy transfer learning, concept drift, data-driven decision support systems, and recommender systems. She has developed machine learning models, intelligent recommender systems, and AI-driven decision support systems through collaborations with industry partners including Optus, Sydney Trains, Domain Holdings Australia Ltd, and Workforce Health Assessors Transport NSW. Her recent publications demonstrate a strong focus on addressing challenges in non-stationary environments, out-of-distribution detection, multi-stream concept drift, and applying AI to healthcare applications such as stroke risk prediction and cancer risk assessment. She has pioneered approaches combining traditional AI techniques with large language models for more robust and explainable systems, particularly in legal case recommendation and women's health applications. Officer of the Order of Australia (AO) IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow Australian Laureate Fellow in AI and Industry Laureate Fellow in AI-for-Health UTS Chancellor's Research Medal for Research Excellence (2019) IEEE Transactions on Fuzzy Systems Outstanding Paper award (2019, 2022) Australian Most Innovative Engineer award (2019) NeurIPS 2022 Paper Award Australasian AI Distinguished Research Contribution Award (2022) Australian NSW Premier Prize on Excellence in Engineering (2023) Professor Lu has supervised 60 PhD students to graduation and serves as Editor-In-Chief for Knowledge-Based Systems journal. She has secured 47 ARC grants and over 110 industry projects since 2017, with funding from ARC Discovery projects, ARC Laureate Fellowships, and industry partners. Her leadership has established UTS as a leading center for AI research in Australia, with significant impact across multiple sectors including transportation, telecommunications, healthcare, and education. As Director of the Australian Artificial Intelligence Institute, Professor Lu has built a thriving research ecosystem that bridges academic research with practical industry applications. Her work on concept drift and transfer learning addresses fundamental challenges in adapting machine learning models to changing environments, with direct applications to real-world problems requiring continuous learning and adaptation.

Anna-Carolina HaenschView profile

Assistant Professor

• Missing Data Analysis
• Synthetic Data Generation
• Big Data in Social Sciences
• +6 more

Anna-Carolina Haensch is a Lecturer at the University of Munich in the Chair of Statistics and Data Science in Social Sciences and the Humanities and an Assistant Professor at the University of Maryland in the International Program in Survey and Data Science. Her interdisciplinary work bridges statistics, computational social science, and natural language processing, with a focus on methodological innovation in social research. Education: PhD in Sociology, University of Mannheim (2017-2021) M.Sc. in Survey Statistics, University of Bamberg (2014-2017) B.A. in Political Science/Sociology, Ludwig-Maximilians-Universität München (2011-2014) Dr. Haensch's research centers on missing data, synthetic data, and big data applications in social sciences. She develops advanced statistical methods for survey data harmonization, multiple imputation techniques, and leverages natural language processing to analyze complex social phenomena. Her work consistently addresses methodological challenges in social research with practical applications for data collection and analysis. Her recent publications reveal a significant trend toward integrating large language models with traditional survey methodology, exploring how AI can enhance data collection, analysis, and interpretation in social science research. She has made substantial contributions to understanding missing data patterns, developing synthetic data approaches, and examining the societal implications of AI tools across multiple domains including mental health, political science, and housing policy. Scientific Awards: 2022 AAPOR Burns "Bud" Roper Fellow Award 2022 AAPOR Warren J. Mitofsky Innovators Award (as part of CTIS team) 2022 AAPOR Policy Impact Award (as part of CTIS team) 2011-2017 Max-Weber-Programm (undergraduate and graduate stipend) Dr. Haensch actively mentors the next generation of researchers, currently supervising 3 PhD theses on statistical education, machine learning applications in social sciences, and synthetic data generation with LLMs. She has guided approximately 6 master's theses and 15 bachelor's theses at the University of Munich since 2022, with topics primarily related to synthetic data, multiple imputation, and LLM applications. Her teaching spans statistical methods, data science techniques for survey researchers, and specialized topics in big data analysis. She has secured teaching grants including a €20,000 promotion for the RAINER project (R Assistant IN Error Resolution) for 2024-2025 and a €10,000 LMU-NYU Scholarship in 2023. She serves on several important boards including the Eurostat EMOS Board (2024-2026), the Ethics Commission of Faculty 16 at LMU (2023-2025), and as Women's Representative at the Institute for Statistics, LMU (2024-2026). Her collaborative work with the University of Maryland Social Data Science Center and involvement in the Global COVID-19 Trends and Impact Survey demonstrates her commitment to large-scale data collection initiatives and real-time social research.

