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Raphael SznitmanView profile

Professor

• Computer Vision
• Probabilistic Methods
• Statistical Learning
• +2 more

Prof. Dr. Raphael Sznitman serves as Director of the ARTORG Center for Biomedical Engineering Research and Head of the Artificial Intelligence in Medical Imaging group at the University of Bern, Switzerland, holding a Full Professor position in AI for Medical Imaging since 2015. Education: PhD in Computer Science, Johns Hopkins University (2011) MSc in Computer Science, Johns Hopkins University (2009) BSc in Cognitive Systems, University of British Columbia (2007) Research Interests: Sznitman's work centers on computational vision , probabilistic methods , and statistical learning applied to medical imaging challenges. His group develops AI algorithms for ophthalmic diagnostics, surgical robotics, and medical image analysis, with emphasis on OCT, surgical phase recognition, and domain adaptation techniques. Key application areas include retinal disease detection and cataract surgery automation. Publication Trends: His 2021-2025 publications reveal concentrated efforts in deep learning for medical imaging , particularly in ophthalmology (OCT analysis) and surgical video understanding. Emerging themes include LLM applications for clinical monitoring, unsupervised out-of-distribution detection for surgical safety, and physics-informed AI for multimodal medical data fusion. Research Leadership: As ARTORG Center Director, Sznitman oversees interdisciplinary research bridging computer science and clinical medicine. His group collaborates extensively with Bern University Hospital clinicians on translational projects, securing funding for AI-driven diagnostic tools and surgical assistance systems. Current initiatives focus on real-time intraoperative guidance and spaceflight ophthalmology applications. Laboratory: The Artificial Intelligence in Medical Imaging group operates within ARTORG's dedicated facilities, maintaining partnerships with surgical robotics labs and ophthalmology departments for clinical validation of AI systems. Their work integrates multimodal data streams including OCT, VR perimetry, and surgical video feeds.

• Machine Learning
• Deep Learning
• Signal Processing
• +9 more

Prof. Dr.-Ing. Ahmad Osman is a Professor at the Saarland University of Applied Sciences (htw saar), specializing in Test Technologies and Test Methods within the Faculty of Engineering. He also holds an Adjunct Professor position at Laval University in Quebec, Canada, in the Department of Electrical Engineering and Computer Science. His research focuses on Artificial Intelligence applications in Signal and Image Processing for Non-destructive Testing (NDT) , with extensive work on Deep Learning , 3D Ultrasound Tomography , and Sensor Data Fusion in industrial contexts. Engineering Artificial Intelligence Signal Processing Image Processing Non-destructive Testing Quality Control Augmented Reality Osman leads the AutomaTiQ research group and serves as Head of the Algorithms/Signal and Data Processing Department at Fraunhofer IZFP . His recent publications (2017–2022) emphasize Deep Learning for defect detection in CFRP , Terahertz Imaging for artwork diagnostics, and Acoustic Sensors for agricultural quality control. He has organized international conferences on Structural Health Monitoring and contributed to Springer books on NDT technologies. His projects include ComforTex-AI (2024) and development of 3D positioners for ultrasound measurements. Collaborations span institutions in Germany, Canada, Italy, and Brazil, with advisory roles in the German Society for NDT and technical committees for conferences in Montreal and Egypt.

Nivee Pradip AminView profile

Assistant Clinical Professor

• Cardiovascular Disease
• Women's Cardiovascular Health
• Preventive Cardiology
• +3 more

