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Serge A. ShapiroView profile

Professor

• Seismology
• Geophysics
• Rock Physics
• +5 more

Prof. Serge A. Shapiro is a Full Professor of Geophysics at Freie Universität Berlin since 1999 and Director of the PHASE consortium since 2004. He holds a Diploma in Applied Geophysics from Lomonosov Moscow State University (1982), a PhD from the Moscow Research Institute of Geosystems (1987), and a Habilitation from Karlsruhe University (1995). His research focuses on seismogenic processes, induced seismicity, rock physics, and subduction zone dynamics, with applications to geothermal energy, CO2 storage, and hydraulic fracturing. Education: Diploma in Applied Geophysics, Lomonosov Moscow State University (1982) PhD in Geophysics, Moscow Research Institute of Geosystems (1987) Habilitation, Karlsruhe University (1995) Research Interests: Induced seismicity from fluid operations CO2 storage and hydraulic fracturing risks Seismic hazard assessment Rock physics under stress Key Contributions: Developed the Seismogenic Index Model for induced earthquakes Pioneered DAS-based seismic monitoring techniques Advanced understanding of fault stability and pressure diffusion effects Awards: Virgil Kauffman Gold Medal (2013) for work in microseismic monitoring and rock physics Grants & Projects: PHASE consortium leader (2004–present) Utah FORGE EGS project advisor Labs/Teams: Seismology Group, Freie Universität Berlin PHASE university consortium

• Materials Science and Engineering
• Additive Manufacturing
• Materials Informatics
• +7 more

S. Mohadeseh Taheri-Mousavi is an Assistant Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. She joined CMU in September 2022 after postdoctoral appointments at MIT and Brown University. Her research is supported by major grants from NASA STRI, DARPA, the Army Research Laboratory, and the Naval Nuclear Laboratory, and she is affiliated with the NextManufacturing Center and the Wilton E. Scott Institute for Energy Innovation. Her educational background includes a Ph.D. from EPFL, Switzerland, and M.Sc. and B.Sc. degrees from Sharif University of Technology, Iran. She was awarded both early and advanced Swiss National Science Foundation fellowships during her postdoctoral studies. Taheri-Mousavi’s research focuses on the intersection of materials science, mechanical engineering, and computer science. She develops multi-scale computational models and AI-driven frameworks—such as AlloyGPT and generative AI agents—to design next-generation structural alloys, particularly for additive manufacturing and extreme environments. Her work emphasizes materials sustainability, industrial decarbonization, and uncertainty quantification in alloy design. The integration of machine learning with Integrated Computational Materials Engineering (ICME) and CALPHAD methods enables rapid exploration of high-dimensional composition and processing spaces. Her recent publications (2023–2025) show a strong trend toward AI/ML applications in alloy discovery, hydrogen embrittlement modeling, and high-temperature aluminum and tungsten alloys. These works reflect a deep commitment to accelerating materials innovation through human-AI collaboration and smart experimental validation. Her scientific honors include prestigious Swiss National Science Foundation fellowships. She has also received seed funding from the Scott Institute for Energy Innovation to study hydrogen embrittlement. She advises a dynamic team of doctoral students and a postdoctoral researcher, working on topics including hydrogen embrittlement, generative AI for welding, and gradient alloys. Her research is funded by high-impact grants from NASA, DARPA, the Army, and the Naval Nuclear Laboratory, supporting transformative projects in structural alloy design. She leads the Taheri-Mousavi Group, which operates within CMU’s Materials Characterization Facility and the NextManufacturing Center. The group focuses on developing novel AI-integrated computational frameworks to guide efficient and intelligent experimentation in alloy development.

