جستجوی استاد راهنمای دانشگاهی

Mahdi Soltanolkotabiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Generative AI
• +۷ مورد دیگر

Mahdi Soltanolkotabi is a Professor in the Departments of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering at the University of Southern California's Viterbi School of Engineering. He serves as the inaugural Director of the USC Center on AI Foundations for Science (AIF4S). His academic journey includes a Ph.D. in Electrical Engineering from Stanford University (2014) under Emmanuel Candes, followed by a postdoctoral position at UC Berkeley's AMPLAB mentored by Ben Recht and Martin Wainwright. Dr. Soltanolkotabi's research spans both theoretical and applied dimensions of data science. On the theoretical side, he develops mathematical foundations for modern data science, focusing on generative AI, deep learning, machine learning, signal processing, and computational imaging. His work draws upon nonconvex optimization, high-dimensional probability, statistical estimation, empirical processes, and learning theory. On the applied side, he develops reliable AI systems for healthcare and scientific applications, collaborating with physicians and domain scientists to enhance AI reliability, develop new architectures, and create rigorous evaluation frameworks. His recent publications demonstrate strong focus on medical AI applications, image reconstruction, and theoretical foundations of deep learning. His work bridges the gap between theoretical guarantees and practical implementations, particularly in medical imaging where reliability is critical. His research group has made significant contributions to understanding the behavior of nonconvex optimization algorithms in high-dimensional settings. David and Lucile Packard Fellow Information Theory Society Best Paper Award NIH Director's new innovator award Sloan Research Fellowship NSF Career award Airforce Office of Research Young Investigator award (AFOSR-YIP) Viterbi school of engineering junior faculty research award Faculty awards from Google and Amazon Dr. Soltanolkotabi has received multiple research grants including Amazon Research Awards for projects on "Artificial intelligence for fast and portable medical imaging" and "Reliable AI for Generation of Medical Reports from MRI Scans." He actively collaborates with medical professionals and leads educational outreach initiatives with local schools through USC's Viterbi Adopt-a-School program. His work demonstrates a strong commitment to translating theoretical advances into practical healthcare solutions while maintaining rigorous mathematical foundations.

• Machine Learning
• Computer Vision
• Neural Networks
• +۵ مورد دیگر

Mehrtash Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University. He joined Monash in 2018 after five years at Canberra Research Laboratory-NICTA working with Prof. Richard Hartley and Prof. Fatih Porikli, and earlier at Queensland Research Laboratory-NICTA with Prof. Brian Lovell. His research focuses on machine learning, computer vision, and geometric learning with applications in medical imaging and diffusion models. Recent Research Trends (2025): 3D Gaussian splatting compression, diffusion transformers for visual correspondence, hyperbolic geometry in hierarchical structures, and robust learning from noisy labels. Scientific Awards: Outstanding Reviewer, CVPR'21 Advising Highlights: Mentored students contributing to papers at ICCV'24, CVPR'25, ICLR'25, and Nature Machine Intelligence. Labs & Teams: Collaborates with Data61-CSIRO, ARC, and US Air Force Research Laboratory.

• Structural Health Monitoring
• Civil Infrastructure
• Machine Learning for Engineering
• +۷ مورد دیگر

