جست‌وجوی استاد و پژوهشگر

Anthony Danalisمشاهده پروفایل

عضو هیئت علمی

• High-Performance Computing (HPC)
• Performance Analysis and Optimization
• System Benchmarking
• +۴ مورد دیگر

Anthony Danalis is a Research Assistant Professor at the University of Tennessee's Innovative Computing Laboratory (ICL), affiliated with the Department of Electrical Engineering and Computer Science. His research focuses on high-performance computing (HPC), including performance analysis, system benchmarking, compilers, MPI, and accelerators. He holds a Ph.D. and two M.S. degrees in Computer Science from the University of Delaware and the University of Crete, along with a B.S. in Physics from the University of Crete. Education: Ph.D. in Computer Science, University of Delaware M.S. in Computer Science, University of Delaware M.S. in Computer Science, University of Crete B.S. in Physics, University of Crete Research Interests: High-Performance Computing (HPC) Performance Analysis and Optimization System Benchmarking Compilers and Runtime Systems Parallel Programming Models (e.g., MPI) Accelerator Technologies Publications Highlight Trends in: Advancements in the PAPI library for exascale computing Dataflow-based execution for scientific applications Hardware performance counter analysis Compiler-driven optimizations Scientific Awards: Best Research Poster Finalist at SC 2017 Advising and Grants: No formal advisees listed Contributions to ICL's leadership and collaborative projects Labs and Teams: Innovative Computing Laboratory (ICL) Participation in projects like DAGuE, DPLASMA, and SHOC

Kyle E. Niemeyerمشاهده پروفایل

دانشیار

• Combustion Modeling
• Computational Fluid Dynamics
• Chemical Kinetics
• +۵ مورد دیگر

Kyle E. Niemeyer is an Associate Professor at Oregon State University's School of Mechanical, Industrial, and Manufacturing Engineering. He leads the Niemeyer Research Group focused on computational modeling of combustion, reactive flows, and environmental systems. He also serves as Associate Head for Undergraduate Programs and Associate Editor-in-Chief of the Journal of Open Source Software (JOSS). Ph.D. in Mechanical Engineering, Case Western Reserve University (2013) M.S. and B.S. in Aerospace Engineering, Case Western Reserve University (2010, 2009) His research spans combustion modeling, GPU acceleration for chemical kinetics, ocean biogeochemical simulations, and open science practices. Key contributions include: Developing open-source tools like pyMARS and MC/DC Advancing GPU-based chemical kinetics integration algorithms Modeling smoldering combustion in biomass fuels Applying combustion-derived model reduction to ocean systems Recent publications focus on exascale Monte Carlo neutron transport, wildfire fuel modeling, and Cantera software optimization. Awards include AAAS Science & Technology Policy Fellowship and Welty Faculty Fellowship. AAAS Science & Technology Policy Fellow (2022–2023) Welty Faculty Fellow (2021–2024) Better Scientific Software Fellow (2019) His group actively engages in open-source software development and computational methods research for energy, aerospace, and environmental applications.

• Leukemia
• Biostatistics
• Computational Biology
• +۶ مورد دیگر

Tim Triche, Jr., Ph.D., is an Associate Professor at the Van Andel Institute in the Department of Epigenetics . He earned his A.B. in chemistry from Cornell University , followed by an M.S. in biostatistics and a Ph.D. in statistical genetics from the University of Southern California . Before joining Van Andel in 2017, he was a postdoctoral fellow at USC's Norris Comprehensive Cancer Center focusing on cellular senescence in blood disorders. As a key member of The Cancer Genome Atlas Research Network since 2011 with over a dozen high-impact publications in Nature , Cell , and NEJM , Dr. Triche specializes in epigenetics , biostatistics , and computational biology . His lab develops innovative approaches for pediatric AML research, integrating next-generation sequencing with clinical trial design to improve patient outcomes. His work emphasizes statistical learning for patient stratification, molecular profiling of hematological cancers, and interpretable machine learning in biomedical contexts. He leads the Bioinformatics and Biostatistics Core as faculty advisor and maintains active collaborations across institutions. 2025 Nature Cancer study on developmental heterogeneity in cancer susceptibility 2024 NAR methods paper on BISCUIT multi-omics tools 2023 PLOS One validation of MAX regulation in pituitary adenomas 2022 Nature Metabolism obesity subtyping analysis Scientific contributions include: Chan Zuckerberg Initiative grant (2022) for biomedical computing NCI SPORE grant (2021) as co-recipient Key role in Pediatric AML molecular mapping (2017)

