Federica Porta is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. Her academic career focuses on numerical analysis, optimization, and stochastic gradient methods, particularly in machine learning and image restoration. Her research interests include: Numerical Analysis and Statistics for Computer Engineering Numerical Optimization for Artificial Intelligence Stochastic Gradient Descent with Variance Control Deep Image Prior Frameworks Regularization Techniques for Biomedical Imaging Federica's recent publications (2021–2025) demonstrate expertise in hybrid gradient projection methods, adaptive learning rate selection, and deep learning applications for image segmentation and classification. She collaborates extensively with researchers like Giorgia Franchini, Valeria Ruggiero, and Luca Zanni, applying these methods to both convex and non-convex optimization problems. She teaches courses such as Numerical Analysis and Statistics for Computer Engineering, Numerical Analysis for Mathematics, and Numerical Optimization for Artificial Intelligence. Her teaching emphasizes MATLAB/Python implementation of numerical methods, convergence properties, and computational complexity analysis. Contact: federica.porta@unimore.it | Office: Mathematics Building, Via Campi 213/b
Audun Theodorsen is an Associate Professor at the Department of Physics and Technology, UiT The Arctic University of Norway, specializing in Complex Systems Modeling within plasma physics and nuclear fusion. His research focuses on turbulence , stochastic modeling , and intermittent fluctuations in fusion plasmas, particularly in the scrape-off layer of tokamaks like Alcator C-Mod. He also contributes to understanding cosmic dust dynamics using data from Solar Orbiter and Parker Solar Probe. Key Collaborators: Odd Erik Garcia, Samuel Kociscak, Ingrid Brigitte Mann, Ralph Kube, Juan Manuel Losada Teaching: Courses in Statistical Physics , Stochastic Modeling , and Nonlinear Dynamics His recent publications include stochastic models for plasma filaments and Bayesian analyses of dust impact ionization , spanning journals like Physics of Plasmas , Astronomy and Astrophysics , and Journal of Geophysical Research: Atmospheres . His work bridges fusion energy research and space science , emphasizing universal statistical patterns across diverse physical systems. For details on his research projects, see ITER and Fusion Research Center Norway .
Susie N. Hong-Zohlman serves as Associate Professor in the Department of Medicine at the University of Maryland School of Medicine with secondary appointments in Diagnostic Radiology and Nuclear Medicine. She directs the Echocardiography Laboratory at University of Maryland Medical Center and co-directs Ambulatory Cardiology Echo Laboratories, collaborating with Thoracic Radiology specialists on cardiovascular imaging. Her educational background includes: Bachelor of Arts, University of Pennsylvania Medical Degree, Thomas Jefferson University Residency in Internal Medicine, Mount Sinai Hospital General Cardiology Fellowship, NYU Langone Medical Center Advanced Imaging Fellowship in Cardiovascular MRI, Beth Israel Deaconess Master of Science in Epidemiology, Harvard School of Public Health Dr. Hong-Zohlman specializes in advanced noninvasive cardiovascular imaging with focus on sex differences in cardiovascular disease, particularly infiltrative cardiomyopathies and pericardial diseases. She maintains Level 3 expertise in echocardiography and cardiovascular MRI, plus Level 2 certification in nuclear cardiology and cardiac CT. Her work bridges clinical diagnostics with multimodality imaging research to address gender disparities in cardiovascular conditions. Her 15 most recent publications demonstrate leadership in cardiovascular imaging innovation, spanning technical advancements in MRI/CT acquisition, clinical applications for hypertrophic cardiomyopathy and pericardial disease, and epidemiological studies on cardiovascular disease in women. Key themes include risk stratification methodologies, imaging biomarker validation, and pandemic-era healthcare adaptation. Scientific recognition includes: Divisional Teaching Award for Cardiovascular Medicine (2016, 2017, 2020) Theodore E. Woodward Prize (2020) Dr. Hong-Zohlman's collaborative work with Drs. Jeudy and White in Thoracic Radiology integrates cardiovascular MRI and CT across research and clinical domains. She directs echocardiography services while advancing imaging protocols for complex conditions like infiltrative cardiomyopathies through multimodality approaches.
Philip Eisenlohr is an Associate Professor in the Department of Physics & Astronomy at Michigan State University . His research focuses on computational materials science, crystal plasticity, and multi-physics simulation frameworks, particularly through the development and application of the DAMASK software toolkit. Primary affiliation: Michigan State University (Department of Physics & Astronomy) Academic rank: Associate Professor Research interests revolve around materials deformation mechanisms , microstructure modeling , and computational tools for integrated materials engineering . Recent work emphasizes multiscale simulation , additive manufacturing , and grain boundary effects in crystalline materials . Key trends in publications include crystal plasticity simulation , DAMASK framework development , and experimental-computational synergy for quantifying critical resolved shear stress , phase transformations , and damage evolution . Articles span 2025 to 2020 , with increasing focus on multi-physics coupling and industrial applications in recent years.
