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Eileen R. MartinView profile

Associate Professor

• Computational Science
• Geophysics
• Applied Mathematics
• +9 more

Eileen R. Martin is an Associate Professor at Colorado School of Mines, jointly appointed in Geophysics and Applied Math and Statistics with a 60/40 split. She collaborates with two industry-aligned consortia: the Center for Wave Phenomena (CWP) and the Center to Advance the Science of Exploration to Reclamation in Mining (CASERM). PhD, Institute for Computational and Mathematical Engineering, Stanford University (2018) MS, Geophysics, Stanford University BS, Mathematics and Computational Physics, University of Texas at Austin Her research bridges computational science and geophysics with applications to subsurface characterization using advanced sensing technologies. She focuses on distributed acoustic sensing (DAS) , seismic imaging , inverse problems , and machine learning for geoscientific data analysis. Recent work explores permafrost thaw monitoring , glacier hydrology , and mine seismicity through scalable algorithms. Selected 2024-2025 Publications : Modeling permafrost thermodynamics with differentiable computing DAS applications in cryoseismic cataloging and glacier monitoring Urban traffic monitoring via multicomponent seismic data Open-source DASCore Python library development Lossy compression effects on seismic data integrity Scientific Awards : NSF CAREER grant SIAM Geosciences Early Career Prize SEG J. Clarence Karcher Award Presidential Early Career Award for Science and Engineering (PECASE) Academic Leadership : Advising 13 graduate students (MS/PhD) across Geophysics, Applied Math, and Hydrologic Engineering Teaching courses in Parallel Scientific Computing, Digital Signal Processing, and Mathematical Geophysics Co-leading weekly group meetings with industry consortia integration Labs & Collaborations : Center for Wave Phenomena (CWP) at Mines CASERM: Mining Exploration to Reclamation Consortium Stanford Exploration Project (alumni) Lawrence Berkeley National Lab affiliate

• UAV trajectory planning
• Autonomous systems
• Medical imaging
• +5 more

Xinwei Wang is a Professor at Nanjing University of Aeronautics and Astronautics with an extensive publication record spanning over two decades. Their research primarily focuses on autonomous systems, UAV trajectory planning, medical imaging, and computer vision applications. Wang has established significant collaborative networks with researchers including Yan Zhou, Liang Sun, Xichao Su, and Lei Wang across multiple Chinese institutions. Wang's research interests center on the intersection of robotics, artificial intelligence, and practical engineering applications. Their work demonstrates particular expertise in UAV coordination systems, medical diagnostic technologies, and underwater imaging solutions. Recent publications reveal a growing emphasis on explainable AI systems for medical applications and safety-critical trajectory planning for autonomous vehicles. The publication trends show a consistent output of high-impact research, with recent work increasingly focusing on practical implementations of AI systems in medical diagnostics, autonomous vehicle navigation, and aerospace applications. Wang's research bridges theoretical control systems with real-world engineering challenges, particularly in safety-critical domains requiring precise motion planning and reliable decision-making. Wang has contributed significantly to both theoretical frameworks in optimal control and practical implementations in medical imaging and autonomous systems. Their work on UAV cooperative task assignment and flight deck operations demonstrates strong connections to aerospace engineering applications, while medical imaging research shows interdisciplinary collaboration with healthcare professionals.