Dr. Andy Nguyen is a Senior Lecturer in Structural Engineering at the University of Southern Queensland, within the School of Engineering. He is an active researcher and educator, specializing in the Structural Health Monitoring (SHM) of critical civil infrastructure such as bridges, buildings, and transport tunnels. Bachelor of Engineering (BEng), NUCE, 1999 Master of Engineering (MEng), NUCE, 2003 Doctor of Philosophy (PhD), Queensland University of Technology (QUT), 2014 Dr. Nguyen's research is at the forefront of integrating advanced technologies into civil engineering. His primary focus is on developing and deploying sophisticated SHM systems that utilize sensors, data analytics, and machine learning to provide real-time insights into the structural integrity of ageing infrastructure. His work aims to enable proactive maintenance, extend the lifespan of structures, and enhance public safety. He has successfully implemented monitoring systems on major bridges and high-rise buildings in Queensland and New South Wales, with systems capable of even detecting distant earthquake events. His research interests span Structural Health Monitoring, Machine Learning for Engineering, Damage Detection, Finite Element Model Updating, Sustainable Building Materials like bamboo, and the application of AI for automated condition assessment of transport infrastructure. The analysis of his recent publications reveals a strong and consistent research trajectory centered on the application of data-driven and AI methods to solve practical problems in civil infrastructure. His work frequently combines signal processing techniques (like Stockwell Transform) with deep learning models for tasks such as crack detection in concrete and pavement. He also conducts significant research on model updating for complex structures like cable-stayed and arch bridges, using vibration data and optimization algorithms. The integration of machine learning for overload classification and the development of cost-effective, automated monitoring systems are key trends in his recent output. Advanced Queensland Fellow (2024-2027) Dr. Nguyen is actively involved in research supervision and collaboration. He is currently supervising several postgraduate students on projects related to AI-powered condition assessment, bamboo as a sustainable building material, and railway track design. He receives research funding from the Queensland Government through his Advanced Queensland Fellowship. His research has direct practical applications, as evidenced by his public engagement, such as writing for The Conversation on safeguarding ageing bridges, and his work with the Australian Network of Structural Health Monitoring. Dr. Nguyen's work embodies the development of a next-generation 'Living' Laboratory for engineering education, where research, teaching, and real-world infrastructure monitoring are integrated. His current projects involve creating smart, automated fault detection systems and advancing 'digital twin'-based monitoring platforms for infrastructure.

Jia-Jie Zhuمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Optimal Transport
• +۵ مورد دیگر

Jia-Jie Zhu is a machine learner and applied mathematician currently serving as head of an independent research group at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, with an upcoming appointment as tenured associate professor at KTH Royal Institute of Technology in Stockholm. Previously, he conducted postdoctoral research in machine learning at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, following doctoral studies in optimization and numerical analysis at the University of Florida. His research focuses on the mathematical foundations of machine learning and optimization, particularly at the intersection of computational algorithms, dynamical systems, and probability theory. Key areas include: Robust probabilistic machine learning algorithms Kernel methods for distribution manipulation Variational methods for optimization over probability distributions Gradient flows and optimal transport theory Wasserstein and Fisher-Rao geometry Applications in generative modeling and causal inference Dr. Zhu's recent work reveals deep connections between partial differential equations, kernel methods, and machine learning, resulting in theoretically grounded algorithms for handling distribution shifts. His publications demonstrate a consistent trajectory from foundational optimization theory to cutting-edge applications in robust learning and generative modeling, with increasing emphasis on the mathematical structures underlying modern ML systems. He has secured significant research funding, including a DFG Project on 'Optimal Transport and Measure Optimization Foundation for Robust and Causal Machine Learning' within the Priority Program 'Theoretical Foundations of Deep Learning' (SPP 2298), and actively organizes academic events such as the Workshop on Optimal Transport from Theory to Applications (OT-DOM) and upcoming sessions at ICSP 2025 and SwissMAP. As an educator, Dr. Zhu teaches nonparametric statistics at Humboldt University of Berlin and serves as area chair for major conferences including AISTATS 2025. He maintains an active research group with opportunities for master's students, PhD candidates, and postdoctoral researchers interested in the mathematical frontiers of machine learning.

Weifeng Liمشاهده پروفایل

دانشیار

• Artificial Intelligence Security
• Cybersecurity Applications
• Machine Learning
• +۴ مورد دیگر

