جست‌وجوی استاد و پژوهشگر

Janna Hastingsمشاهده پروفایل

استادیار

• Medical Knowledge Systems
• Artificial Intelligence in Healthcare
• Ontology Development
• +۴ مورد دیگر

Janna Hastings is Assistant Professor for Medical Knowledge and Decision Support at the Institute for Implementation Science in Health Care (Faculty of Medicine) at the University of Zurich since August 2022, while also serving as Vice-Director of the School of Medicine at the University of St. Gallen. Her research focuses on digitalization in clinical contexts, examining how AI-driven knowledge systems reshape clinical practice, professional identity, and doctor-patient relationships. Education: PhD in Computational Biology (University of Cambridge, 2019), part-time MSc in Computer Science (University of South Africa, 2011), MSc in Philosophy (Open University, 2012) Prior Roles: Group Coordinator at European Bioinformatics Institute (2006-2015), Postdoctoral Researcher at Otto-von-Guericke University Magdeburg (2019-2022), Co-Leader of Human Behaviour-Change Project at University College London (2017-2022) Her research spans ontology development for biomedical domains (ChEBI, Human Behaviour Ontology), AI applications in healthcare decision-making, and behavior change interventions. Key projects include: Building ChEBI molecular ontology Developing Human Behaviour-Change Project knowledge system Ontology-driven mental health frameworks LLM applications in radiation oncology Time-series modeling of metabolism in ageing She explores the capabilities and limitations of clinical AI systems, with publications in JMIR and Lancet Digital Health , covering topics like bias prevention in generative AI and proteomic biomarker discovery. Her work bridges biomedical research with implementation science and digital ethics.

Hassan Ghasemzadehمشاهده پروفایل

دانشیار

• Digital Health
• Machine Learning
• Algorithm Design
• +۷ مورد دیگر

Hassan Ghasemzadeh is an Associate Professor and Program Director in the College of Health Solutions at Arizona State University (ASU), where he is also on the graduate faculty for biomedical informatics, computer science, computer engineering, and biomedical engineering. Prior to joining ASU, he served as an assistant/associate professor of computer science at Washington State University (2014-2021) and as a postdoctoral research manager at UCLA (2011-2013). Education: PostDoc, Computer Science, University of California Los Angeles PhD, Computer Engineering, University of Texas at Dallas MS, Computer Engineering, University of Tehran BS, Computer Engineering, Sharif University of Technology Dr. Ghasemzadeh's research focuses on digital health, machine learning, and algorithm design, with applications spanning wearable technologies, chronic disease management, and behavioral health. His work bridges computer science with healthcare, developing novel algorithms and systems that use wearable sensors to monitor and improve health outcomes. His research has particular emphasis on diabetes management, Parkinson's disease detection, and stress monitoring through advanced sensor analysis and machine learning techniques. His recent publications demonstrate a strong focus on leveraging large language models, counterfactual reasoning, and advanced deep learning techniques to address challenges in digital health. The research spans multiple domains including glucose prediction, Parkinson's disease assessment, cannabis use monitoring, and activity recognition, showing a consistent thread of applying cutting-edge AI to solve real-world health problems with wearable sensor data. Scientific Awards: 2025 Best Poster Award, ASU College of Health Solutions Faculty Research Day 2024 Research Award, ASU College of Health Solutions 2024 Best Poster Award, ASU College of Health Solutions Faculty Research Day 2018 Early Career Development Award, National Science Foundation (NSF CAREER) 2018 Early Career Award, WSU School of EECS Dr. Ghasemzadeh actively mentors numerous graduate students in the Embedded Machine Intelligence Lab (EMIL), with current PhD students including Eric Junyoung Kim, Ebrahim Farahmad, Saman Khamesian, Shovito Barua Soumma, Pegah Khorasani, and others. His research has been funded by prestigious organizations including the National Science Foundation, with projects often focusing on developing innovative wearable health monitoring systems that have led to commercial applications such as WANDA and Sense4Baby. Dr. Ghasemzadeh leads the Embedded Machine Intelligence Lab (EMIL), which focuses on developing machine learning algorithms for embedded and wearable systems. The lab creates solutions that address real-world health challenges through interdisciplinary research that combines computer science, electrical engineering, and clinical medicine. Current projects include glucose prediction systems, Parkinson's disease detection tools, and personalized hydration monitoring applications.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Kjartan Martin Johannesen serves as an Instructor at the Department of Computer Science (DIKU), University of Copenhagen, affiliated with the Pioneer AI research section. His role centers on teaching within DIKU's academic framework while contributing to the department's research ecosystem. His research and teaching interests align with Pioneer AI's core focus areas: artificial intelligence, machine learning, computer vision, medical imaging, statistics, and geometric data analysis. The section specializes in fine-grained classification, self-supervised learning, multimodal AI systems, medical image modeling, and evolutionary morphometry, leveraging advanced techniques in 2D/3D generative models and domain adaptation. The Pioneer AI section operates from the Observatory in Copenhagen Botanical Garden as part of the Pioneer Center for Artificial Intelligence. It maintains robust collaborations with Danish institutions, international partners, and industry, while hosting major conferences including ECCV 2026. Teaching responsibilities include machine learning, deep neural networks, vision processing, and data science courses that integrate the section's research advancements.