Miguel A. NunesView profile

Researcher

• Small Satellites
• CubeSats
• Multi-Agent Robotic Systems
• +7 more

Miguel A. Nunes serves as Assistant Researcher and Deputy Director at the Hawaiʻi Space Flight Laboratory (HSFL) within the University of Hawaiʻi at Mānoa's College of Engineering. Specializing in small satellite design and multi-agent robotic systems , he leads systems engineering for missions like Neutron-1 and HyTI, while teaching space systems design in programs including the Vertical Integrated Project Aerospace Technologies and Earth and Planetary Exploration Technology (EPET) capstone courses. PhD in Aerospace Engineering (2010) focusing on autonomous satellite swarm control Developed COSMOS mission operations system for managing multiple spacecraft Key work in thermal control systems for hyperspectral imagers and satellite constellations His 10+ years of subsystem expertise spans ADCS, OBC, flight software, and radio communications. Recent publications highlight advancements in T2SL detector arrays and active thermal management for CubeSats. As Deputy-PI for HyTI mission, he contributes to volcanic monitoring and agricultural sensing applications through high-resolution thermal imaging. Research trends show consistent focus on distributed satellite systems and autonomous operations , transitioning from foundational work on multi-agent control to current applications in Earth observation constellations . His collaborations span JPL, NASA, and Saraniasat Inc., with technical contributions to 6U CubeSat standardization and onboard computing architectures.

• Documentary Film
• Ethnographic Film
• Experimental Cinema
• +9 more

Katharine Round is a Lecturer (Teaching) in Ethnographic and Documentary Film at the Department of Anthropology, University College London (UCL). With over two decades of professional experience in creative non-fiction filmmaking, she has worked with prestigious institutions like Netflix, BBC, BFI, Arts Council England, and Creative Europe. Her film practice challenges documentary conventions through formal innovation and hybrid approaches. Co-founder of Disobedient Films (2014) Co-founder of Doc Heads (2009) Studio Lead on UCL's MA Ethnographic and Documentary Film Developer of the DocFiction pathway (launched 2022) Her research focuses on: Experimental documentary forms Boundary-pushing ethnographic film Climate change communication through Climate Symphony project Hybrid reality-fiction narratives in Ghost Town project Memory and trauma representation Interdisciplinary approaches to film Selected scientific awards: UCL-Tohoku Strategic Partnership Award for post-tsunami research Professional activities include presentations at: Holy Motors conference (2025) The Researcher in the Room event (2025) Taxi to the Other Side conference (2025)

• Accelerator Physics
• Beam Dynamics
• Radio-Frequency Photoinjectors
• +5 more

Thorsten Kamps is a Professor of Physics at Humboldt University of Berlin and Head of the Department of High-Brilliance Electron Beams at Helmholtz-Zentrum Berlin . He is also Deputy Project Manager for the Superconducting RF Electron Accelerator Laboratory SEALAB . Education: Graduate Physicist (Dipl.-Phys.) from TU Dortmund (formerly University of Dortmund), Doctor rerum naturalium (Dr. rer. nat.) from Humboldt University of Berlin Research Interests: Particle accelerator beam dynamics, diagnostics, superconducting radio-frequency photoinjectors (SRF photoinjectors), photocathode growth, instrumentation for bright electron beams, free-electron lasers, ultrafast scattering sources, and future trends of accelerator-driven light sources Expertise: Accelerator physics, beam physics of electron beams, photoinjectors, storage rings, electron beam diagnostics, free-electron lasers, and project management of large-scale accelerator projects Labs/Teams: Involved with SEALAB (Superconducting RF Electron Accelerator Laboratory), BERLinPro (Berlin Energy Recovery Linac Project), and collaborations with institutions like Royal Holloway University of London, DESY, and DELTA/University of Dortmund Publications Trends: Thorsten Kamps’ recent works focus on SRF photoinjectors, beam diagnostics, photocathode development, and thermal load studies. His research spans applications in ultrafast electron diffraction, laser-driven acceleration, and energy-recovery linacs, with collaborations across Europe and the US. Topics include interferometric beam monitoring, multi-alkali antimonide materials, and solenoid alignment for beam control. Teaching: Promotes education in accelerator physics through BSc and MSc courses, and supervises BSc, MSc, and PhD students. Develops practical experiments for internships in accelerator physics.