John N. DuPontView profile

Professor

• Welding Metallurgy
• Casting
• Failure Analysis
• +15 more

John N. DuPont is the R.D. Stout Distinguished Professor of Materials Science & Engineering at Lehigh University , where he also serves as Associate Director of the Energy Research Center and holds a joint appointment in the Mechanical Engineering Department. He was a founding Director of the National Science Foundation Manufacturing and Materials Joining Innovation Center, a globally prominent welding research facility. Education: Ph.D., Materials Science and Engineering, Lehigh University M.S., Materials Science and Engineering, Lehigh University B.S., Metallurgical Engineering, Ohio State University Research Interests: Welding Metallurgy Failure Analysis and Product Litigation Laser Engineered Net Shaping Solidification and Alloy Development Energy Science, National Defense, and Large Structures Scientific Awards: ASM Scholar (1990) AWS National Fellowship Award (1995) AWS Harold H. Jennings Award (1996, 2000) AWS William Sparegan Award (1999, 2000, 2008, 2010) National Science Foundation Presidential Early Career Award (PECASE) (2000) Department of Defense Manufacturing Technology Achievement Award (2015) Steel Founders Society of America Thomas E. Barlow Award of Honor (2020) Grants and Professional Activities: Dr. DuPont has raised over $20 million in research funding from agencies such as the National Science Foundation, Office of Naval Research, and Department of Energy. He has conducted 300+ consulting projects in failure analysis and materials testing for corporations, law firms, and the Department of Defense. His academic contributions include teaching courses on Welding Metallurgy, Diffusion and Phase Transformations, and Advanced Solidification.

• Compassion at work
• Mindfulness at work
• Organisational justice
• +7 more

Samantha Su-Hsien Sim is an Assistant Professor at Nova School of Business and Economics (NOVA SBE) in Portugal, where she teaches Organisational Behaviour and Human Resource-related courses. She is affiliated with the Leadership for Impact Centre at Nova SBE and the Mindfulness Initiative at Singapore Management University, and serves as a faculty adviser for the student-led Nova Mindfulness Club. Doctorate in Business, Singapore Management University Bachelor in Social Science, Singapore Management University Master in Social Science, Tilburg University Her research centers on compassion and mindfulness in the workplace, organisational justice, and cross-cultural differences. She investigates how compassion influences workplace behavior and leadership, and how mindfulness affects cooperation and negotiation outcomes. Her work bridges organizational psychology and management, with implications for employee well-being, ethical leadership, and inclusive organizational cultures. Her recent publications explore mindfulness-based training, corporate well-being programs, and algorithmic fairness in employment. The research spans topics like psychological well-being, negotiation dynamics, and public policy, showing a strong interdisciplinary focus on human-centered organizational practices. Published in top journals including Journal of Applied Psychology , Academy of Management Journal , and Organizational Behavior and Human Decision Processes She advises student research through the Nova Mindfulness Club and contributes to research grants related to leadership, mindfulness, and organizational justice. Her collaborative network includes scholars from Singapore, Europe, and North America, reflecting her cross-cultural research orientation. She is active in research centers focused on leadership impact and mindfulness, contributing to both academic and practical applications in organizational settings.

• Machine learning
• Artificial intelligence
• Big data analytics
• +8 more

Gianluca Setti is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, where he has been serving since 2017. He previously held positions at the University of Ferrara from 1997 to 2017. His institutional roles include being the Contact Person for the Research Quality Evaluation process, Member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and Member of the University Quality Assurance Committee. He serves as Editor-in-Chief of the Proceedings of the IEEE, the first non-US editor to hold this position. Dr. Setti's research spans multiple interdisciplinary fields including machine learning, artificial intelligence, big data analytics, Internet of Things, biomedical signal processing, power electronics, and electromagnetic compatibility. His work bridges theoretical foundations with practical applications, particularly focusing on compressed sensing, neural networks, and circuit design for specialized applications. His research has significant implications for healthcare, sustainable infrastructure, and next-generation electronics. His publication record reveals a consistent trajectory from foundational work in chaotic systems and neural networks to contemporary applications in AI, IoT, and edge computing. The most recent publications demonstrate his focus on anomaly detection at the edge, neural oracles for biosignal processing, and power electronics innovations. His work shows strong integration between theoretical signal processing and practical circuit implementation. 1998 Caianiello prize (best Italian Ph.D. thesis on Neural Networks) IEEE Fellow (2006) IEEE Circuits and Systems Society Distinguished Lecturer (2004, 2015) 2004 IEEE CAS Society Darlington Award 2013 IEEE CAS Society Meritorious Service Award 2013 IEEE CAS Society Guillemin-Cauer Award 2019 IEEE Transactions on Biomedical Circuits and Systems best paper award Multiple best paper awards at major conferences including ECCTD2005, EMCZurich2005, ISCAS2011, PRIME2019, and EMCCOMPO2019 Dr. Setti has supervised numerous PhD students across various research domains including electromagnetic compatibility, signal and power integrity, communication networks, mechatronics and robotics. His research is supported by significant funding including national PRIN projects, EU-funded JTI-ECSEL initiatives, and commercial contracts. He leads the VLSILAB Group at DET, focusing on circuit architectures, embedded systems, and AI applications. His current projects include DECORI (anomaly detection), StorAIge (embedded storage for AI), PROGRESSUS (energy infrastructure), CONNECT (smart grid), and CONVERGENCE (wearable healthcare applications).