Patrick Forréمشاهده پروفایل

استادیار

• Causal Inference
• Machine Learning Theory
• Information Theory
• +۴ مورد دیگر

Patrick Forré is an Assistant Professor and Lab Manager of the AI4Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. His work bridges theoretical machine learning and scientific applications, fostering interdisciplinary collaboration across informatics, mathematics, ecology, chemistry, physics, biology, and astrophysics. His research centers on mathematical foundations of machine learning including causal inference, graphical models, information theory, conditional independence structures, and geometric deep learning. He specializes in applying these techniques to scientific data problems, particularly in electro-catalysis and nitrogen fixation, where machine learning enhances molecular simulations and quantum chemical modeling. His theoretical work addresses non-linear structural causal models with cycles and latent confounders. The AI4Science Lab under his management focuses on detecting hidden patterns in scientific data through projects like electrode-electrolyte interface modeling, nitrogen-fixing coordination complexes analysis, and classical DFT neural approximations. Located in LAB42 Building at Amsterdam Science Park, the lab connects diverse scientific disciplines through machine learning innovation while organizing colloquia, workshops, and PhD defenses.

• Computer Graphics
• Computer Vision
• Video Processing
• +۴ مورد دیگر

Prof. Dr. Thorsten Thormählen is a University Professor for Computer Graphics and Multimedia Programming in the Department of Mathematics and Computer Science at Philipps University Marburg, Germany, a position he has held since October 2012. Prior to this, he served as Substitute Professor for Interactive Graphics Systems at the University of Tübingen (2011-2012) and led the independent research group "Image-based 3D Scene Analysis" at the Max Planck Center for Visual Computing and Communication (2007-2012). His academic background includes a PhD (Dr.-Ing.) from the University of Hannover (2000-2005), where he also worked as a full-time scientific assistant, and a Diploma in Electrical Engineering from the University of Duisburg (1994-1999). Prof. Thormählen's research centers on Visual Computing , spanning Computer Graphics , Computer Vision , Video Processing , and Human-Computer Interaction . His work pioneers tools for 3D reconstruction (e.g., Multi-View Photometric Stereo), video manipulation (e.g., MovieReshape), and multimedia processing (e.g., GSN Composer), with groundbreaking contributions to accessibility through haptic and auditory interfaces for blind 3D modelers. His lab develops practical systems that bridge theoretical computer vision with real-world applications. Analysis of his 15 most recent publications reveals a consistent focus on photometric stereo techniques for 3D reconstruction, video processing innovations, and accessibility-driven interfaces. His work increasingly emphasizes inclusive design, particularly for visually impaired users, while maintaining technical rigor in camera calibration, surface modeling, and multi-sensor fusion. At the Max Planck Center, he established and led the "Image-based 3D Scene Analysis" research group, which advanced methods for reconstructing 3D environments from video sequences. His current work at Marburg continues this trajectory, integrating computer vision, graphics, and human-centered design to solve complex problems in digital content creation and accessibility.