Jie Wuمشاهده پروفایل

استادیار

• AI for Software Engineering
• Software Engineering for AI
• Large Language Models
• +۵ مورد دیگر

Jie Wu is an Assistant Professor in the Department of Computer Science at Michigan Technological University, a Carnegie R1 (Very High Research Activity) institution. Previously, he was a postdoctoral researcher at the University of British Columbia working with Dr. Fatemeh Fard at the intersection of Software Engineering and AI. Dr. Wu received his PhD in Systems Engineering from George Washington University. His undergraduate and master's studies were both in Computer Science at Shanghai Jiao Tong University's elite ACM Class program. Before academia, he worked for nearly a decade as a software engineer in the industry at Snap Inc., Microsoft, and ArcSite (a startup). Dr. Wu's research focuses on Trustworthy AIware, with particular interest in transforming "AI for Software Engineering" and "AI system development" from art into rigorous science and engineering disciplines. His work emphasizes human-centered AI, AI alignment, and practical software engineering, grounded in a systems-thinking mindset. His primary research areas include AI for Software Engineering (AI4SE), Software Engineering for AI (SE4AI), Large Language Models (LLMs), Data Science, and Systems Science and Engineering. His recent publications demonstrate a strong focus on evaluating and improving communication capabilities of code-generating LLMs, automated program repair using LLMs, and applying AI to software engineering challenges. His work often bridges theoretical foundations with practical applications in industry settings, with notable contributions including the HumanEvalComm benchmark for evaluating communication skills in code generation. Distinguished Paper Award Candidate at CAIN 2024 for V-Model research Reviewer for top-tier journals including IEEE TSE and ACM TOSEM Program Committee Member for RAIE 2025, CAIN 2025, and SANER 2025 Dr. Wu is actively recruiting PhD students to join his research group at Michigan Tech to work at the intersection of Software Engineering and AI. He is passionate about bridging academic research and industry practice to accelerate innovation and create meaningful societal impact, welcoming collaborations with industry partners interested in applying cutting-edge AI research to real-world challenges.

Gita Alaghbandمشاهده پروفایل

استاد

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Professor Gita Alaghband is Chair and PhD Director at the Department of Computer Science and Engineering, University of Colorado Denver. Her work bridges high-performance computing with AI applications in computer vision, facial recognition, and deep learning optimization. Current research focuses on real-time multi-human tracking for autonomous systems Develops explainable AI solutions for medical and financial domains Leads the Parallel Distributed Systems (PDS) Lab Key publication trends include: 2025: Medical imaging AI for pediatric trauma analysis 2024: Financial forecasting with causal econometrics and adversarial defense systems 2023: Trajectory prediction models and HEVC compression frameworks Scientific recognition: IRC Best Paper Award (2020) for facial recognition research Students advised include industry leaders at Google, VMWare, and Nissan, with research spanning parallel computing, medical imaging AI, and autonomous systems.

• Cell Specification and Differentiation
• Cellular/Molecular/Developmental Neuroscience
• Neural Degeneration and Repair
• +۱ مورد دیگر

