جست‌وجوی استاد و پژوهشگر

Ruiyang Wuمشاهده پروفایل

استادیار

• Data Science
• Quantitative Modeling
• Statistics
• +۲ مورد دیگر

Ruiyang Wu is an Assistant Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, City University of New York. He holds a PhD in Mathematics (2022), an MS in Statistics and Data Science (2020), and a BS in Mathematics and Applied Mathematics (2016) from the University of Arizona and Peking University, respectively. Education: PhD, Mathematics, University of Arizona, 2022 MS, Statistics and Data Science, University of Arizona, 2020 BS, Mathematics and Applied Mathematics, Peking University, 2016 His research focuses on high-dimensional statistics, statistical learning, and quantitative modeling. He develops methodologies for dimensionality reduction, classification, and distributed algorithms in sparse additive models, with applications in business analytics. Recent publications address dynamic supervised principal component analysis, distributed algorithms for high-dimensional sparse models, and quadratic discriminant analysis techniques. These works span journals like the Journal of Computational and Graphical Statistics and Journal of the American Statistical Association . Scientific Awards: Galileo Circle Scholarship (2021) Data Science Academy Fellowship (2020) Galileo Circle Scholarship (2019) He actively contributes to academic service as a reviewer for journals such as the Journal of Computational and Graphical Statistics and Computational Statistics & Data Analysis , and has served as a session chair at international conferences.

Amanda Bienzمشاهده پروفایل

استادیار

• High Performance Computing
• Parallel Computing
• Numerical Methods
• +۴ مورد دیگر

Amanda Bienz serves as an Assistant Professor in the Department of Computer Science at the University of New Mexico (UNM), where she leads the Scalable Solvers Lab and acts as faculty advisor for Women in Computing. Her academic roles include teaching operating systems and parallel computing courses while spearheading efforts to restructure New Mexico's CS4ALL curriculum for statewide computer science education expansion. Her research centers on overcoming communication bottlenecks in high-performance computing systems, specifically targeting the performance gap between emerging exascale hardware and real-world applications. Key focus areas include developing portable communication optimizations, enhancing MPI collective operations, creating topology-aware message passing extensions, and benchmarking heterogeneous architectures. Her work directly addresses critical challenges in scaling parallel applications through innovations in sparse solvers, neighborhood collectives, and node-aware communication strategies for GPU-accelerated systems. Analysis of her 2022-2024 publications reveals consistent emphasis on communication optimization across diverse HPC domains. Her research demonstrates particular expertise in irregular communication patterns, locality-aware algorithms, and performance modeling for heterogeneous architectures. Significant contributions include novel approaches to sparse dynamic data exchange, compressed linear algebra algorithms, and persistent communication techniques that reduce synchronization overhead in large-scale simulations. Scientific Awards: NSF CAREER Award for "Towards Exascale Performance of Parallel Applications" Dr. Bienz actively mentors students through the Scalable Solvers Lab, welcoming new researchers interested in high-performance computing. Her NSF CAREER grant provides substantial research funding supporting both technical innovation and educational initiatives. The CS4ALL curriculum restructuring project demonstrates her commitment to broadening computer science access throughout New Mexico's K-12 education system. The Scalable Solvers Lab develops open-source tools including the Raptor algebraic multigrid solver and MPI-Advance communication library. Current projects focus on benchmarking heterogeneous architectures (Summit/Lassen supercomputers), optimizing FFT implementations, and creating node-aware communication strategies for conjugate gradient methods. The lab maintains active GitHub repositories with substantial community engagement, including contributions to CUDA-aware MPI implementations and halo exchange libraries for multi-GPU systems.

• Mathematical Biology
• Disease Modeling
• Physiological Modeling
• +۲ مورد دیگر

Erica Rutter is a faculty member in the Department of Applied Mathematics at the University of California, Merced, where she joined in July 2019. Prior to this position, she was a postdoctoral scholar at North Carolina State University. Dr. Rutter received her B.Sc. from the University of Michigan in 2009 in Applied Mathematics and Japanese. After her undergraduate studies, she worked for several years as a Research Technician at the Center for Radiative Shock Hydrodynamics in the Atmospheric Oceanic and Space Sciences Department at the University of Michigan. She then pursued her doctoral studies, obtaining a PhD in Applied Mathematics from Arizona State University in 2016. Her research interests span mathematical biology with a focus on disease modeling, physiological modeling, and ecotoxicology. She employs techniques from various mathematical disciplines, including machine learning, to address complex biological problems. Her work often involves developing mathematical models to understand biological processes and applying computational methods to analyze biological data. Dr. Rutter's publication record demonstrates an interdisciplinary approach across mathematical oncology, ecotoxicology, and biomedical image analysis. Her research shows a progression toward increasingly sophisticated methods for modeling biological systems, with emphasis on tumor heterogeneity, glioblastoma growth, and equation learning from limited biological data. She maintains an active research program at UC Merced, contributing to the academic community through her teaching and research mentorship in the Department of Applied Mathematics.