Erhan BayraktarView profile

Professor

• Mathematical finance
• applied probability
• machine learning
• +4 more

Erhan Bayraktar is a Professor of Mathematics at the University of Michigan, holding the Susan Smith Chair. He serves as Director of the Quantitative Finance and Risk Management Masters Program, which he established in 2015. His academic career at the University of Michigan spans since 2004, progressing from T. H. Hildebrandt Research Assistant Professor to his current full professorship. Professor Bayraktar earned his Ph.D. from Princeton University in 2004, following dual Bachelor's degrees in Electrical Engineering and Mathematics from Middle East Technical University in Turkey. His academic journey reflects a strong foundation in both theoretical and applied mathematical disciplines. Bayraktar's research focuses on mathematical finance, applied probability, machine learning, mean field games, stochastic analysis, stochastic control, and optimal stopping. His work bridges theoretical mathematics with practical applications in finance and risk management. He has developed sophisticated mathematical frameworks for analyzing complex financial systems, market behaviors, and optimal decision-making under uncertainty. His contributions to mean field games have provided new insights into large-scale interacting systems, while his work on stochastic control has advanced methodologies for optimal decision processes. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on Wasserstein space analysis, graphon particle systems, and applications of machine learning to financial mathematics. His research shows increasing interdisciplinary connections between traditional mathematical finance and modern computational approaches. Susan M. Smith Professorship (2010-present) National Science Foundation CAREER Grant (2010-2016) SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize (2010) Professor Bayraktar has mentored 14 Ph.D. students (13 graduated) and approximately 40 post-doctoral researchers. His students hold prestigious positions in academia and industry, including tenure-track positions at Boston University, University of Colorado, University of Sydney, and University of Toronto. He has secured continuous funding from the National Science Foundation, including the current grant DMS-2507940 (2025-2028) and previous grants totaling over 15 years of continuous NSF support. As Director of the Quantitative Finance and Risk Management Masters Program, Bayraktar has built a robust academic community through the Financial/Actuarial Math seminar series, which hosts about 10 outside speakers annually, and by organizing international workshops in stochastic analysis for finance and insurance in Ann Arbor.

Kede MaView profile

Associate Professor

• Image Quality Assessment
• Computational Photography
• High Dynamic Range Imaging
• +7 more