Sam GoreeView profile

Assistant Professor

• computer vision
• human-computer interaction
• digital humanities
• +4 more

Sam Goree is an assistant professor of computer science at Stonehill College, specializing in the intersection of computer vision, human-computer interaction, and digital humanities. His work critically examines how AI systems evaluate aesthetic quality and human-centered aspects of AI, with particular focus on subjectivity in machine learning models and evaluation methodologies. PhD in Informatics from Indiana University's Luddy School of Informatics, Computing and Engineering Goree's research explores aesthetic phenomenon problems in computer vision, particularly image aesthetic quality assessment. He investigates how subjective human perceptions of beauty and aesthetics can be modeled and evaluated in AI systems, challenging traditional approaches that assume a universal standard of beauty. His work draws from feminist theory, philosophy of science, and media archaeology to develop more human-centered evaluation methods for AI systems. Goree is particularly interested in how subjectivity manifests in both data and models, and how this affects algorithmic bias. His recent publications reveal a strong trend toward human-centered evaluation of AI systems, with increasing focus on subjectivity, personalization, and philosophical foundations of aesthetics in computer vision. Goree's work bridges technical computer vision research with critical humanities perspectives, examining how historical contexts shape current AI practices. He has developed novel evaluation frameworks that incorporate qualitative human feedback, moving beyond traditional quantitative metrics to better understand how AI systems interact with human perception and values. Goree actively engages with teaching challenges in the era of generative AI, developing pedagogical approaches that help students develop critical thinking skills while navigating new AI tools. His teaching philosophy emphasizes maintaining human-centered learning experiences even as AI transforms educational landscapes. He advocates for assignments that require contextual understanding and creativity, which are difficult for AI to replicate. His research is organized around understanding the "aesthetic gap" - the difference between information extractable from image pixels and the feelings images evoke in humans. Goree's lab explores how to build more transparent and accountable AI systems that acknowledge their subjective nature rather than claiming false objectivity.

• Information Retrieval
• Machine Learning
• Data Mining
• +5 more

Professor Klaus Berberich is a faculty member at htw saar (Saarland University of Applied Sciences), where he serves as Professor in the Databases & Information Systems department within the Faculty of Engineering. He is the Laboratory manager of the software laboratory (SWL) and Chairman of the examination boards for Practical Computer Science, Communication Informatics and Production Informatics. His research focuses on Information Retrieval, Machine Learning, Data Mining, and Web Archives, with significant contributions to knowledge graphs, temporal information retrieval, and neural information retrieval models. Professor Berberich has developed innovative approaches for quantity extraction from web tables, structuring text into tables, and knowledge graph querying. His publication record shows a consistent trend toward increasingly sophisticated neural approaches to information retrieval, evolving from traditional temporal search techniques to modern deep learning models. Recent work demonstrates strong integration of knowledge graphs with neural information retrieval systems, particularly in handling quantities and temporal aspects of information. Professor Berberich has received numerous prestigious awards throughout his career: 2020: Test of Time Award, ECIR 2020 2018: Honorable Mention for Best Poster Award, WWW 2018 2017: Prominent Paper Award, Artificial Intelligence Journal 2014: Highly Commended Poster Presentation Award, IIiX 2014 2013: Honorable Mention for Best Paper Award, CIKM 2013 2011: Best Demo Award, WWW 2011 2009: Best Late-Breaking Result Award, WSDM 2009 As an active researcher and educator, Professor Berberich serves on numerous program committees for major conferences including WSDM, CIKM, SIGIR, and ICTIR. He has been a consistent reviewer for prestigious journals in the field and is a member of the executive committee of the Information Retrieval specialist group of the German Informatics Society. His teaching portfolio includes courses in Databases, Information Retrieval, Data Science, Machine Learning, and Deep Learning. Professor Berberich leads the software laboratory (SWL) at htw saar and has been instrumental in developing research infrastructure for knowledge-centric tasks, including the GYANI indexing infrastructure. His research group has made significant contributions to temporal information retrieval, particularly in the context of web archives and news archives.

Gokberk CinbisView profile

Associate Professor

• data-efficient machine learning
• few-shot learning
• zero-shot learning
• +6 more

Gökberk Cinbiş is an Associate Professor at the Department of Computer Engineering, Middle East Technical University (METU), leading research in data-efficient machine learning with minimal supervision. His work spans zero-shot, few-shot, weakly-supervised, and self-supervised learning, with applications in vision-language integration and large-scale image/video understanding. PhD from Université de Grenoble (2014) M.A. from Boston University (2010) His research focuses on generative models, meta-learning, and robust computer vision systems. Recent work explores sparsity in parameter-efficient fine-tuning, token embeddings for vocabulary extension, and domain-aware LoRA components for personalization. Notable trends in his publications include: Advancements in zero-shot and few-shot learning Applications in remote sensing (SAR2ET for evapotranspiration) Meta-learning and hybrid augmentation techniques Sign language recognition and industrial vision benchmarks Scientific honors include: Science Academy - Young Scientist Award (BAGEP 2024) Google Faculty Research Award (2019-2020) Best PhD Thesis Prize by AFRIF (2015) Alper Atalay Best Student Paper Award (2016) He has supervised MSc students Gencer Sumbul and Berkan Demirel, secured TUBITAK grants on meta-learning and any-shot learning, and organized workshops on industrial inspection at CVPR and ECCV. Cinbiş leads METU ImageLab, focusing on computer vision and machine learning.