Hynek Wichterle is a Professor holding joint appointments in the Departments of Pathology & Cell Biology, Neuroscience (in Neurology), and Rehabilitation and Regenerative Medicine at Columbia University Medical Center. He serves as Co-Director of the Columbia Stem Cell Initiative and Vice-Chief of the Division of Regenerative Medicine in the Department of Rehabilitation & Regenerative Medicine. He also holds the administrative title of Co-Director of the Motor Neuron Center. Dr. Wichterle received his M.S. degree from Charles University in Prague and his Ph.D. degree from The Rockefeller University. He trained at Columbia University, where he became an assistant professor in 2004 and associate professor in 2012 before achieving his current professorship. Wichterle's research focuses on modeling nervous system development in vitro using stem cell technologies. His laboratory has pioneered efficient methods for differentiating pluripotent embryonic stem cells into specific subtypes of spinal motor neurons and interneurons. His work combines stem cell differentiation with CRISPR-based genome editing and inducible transgene expression to decode transcriptional programs controlling neural development. A major focus of his research involves using both mouse and human pluripotent stem cells to model motor neuron degenerative diseases like amyotrophic lateral sclerosis (ALS), with the goal of discovering new neuroprotective drugs. Analysis of his recent publications reveals a consistent focus on developmental neuroscience, stem cell biology, and neurodegenerative disease modeling. His work spans molecular mechanisms of neuronal specification, chromatin regulation, and therapeutic applications for motor neuron diseases. Over time, his research has evolved from foundational work on motor neuron differentiation to increasingly sophisticated models of disease mechanisms and drug discovery approaches. Dr. Wichterle has made significant contributions to understanding the transcriptional programs that control motor neuron identity and function. His laboratory has assembled global maps of genomic regulatory elements controlling motor neuron expression programs, effectively decoding the 'syntax and grammar' of the transcription factor language that specifies neuronal cell identity during embryonic development. His research program benefits from extensive collaborations across multiple disciplines, including molecular biology, genomics, and therapeutic development. The laboratory's unique stem cell-based screening platforms have enabled the identification of compounds that protect motor neurons from degeneration, bridging basic science with potential clinical applications for ALS and related disorders.

• Cybersecurity
• Machine Learning
• Digital Networks
• +۷ مورد دیگر

Yasser Morgan is a Teaching Professor at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology, specializing in the Department of Systems and Enterprises. He can be contacted at ymorgan@stevens.edu. PhD in Computer Science from Carleton University (2005) MS in Computer Science from ISSR, Cairo University (1997) His research spans multiple domains including Digital Networks (cellular and vehicular), Machine Learning with a focus on Artificial Neural Networks (ANN), and Cybersecurity solutions. He has pioneered standards for vehicular communications (WAVE) through IEEE-802.11p and 3GPP collaborations, impacting connected vehicle systems. His recent work focuses on cybersecurity frameworks, resource allocation in energy-efficient networks, and honeypot research. The 10 most recent publications highlight expertise in Cybersecurity (honeypot systems, passive attacks), Wireless Communications (UAVs, LTE, vehicular networks), and Machine Learning (real-time recognition systems). Key subfields include network coding, cognitive radio, energy harvesting, and secure communication protocols. Canadian Advanced Technologies Association Award 'Wilfrid Laurier University, Lazaridis School of Business Public Sector Leadership' for exceptional contribution to Public Safety Dr. Morgan has secured over $4.5 million in grants from NSERC, DRDC, and government agencies, with notable inventions including the international patent A Method and System for Cooperative Stochastic Positioning in a Mobile Environment (PCT No. EP2598913 A4). His consulting services in cybersecurity and teaching portfolio (SSW 540, SSW 567) reflect his interdisciplinary expertise in systems engineering and information security.

Turaj Ashuriمشاهده پروفایل

استاد

• Multiphysics Modeling
• Design Optimization
• Renewable Energy Systems
• +۵ مورد دیگر