Kede Ma is an Associate Professor in the Department of Computer Science at City University of Hong Kong (CityUHK). He received his B.E. from the University of Science and Technology of China (USTC) in 2012, and MASc and Ph.D. degrees from the University of Waterloo in 2014 and 2017, respectively. From 2018 to 2019, he was a Research Associate with the Howard Hughes Medical Institute and New York University. Prof. Ma has been named to the Highly Cited Researchers list by Clarivate Analytics in 2024 and currently serves on the editorial boards of IEEE Transactions on Image Processing, IEEE Transactions on Information Forensics and Security, and IEEE Signal Processing Letters. Prof. Ma leads the Multimedia Analytics (MA) Laboratory, an interdisciplinary research group focused on computational vision, computational modeling of human visual perception, perceptual multimedia signal processing, quality assessment, and multimedia forensics. His research spans computational photography, high dynamic range imaging and rendering, omnidirectional video analysis, camera processing pipeline design, and artificial intelligence safety in multimedia systems. His work integrates machine learning techniques including reinforcement learning, generative modeling, self-supervised learning, and continual learning for multimedia signal processing applications. His recent publications demonstrate a strong focus on image quality assessment, deep learning for multimedia processing, and multimedia forensics. His work bridges theoretical computer vision principles with practical applications in multimedia systems. The research trends show increasing integration of foundation models with specialized multimedia processing tasks, particularly in quality assessment and security applications. Highly Cited Researchers list by Clarivate Analytics (2024) Best Paper Award at IEEE International Conference on Virtual Reality and Visualization (2021) Best Paper Runner-Up at International Joint Conference on Artificial Intelligence Workshop (2021) Top 10% Award at IEEE International Conference on Image Processing (2015) Finalist for the Governor General's Gold Medal, University of Waterloo (2017) Spotlight presentation at NeurIPS (2022) Highlight paper at ICCV (2025) Oral presentation at ICLR (2025) Prof. Ma advises numerous PhD students and postdoctoral fellows in the MA Laboratory. His research is supported by various grants enabling work in multimedia analytics, image processing, and computer vision. The laboratory maintains active collaborations with researchers at institutions including SUSTech, ZJU, and HIT. Current projects focus on advancing image quality assessment methodologies, developing more robust deep learning techniques for multimedia forensics, and exploring new approaches to HDR imaging and omnidirectional video processing. The Multimedia Analytics Laboratory maintains a strong focus on both theoretical foundations and practical applications of multimedia processing. Current research directions include integrating large language models with image quality assessment, developing more robust deepfake detection methods, and advancing techniques for continual learning in multimedia applications. The lab emphasizes rigorous evaluation methodologies and maintains multiple datasets for multimedia quality assessment research.

Ernest DavisView profile

Professor

• Artificial Intelligence
• Commonsense Reasoning
• Knowledge Representation
• +4 more

Ernest Davis is a Professor at the Department of Computer Science , Courant Institute of Mathematical Sciences , New York University . His research focuses on representing commonsense knowledge in AI systems , with an emphasis on spatial and physical reasoning , and he collaborates with Gary Marcus on integrating AI and psychological models. He has authored over 50 scientific papers and three books, including Linear Algebra and Probability for Computer Science Applications (2012). His teaching includes courses on Artificial Intelligence and Fundamental Algorithms. Research Trends: His recent work examines benchmarks for commonsense reasoning , limitations of large language models (e.g., GPT-4, DALL-E 2), mathematical reasoning in AI, and the Winograd Schema Challenge . Professional Activities: He has served as an ACM reviewer, program committee member for 50+ conferences, and area editor for ACM Transactions on Computational Logic . He contributes book reviews to Computing Reviews , SIAM News , Artificial Intelligence journal, and others. Non-Technical Writing: Davis writes for general audiences on topics spanning computer science, mathematics, cognitive psychology, and literary themes, published in outlets like The New Yorker , Wired , and The Times Literary Supplement .