Bo HuiView profile

Assistant Professor

• Data Mining
• Machine Learning
• Graph Neural Networks
• +6 more

Bo Hui is an Assistant Professor of Computer Science at The University of Tulsa's College of Engineering & Computer Science. He received his Ph.D. in Computer Science and Software Engineering from Auburn University in 2023 and earned his B.S. in Computer Science from Xi'an Jiaotong University in 2013. Prior to his Ph.D., he worked as a senior software engineer in the industry. Education: Ph.D., Computer Science and Technology, Auburn University, 2023 B.S., Computer Science and Technology, Xi'an Jiaotong University, 2013 Dr. Hui's research focuses on data mining and machine learning, particularly in designing machine learning methods for complex real-world data while addressing social concerns such as privacy in AI. His work spans multiple application domains including traffic prediction, social recommendation systems, and biological data analysis. He has made significant contributions to graph neural networks, knowledge graph unlearning, and federated learning systems. Dr. Hui's publication record demonstrates a strong focus on machine unlearning (the 'right to be forgotten' in AI models), graph neural networks, and traffic prediction systems. His work bridges theoretical machine learning with practical applications, with a growing emphasis on privacy-preserving AI techniques. He frequently publishes in top-tier venues including ICCV, ICLR, KDD, and AAAI. Awards and Honors: NSF award #2348177 for machine unlearning research AAAI student scholarship award Dr. Hui currently advises two Ph.D. students (Ruimeng Ye and Yang Xiao) who began their studies in Fall 2024. His research is supported by an NSF grant (#2348177) focused on machine unlearning. He has served as a reviewer for major conferences including ICLR, AAAI, KDD, CVPR, ACL, and EMNLP. His teaching includes Data Mining (2024 Spring) and Interaction Design (2023 Fall). His research group appears to focus on data mining and machine learning with applications to real-world problems requiring privacy-aware solutions.

Yihao LiuView profile

Assistant Professor

• Medical Image Analysis
• Image Registration
• Deep Learning
• +3 more

Yihao Liu serves as Research Assistant Professor in Vanderbilt University's School of Engineering, Department of Electrical and Computer Engineering, focusing on clinically relevant image analysis for personalized treatment strategies and large-scale medical data interpretation. His work bridges engineering and clinical applications through advanced computational methodologies. Education: PhD in Electrical and Computer Engineering, Johns Hopkins University (May 2024) Dr. Liu's research spans medical image analysis, deformable registration, deep learning, and computer vision with applications in CT, MRI, and OCT imaging. He develops AI-driven tools for pulmonary nodule diagnosis, MS lesion analysis, body composition assessment, and dermatological imaging, emphasizing clinical translation and multi-modal data fusion. His 2024-2025 publications reveal concentrated innovation in diffusion models for field-of-view extension, bi-directional lesion synthesis, and unsupervised registration techniques, demonstrating leadership in foundational registration models and longitudinal analysis for precision medicine. Dr. Liu directs the Vanderbilt Lab for Immersive AI Translation (VALIANT) and collaborates with the Medical-image Analysis and Statistical Interpretation Lab (MASI) under VISE Affiliate Bennett Landman, PhD. He holds the Stevenson Chair in Electrical and Computer Engineering and participates in NIH grant writing initiatives through Vanderbilt's medical partnerships.

Alan J. MichaelsView profile

Professor

• National Security & Restricted Research
• RF Spectrum Dominance
• Spread Spectrum Satellite Communications
• +3 more

Alan J. Michaels is a Professor and Director of the NSI Spectrum Dominance Division at Virginia Tech, leading research in digital communications, electronic warfare, and quantum algorithms. He holds affiliations with the Bradley Department of Electrical and Computer Engineering and the Department of Mathematics. Previously, he served at Harris Corporation in engineering leadership roles. Education includes multiple degrees from Georgia Institute of Technology (B.S., M.S., Ph.D. in ECE; B.S., M.S. in Applied Mathematics; M.S. in Operations Research) and an MBA from Carnegie Mellon University. Research focuses on RF spectrum dominance, applied mathematics, and cybersecurity. Notable contributions include 44 U.S. patents, over 80 peer-reviewed publications, and leadership in $171M+ research projects. He pioneered initiatives like the Vertically Integrated Projects (VIP) model for undergrad research and the Southwest VA node of the Commonwealth Cyber Initiative (CCI). Awarded Fellow of the National Academy of Inventors for his inventive impact. His work bridges academia and industry, emphasizing practical applications in restricted research domains.