Dr. Nivee Pradip Amin serves as Clinical Assistant Professor of Medicine at Weill Cornell Medical College and Assistant Attending Physician at NewYork-Presbyterian Hospital, holding leadership roles as Associate Director for Consultative Cardiology and Director of the Women's Heart Program & Preventive Cardiology. Her clinical expertise spans general cardiology, preventive cardiology, and echocardiography with focus on high-risk cardiovascular prevention. Her educational credentials include: B.A., B.S. in International Studies and Business from University of Pennsylvania (2002) M.D. from Johns Hopkins University School of Medicine (2007) M.H.S. in Clinical Investigation from Johns Hopkins Bloomberg School of Public Health (2013) Research centers on sex-based disparities in cardiovascular outcomes, women's heart health across the lifespan, and preventive strategies for coronary/valvular disease. She investigates intersections between cardiology and obstetrics/oncology, emphasizing personalized risk assessment and early intervention to improve quality of life. Analysis of her publications reveals consistent focus on women's cardiovascular biomarkers (e.g., breast arterial calcium), obstetric complications as long-term risk factors, and disparities in revascularization outcomes. Her work leverages large-scale database analysis to address gaps in preventive care for high-risk populations including cancer patients and those with cirrhosis. Dr. Amin maintains active board certifications in Internal Medicine, Cardiovascular Diseases, Nuclear Cardiology, and Echocardiography through the American Board of Internal Medicine, and holds professional memberships in the American College of Cardiology, American Society for Preventive Cardiology, and American Heart Association. She provides inpatient consultative care on cardiac telemetry units while leading the Women's Heart Program, which integrates preventive cardiology services with gender-specific treatment protocols. Fluent in Spanish, she emphasizes culturally competent care tailored to individual patient needs across diverse populations.

Reinhard HeckelView profile

Professor

• Machine Learning
• Deep Learning
• Computational Imaging
• +5 more

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.

• Fatigue strength of aluminum structures
• Fracture mechanics
• Railway bridge assessment
• +4 more

Dorina Siebert is a Researcher at the Chair of Metal Construction within the School of Engineering at the Technical University of Munich. She has been working as a research assistant at the Chair since 2019, contributing to various research projects related to steel and aluminum construction, fracture mechanics, and additive manufacturing in construction. Education: M.Sc. in Civil Engineering from Technical University of Munich (2012-2019) Affiliation: Chair of Metal Construction, School of Engineering, Technical University of Munich Contact: dorina.siebert@tum.de, Room 0101.Z1.038, +49 (89) 289-22527 Dorina's research primarily focuses on the fatigue strength of aluminum structures, fracture mechanics in railway bridges, and the application of additive manufacturing techniques in construction. Her work on powder bed-based laser beam melting of metal has significant implications for modern construction methods. She also investigates safe operating time intervals for historic steel bridges and has contributed to the development of a mobile vehicle barrier, demonstrating the practical applications of her theoretical work. Her publication record shows a strong trend toward computational and experimental analysis of material behavior under stress, particularly in aluminum alloys and steel structures. She has published extensively on fatigue properties, fracture mechanics calculations, and additive manufacturing applications, with a clear progression toward more complex modeling techniques and practical engineering solutions. Her work bridges theoretical computational models with real-world infrastructure challenges. Dorina teaches courses including 'Constructing with aluminum' for the Summer semester 2025 and 'Fracture mechanics and fatigue' for the Winter semester 2024/25. She also leads a seminar on plate buckling and steel bridge construction, sharing her specialized knowledge with engineering students. Her teaching directly reflects her research expertise, creating a strong connection between theoretical knowledge and practical application for her students.

Olivia Di MatteoView profile

Assistant Professor

• Quantum Computing
• Quantum Software Engineering
• Quantum Compilation
• +5 more

Olivia Di Matteo serves as an Assistant Professor in the Department of Electrical and Computer Engineering within UBC's Faculty of Applied Science, leading the Quantum Software and Algorithms Research (QSAR) group since her January 2022 appointment. Her academic foundation includes a BSc from Lakehead University and MSc/PhD in Physics (Quantum Information) from the University of Waterloo, completed in 2019. Dr. Di Matteo's research centers on quantum software engineering , with pioneering work in quantum compilation , circuit optimization , and debugging tools . She champions open-source quantum frameworks and develops accessible educational resources to democratize quantum computing. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in quantum programming infrastructure, particularly circuit analysis (33%), bug classification (20%), and qubit network optimization (15%), with strong emphasis on practical software tooling over theoretical physics. No scientific awards were documented in the source materials. She advises graduate students in the QSAR group while contributing to open-source quantum ecosystems through projects like PennyLane and The Ionizer transpiler, and teaches courses including CPEN 400Q (Gate-model quantum computing) and ELEC 221 (Signals and Systems). The QSAR group operates at the intersection of quantum software development and education, focusing on making quantum programming accessible through visual tools, real-time debugging environments, and hardware-agnostic compilation techniques.