Daniel M Nosenchuck is an Associate Professor at Princeton University's School of Engineering, Department of Mechanical and Aerospace Engineering. His work bridges experimental fluid mechanics and supercomputer architecture , focusing on flow control and simulation technologies. PhD in Aeronautics (Caltech, 1982) MS in Aeronautics (Caltech, 1977) BS in Aerospace and Mechanical Engineering (Syracuse, 1976) Research Highlights : Active control of turbulence and boundary layers using electromagnetic and thermal methods Invention of the Navier-Stokes Computer for advanced hydrodynamic simulations Development of laser-sheet scanning techniques for 3D flow visualization Patents in electromagnetic boundary layer control and dynamic reconfiguration systems Prominent Research Themes from 1982-1994 include: Experimental fluid mechanics with applications in aerospace and marine systems Parallel computing architectures for turbulence simulation Interdisciplinary studies in bioluminescence and hydrodynamics Active control of wake vortices and transitional flows Development of optimizing compilers for supercomputers Innovative visualization techniques for complex flows Scientific Awards : NSF Presidential Young Investigator Award (1984-1989) IBM Faculty Development Award (1984-1985) Princeton Rheinstein Award (1986) National EMMY for Special Visual Effects (1984) GTE Emerging Scholar (1987) William F Ballhaus Prize (1982) Undergraduate Engineering Council Teaching Award (1993) Advising & Grants : Advisor to multiple co-authors in fluid mechanics and computer science NSF funding through Presidential Young Investigator program Industrial and DoD consulting projects Labs & Facilities : Moody Hydrodynamics Laboratory (Princeton) Low-Speed Water Channel High-Speed Water Tunnel
Slavica Jonic is a Research Director at CNRS, affiliated with Sorbonne University at the Institute of Mineralogy, Materials Physics, and Cosmochemistry (IMPMC-UMR 7590) in Paris, France. She obtained her PhD in Image Processing from EPFL (Switzerland) in 2003 and a Research Director Habilitation from UPMC (France) in 2015. She leads the 'Image analysis for biomolecular structural and dynamics studies' subgroup under the BiBiP team and co-leads the IMPMC transversal axis on 'Theory, Artificial Intelligence, Big Data.' Her research focuses on developing algorithms that integrate image analysis , molecular mechanics simulation , and artificial intelligence to explore conformational dynamics of biomolecules via in vitro and in situ cryo-EM and cryo-ET. Key software contributions include ContinuousFlex , HEMNMA , StructMap , MDTOMO , and MDSPACE , which are used to study complexes like nucleosomes, ribosomes, SARS-CoV-2 spike, and ATPase p97. Her work is supported by ANR , CNRS , Sorbonne University , and GENCI . She mentors PhD students and Master interns and serves on editorial boards for journals like BMC Methods and Frontiers in Molecular Biosciences . She also organizes workshops on cryo-EM and AI.
Dr. Pete Santago is a Professor in the Department of Computer Science at Wake Forest University. He was previously a faculty member at the Wake Forest University School of Medicine for 24 years, where he established an engineering group in Radiology and co-founded the Biomedical Engineering Department. His research spans biomedical informatics, pattern recognition, machine learning, and image/signal processing, with current interests in stochastic processes, data mining, and computer organization. Education: Ph.D. in Electrical Engineering from North Carolina State University Education: M.S. in Computer Science from Virginia Tech Dr. Santago’s research bridges biomedical engineering and computer science, with applications in medical imaging, data analysis, and educational innovation. His work emphasizes practical solutions for image processing, diagnostic tools, and hands-on STEM learning environments like the WakerSpace makerspace. His 15 most recent publications highlight expertise in medical imaging (e.g., PET, CT, MRI), algorithm development (segmentation, reconstruction), and educational initiatives. Techniques such as convex sets, MAP estimation, and geodesic active contours recur in his contributions to biomedical informatics and computer science. As Faculty Director of WakerSpace, Dr. Santago focuses on service work and student mentorship, guiding independent projects that integrate data mining and computer organization principles.