Weifeng Li serves as an Associate Professor in the Department of Management Information Systems at the University of Georgia's Terry College of Business. His academic foundation includes a Ph.D. in Management Information Systems from the University of Arizona (2017) and a B.S. from Shanghai Jiao Tong University (2012). Ph.D., Management Information Systems, University of Arizona (2017) B.S., Management Information Systems, Shanghai Jiao Tong University (2012) Dr. Li's research centers on AI security and cybersecurity applications , with methodological expertise in machine learning, natural language processing, and Bayesian modeling. His work spans critical domains including adversarial robustness in AI systems, dark web threat intelligence, disinformation detection, and phishing defense mechanisms. He develops frameworks for proactive cyber defense through generative adversarial learning and interpretable multi-modal models. His publication portfolio reveals a strong trajectory in top-tier venues, with recent work focusing on adversarial robustness (RADAR framework), dark web community analysis, and interpretable AI for security applications. Research consistently bridges theoretical machine learning advances with practical cybersecurity implementations, particularly in financial technology and social media contexts. Dr. Li's research has received funding from the National Science Foundation's Secure and Trustworthy Cyberspace (SaTC) program, supporting his work on AI security frameworks. His collaborations span multiple institutions and research groups focused on cyber threat intelligence. He contributes to cybersecurity infrastructure through systems like the AZSecure text mining platform for dark web monitoring and hacker community analysis. His work enables proactive threat detection through nonparametric topic modeling and generative adversarial approaches to counter cybercriminal tactics.

Luis Merino Cabañasمشاهده پروفایل

استاد

• Robotics
• Systems Engineering
• Automation
• +۵ مورد دیگر

Luis Merino Cabañas is a Professor at the Universidad Pablo de Olavide , affiliated with the Deporte e Informática department and leading the SRL Service Robotics Laboratory . His research focuses on robotics, systems engineering, and automation, with a specialization in human-robot interaction and path planning. Education : PhD in Systems Engineering from the Universidad de Sevilla (2007), where his thesis explored cooperative perception techniques for multiple unmanned aerial vehicles in forest fire detection. Research Trends : Recent work (2023–2025) emphasizes 3D path planning, sensor fusion (LiDAR, radar, inertial systems), neural distance fields for safe navigation, and socially aware robotics. His studies integrate AI, genetic programming, and multi-modal perception for applications in construction, healthcare, and GNSS-denied environments. Labs & Teams : He leads the SRL Service Robotics Laboratory , contributing to projects like the Skyeye team and BIM2ROS integration for construction robotics.

Md. Zoheb Hassanمشاهده پروفایل

استادیار

• Spectrum sharing and management
• Wireless communication systems
• Communications and network control and regulation systems
• +۱ مورد دیگر

Md. Zoheb Hassan serves as an Assistant Professor in the Department of Electrical Engineering and Computer Engineering at Laval University, where he leads cutting-edge research in wireless communications and spectrum management. His academic role includes graduate recruitment and active participation in the university's research ecosystem, particularly through the Establishment of the Next Generation of Professors program funded by FRQNT. Dr. Hassan's research centers on spectrum sharing and management, wireless communication systems, and communications network control systems. He pioneers the integration of digital twin technology and machine learning to solve critical challenges in next-generation networks, including interference management in 5G/6G aerial corridors, Internet of Vehicles, and satellite-terrestrial integration. His work emphasizes practical implementations such as proof-of-concept demonstrations for tactical networks and proactive resource allocation in dynamic environments. Analysis of his 2024-2025 publications reveals a dominant trend toward AI-driven wireless resource optimization, with 12 of 15 recent papers featuring digital twins for interference management, spectrum sharing, and energy efficiency. Key thematic clusters include vehicular communications (4 papers), underwater IoT networks (2 papers), and hardware-impairment resilient designs (3 papers), demonstrating his focus on bridging theoretical advances with real-world deployment challenges across diverse network topologies. Dr. Hassan has secured significant competitive funding for his research initiatives: Digital Twin-Enhanced Interference Management for Next-Generation Radio Access Networks in the FR3 Band (FRQNT, 2025-2027) Center for Radio Frequency and Communications Systems, Technologies and Applications (FRQNT, 2024-2030) Context-Aware Spectrum Sharing and Management for Next Generation Wireless Networks (NSERC, 2024-2029) Development of innovative technologies for modeling predictive systems in urban mobility (MITACS, 2022-2026) Springboard to Discovery supplement for Context-Aware Spectrum Sharing (NSERC, 2024-2025) He actively mentors doctoral candidates, currently supervising Mahima Karim (PhD in Electrical Engineering, expected 2025) and Mohammadamin Parhizgar (PhD in Electrical Engineering, expected 2024). His supervisory approach combines theoretical rigor with practical problem-solving, focusing on spectrum management algorithms and digital twin implementations for next-generation networks. While specific laboratory affiliations aren't detailed in the source material, his projects indicate strong alignment with Laval University's wireless research infrastructure and the Center for Radio Frequency and Communications Systems.