Dr. Turaj Ashuri is an Assistant Dean of Research Initiatives and Graduate Programs (interim) at the College of Engineering and Engineering Technology, Kennesaw State University. He previously held faculty positions at the University of Texas at Dallas and Arkansas Tech University, where he served in leadership roles such as Director of the Interdisciplinary Research Center and Graduate Program Director. Before academia, he accumulated over a decade of industry experience as a Structural Design Engineer, Research Engineer, and R&D Project Manager. PhD, Aerospace Engineering, Delft University of Technology MSc, Aerospace Engineering, Sharif University of Technology BSc, Mechanical Engineering, Tehran Azad University Postdoctoral Research Fellowship, University of Michigan - Ann Arbor Dr. Ashuri's research focuses on Multiphysics Modeling and Design Optimization , spanning theoretical and applied domains. His work develops numerical methods for unsteady flow fields, nonlinear composite structures, and machine learning algorithms, while applying these techniques to soft robots, unmanned aerial vehicles, and renewable energy systems. He actively explores Uncertainty Quantification and Optimal Control in energy systems, with particular emphasis on wind turbine technology and biomedical soft robotics. His recent publications demonstrate expertise in Wind Farm Optimization (2016-2018), Soft Robotics (2020), and Hydrogen Fuel Cell Vehicles (2019). These works intersect Computational Design , Control Theory , and Renewable Energy Systems , reflecting his multidisciplinary approach. Scientific recognitions include: Associate Fellow, American Institute of Aeronautics and Astronautics (AIAA) Chair, 2023 AIAA Aviation Forum Discipline Committee Dr. Ashuri's lab at KSU conducts research in Multidisciplinary Design Optimization and Machine Learning applications for intelligent mechanical systems. The lab receives funding from NASA and NIH , supporting work on soft robots, wind turbine engineering, and energy systems optimization.

Brian Borchersمشاهده پروفایل

استاد

• Optimization
• Inverse Problems
• Geophysical Inverse Problems
• +۳ مورد دیگر

Roles and Affiliations : Professor of Mathematics at New Mexico Tech. Member of the Mathematics Department since at least 1992, actively involved in teaching and research. Maintains a research-focused website with links to publications, projects, and codes. Education : BS in Computer Science, MS and PhD in Mathematics from Rensselaer Polytechnic Institute. Research Interests : Specializes in optimization and inverse problems, including linear/nonlinear programming, geophysical inverse problems, and applications in hydrology and geophysics. Co-authored a widely used textbook Parameter Estimation and Inverse Problems (3rd ed., 2018). Active in developing computational tools like CSDP for semidefinite programming and CRONUScalc for cosmogenic nuclide dating. Publications : Over 50 peer-reviewed articles in journals such as Computational Optimization and Applications , Soil Science Society of America Journal , and International Journal of Remote Sensing . Focus areas include optimization algorithms, geophysical modeling, and remote sensing applications. Consulting and Grants : Available as a consultant for numerical analysis and inverse problems. Supervised projects involving data assimilation, convex optimization, and compressive sensing. Mentored students on funded research projects. Labs/Teams : Collaborates with interdisciplinary teams in geophysics (e.g., Rick Aster, Cliff Thurber) and soil science (Jan Hendrickx). Involved in sensor test facilities for landmine detection and environmental monitoring.

Hussam N. Mahmoudمشاهده پروفایل

استاد

• Sustainable and Resilient Infrastructure
• Community Resilience
• Multi-Hazard Structural Engineering
• +۴ مورد دیگر

Hussam N. Mahmoud is the George T. Abell Professor in Infrastructure and Director of the Structural Laboratory at Colorado State University's Department of Civil and Environmental Engineering. He leads the SoPHIE Research Group , focusing on Sustainable and Resilient Infrastructure and Communities across three major research thrusts: community resilience quantification, multi-hazard structural performance, and deteriorated infrastructure lifecycle assessment. Key research areas include climate change adaptation , multi-hazard design , and life-cycle cost analysis for infrastructure subjected to earthquakes, wildfires, hurricanes, and climate-related deterioration. His work integrates machine learning (e.g., neural networks for wind damage prediction), hybrid simulation , and multi-resolution modeling of structural systems. Recent publications highlight interdependent recovery of healthcare/school networks , coastal community resilience to cyclones, and fatigue repair methodologies using fiber composites. Scientific Recognition Keynote speaker at international conferences (STESSA 2015, NEES Annual Meeting 2013) Invited to prestigious events: G7 One Health Summit , NASEM New Voices program Recipient of the George T. Abell Professorship, a distinguished academic honor Professional Leadership Chaired NASEM workshop on Natural Infrastructure Active in global collaborative frameworks through the InterAcademy Partnership Organizing committee member for triennial international academic conferences