• Statistical Signal and Image Analysis
• Biomedical Applications
• Environmental Statistics
• +۳ مورد دیگر

Armin Schwartzman is a Professor at the Halıcıoğlu Data Science Institute and Department of Biostatistics at the University of California, San Diego (UCSD). He holds a PhD in Statistics from Stanford University, an MSc in Electrical Engineering from the California Institute of Technology, and a BSc in Electrical Engineering from Technion-Israel Institute of Technology. His research focuses on statistical methods for signal and image analysis with applications in biomedical and environmental domains, including spatial inference, high-dimensional data analysis, and random field theory. Education : PhD in Statistics, Stanford University, 2006 MSc in Electrical Engineering, California Institute of Technology, 1996 BSc in Electrical Engineering, Technion-Israel Institute of Technology, 1995 Research Interests : Dr. Schwartzman develops statistical methodologies for analyzing complex data, particularly in neuroimaging, climate science, and genomics. Key areas include spatial inference for image analysis, multiple testing in high-dimensional data, and applications of random field theory. His work bridges theoretical statistics and practical challenges in biomedical and environmental research. Grants & Funding : NIH R01EB026859 (2019–2023): Spatial inference methods for image analysis NIH R21EB013795 (2012–2015): Voxelwise analysis of imaging response in neuro-oncology NIH R01CA157528 (2012–2019): Multiple testing methods for random fields Advising : Current advisees include Sam Davenport, Anubhav Singh-Sachan, and others. Former students have transitioned to roles at institutions like Moderna, University of Florida, and Cruiser. Labs & Collaborations : His research group works on projects such as: Random field theory for noise modeling in images Spatial inference in neuroimaging and climate data Machine learning for medical imaging (e.g., glaucoma diagnosis) Mountain glacier monitoring via satellite imagery

Susanne Wenzelمشاهده پروفایل

پژوهشگر

• Pattern recognition
• Image interpretation
• Machine Learning
• +۶ مورد دیگر

Dr. Susanne Wenzel is a postdoctoral researcher and teaching assistant at the Photogrammetry group (IGG - Institute of Geodesy and Geoinformation) at the University of Bonn, and a scientific coordinator at Forschungszentrum Jülich since February 2018. Her academic journey began with studies in Geodesy at the Technical University of Berlin and University of Bonn, following professional training as a surveying technician at the Berlin Senate of Urban Development. Her research focuses on pattern recognition and image interpretation, particularly applying machine learning and deep learning techniques to photogrammetry and remote sensing problems. Wenzel's work prominently features Markov Marked Point Processes and the analysis of symmetries and repeated structures in images, with applications ranging from facade interpretation to oceanographic analysis. Her interdisciplinary approach bridges computer vision, geospatial analysis, and machine learning. The analysis of her 15 most recent publications reveals a strong trend toward applying advanced machine learning techniques, particularly deep learning and self-taught learning approaches, to photogrammetric and remote sensing problems. Her research spans multiple domains including urban modeling (facade interpretation), environmental monitoring (ocean eddies, sea level anomalies), and forensic applications (latent trace detection), demonstrating remarkable versatility while maintaining a core focus on image interpretation methodologies. Faculty Teaching Award 2014 Faculty Award for the best student in 2007 in Geodesy and Geoinformation Turbo-Preis 2007 of Society for Geodesy, Geoinformation and Land Management (DVW) Dr. Wenzel has supervised numerous Master's and Bachelor's students on diverse topics including neural network applications for ocean eddy tracking, hyperspectral imaging for latent trace detection, and deep learning for remote sensing image classification. Her teaching portfolio includes Photogrammetrie I and II courses since 2009, and she managed the development of the Geodetic Engineering Master's program at IGG from 2015-2018. Her research has been supported through positions at both the University of Bonn and Forschungszentrum Jülich, where she contributes to interdisciplinary projects bridging geospatial analysis and machine learning.