William HarbertView profile

Professor

• Geophysics
• Seismic Processing
• Microseismic Analysis
• +5 more

William Harbert is a Professor in the Department of Geology and Environmental Science at the University of Pittsburgh, where he leads research in geophysics and subsurface characterization. His work bridges fundamental geophysical principles with practical applications in energy and environmental systems. Education: MS in Exploration Geophysics from Stanford University PhD in Geophysics from Stanford University Research focuses on seismic analysis across multiple scales, from micro-CT to surface seismic. His group specializes in advanced processing of microseismic, reflection seismic, and VSP data to image subsurface structures and understand pore-scale dynamics. Current work integrates deep learning for geophysical object detection and classification, with emphasis on organic shale systems and CO 2 storage monitoring. Key areas include rock physics, microseismicity analysis, and environmental geophysics for water quality assessment. Publication trends show strong emphasis on energy-related geophysics, particularly hydraulic fracturing monitoring, CO 2 sequestration verification, and unconventional reservoir characterization. Recent work increasingly incorporates machine learning techniques and addresses environmental monitoring challenges in subsurface operations. Scientific recognition: DOE ORISE Research Associate Resident Institute Fellow of the NETL-Institute for Advanced Energy Solution Professional engagements include membership on the Altarock Review Board for DOE-funded geothermal projects and prior service on the Scientific Advisory Board for the In Salah CO 2 Injection Project. His research involves extensive collaboration with national laboratories and industry partners on subsurface monitoring technologies. His laboratory group develops advanced geophysical processing techniques for subsurface imaging across multiple scales, with current projects focusing on microseismic monitoring of shale reservoirs and CO 2 storage sites.

Joel M. CohenView profile

Professor

• Mathematics
• Algebraic Topology
• Harmonic Analysis
• +4 more

Joel M. Cohen is a Professor of Mathematics at the University of Maryland, College Park. He specializes in Algebraic Topology , Harmonic Analysis , and Functional Analysis , with a focus on Operator Theory and Differential Equations on Trees . His work bridges abstract mathematical theory with applications in Mathematical Physics and Geometric Analysis . His recent publications include studies on Carleson measures, harmonic structures on trees, and the Radon transform. He has received the 2006 Lester R. Ford award for his article in the American Mathematical Monthly. Key Collaborators: Flavia Colonna, David Singman, Massimo Picardello Courses Taught: Math 136 (Calculus for Life Sciences), Math 734 (Algebraic Topology) Cohen has served in various academic roles, including Former Member of the University Senate and Founding Member of the Coalition on Intercollegiate Athletics . His research has been published in journals such as Ann. Inst. Fourier Grenoble, American Journal of Mathematics, and Advances in Applied Mathematics.

Anru ZhangView profile

Associate Professor

• Tensor Learning
• High-Dimensional Statistics
• Machine Learning
• +3 more

Anru Zhang is the tenured Eugene Anson Stead, Jr. M.D. Associate Professor with joint appointments in Biostatistics & Bioinformatics, Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. He holds a Ph.D. from the University of Pennsylvania (2015, advised by T. Tony Cai) and a B.S. in Mathematics from Peking University (2010). Current roles: Associate Professor at Duke (2024–present), previously Assistant Professor at UW-Madison (2018–2021) Research focus: Tensor learning, high-dimensional statistics, EHR analysis, and healthcare applications Mentorship: Supervises active research team including postdocs (Jianbin Tan, Qiuyi Wu) and PhD students (Runshi Tang, Yinrui Sun) Research Trends : His recent publications emphasize tensor methods in biomedical data (EHR, microbiome, Alzheimer’s), Riemannian optimization for high-dimensional problems, and hybrid statistical-computational approaches. Key themes include healthcare AI, EHR analysis, and non-convex optimization. Scientific Awards : COPSS Emerging Leader Award (2024) IMS Tweedie New Researcher Award (2022) ASA Gottfried E. Noether Junior Award (2021) NSF CAREER Award (2020) AMIA Data Science Outstanding Paper Award (2023) Advising & Grants : Mentored 16+ students/postdocs, including Yuetian Luo (IMS Lawrence D. Brown Award) and Yuchen Zhou (IMS Hannan Travel Award). Current grants include NIH-funded projects on sepsis detection, mental health AI, precision genetic testing, and telehealth interventions, plus NSF CAREER funding for statistical inference in high-dimensional structures. Labs & Teams : Leads a research group at Duke focusing on tensor learning, statistical theory, and healthcare AI applications. Collaborates with Duke’s AI Health initiative and serves as Associate Editor for leading journals like Annals of Statistics and JASA.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +3 more