Kayhan Batmanghelich is an Assistant Professor in the Department of Electrical and Computer Engineering at Boston University , where he also holds affiliations as a Hariri Institute Junior Faculty Fellow and AIR Affiliate . Previously, he served as an Assistant Professor at the University of Pittsburgh's Department of Biomedical Informatics with a secondary appointment in their School of Computing and Information. Education : Ph.D. in Computer Science from the University of Pennsylvania (2012) Research Interests : Focus on overcoming AI challenges in healthcare, including Explainability, Data Efficiency, Multimodal Data Fusion, and Causality. His research develops AI systems for medical imaging, genetics, and electronic health records, particularly targeting diseases like Chronic Obstructive Pulmonary Disease (COPD) , Alzheimer’s Disease , and Non-Alcoholic Fatty Liver Disease (NAFLD) . Key innovations include biomechanically-informed neural networks for lung tissue stiffness estimation and graph-based AI for multi-scale pulmonary disease analysis. Recent publications highlight multimodal frameworks for COPD subtyping, vision-language models in mammography, and diffusion models for 3D CT synthesis. His work has been recognized with the NSF CAREER Award and Google Academic Research Award (GARA) , alongside Hariri Institute grants. Awards : NSF CAREER Award (2025) Google Academic Research Award (2024) Hariri Focused Research Program Awards (2024) His BatmanLab trains students in medical AI and collaborates with institutions like the University of Pittsburgh School of Medicine and Brigham and Women’s Hospital. Funding sources include NIH, NSF, and industry partnerships.
Wendy Osborn is an Associate Professor in the Department of Mathematics and Computer Science at the University of Lethbridge . She holds a Ph.D. (2005) from the University of Calgary, an M.Sc. (1998) from the University of Windsor, and a B.C.S.(Hons) (1996) from the University of Windsor. Research Interests: Dr. Osborn specializes in Databases , with a focus on Spatial , Distributed , and Multimedia systems. Her work also explores Mobile Information Systems , Recommender Systems , and Digital Libraries , emphasizing efficient querying and classification in dynamic environments. Publications: Her research addresses challenges in spatial data streams, mobile device processing, and distributed query optimization, reflecting collaborations with database indexing techniques like the mqr-tree and area code tree. Key trends include iterative classification, approximate querying, and energy-efficient system design.
Josef Pichler serves as a Professor at the Research Center Hagenberg within the University of Applied Sciences Hagenberg, specializing in the Information & Communications Technology focal area. His academic profile demonstrates continuous research activity with publications spanning from 1998 through 2024, showing particularly productive periods in 2008-2010, 2013-2014, and consistently from 2017 onward. Dr. Pichler's research focuses on practical software engineering solutions with strong industrial applications. His primary interests include software systems , reverse engineering , documentation generation , and domain-specific languages . His work bridges theoretical computer science with practical industry needs, developing tools that address real-world software maintenance and comprehension challenges. Analysis of his recent publications reveals a clear research trajectory toward integrating artificial intelligence with traditional software engineering practices. His 2023-2024 work particularly emphasizes AI-assisted programming, semantic differencing techniques, and low-code development platforms for industrial applications like welding robot control. This demonstrates his ability to adapt to emerging technologies while maintaining focus on practical software engineering problems. Dr. Pichler has delivered numerous presentations on topics ranging from ChatGPT applications in computer science education to advanced semantic differencing approaches. His activities indicate active engagement with both academic and industry communities, particularly in the German-speaking region. While specific grant information isn't detailed in the provided text, his research output suggests involvement in projects related to software documentation, reverse engineering tools, and AI-assisted development environments. His supervision of student work (indicated by 'Supervised Work (3)') demonstrates his commitment to academic mentorship alongside his research activities. The Research Center Hagenberg, where Dr. Pichler is based, appears to be a significant hub for ICT research with strong industry connections, as evidenced by his publications on industry collaboration and practical software solutions.
Dr. Mayanglambam Suheshkumar Singh is an Associate Professor in the School of Physics at Indian Institute of Science Education and Research Thiruvananthapuram (IISER-TVM). He leads the Biomedical & nano-Bioscience Engineering Lab (BnBEng.LAB), focusing on non-destructive imaging modalities for biological and clinical applications. His research spans photoacoustic imaging, light sheet microscopy, OCT, and AI-driven image reconstruction. Research Areas: Photoacoustic Imaging (Microscopy & Tomography) Light Sheet Fluorescence Microscopy Optical Coherence Tomography Deep Learning for Biomedical Imaging Tissue Mimicking Phantoms Scientific Awards: SRF-CSIR Scholarship (2005) His funded projects include DBT-led OCT-US-MEMS-μPAE ophthalmic imaging system development and MNRE-supported AI-based hydrogen leak detection sensors. He serves as Principal Investigator in multi-institution collaborations with IISc and LVPEI.
Lukas Mehl is a Researcher at the Computer Vision Department within the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. His work focuses on scene flow estimation, optical flow, and adversarial attacks in motion estimation. Education : Not explicitly mentioned in the text. Research Interests : He develops multi-frame fusion techniques for scene flow estimation, creates high-resolution benchmark datasets like Spring, and explores adversarial weather effects on motion estimation. His methods improve accuracy in automotive and computer vision applications. Scientific Awards : No awards mentioned in the text. Advising & Grants : Supervised multiple Master's and Bachelor's theses on topics including occlusion-aware optical flow and motion-compensated frame interpolation. Participated in research projects like Pre-training ProFlow. Labs & Teams : Collaborates with the VIS team at the University of Stuttgart, contributing to tools like the Spring benchmark website for evaluating motion estimation algorithms.