Konstantinos Barmpasمشاهده پروفایل

پژوهشگر

• Brain-Computer Interfaces (BCIs)
• Deep Learning
• Neurosciences
• +۳ مورد دیگر

Konstantinos Barmpas is a Postdoctoral Research Associate at Imperial College London's Department of Computing within the Faculty of Engineering. His research focuses on developing Generative Foundation Models for biosignals in Brain-Computer Interfaces (BCIs), emphasizing causal reasoning and deep learning applications. He holds a PhD in Computer Science (AI) from Imperial College London and an MEng from Imperial College London/ETH Zurich. Education : PhD in Computer Science (Imperial College London, 2020–2024) MEng in Electrical & Electronic Engineering (Imperial College London, 2016–2020) Master Year Abroad at ETH Zurich (2019–2020) His research bridges deep learning with BCIs, addressing topics like differentiable signal processing, causal modeling, and ethical challenges in GenAI-powered neural interfaces. He leads projects like MyBrainCommands and MyBrainStates , approved by Imperial's Research Governance team. His work has been featured in outlets like Sky News, BBC, and The Times. Barmpas contributes extensively to academia: he co-organized the London Geometry and Machine Learning (LOGML) Summer School, led the Imperial Computing Conference (ICC), and served on Imperial's Equity, Diversity, and Culture Committee. He reviews for top AI journals/conferences (e.g., IEEE Transactions, NeurIPS workshops) and mentors students through initiatives like Microsoft Student Ambassador programs. He is a Fellow of the Higher Education Academy (FHEA) and an ELLIS Society member. His innovations include the CogiGame EEG-VR platform for stroke rehabilitation and causal frameworks for brainwave modeling. Over 15 peer-reviewed publications span topics like EEG signal processing, neural network geometry, and BCI ethics. Patents include classification of brain activity signals (GB2605270).

Diana Chua Halikiasمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Scientific Machine Learning
• Randomized Linear Algebra
• +۴ مورد دیگر

Diana Chua Halikias is a Ph.D. Candidate in Mathematics at Cornell University, with a focus on numerical analysis and scientific machine learning. Starting in January 2026, she will join New York University as a Courant Instructor in the Department of Mathematics, collaborating with Chris Musco. Her current role as a Simons-Berkeley Research Fellow underscores her expertise in computational mathematics and machine learning. Education: B.S. in Mathematics from Yale University (2020) Ph.D. in Mathematics from Cornell University (2025) Her research bridges randomized linear algebra, matrix theory, and operator learning, with applications in scientific computing and machine learning. Recent work includes hierarchical matrix approximation and data-efficient PDE learning. She has held internships at the Flatiron Institute and Lawrence Berkeley National Laboratory. Scientific Awards: Simons-Berkeley Research Fellow Graduate Student Funding and Awards at Cornell Halikias actively mentors undergraduate researchers, including directing projects in machine learning and spectral graph theory. She also contributes to outreach through the Cornell REU program and local math circles in Ithaca.

Georgios Angelisمشاهده پروفایل

پژوهشگر ارشد

• Medical Imaging
• Biomedical Engineering
• PET/SPECT Image Reconstruction
• +۳ مورد دیگر

Dr Georgios Angelis is a National Imaging Facility Fellow at the University of Sydney, specializing in PET/SPECT image reconstruction and pharmacokinetic modeling. His research focuses on developing advanced imaging techniques for neuroscience and oncology applications, including motion correction algorithms, parametric image analysis, and deep learning approaches. He holds a PhD in Biomedical Engineering from the University of Manchester and has held postdoctoral roles at the University of Sydney's Brain and Mind Centre and the Northern Sydney Cancer Centre. His work bridges imaging physics and clinical practice, with contributions to open-field PET systems and tumor tracking for radiotherapy. Key research areas include: medical imaging algorithms, neurochemical PET analysis, machine learning in imaging, and translational imaging tools for freely moving subjects. Recent work emphasizes integration of MRI and PET data for improved spatial-temporal resolution, and development of Bayesian methods for neurotransmitter quantification. His projects frequently involve collaborations in oncology and neuroscience, with applications to both preclinical and clinical settings.