Stefanie CzischekView profile

Assistant Professor

• Quantum many-body systems
• Quantum condensed matter
• Quantum computation and simulation
• +4 more

Dr. Stefanie Czischek is an Assistant Professor in the Department of Physics at the University of Ottawa, leading the APRIQuOt research group focused on artificial and physically realizable intelligence for quantum applications. She joined uOttawa in 2022 after postdoctoral work at the University of Waterloo. Her research bridges quantum technologies and neural networks, with expertise in quantum simulation, neuromorphic computing, and machine learning applications in quantum physics. Research Interests: Quantum computation/simulation using neural networks Neuromorphic hardware implementations Quantum many-body systems Machine learning for quantum control and tomography Her publications demonstrate strong interdisciplinary focus, combining quantum physics with cutting-edge ML techniques. Recent works explore transformer models for quantum simulation, neural network quantum states, and quantum sensing applications. The research shows consistent evolution toward hardware-algorithm co-design for quantum problems. Awards: Springer Thesis Award (2020) for doctoral research on neural-network simulation of quantum systems. Research Group & Advising: Leads the APRIQuOt lab with 1 postdoc, 6 graduate students, and 1 undergraduate. Current projects include large language models for quantum states, quantum optimal control via reinforcement learning, and neuromorphic quantum simulations. The group collaborates with experimental teams and maintains strong industry-academia partnerships.

• Deep Learning
• Self-supervised learning
• Efficient training of deep learning methods
• +3 more

Mustafa Taha Koçyiğit is a Full-time Assistant Professor at Bogazici University. His research focuses on Deep Learning, Self-supervised learning, Efficient training of deep learning methods, Computer vision, Efficient training of large language models, and Language grounded vision models. His recent work addresses computational efficiency in training methods and novel applications of deep learning across domains like aerospace defect detection and computer vision. His publications span advancements in self-supervised learning strategies (2023), efficient training for computer vision tasks (2023), and theoretical contributions like unsupervised batch normalization (2020). The 2025 work demonstrates cross-disciplinary impact in aerospace engineering through AI-driven defect detection via X-ray tomography. Notable Contributions: Bridging efficiency and accuracy in deep learning pipelines Technical Strengths: Neural architecture design, optimization strategies, and domain-specific model adaptation

Nai-Hui ChiaView profile

Assistant Professor

• Quantum Algorithms
• Quantum Complexity Theory
• Quantum Cryptography
• +3 more

Nai-Hui Chia is an Assistant Professor in the Department of Computer Science at Rice University. Previously, he held positions at Indiana University Bloomington (2021-2022), the University of Maryland's Joint Center for Quantum Information and Computer Science (2020-2021), and the University of Texas at Austin (2018-2020). He earned his Ph.D. in Computer Science and Engineering from Penn State University (2018) and a Bachelor's degree from National Taiwan University (2010). His research focuses on quantum algorithms, quantum complexity theory, and quantum cryptography. Key questions include exploring quantum advantages, computational limits, and the impact of quantum computing on computer science. He has contributed to foundational work on quantum circuit complexity, verification, and cryptographic protocols resilient to quantum attacks. Dr. Chia has received prestigious awards, including the NSF CAREER Award (2024), DOE Quantum Testbed Pathfinder Award (2023), and Google Scholar Award (2023). He actively participates in program committees for major conferences like TQC, Asiacrypt, and Crypto. Advising includes students like Yu-Ching Shen and Chia-Ying Lin, along with postdoctoral researchers. His work bridges theoretical computer science and quantum computing, with grants supporting research into quantum circuit size problems and post-quantum cryptography.