Trung LeView profile

Lecturer

• Deep Generative Models
• Optimization for Machine Learning
• Kernel Methods
• +6 more

Trung Le is a Lecturer in the Department of Data Science & AI at Monash University. His research focuses on deep generative models, kernel methods, optimization in machine learning, and Bayesian inference, with applications to supervised learning, adversarial learning, and cyber security. He holds a PhD in Computer Science from the University of Canberra (2013). Key projects include the Australian Research Council-funded initiatives such as 'Can Machines Unlearn? Toward Next-Generation Safe Artificial Intelligence' (2025–2029) and 'Trustworthy Generative AI: Towards Safe and Aligned Foundation Models' (2024–2026). His work contributes to UN SDG 4 (Quality Education) through advancements in AI ethics and safety. Education: PhD in Computer Science, University of Canberra (2013) Research Areas: Continual Learning, Diffusion Models, Domain Adaptation, Adversarial Machine Learning Awards: KDD 2023 Best Student Paper Award, Imagine Cup 2010 Australia First Prize Trung has published extensively in top-tier venues like NIPS, ICLR, and JMLR, with over 100 outputs since 2009. His recent work emphasizes robustness in AI systems, including projects on adversarial defense and ethical concept erasure in generative models.

George DrettakisView profile

Researcher

• Computer Graphics
• Rendering
• Image-Based Rendering
• +12 more

George Drettakis is a Senior Researcher at INRIA Sophia-Antipolis and leads the GRAPHDECO research group. He has held professorial roles at institutions including MIT, University of Reims, University of Toronto, and École Normale Supérieure. His research focuses on rendering for computer graphics and sound, with emphasis on image-based rendering, perceptual rendering, and audio-visual cross-modal effects. He has also explored interactive illumination, shadows, relighting, and generative models. Current Students: G. Kopanas (Neural Rendering), N. Violante (Generative Models), A. Petitjean (co-supervised), Y. Poirier-Ginter (co-supervised), P. Panantonakis (starting fall 2023). Postdoctoral Researchers: A. Gauthier at INRIA. His recent work includes 3D Gaussian Splatting , Diffusion-based Relighting , and Neural Radiance Fields . He has received the Eurographics Outstanding Technical Contributions Award (2007) and was named an Eurographics Fellow . He manages projects like ERC Advanced Grant FUNGRAPH and has participated in H2020 EMOTIVE , ANR SEMAPOLIS , and CROSSMOD . His group collaborates internationally and has hosted researchers from institutions such as UC Berkeley, Imperial College London, and TU Wien.

Elena Simona LohanView profile

Professor

• Wireless Positioning
• GNSS Algorithms
• Wearable Computing
• +7 more

Elena Simona Lohan is a Professor at Tampere University's Faculty of Information Technology and Communication Sciences , leading the Signal Processing for Wireless Positioning research group. She also holds a Visiting Professor position at Universitat Autonoma de Barcelona's SPCOMNAV group. Education : MSc in Electrical Engineering (Politehnica University of Bucharest, 1997), DEA in Econometrics (École Polytechnique, Paris, 1998), PhD in Telecommunications (Tampere University of Technology, 2003) Research Focus : GNSS algorithms, interference detection, wearable computing, and 5G positioning convergence. Her work spans signal processing for GNSS (Galileo, GPS, GLONASS, BeiDou), indoor/outdoor localization, and IoT communication. She coordinates the H2020 MSCA European Joint Doctorate A-WEAR and serves as Associate Editor for the Journal of Navigation and IET Radar, Sonar & Navigation . Notable projects include GRAMMAR (Galileo receivers), UJI IndoorLoc Platform , and SP4TE collaborations. She has authored over 250 publications and 6 patents.

• Civil Engineering
• Materials Science
• Concrete Durability
• +6 more

Rachid Cherif is a Teacher-Researcher at the University of Limoges, France, affiliated with the E3-TDVM team. His work bridges civil engineering, materials science, and musicology, with a focus on chloride ion transport modeling in cementitious materials and musical discourse analysis in Tunisian traditional and modern music. Key research areas: Concrete durability, multi-ion transport, recycled aggregates, AI applications in construction, ethnomusicology, and music pedagogy. Email: rachid.cherif@univ-lr.fr His publications (2010–2025) span 15+ articles on topics like: Chloride migration models with thermodynamic equilibria Predictive AI frameworks for recycled concrete properties Non-invasive corrosion diagnostics Ethnomusicological studies of Tunisian popular music Hygrothermal behavior of bio-based concretes Collaborations include researchers like Abdelkarim Aït-Mokhtar, Carmen Andrade, and Emilio Bastidas-Arteaga. No scientific awards or students are explicitly mentioned. His work also explores music education in Tunisian primary schools and interplay between authenticity and modernity in Tunisian music.