Edmund Yehمشاهده پروفایل

استاد

• Networking and systems for data-intensive engineering, science, health applications
• Caching, fog/edge computing
• Networked distributed learning
• +۷ مورد دیگر

Edmund Yeh is Professor of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. He has previously held faculty positions at Yale University and visiting roles at MIT, Stanford, Princeton, UC Berkeley, NYU, EPFL, and National Taiwan University. His research spans networking, distributed systems, and data-centric computing, with strong emphasis on edge computing, caching, network coding, and interdependent infrastructure resilience. His research interests include: Networking and systems for data-intensive engineering, science, and health applications Caching, fog/edge computing Networked distributed learning Wireless network optimization Coding for low latency, network coding, polar codes Interdependent networks, cascading failure, information dissemination Network economics Recent publications show a strong focus on joint optimization of caching, routing, and computation in edge and data-centric networks, emphasizing low-latency, energy efficiency, and robustness. His work often integrates machine learning with networking, particularly in federated and distributed learning systems under communication constraints. His scientific honors include: Best Paper Awards at WiOpt (2023), ICN (2017), ICC (2015), and ICUFN (2012) Best Poster Award at IPSN (2023) IEEE Communications Society Distinguished Lecturer (2021–2022) Alexander von Humboldt Research Fellowship Army Research Office Young Investigator Award Winston Churchill Scholarship National Science Foundation and ONR Graduate Fellowships Prof. Yeh has led or participated in over $38 million in funded research from NSF, DARPA, DTRA, and industry partners such as Cisco, Intel, and Raytheon. He has served as PI or co-PI on major projects including the Named Data Networking (NDN) initiative, SANDIE, N-DISE, and the PAWR Project Office. He mentors numerous graduate students and collaborates extensively with researchers in networking and machine learning. He also contributes to the broader community through editorial roles and service on U.S. National Academies panels. He is actively involved in research labs and collaborative teams focused on data-centric networking, edge computing, and resilient infrastructure systems.

Shanghong Xieمشاهده پروفایل

استادیار

• Machine Learning
• Network Analysis
• Precision Medicine
• +۱۲ مورد دیگر

Shanghong Xie is an Assistant Professor in the Department of Statistics at the University of South Carolina, College of Arts and Sciences. She is also a member of the Carolina Autism and Neurodevelopment (CAN) Research Center and the Institute for Mind and Brain (IMB), reflecting her interdisciplinary focus on statistical methods for neuroscience and public health. Dr. Xie earned her PhD in Biostatistics from Columbia University in 2019 and completed her postdoctoral training there. Her research centers on developing machine learning and generative models to tackle complex challenges in functional data analysis, network modeling, causal inference, and precision medicine. She applies these methods to neuroimaging (fMRI, DTI, EEG), genetic biomarkers, and public health data, particularly in mental health and neurological disorders. Her recent publications demonstrate a strong trajectory in methodological innovation with high-impact applications, including COVID-19 forecasting models adopted by the CDC, mediation analysis with neuroimaging, and personalized treatment regime estimation. The research trends highlight expertise in high-dimensional data, graphical models, and integrative learning across multi-modal biomarkers. American Statistical Association (ASA) Statistics in Imaging Section First Prize Student Paper Award American Statistical Association (ASA) Mental Health Statistics Section Best Student Paper Award International Conference on Health Policy Statistics (ICHPS) Student Travel Award Dr. Xie advises several graduate students and co-founded the Functional and Complex Data Analysis (FUNCODA) Working Group, fostering collaboration on advanced data modeling. She has been involved in significant grant-supported work, notably contributing to the CDC’s ensemble forecasting effort during the pandemic through her survival-convolution model. Her lab emphasizes computational statistics and open science, with code available on GitHub.