François Pomerleauمشاهده پروفایل

استاد

• Robotics
• 3D Reconstruction
• Autonomous Navigation
• +۶ مورد دیگر

François Pomerleau is a full-time Professor at the Department of Computer Science and Software Engineering at Université Laval since 2017. His research focuses on 3D environment reconstruction , autonomous navigation , search-and-rescue robotics , and scientific methodology in robotics . He has held postdoctoral fellowships at the University of Toronto and Université Laval, with technology transfer experience at Alstom Inspection Robotics and Robotiq. Ph.D. in Mechanical Engineering (2013) from ETH Zurich M.Sc. in Electrical Engineering (2009) and B.Ing. in Computer Engineering (2006) from Université de Sherbrooke His research integrates robotics , computer science , and environmental monitoring , with a focus on point cloud registration , Lidar-based SLAM , and trajectory planning for unstructured environments. Recent work includes UAV-assisted terrain awareness , exposure time emulation for vision algorithms , and multi-season datasets for autonomous navigation . François’s recent publications emphasize 3D mapping , SLAM robustness , and environmental adaptation across forestry, subarctic, and alpine domains. His team develops tools for autonomous vehicles , search-and-rescue , and industry 4.0 . Scientific awards include Best Robotic Vision Paper Awards at CRV 2016 and 2020, a Best Paper Award at the ICRA 2024 Workshop, and recognition as a Distal Fellow of the NSERC Canadian Robotics Network (NCRN). He collaborates with industry partners like Robotiq and serves as Associate Editor for IEEE Robotics and Automation Letters , Frontiers in Robotics and AI , and IROS , while contributing to international program committees for robotics conferences.

Ralf Peetersمشاهده پروفایل

استاد

• Signal and Image Processing
• Systems and Control Theory
• Mathematical Modelling
• +۷ مورد دیگر

Ralf Peeters is a Full Professor in Mathematics of Knowledge Engineering at Maastricht University's Faculty of Science and Engineering , Department of Advanced Computing Sciences. He serves as Vice-Dean of Research and Director of the STEM Graduate School, while leading the university's team at the inter-university research school DISC and co-chairing the Mathematics Centre Maastricht. Education: PhD in Mathematics (Free University, Amsterdam, 1994) Technical Mathematics (Delft University of Technology, 1988) Research Interests span applied mathematics, systems and control theory, signal/image processing, artificial intelligence, and biomedical engineering applications. His work bridges mathematical techniques with real-world challenges in healthcare and industrial systems. Recent Publications highlight advancements in deep learning for cardiac signal reconstruction, tensor-based signal decomposition, and recurrence plot analysis. These works integrate machine learning with clinical diagnostics, particularly in electrocardiographic imaging and arrhythmia characterization. Key Collaborations: Mathematics Centre Maastricht Dutch Mathematics Platform Dutch Institute of Systems and Control Leadership Roles: Vice-Dean of Research (FSE), Director of STEM Graduate School, Head of DISC-affiliated team, and Co-Chair of Mathematics Centre Maastricht. He has supervised over 25 PhD projects, emphasizing applied research across health and industrial domains.

Seongjin Choiمشاهده پروفایل

استادیار

• Urban Mobility Data Analytics
• Spatiotemporal Data Modeling
• Deep Learning & Artificial Intelligence
• +۳ مورد دیگر

Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.