Nikolaos Sahinidis is the Gary C. Butler Family Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering and the School of Chemical and Biomolecular Engineering at Georgia Institute of Technology. His research bridges computer science and operations research with applications across engineering and scientific domains, focusing on developing rigorous optimization methods for complex real-world problems. Dr. Sahinidis's research spans global optimization of mixed-integer nonlinear programs, informatics problems in chemistry and biology, process and energy systems engineering, and chemical product design. His work integrates theoretical algorithm development with practical applications in medical diagnosis, protein structure analysis, and environmentally benign chemical design. He has made significant contributions to inverse imaging problems in X-ray crystallography, biochemical network design, and black-box optimization. His recent publications demonstrate a clear trajectory toward integrating machine learning with traditional optimization approaches, particularly in derivative-free optimization, global optimization of nonconvex problems, and mixed-integer nonlinear programming. His work increasingly focuses on sustainable engineering applications, including rare earth element recovery, water network optimization, and perovskite solar cell design, reflecting a strong commitment to addressing contemporary engineering challenges. NSF CAREER award INFORMS Computing Society Prize Beale-Orchard-Hays Prize from the Mathematical Optimization Society Computing in Chemical Engineering Award Constantin Carathéodory Prize National Award and Gold Medal from the Hellenic Operational Research Society Member of the U.S. National Academy of Engineering Fellow of AIChE Fellow of INFORMS Dr. Sahinidis has secured substantial funding from the National Science Foundation, U.S. Environmental Protection Agency, and industry partners for his research. His group has developed several influential software tools including CMOS for protein structure alignment, GPU-BLAST for accelerated sequence alignment, R3 for protein side-chain conformation prediction, and SAS-Pro for protein structural alignment. The Sahinidis Optimization Group maintains active openings for graduate students and researchers nearly every year, fostering the next generation of optimization scientists. The Sahinidis Optimization Group at Georgia Tech is a leading research center in mathematical optimization and its applications. The group maintains strong collaborations with researchers across multiple disciplines and institutions, including the Hauptman-Woodward Medical Research Institute. Their work spans theoretical algorithm development to practical implementations in chemical engineering, bioinformatics, and materials science, with a consistent focus on developing rigorous, efficient methods for challenging optimization problems.
Panagiotis Tsiotras holds the David & Andrew Lewis Endowed Chair in the Daniel Guggenheim School of Aerospace Engineering at Georgia Tech and serves as Associate Director at the Institute for Robotics and Intelligent Machines (IRIM). His research focuses on nonlinear/optimal control, AI integration, planning, and decision-making for autonomous ground, aerial, and space systems. Education: Dipl.Eng. Mechanical Engineering, National Technical University of Athens, 1986 M.S. Aerospace Engineering, Virginia Tech, 1987 M.S. Mathematics, Purdue University, 1992 Ph.D. Aeronautics and Astronautics, Purdue University, 1993 Research: Primary interests include autonomous vehicle control, robotics, AI-driven decision-making, and multi-agent systems. His work bridges theoretical control frameworks with applications in aerospace robotics, featuring advanced algorithms for navigation, safety verification, and large-scale system coordination. Recent themes involve stochastic control, distribution steering, and real-time safety guarantees. Awards: IEEE Technical Excellence Award in Aerospace Controls NSF CAREER Award Outstanding Aerospace Engineer Award (Purdue) Sigma Xi Excellence in Research Award Fellow of AIAA, IEEE, and AAS Leadership: Directs the Dynamics and Control Systems Laboratory (DCSL) and collaborates with the Vertical Lift Research Center of Excellence (VLRCOE) and Decision and Control Laboratory (DCL). Editorial roles include Chief Editor of Frontiers in Robotics & AI and Associate Editor for multiple control journals.
Dr. Slađana Spasić is a Senior Researcher at the University of Belgrade - Institute for Multidisciplinary Research , specializing in Living Systems Sciences and Biophysics . She holds a PhD in Mathematical Sciences from the Faculty of Mathematics, University of Belgrade, with additional degrees in Applied Mathematics and Artificial Intelligence . Research Interests : Fractal analysis of physiological signals, nanobionics, biophysics, environmental bioindicators, and nonlinear dynamics in living systems. Recent Work : Focus on carbon dots enhancing photosynthesis, magnetic field effects on neuronal activity, and fractal dimension methods in biomedical imaging. Projects : 2019–2023 COST Action CA18102 Aquatic Animal Tracking Network; 2020–2022 Nanobionic plant productivity project. Professional Activities : Guest editor for Frontiers journals, certified reviewer for Serbian National Accreditation Body, member of Serbian Mathematical Society and Neuroscience Society.