• Signal processing
• Audio processing
• Spatial audio
• +7 more

Professor Christian Ritz serves as Director of SMART and Professor in the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong. He has held his professorship since 2017 and served as Associate Dean (Research) in 2022 and Associate Dean (International) from 2016-2021. Professor Ritz's research focuses on signal processing for speech, audio, acoustics and visual information. His current projects include microphone array signal processing for enhancing sound recorded in classrooms, sound scene classification for dementia care environments, spatial audio for Virtual and Augmented Reality, and undersea surveillance. His work spans multiple technical domains including signal processing, audio engineering, computer vision, and communications engineering. His recent publication trends show a strong focus on neural network applications for audio processing, particularly for classroom monitoring and spatial audio reproduction. His work integrates deep learning with traditional signal processing techniques to address challenges in spatial audio synthesis, room impulse response generation, and directional audio reproduction. He has also contributed to applications in mining automation, public safety surveillance, and positioning systems. Professor Ritz is a member of the Centre for Signal and Information Processing (CSIP), part of the Signals, Information and Communications Research Institute (SICOM) at the University of Wollongong. His research is funded by the ARC, overseas research institutes, and defense organizations. His grant portfolio is extensive, including projects such as AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods, Multi-Modal Satellite-based Vessel Surveillance, Net Zero Industry and Innovation Program, Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane, and numerous projects related to spatial audio and underwater surveillance. He has secured funding from diverse sources including the Australian Research Council, defense organizations, and international collaborations. Professor Ritz maintains an active research laboratory environment focused on signal and information processing. His work spans multiple application domains from classroom monitoring to underwater mine detection, with strong connections to both academic and industry partners. His research group appears to focus on practical applications of signal processing techniques to solve real-world problems across various domains.

• Cognitive Robotics
• Augmented Reality
• Traffic Telematics
• +3 more

Prof. Dr.-Ing. Gerd-Jürgen Giefing serves as Professor of Information and Communication Technology at Georg Agricola University of Applied Sciences since 2003, concurrently leading the Electrical and Information Engineering Master's Program, Digital Signal Processing Laboratory, and serving as Deputy Head of the Software Engineering Laboratory. His academic foundation includes: Electrical engineering studies with data processing focus at University of Karlsruhe and Technical University of Munich (1983-1988) Doctorate in neuroinformatics and technical vision from Ruhr University Bochum (1988-1993) Research spans cognitive robotics with emphasis on behavior-oriented scene analysis and distributed communication frameworks, augmented reality systems, and traffic telematics applications including driver face recognition. His foundational work in biologically inspired computer vision established video-based facial capture systems using multiprocessor architectures, later evolving into cognitive robotics frameworks. Current investigations focus on brain-computer interfaces and nomadic point cloud calibration for mobile robotics. Publication trends reveal a progression from neurobiological vision models (1990s) to cognitive robotics infrastructure (2010s), consistently addressing real-world applications in automation and human-machine interaction through IEEE conference proceedings. Key recognitions: Innovation Award '94 from Bochum Technology Transfer Association European Information Technology Award 1996 from European Council for Applied Sciences and Engineering As IEEE Systems Man and Cybernetics Society member, he maintains active research leadership without documented grant specifics. His laboratory direction fosters applied research in signal processing and software engineering for cognitive systems development.