• Data Mining
• Machine Learning
• Reinforcement Learning
• +۹ مورد دیگر

Dr. Yanjie Fu is an Associate Professor in the School of Computing and AI at Arizona State University, part of the Ira A. Fulton Schools of Engineering. He maintains his office in BYENG 506 at the Tempe campus and can be reached at yanjie.fu@asu.edu. Dr. Fu received his Ph.D. from Rutgers University in 2016, the B.E. degree from the University of Science and Technology of China, and the M.E. degree from the Chinese Academy of Sciences. His industry research experience includes positions at Microsoft Research Asia and IBM Thomas J. Watson Research Center. His research focuses on developing disruption-robust machine intelligence that can handle imperfect and complex data. Dr. Fu's work spans two major efforts: Data for AI (D4AI), exploring how structure knowledge of data can guide AI, and AI for Data (AI4D), investigating how AI can augment, reprogram, and knowledgeize data. His current research interests include space-time intelligence, data-centric AI, sim2decision, multimodal reasoning, and LLM with agentic AI. His lab has contributed projects including D4AI-spatial, D4AI-timeseries, D4AI-causal outliers, AI4D-RL, AI4D-Gen, and AI4D-LLM. Dr. Fu's recent publications reveal a strong trend toward integrating causal reasoning with deep learning for robust anomaly detection, advancing time series forecasting with novel normalization techniques, and applying generative AI to urban planning. His work increasingly bridges traditional machine learning with large language models, particularly focusing on data-centric approaches for tabular data transformation and feature engineering. US NAE FOE early career engineer (2023) US NSF CAREER (2021) NSF CRII (2018) ACM KDD18 Best Student Paper Finalist IEEE ICDM Best Paper Finalist (2014, 2021, 2022) ACM SIGSpatial Best Paper Runner-up (2020) 2022 Baidu Scholar global top Chinese young scholars in AI 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention Dr. Fu has successfully mentored multiple Ph.D. students who have secured tenure-track faculty positions at prestigious institutions including University of Kansas, Chinese Academy of Sciences, Great Bay University, Portland State University, and University of Macau. His research has been supported by significant grants including the NSF CAREER award, and he currently serves as Associate Editor of ACM Transactions on Knowledge Discovery from Data. He is also a senior member of both ACM and IEEE. Dr. Fu leads a research group focused on developing trusted and safe machine intelligence. The lab connects computing issues across representation learning, self-supervised learning, interactive learning, adaptive learning, and stream learning to build disruption-robust frameworks. The group executes two key steps: data representation construct (integrating structure knowledge, self-optimization, explainability) and learning strategy construct (integrating robust representations with adaptive and interactive learning).

• Wireless Communications
• Statistical Signal Processing
• Estimation and Equalization
• +۳ مورد دیگر

Cihan Tepedelenlioglu is an Associate Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. His work bridges wireless communications, statistical signal processing, and renewable energy systems, with a focus on photovoltaic array monitoring, fault detection, and optimization. PhD, MS, and BS in Electrical Engineering from University of Minnesota, University of Virginia, and Florida Institute of Technology 2001 NSF CAREER Award recipient Research interests span wireless communications , graph signal processing , stochastic optimization , and machine learning applications to solar energy systems . Key projects include quantum machine learning for PV topology optimization, consensus algorithms for distributed networks, and real-time fault detection using neural networks. Recent articles emphasize machine learning in energy systems (2023-2025), with 12 publications on photovoltaic monitoring and 3 on consensus algorithms. Earlier work focused on channel estimation in OFDM systems and fading models in wireless communications. Scientific awards : NSF CAREER Award (2001) Major grants include NSF funding for networked solar array management (2013-2016), nonlinear distributed consensus (2013-2016), and statistical processing of solar data (2009-2012). Teaching roles include EEE 350 Random Signal Analysis and graduate research supervision in signal processing and wireless communications. Collaborates extensively with Andreas Spanias, Mahesh Banavar, and other researchers on cyber-physical systems for energy applications.