Chao LiuView profile

Researcher

• Surgical Robotics
• Teleoperation
• Haptics
• +2 more

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

Sadaf SobhaniView profile

Assistant Professor

• Thermal Management
• Energy Conversion
• Heat Transfer
• +12 more

Sadaf Sobhani is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Her research focuses on thermal management and energy conversion with applications in high-efficiency, low-emission energy systems, spacecraft thermal control, and electrochemical reactors for carbon dioxide conversion. Dr. Sobhani's educational background includes a B.S. (2014), M.S. (2015), and Ph.D. (2019) in Mechanical Engineering from Stanford University. During her doctoral studies, she worked as a research associate at the NASA Ames Research Center and later joined the Lawrence Livermore National Laboratory as a postdoctoral researcher. Her research program integrates computational modeling, experimental techniques, and advanced manufacturing to investigate flow, heat transfer, and chemical reactions in porous media. She leverages the connection between micro-scale features and macro-scale transport properties to develop innovative solutions for energy systems. Her work spans multiple disciplines including combustion engineering, electrochemical systems, and thermal management for spacecraft. Dr. Sobhani's publications demonstrate a consistent focus on porous media combustion, heat transfer optimization, and advanced diagnostic techniques. Her recent work has increasingly incorporated additive manufacturing and machine learning approaches to solve complex thermal management challenges, particularly for space applications and carbon dioxide conversion systems. Gallery of Fluid Motion Award, American Physical Society (2018) Accel Innovation Scholarship, Stanford Technology Ventures Program (2017) Graduate Public Service Fellowship, Haas Center for Public Service (2016) Schneider/MAP Sustainable Energy Fellowship, Haas Center for Public Service (2016) Graduate Research Fellowship, National Science Foundation (2015) AIAA Niagara Frontier Section 2025 Young Professional of the Year Award NASA Early Career Faculty Award (2023) NASA Early Stage Innovations Award (2023) Dr. Sobhani leads an active research group and has secured significant funding including a NASA Early Career Faculty Award and a FuzeHub grant with industry partners Lithoz America and Dimensional Energy. She has developed a new spacecraft thermal management course at Cornell and is actively mentoring students in her laboratory research. The Sobhani Lab, located at 182 Grumman Hall, spans approximately 850 sq. ft. and focuses on spacecraft thermal control, combustion research, non-intrusive diagnostic methods, and ceramic additive manufacturing. The lab utilizes advanced facilities including the Cornell NanoScale Science and Technology Facility and the Cornell High Energy Synchrotron Source.

• X-ray Optics
• Nanoimaging
• Synchrotron Radiation
• +3 more

Ulrich Vogt is a Professor in Applied Physics at Kungliga Tekniska Högskolan (KTH) and leads the X-ray Optics and Nanoimaging group within the Bio-Opto-Nano unit. He serves as Vice-head of the Applied Physics department for undergraduate education. His research focuses on developing advanced X-ray microscopy techniques, particularly at synchrotron facilities like MAX IV’s NanoMAX beamline. He specializes in X-ray optics, nanoimaging, and diffractive optical elements for applications in materials science, biology, and medicine. Key contributions include the design of the NanoMAX beamline, optimization of X-ray zone plates via metal-assisted chemical etching, and advancements in multi-beam ptychography. Vogt has pioneered compact X-ray microscopy systems using laser-plasma sources and liquid-jet targets. His work integrates nanofabrication, computational imaging, and synchrotron instrumentation to achieve sub-100 nm resolution in hard and soft X-ray regimes. Teaching responsibilities include courses on experimental physics, photonics, and X-ray applications. His lab collaborates internationally on projects like the European XFEL, emphasizing high-brightness sources and radiation-resistant optics. Recent innovations include adaptive multi-beam ptychography and stereo X-ray imaging for 3D nanoscale visualization. Research highlights span over 100 peer-reviewed articles, with a focus on coherence characterization, beamline instrumentation, and nanostructured materials. Vogt’s grants include a Röntgen-Ångström Cluster award supporting multi-beam ptychography and cryo-microscopy advancements.