Florin Rusuمشاهده پروفایل

استاد

• Database systems
• Large-scale data management
• Query processing and optimization
• +۴ مورد دیگر

Florin Rusu is a Professor and Chair of the Department of Computer Science and Engineering at the University of California Merced, School of Engineering. He joined UC Merced in 2010 and has served in multiple administrative positions including as chair of the School of Engineering's Executive Committee and currently as chair of the Department of Computer Science and Engineering. His educational background includes a B.Eng. degree from the Technical University of Cluj-Napoca, Faculty of Automation and Computer Science (2004), and M.Sc. and Ph.D. degrees from the University of Florida in Computer Science (2008 and 2009). Rusu's research focuses on database systems and large-scale data management, with particular emphasis on designing infrastructure for Big Data analytics. His specific research areas include query processing and optimization, approximate and randomized algorithms, scalable machine learning, multi-dimensional array data management, and in-situ data processing. His work bridges theoretical aspects with practical system design issues. His research has been funded by multiple prestigious organizations including the US Department of Energy (DOE), National Science Foundation (NSF), California Department of Education, Hellman Foundation, LogicBlox, and TigerGraph. His recent publications show a continued focus on database query optimization, particularly around cardinality estimation, sketch-based methods, and innovative approaches to query plan generation. His work spans both theoretical contributions and practical implementations, with several projects transitioning into real-world database systems. Scientific Awards: DOE Early Career Award (2014) Hellman Faculty Fellowship (2013) Rusu has advised numerous graduate students through their Ph.D. and Master's programs, with many going on to successful careers at major tech companies (Google, Meta, TigerGraph) and academic positions. His research group has secured substantial funding from NSF (COMPASS project 2020-2025), DOE Early Career Award (2014-2021), TigerGraph, California Department of Education, and Hellman Foundation. His research group maintains active projects in Database Query Optimization, Scalable Gradient Descent Optimization, Array Databases, In-Situ Data Processing, GLADE, Online Aggregation, and Sketches, demonstrating a comprehensive research program spanning multiple aspects of database systems and large-scale data management.

Raag Airanمشاهده پروفایل

استادیار

• Neuroimaging
• Neurointervention
• Ultrasound Techniques
• +۳ مورد دیگر

Raag Airan is an Assistant Professor of Radiology (Neuroimaging and Neurointervention) and by courtesy, Materials Science and Engineering at Stanford University. His work bridges Noninvasive nervous system manipulation Ultrasound and nanoparticle engineering Theranostic applications Behavioral neuroscience His research focuses on Ultrasound-mediated drug delivery systems Neuroimaging of functional connectivity Neuromodulation for chronic pain and psychiatric disorders Glymphatic system modulation Recent publications highlight advancements in Acoustically activatable liposomes Sex-based pharmacological responses Algorithmic psychiatry frameworks Stem cell therapy imaging Deep learning-enhanced ultrasound

• Electronic Structure Theory
• Machine Learning in Materials Science
• Data Driven Materials Science
• +۶ مورد دیگر