Kwon Oh-jinView profile

Professor

• Image/video compression
• Image quality enhancement
• Image fusion
• +6 more

Professor Kwon Oh-jin is affiliated with Sejong University , where he serves in the Department of Electronic Engineering . His academic career spans over three decades, including a PhD in Electrical and Computer Engineering from University of Maryland (1994) and MS in Signal/Image Processing from University of Southern California (1991). His research focuses on image/video analysis , compression algorithms , watermarking systems , and steganography . Recent work explores machine learning-driven compression , 360-degree imaging , and JPEG standards implementation . His 15 most recent articles address topics like cybersecurity using CNNs , dynamic gesture recognition , and novel multimedia platforms for emerging standards. Professor Kwon leads the Visual Communication Research Lab (Chung923), supervising numerous PhD and MS students working on point cloud coding , JPEG privacy , and AI-based image coding . He has received multiple government and industry grants from organizations including the Agency for Defense Development and Ministry of National Defense .

Christopher W. BairdView profile

Associate Professor

• Pediatric Cardiac Surgery
• Congenital Heart Defects
• Aortic Valve Disease
• +3 more

Christopher W. Baird is an Associate Professor of Surgery at Harvard Medical School and a key faculty member in the Department of Cardiac Surgery at Boston Children's Hospital. He holds leadership roles as Director of the Heart Valve Program and Co-Director of the Vascular Ring & Airway Program , focusing on congenital heart defects and structural valve repair. Biology (BS) - Appalachian State University Medicine (MD) - University of North Carolina at Chapel Hill His research addresses pediatric congenital heart surgery , with a focus on vascular ring decompression, aortic and pulmonary valve reconstruction, and airway stabilization. He has pioneered techniques like the Ozaki method for congenital valve repair and developed analytical models for surgical planning. Recent publications highlight outcomes in vascular ring reoperation, biventricular repair strategies, and anticoagulation therapy post-valve replacement. His work bridges clinical practice and biomedical innovation, particularly in low-resource surgical solutions.

Bob MahmoodiView profile

Lecturer

• Wireless communication system
• Digital signal processing
• Control
• +6 more

Dr. Bob Mahmoodi serves as a Lecturer in the Department of Electrical and Computer Engineering at the University of St. Thomas School of Engineering, where he has taught for 10 years since 2012. Concurrently, he holds an adjunct instructor position at the University of Minnesota's Electrical and Computer Engineering department for 35 years. His industry background includes over 30 years at 3M in R&D focused on wireless RFID and biometric sensors, plus three years at Honeywell developing airborne radar systems. His educational qualifications include: PhD in Electrical Engineering and Control Sciences from the University of Minnesota MS in Electrical Engineering from the University of Minnesota BS in Electrical Engineering from the University of Minnesota Dr. Mahmoodi's research spans wireless communication, digital signal processing, control systems, medical instrumentation sensors, and FPGA/IC design. His work integrates analog/digital systems with applications in biometric security and image compression. Current teaching focuses on Electronics I laboratories, Engineering Design Clinic II, and graduate-level Digital Signal Processing coursework emphasizing machine learning applications. His publication history from 1981-2013 reveals consistent innovation in image enhancement algorithms and radar signal processing, with increasing specialization in medical imaging after 1984. Key thematic developments include the transition from military radar applications to medical/biometric systems and the evolution of real-time processing techniques for commercial printing technologies. Dr. Mahmoodi holds seven U.S. patents covering image enhancement (1986, 1994), projection displays (2006), and oil quality monitoring systems (2012-2013). His professional service includes IEEE Twin Cities Chapter chairmanship (1989-1990), IS&T Conference chairmanship (1991), and ACR-NEMA standards committee membership for image compression. As an active design clinic instructor, he mentors student teams in industrial problem-solving through the Senior Design Clinic program. His industry experience directly informs classroom instruction, particularly in sensor integration and system modeling applications. Laboratory development for electronics courses leverages his 3M/Honeywell background in practical circuit design.

V. MenkovskiView profile

Associate Professor

• Graph Neural Networks
• Machine Learning
• Deep Learning
• +7 more

Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.