Miaki IshiiView profile

Professor

• Seismology
• Geophysics
• Earthquake Studies
• +5 more

Miaki Ishii is a Professor of Earth and Planetary Sciences at Harvard University, affiliated with the Department of Earth and Planetary Sciences. She leads the Harvard Seismology Group and has held academic roles at Harvard since 2006, progressing from Assistant to Associate Professor and then Full Professor. Education: Ph.D. in Geophysics (2003), Harvard University Hon.B.Sc. in Physics (1998), University of Toronto Research Interests: Ishii specializes in seismic imaging of Earth's internal structure, including the mantle and core. Her work focuses on earthquake mechanisms, signal processing, and theoretical seismology. She uses seismic data to study rupture dynamics, subduction zone processes, and free oscillations of the Earth. Key Contributions: Notable projects include analyzing the 2011 Tohoku-Oki earthquake rupture, developing the DigitSeis software for analog seismogram digitization, and studying inner core anisotropy using normal mode splitting. Her research integrates high-performance computing and waveform inversion techniques. Awards: James B. Macelwane Medal (2009) Kavli Fellow (2012) Charles F. Richter Award (2008) Alice Wilson Award (2004) Labs/Teams: Directs the Harvard Seismology Group, collaborating internationally on seismic networks like Hi-net and USArray. Her work bridges computational seismology with observational geophysics.

• Inverse Problems
• Nonlinear Acoustics
• Medical Imaging
• +5 more

Yang Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI), working under Prof. Katya Krupchyk. His research focuses on inverse problems in imaging sciences, nonlinear hyperbolic equations, and medical imaging applications. He previously held a postdoctoral position at the University of Washington, Seattle, under Prof. Gunther Uhlmann. His work integrates microlocal analysis and partial differential equations to address challenges in wave propagation, nonlinear acoustics, and elasticity. Education & Career: PhD from Purdue University (advisor: Prof. Plamen Stefanov) Postdoc: University of Washington, Seattle (2020–2024) Research Interests: Dr. Zhang's work spans inverse problems for nonlinear hyperbolic equations, acoustic imaging, and integral transforms in medical contexts. He develops novel methodologies using multi-fold linearization, wave interactions, and advanced calculus techniques. His studies on Rayleigh and Stoneley waves in elasticity further demonstrate his expertise in microlocal analysis. Key Contributions: His research bridges theoretical mathematics and applied imaging, with notable publications on inverse scattering, damping effects in wave equations, and Compton camera imaging. He is an active member of the Inverse Problems International Association (IPIA). Grants & Collaborations: Collaborations with prominent figures like Prof. Gunther Uhlmann and Prof. Katya Krupchyk highlight his network in inverse problems. His work often involves both analytical and computational approaches, with applications in medical diagnostics and geophysics.

Indrani BhattacharyaView profile

Assistant Professor

• Multimodal Machine Learning
• Medical Image Analysis
• Social Signal Processing
• +3 more

Indrani Bhattacharya, PhD, is an Assistant Professor in the Department of Biomedical Data Science and the Center for Precision Health and Artificial Intelligence (CPHAI) at Dartmouth College's Geisel School of Medicine. Her research focuses on developing human-centered AI systems for healthcare, particularly in multimodal medical imaging and behavioral health analytics. She holds a BS in Electrical Engineering from Jadavpur University (India), and MS/PhD from Rensselaer Polytechnic Institute (USA). Postdoctoral training at Stanford University's Department of Radiology further specialized her in biomedical imaging informatics. Research interests include: Integrating imaging and non-imaging data for precision medicine AI-driven prostate cancer detection/classification Multimodal behavior estimation for doctor-patient interactions Privacy-preserving sensor systems for group interaction analysis Her work bridges computer vision, medicine, and social science, with recent breakthroughs in MRI-ultrasound fusion AI outperforming radiologist interpretations in multi-center studies. Active in AI ethics and translational research, she leads teams developing clinical decision support tools for oncology and behavioral health. Key career milestones include: Postdoctoral scholar at Stanford University School of Medicine (2016-2021) Academic research staff at Stanford Radiology (2021-2022) Founding member of Dartmouth CPHAI precision health initiatives Labs/Teams: Leads the Biomedical AI for Healthcare group at Dartmouth, collaborating with Stanford and industry partners on AI-driven diagnostic systems.