Professor Patrick Rinke leads the Chair of AI-based Materials Science at the Technical University of Munich (TUM), within the TUM School of Natural Sciences and Department of Physics. His research group develops advanced electronic structure and machine learning methods to address critical challenges in materials science, surface science, physics, chemistry, and nanoscience. Professor Rinke's research spans multiple cutting-edge domains including electronic structure theory development, machine learning applications for materials science, data-driven materials discovery, biomaterials engineering, atmospheric science applications, clean energy materials, and hybrid materials systems. His work integrates advanced computational methods with practical applications across diverse scientific fields, particularly focusing on how artificial intelligence can transform traditional materials research. Analyzing his recent publications reveals strong trends in applying machine learning techniques to materials discovery, with particular emphasis on Bayesian optimization methods, active learning approaches for molecular data, and efficient dataset generation strategies. His research spans from fundamental electronic structure theory to practical applications in biomaterials, atmospheric science, and renewable energy technologies. Professor Rinke has received several prestigious awards including the August-Wilhelm Scheer visiting professorship (2017), a German Science Foundation research scholarship (2007), the Outstanding Postdoctoral Research Achievement Award from UC Santa Barbara (2009), recognition as an Outstanding Referee for Physical Review journals (2014), and the Institute of Physics Computational Physics Group Thesis Prize (2003). Professor Rinke actively contributes to the academic community through teaching and supervision. For the Winter term 2025/26, he is teaching courses including Academic Writing Skills, Introduction to Machine Learning for Materials Science, Current Topics in AI-Based Materials Science, and Machine Learning for Natural Sciences. His research group includes several team members working on diverse projects spanning the intersection of AI and materials science.

Debabrota Basuمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۶ مورد دیگر

Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Dimitri Mavrisمشاهده پروفایل

استاد

• Advanced Design Methods
• Aircraft Conceptual and Preliminary Design
• Air-Breathing Propulsion Design
• +۴ مورد دیگر

Dimitri Mavris is a Professor at the School of Aerospace Engineering , Georgia Institute of Technology, holding distinguished titles including Boeing Chair in Advanced Aerospace Systems Analysis and S.P. Langley Distinguished Professor . He directs the Aerospace Systems Design Laboratory (ASDL) and serves as Executive Director of the Professional Master’s in Applied Systems Engineering (PMASE) . Education B.S., Aerospace Engineering, Georgia Institute of Technology (1984) M.S., Aerospace Engineering, Georgia Institute of Technology (1985) Ph.D., Aerospace Engineering, Georgia Institute of Technology (1988) Dr. Mavris specializes in advanced design methods , aircraft preliminary design , and non-deterministic design theory , focusing on multi-disciplinary optimization and system of systems architecting . His work spans propulsion systems, space exploration, and sustainable aviation technologies. Recent research trends include machine learning for aerospace safety , hybrid-electric propulsion analysis , and digital twin applications in energy systems. His lab produces 100+ annual publications, reflecting leadership in MBSE , UAS certification , and lunar operations modeling . Scientific Awards Class of 1934 Distinguished Professor Award (2024) NSF CAREER Award (1997) Fellow of AIAA (2009-present) NASA Blue Marble Award (2017) SGA Faculty of the Year (2012-2013) As an advisor to Georgia Tech's AIAA student chapter, Dr. Mavris mentors 1,484 alumni (300 Ph.D., 1,184 M.S.) and leads industry partnerships with FAA , Airbus , and Siemens . His labs include the Vertical Lift Research Center and Strategic Energy Institute , with global initiatives like the ICAS presidency (2023-2024).

Thi Phuong Khanh Nguyenمشاهده پروفایل

پژوهشگر

• Prognostics and Health Management (PHM)
• Predictive Maintenance
• Condition-Based Maintenance
• +۸ مورد دیگر

Dr. Thi Phuong Khanh Nguyen is a researcher at the Ecole Nationale d'Ingénieurs de Tarbes (ENIT) , affiliated with the College of Engineering and Department of Systems . Her work focuses on Prognostics and Health Management (PHM) , predictive maintenance, and industrial data analytics, combining machine learning with physics-informed modeling to address uncertainty in system degradation. Teaching: Mathematics for engineers, Probability, Statistics, Operating safety Research: Health indicators, diagnostics, prognostics, multimodal data fusion Methods: Data mining, physical and data-driven models, decision support systems Tools: FAST, Petri nets, UML, HMM, RNN, CNN, Transformer architectures Her recent publications highlight advancements in explainable AI , physics-informed neural networks , and multimodal learning for fault detection, battery RUL prediction, and robotic inverse dynamics. She also explores blockchain and federated learning for decentralized prognostics.

• Astronomy
• Astrophysics
• Machine Learning
• +۵ مورد دیگر

Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.