• Knowledge Graph Construction & Curation
• Neural-Symbolic Reasoning
• Large Language Models (LLMs)
• +۴ مورد دیگر

Dr. Jiaoyan Chen is a Lecturer (teaching & research) in the Department of Computer Science at The University of Manchester, transitioning to Senior Lecturer (Associate Professor) in July 2025. Previously, she was a Senior Researcher at the University of Oxford (2017-2022) and held visiting roles at Zurich University and Heidelberg University. She holds a PhD and BEng in Computer Science from Zhejiang University. Her research focuses on neural-symbolic systems, integrating knowledge graphs/ontologies with large language models and machine learning. Key areas include knowledge graph construction/curation, ontology embeddings, and data-intensive systems. She leads EPSRC-funded projects like 'OntoEm' (427.9K GBP) and collaborates on initiatives such as the Manchester-Melbourne-Toronto AI for Child Protection project. Teaching responsibilities include units like 'Data Engineering Technologies' and 'Advanced Topics in Knowledge Representation.' She actively supervises 9 PhD/postdoc researchers across topics like knowledge-aware AI and semantic search. Professional services include associate editor roles at Transactions on Graph Data and Knowledge and membership in the EPSRC Peer Review College. Her software contributions include OWL2Vec* (ontology embedding framework) and DeepOnto (ontology engineering toolkit). Recent achievements include ACL 2025 acceptances and an IEEE TKDE survey paper on ontology embeddings.

Angela Violiمشاهده پروفایل

استاد

• Bio-Nanotechnology
• Biomedical Computation and Modeling
• Nanoparticle-Biological Interactions
• +۴ مورد دیگر

Angela Violi is the Arthur F. Thunau Professor of Biomedical Engineering at the University of Michigan, with affiliations in Mechanical Engineering, Electrical Engineering and Computer Science, and Chemical Engineering. Her research focuses on computational methods to study nanoparticle interactions with biological systems, leveraging molecular simulations and machine learning. The Violi Lab develops interdisciplinary approaches to address challenges in nanoscience, including nanoparticle-membrane interactions, drug delivery, and environmental applications. Key research areas include bio-nanotechnology, biomembranes, and biomedical computation. Recent work emphasizes geometric deep learning for nonthermal plasma modeling and ensemble methods for feature selection. The lab's innovations aim to reduce computational costs while enhancing predictive accuracy in complex systems. Publications highlight advancements in nanoparticle growth prediction, machine learning-driven reaction network analysis, and domain-agnostic interaction modeling. Collaborative efforts span engineering, biology, and computer science, reflecting a commitment to translational research with societal impact. Angela Violi actively contributes to diversity initiatives in academia, as seen in her participation in DEI lectures and mentorship programs. Labs/Teams: The Violi Lab is a leader in nanobiotechnology, combining experimental and computational methods. Current projects focus on predictive models for nanoparticle behavior, sustainable materials design, and biomedical applications of AI.

Hanna Kurniawatiمشاهده پروفایل

استاد

• Robotics
• Decision-Making Under Uncertainty
• Motion Planning
• +۴ مورد دیگر

Hanna Kurniawati is a Professor at the ANU School of Computing and holds the SmartSat CRC Professorial Chair for System Autonomy, Intelligence, and Decision-Making. She leads the ANU Node and Planning & Control theme of the ARIAM Hub, and founded the Robot Decision Making group. Her research focuses on scalable algorithms for decision-making under uncertainty, particularly in robotics and autonomous systems. She has received prestigious awards, including the RSS 2021 Test of Time Award and multiple best-paper recognitions. She is an active conference organizer and editorial board member for journals like IEEE Transactions on Robotics. Education: BSc Computer Science, University of Indonesia PhD Computer Science (Robot Motion Planning), National University of Singapore (2008) Research Interests: Planning under uncertainty and POMDP frameworks Robot motion planning and computational geometry Integration of planning and reinforcement learning Autonomous system assurance and safety Service and Leadership: Former President of the Australian Robotics and Automation Association (2019–2020) Keynote speaker at ICRA 2025, IROS 2018, and ICAPS 2025 Program Co-Chair for ICRA 2022 and Editor roles for IEEE TRO and RA-L Teaching: Advanced Topics in AI (COMP4620/8620) Advanced Topics in Machine Learning (COMP4680/8650) Algorithms (COMP3600/6466)