Salvador Dura-Bernalمشاهده پروفایل

استادیار

• Computational Neuroscience
• Multiscale Modeling
• Machine Learning in Neuroscience
• +۶ مورد دیگر

Salvador Dura-Bernal, PhD, is an Assistant Professor in the Department of Physiology and Pharmacology at SUNY Downstate Medical Center. He leads a computational neuroscience lab focused on multiscale brain modeling, machine learning applications in neuroscience, and biomimetic neuroprosthetics. His research bridges molecular, cellular, and systems-level neuroscience through biophysically detailed simulations. Developed NetPyNE, an open-source Python package for multiscale neuronal network modeling Collaborates with Nathan Kline Institute and global computational neuroscience organizations Co-Director of SUNY Downstate's Global Center for AI in Mental Health Research interests include: multiscale cortical circuit modeling, neural coding mechanisms, neuroprosthetic systems using spiking networks, and brain-machine interfaces. His work combines supercomputing simulations with experimental data validation, focusing on thalamocortical interactions and neuromodulatory effects. Recent publications demonstrate his team's success in creating biologically accurate models for motor and auditory cortices, with applications in understanding brain disorders and developing treatment strategies. The lab employs machine learning for model optimization and analysis, including evolutionary algorithms and Bayesian networks. 2024: Theoretical thalamus model for deviance detection 2023: Primary motor cortex model validated with in vivo data 2022: Somatosensory thalamocortical NetPyNE implementation Scientific contributions include: SUNY Downstate Annual Research Day best work (2022) Key developer of NetPyNE software Editorial board member for NeuroML NIH reviewer for BRAIN Initiative The lab trains graduate students and postdoctoral fellows, currently mentoring Joao Moreira (M.Sc.) and three postdocs. Research funding includes NIH grants for integrating NetPyNE with The Virtual Brain tool and developing neuroprosthetic systems that could replace damaged brain regions with in silico models.

Toros Arikanمشاهده پروفایل

استادیار

• Signal Processing
• Remote Sensing
• Underwater Acoustics
• +۶ مورد دیگر

Dr. Toros Arikan is an Assistant Professor in the Department of Electrical Engineering at the University of Notre Dame's College of Engineering. His research focuses on signal processing for remote sensing applications, with particular emphasis on underwater acoustics and indoor radio frequency systems. He applies deep learning techniques to solve complex problems in environmental mapping, localization, and tracking. B.S., M.S., and Ph.D. in Electrical and Electronics Engineering from University of Illinois Urbana-Champaign and Massachusetts Institute of Technology Professor Arikan's work addresses fundamental challenges in underwater acoustic localization, reverberant environment modeling, and challenging-environment communications. His recent publications highlight neural network-based solutions for Steiner minimum trees and boundary estimation problems. His research on HF communications systems spans topics including low-latency transmission, Doppler tolerance, and modem design for underwater applications. Earlier work includes biomedical ultrasound signal processing for blood velocity estimation.

Greg D. Fieldمشاهده پروفایل

دانشیار مدعو

• Neurobiology
• Retinal Processing
• Neural Adaptation
• +۷ مورد دیگر

Greg D. Field is an Adjunct Associate Professor of Neurobiology at Duke University and a Faculty Network Member of the Duke Institute for Brain Sciences. His laboratory investigates retinal processing of visual scenes, focusing on functional connectivity, light adaptation, circadian rhythms, and retinal degenerative conditions. Using multi-electrode arrays, transgenic mouse models, and chemogenetics, he examines how retinal circuits encode visual information and how degeneration impacts signaling. Recent work addresses neural adaptation mechanisms, retinal mosaics, and potential therapies for retinal diseases. Research Themes : Retinal circuit organization, neural adaptation, visual signal processing, retinal degeneration, and optogenetics Technologies : Multi-electrode recordings, light-sheet microscopy, computational modeling, and transgenic approaches The 15 most recent publications (2010-2025) demonstrate his work on retinal ganglion cell function, visual hierarchy encoding, and therapies for photoreceptor degeneration. Grants from the National Institutes of Health (2015-2025) support studies on neural population mapping, comparative biology, and retinal circuit restoration. His work bridges theoretical neuroscience with clinical applications, particularly in developing interventions for blindness.