• Machine Learning
• Signal Processing
• Telecommunications
• +۳ مورد دیگر

Andrea Migliorati is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He specializes in telecommunications and industrial/information engineering (IINF-03/A - Area 0009), with a focus on machine learning applications in signal processing and neural network optimization. Academic Role: Assistant Professor Department: Electronics and Telecommunications Research Branch: Telecommunications (IINF-03/A) University: Politecnico di Torino Migliorati's research explores neural network sparsification, quantization techniques, and robust training methods for secure systems. His work bridges telecommunications engineering with modern machine learning, targeting applications in wearable biometrics and edge computing. Key trends include: Advancing sparse trainable neural networks through concave regularization Developing ternary/binary quantization approaches for model compression Enhancing gait identification systems for wearable devices Improving adversarial robustness via Gaussian class-conditional training

• Structural Bioinformatics
• Protein Structure Determination
• NMR Spectroscopy
• +۲ مورد دیگر

Professor Thomas Huber is a distinguished academic at the Australian National University's Research School of Chemistry, where he was appointed Professor in 2013 after serving as an ARC Future Fellow (2010-2014). His career spans appointments at ETH-Zurich, ANU Supercomputer Facility, University of Queensland (Mathematics and Molecular Bioscience departments), and the Research School of Chemistry. Education: Diploma of Chemistry, Technical University Munich PhD, ETH-Zurich Huber's research focuses on structural bioinformatics and computational structural biology , developing innovative tools to determine 3D structures of biological macromolecules using sparse experimental data. His work targets understanding molecular interactions fundamental to life processes and pharmaceutical intervention. Key research areas include NMR spectroscopy, protein structure determination, genetically encoded non-canonical amino acids, and paramagnetic probes for distance measurements. Analysis of his recent publications (2022-2025) reveals dominant trends in protein engineering through genetic code expansion, fluorogenic labeling techniques, and advanced NMR methodologies for probing protein dynamics and ligand binding. His work bridges computational modeling with experimental structural biology, emphasizing cost-efficient solutions for macromolecular structure determination. Scientific Awards: ARC Future Fellow (2010-2014) Huber actively supervises research students and leads multiple collaborative projects including "Protein Structure and Dynamics by Electron/Nuclear Paramagnetic Resonance" and "Non-Canonical Amino Acids for Protein Analysis." His research is supported by significant grants from the Australian Research Council, focusing on protein characterization, drug discovery platforms, and advanced spectroscopy instrumentation. He leads the Huber Group within the Research School of Chemistry, collaborating extensively with researchers like Gottfried Otting and Christian Nitsche on protein analysis and therapeutic development.

Jürgen Hesserمشاهده پروفایل

استاد

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +۷ مورد دیگر

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

Eugene Y. Vassermanمشاهده پروفایل

دانشیار

• Network and distributed system security
• Privacy and anonymity
• Censorship resistance
• +۴ مورد دیگر

Eugene Y. Vasserman is an Associate Professor in the Department of Computer Science at Kansas State University's College of Engineering, where he also serves as Director of the Center for Cybersecurity and Trustworthy Systems. His office is located in 2171 Engineering Hall, and he holds office hours on Tuesdays from 2:30 pm to 4:00 pm and Wednesdays from 11:30 am to 1:00 pm during the Fall 2025 semester. Dr. Vasserman's research spans multiple critical areas of cybersecurity including network and distributed system security, privacy and anonymity, censorship resistance, operating system security, medical and IoT security, usable security, and applied cryptography. His work bridges theoretical security concepts with practical implementations, addressing real-world challenges in diverse domains from medical systems to blockchain technologies. His recent publications reveal a research trajectory that has evolved from foundational network security work to increasingly interdisciplinary research intersecting with artificial intelligence, medical systems, and cybersecurity education. The 15 most recent publications demonstrate his ongoing commitment to both theoretical advances and practical security solutions across multiple domains. Outstanding short paper award for 'Hypersparse Traffic Matrix Construction using GraphBLAS on a DPU' at IEEE HPEC 2023 Best graduate student poster award for 'Empowering pre-service teachers to utilize programming in the classroom' at ASEE Midwest Conference 2013 Dr. Vasserman has mentored numerous graduate students through the Systems and Network Security (SyNeSec) Lab, with alumni now working at organizations including Paycom, Corelight, Sandia National Labs, Microsoft, and Cerner. He teaches multiple cybersecurity courses each semester including CIS 525: Introduction to Network Programming, CIS 755: Systems Security, and CIS 351: Cyber Defense Basics, demonstrating his long-term commitment to cybersecurity education since at least 2010. As Director of the Center for Cybersecurity and Trustworthy Systems, he leads initiatives that address critical security challenges across multiple domains, fostering collaboration between researchers, students, and industry partners to develop trustworthy systems for the future.