Noomane Ben Khalifaمشاهده پروفایل

استاد

• Sustainable Manufacturing
• Metal Forming Technologies
• Additive Manufacturing
• +۴ مورد دیگر

Prof. Dr.-Ing. Noomane Ben Khalifa is a Professor of Manufacturing – Innovative Manufacturing and Head of the Institute for Production Technology and Systems (IPTS) at Leuphana University. He concurrently leads the Institute for Material and Process Design at Helmholtz Center Hereon. His work focuses on sustainable manufacturing processes, particularly extrusion, sheet metal forming, and additive manufacturing, emphasizing eco-friendly production across material value chains. Education: Awarded a Doctorate in Engineering from TU Dortmund University (2012) with distinction for research on helical profile extrusion. Holds a Mechanical Engineering degree from TU Dortmund (2005), specializing in production engineering and forming technology. Research Interests: Sustainable manufacturing processes; material recycling (e.g., aluminum chips); lightweight material development; composite extrusion; die design optimization; and process simulation. His work bridges advanced manufacturing with environmental impact reduction, leveraging AI and machine learning for material-property modeling. Key Article Trends: Recent studies emphasize machine learning applications in materials science, die design for magnesium alloys, and sustainable recycling processes. His work spans from fundamental material behavior analysis to industrial process optimization. 2025: Focus on AI-driven texture-property modeling in Mg alloys and dieless wire drawing improvements 2024: LCA of aluminum recycling and deep-drawing process reliability Awards: 2009 ISPF Award for extrusion research; 2014 ZukunftErfindenNRW for innovative extrusion press design; invited keynote speaker at ICIT & MPT 2014. Advising & Grants: Oversees OPTUM-MAGNA (magnesium nanocomposites), FERNAPRO (sustainable manufacturing tech), and projects like TrICo (innovation cooperation). Collaborates with industry on tool design and eco-processes. Labs & Teams: Leads IPTS and co-leads Helmholtz material design institute. Supervises interdisciplinary teams focusing on compound casting, incremental forming, and smart manufacturing systems.

Jens Hegerمشاهده پروفایل

استاد

• Manufacturing Engineering
• Production Technology
• Simulation
• +۵ مورد دیگر

Jens Heger is a Professor of Engineering specializing in Modeling and Simulation of Technical Systems and Processes at Leuphana University Lüneburg, Germany. He is affiliated with the Institute for Production Technology and Systems (IPTS) and the Research Center for Digital Transformation. His academic career spans over a decade of research and teaching in production engineering, manufacturing systems, and simulation technologies. Dr. Heger completed his Master of Science in Computer Science with a minor in Business Studies at the University of Paderborn (1999-2006). He then earned his doctorate degree at the University of Bremen in the Department of Production Engineering. His dissertation focused on "Dynamic selection of rules for sequence planning in workshop and flexible flow production" under the supervision of Prof. Scholz-Reiter and Prof. Jürgen Branke from the University of Warwick, UK. Professor Heger's research interests center around the intersection of manufacturing engineering, simulation, and artificial intelligence. His work demonstrates a strong focus on applying advanced computational methods to solve complex production challenges. Key research areas include: Manufacturing process optimization through simulation and modeling Application of machine learning and reinforcement learning in production scheduling Development of intelligent control systems for manufacturing processes Energy efficiency and sustainability in production systems Quality control and prediction in sheet metal forming processes Integration of AI technologies in traditional manufacturing environments Analysis of Professor Heger's recent publications reveals a clear trajectory toward increasingly sophisticated applications of artificial intelligence in manufacturing. His work has evolved from traditional simulation and optimization techniques toward integrating deep learning, reinforcement learning, and computer vision technologies into production systems. A significant portion of his research focuses on dynamic scheduling and optimization problems, particularly using reinforcement learning approaches to address complex manufacturing challenges. His publications also demonstrate a growing interest in sustainable manufacturing practices, energy efficiency, and quality control applications through AI technologies. Professor Heger has contributed significantly to the field through numerous publications in manufacturing engineering, production systems, and AI applications. His work spans both theoretical advancements in scheduling algorithms and practical implementations in industrial settings, particularly focusing on small and medium enterprises.