Yongkai Wuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Dr. Yongkai Wu is an Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University, where he focuses on advancing Responsible AI , Causal Inference , and Machine Learning . His research addresses fairness, trustworthiness, and transparency in AI systems through causal modeling and has been published in top-tier venues like AAAI, NeurIPS, and KDD. Education: Ph.D. in Computer Science (2020) and M.S. in Computer Science (2018) from the University of Arkansas; B.Eng. in Electronic Engineering (2014) from Tsinghua University. Dr. Wu’s research spans Responsible AI and Causal Inference , with applications in healthcare, computer vision, and cybersecurity. He explores Causal Fairness in non-IID settings, Responsible LLMs , and Robust Learning via hyperspectral data. His work integrates ethics into AI/ML systems, ensuring equitable outcomes in dynamic environments. His recent articles highlight trends in Fairness through causal inference, Explainable AI in healthcare, and Efficient LLMs . Collaborations with institutions like the University of Maryland and Prisma Health underscore real-world impact. Scientific Awards: Best Paper Award (SIGKDD'25), travel awards from SBP-BRiMS, IJCAI, KDD, and NeurIPS. Dr. Wu’s grants include NSF , SC EPSCoR , Prisma Health , and United States Army CCDC funding for projects on Responsible AI in Healthcare , Hyperspectral AI , and Robust Learning . He mentors students through summer programs and directed research, emphasizing hands-on experience with Python, PyTorch, and ethical AI frameworks.

Johanna Sommerمشاهده پروفایل

پژوهشگر

• Robust Machine Learning
• Machine Learning for Graphs and Networks
• Machine Learning for Temporal and Dynamical Data
• +۳ مورد دیگر

Johanna Sommer is a researcher at the Technical University of Munich , affiliated with the Department of Informatics under the TUM School of Computation, Information and Technology. She contributes to research and teaching in advanced machine learning domains. Education : M.Sc. Computer Science (TUM, passed with distinction), B.Sc. Applied Computer Science (Baden-Württemberg Cooperative State University). Research Interests : Her work spans Robust Machine Learning , machine learning for graphs and sequential data, Bayesian learning with uncertainty quantification, and efficiency improvements in training algorithms. She applies these to tasks like molecular generation and continuous-time modeling. Teaching : Johanna leads seminars and courses on topics including Advanced Machine Learning: Deep Generative Models , Machine Learning for Graphs and Sequential Data , and Large-Scale Machine Learning , often in collaboration with industry partners. Publications highlight her contributions to robustness analysis of combinatorial solvers, molecule generation from 3D shapes, and efficient alternatives to neural ODEs. These works appear at top venues like ICLR and NeurIPS .

Mathias Unberathمشاهده پروفایل

دانشیار

• Computer-assisted medicine
• Computer vision
• Artificial intelligence and machine learning
• +۴ مورد دیگر

Mathias Unberath is the John C. Malone Associate Professor in the Department of Computer Science at Johns Hopkins University, with secondary appointments in Ophthalmology and Otolaryngology—Head and Neck Surgery at the School of Medicine. He is a core faculty member of the Laboratory for Computational Sensing and Robotics (LCSR) and the Malone Center for Engineering in Healthcare, and affiliate faculty at the Institute for Assured Autonomy and Data Science and AI Institute. Education: PhD in Computer Science from Friedrich-Alexander University of Erlangen-Nürnberg (2017), MSc in Optical Technologies (2014), BSc in Physics (2012) His research focuses on computer-assisted medicine, integrating computer vision, machine learning, and medical robotics to develop human-centered solutions through mixed reality and embodied technologies. His work addresses surgical phase recognition, explainable AI, and digital twin representations for clinical workflows. Unberath's 15 most recent publications demonstrate expertise in surgical AI (7/15), medical imaging (12/15), and mixed reality (8/15), with specific subfields including segmentation frameworks (3 papers), cognitive load estimation (4 papers), and surgical robotics (5 papers). NSF CAREER Award NIH NIBIB Trailblazer R21 Google Research Scholar Award Inaugural DSAI Junior Faculty Award IPCAI 2025 Best Paper Award He teaches graduate courses in machine learning, AI system design, and interpretable machine learning. His group, the ARCADE Lab, develops technologies for computer-assisted interventions, emphasizing robustness, explainability, and human-AI collaboration in clinical settings.