Balint Toth is a distinguished academic with dual affiliations: a Research Professor at the Alfréd Rényi Institute of Mathematics in Budapest and a Professor of Probability (Heilbronn Chair) at the University of Bristol 's School of Mathematics. His work bridges Probability Theory , Mathematical Physics , and Statistical Mechanics , focusing on stochastic dynamics, random walks in complex environments, and scaling limits. Key Roles: Co-Editor-in-Chief of Probability Theory and Related Fields , organizer of probability seminars in Budapest-Vienna and Bristol, and former leader of the BME Stochastics Seminar (1999–2020). Teaching: Delivers advanced courses like Probability 2 , Stochastic Differential Equations , and Percolation , emphasizing rigorous mathematical foundations. Research Themes include hydrodynamic limits, self-interacting random walks, diffusion in random media, and symmetry breaking in spin systems. His recent publications explore non-equilibrium stochastic models, anomalous diffusion, and connections between probability and physics. Teaching Materials span bilingual resources (Hungarian/English) for undergraduate and graduate courses in probability and stochastic analysis.

Paul A. YushkevichView profile

Professor

• Biomedical Image Analysis
• Alzheimer's Disease Imaging
• Statistical Shape Modeling
• +5 more

Paul A. Yushkevich is a Professor of Radiology at the Perelman School of Medicine, University of Pennsylvania , with affiliations in the Bioengineering Graduate Group . He leads the Penn Image Computing and Science Laboratory (PICSL) , focusing on advanced biomedical image analysis techniques. Developed first-of-its-kind computational atlas of the hippocampal formation Led NIH R01-funded research on MRI-derived biomarkers for Alzheimer's disease Created open-source software tools: ITK-SNAP and Convert3D Expert in statistical shape modeling and histology-MRI co-registration Research Focus: Specializes in hippocampal segmentation using high-resolution MRI, with applications in Alzheimer's disease research and cardiac imaging . His work combines differential equations and machine learning for accurate image analysis. Scientific Achievements: First-place MICCAI segmentation challenges (2012, 2013) Over 169 PubMed publications in neuroimaging and computational anatomy Developed DTI-TK toolkit for diffusion MRI analysis Collaborations: Works with Alzheimer's Disease Neuroimaging Initiative (ADNI) and multiple international institutions. Supervises graduate students in biomedical image analysis.

• Textile Engineering
• Smart Textiles
• Electronic Textiles
• +7 more

André Catarino is an Assistant Professor in the Department of Textile Engineering at the University of Minho, Portugal, and Deputy Director of the 2C2T – Center for Textile Science and Technology since 2022. He is an integrated researcher at the center, with a strong focus on interdisciplinary research at the intersection of textiles, electronics, and materials science. Education: Postgraduate Specialization in Digital Business, University of Porto, Porto Business School (2020–2021) Ph.D. in Textile Engineering, University of Minho (2005) M.Sc. in Textile Engineering, University of Minho (1998) B.Sc. in Electrical and Computer Engineering, University of Porto (1992) Research Interests: André Catarino's research spans a wide range of domains, including smart and electronic textiles , wearable sensor systems , materials engineering , and functional textiles for health and sports . His recent work also delves into digital marketing and the application of artificial intelligence in textile systems. His expertise encompasses electronics, instrumentation, programming, fabric manufacturing, and textile characterization. He has led or participated in over 25 national and international research projects , including EU-funded initiatives like BE@T, GreenAuto, and Fashion Alive. His work has resulted in 4 Portuguese patents and 1 European patent , alongside numerous prototypes and technology transfers. Publications and Impact: With over 115 scientific publications , including journal articles, book chapters, and conference proceedings, his research output is both broad and impactful. His recent publications focus on smart vests for posture monitoring, textile-based EMG electrodes, and consumer behavior in fashion marketing. Student Supervision: He has supervised or co-supervised more than 45 master’s and doctoral dissertations , covering topics from wearable health monitoring systems to sustainable fashion design and digital marketing strategies. Labs and Teams: As Deputy Director of the 2C2T – Center for Textile Science and Technology , he leads a multidisciplinary team of researchers and engineers. The center is a hub for innovation in textile science, with a strong emphasis on integrating electronics, sustainability, and human-centered design into textile applications.

Benjamin RicaudView profile

Associate Professor

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +3 more

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.