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Kay James

· Associate Professor of Neuroscience and EducationVerified

Georgia Institute of Technology · Curriculum & Teaching

Active 1986–2026

h-index17
Citations1.1k
Papers9356 last 5y
Funding$284k
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About

Dr. Kay James is an Associate Professor of Neuroscience and Education at Teachers College, Columbia University, with a focus on the neural underpinnings of language representation and processing. Her research investigates language and cognitive processing in both typical and pathological situations, including developmental and acquired speech and language disorders, schizophrenia, and second language acquisition in adults. She directs the Neurocognition of Language Lab, which utilizes high-density electroencephalography to explore questions about language, cognition, and learning across the lifespan. Dr. James has a background in neuroscience, linguistics, and speech-language pathology, and her work has contributed to understanding the neural mechanisms involved in language impairments and bilingual language processing. She has been involved in developing assessment tools for early identification of language impairments and has conducted research using various brain imaging modalities, including MEG and fMRI. Her scholarly contributions include numerous peer-reviewed publications and active participation in professional organizations related to neuroscience, linguistics, and speech-language pathology.

Research topics

  • Computer Science
  • Physics
  • Engineering
  • Materials science
  • Structural engineering
  • Artificial Intelligence
  • Mathematical optimization
  • Nanotechnology
  • Mathematics
  • Mathematical analysis
  • Composite material
  • Applied mathematics
  • Algorithm
  • Mechanical engineering

Selected publications

  • Frequency-constrained topology optimization of an airfoil with variable supports using super-Gaussian function parameterization

    Structural and Multidisciplinary Optimization · 2026-05-01

    articleOpen accessSenior author

    Abstract This paper introduces a procedure that optimizes the topology of an airfoil structure and its support locations subject to mass-fraction, natural frequency constraints, and aerodynamic loading. We use a node-based density formulation with a density filter and solid isotropic material penalization (SIMP) interpolation. This ensures a continuous density field and provides a smooth transition between solid and void element states. A super-Gaussian function projects circular supports onto the airfoil’s finite element mesh, applying a soft-min filter to the distance function, thus preserving differentiability. By imposing a Kreisselmeier–Steinhauser (KS) aggregation constraint on the fundamental natural frequency, we address possible eigenvalue crossover issues known to affect frequency-centered structural problems without needing direct eigenmode tracking. In addition, loads obtained from a 2D aerodynamic analysis are interpolated and applied to the boundary nodes of the structural mesh using 1D linear shape functions. We provide new insight into enforcing a “skin” layer in which elements on the outer boundary of the airfoil structure maintain some requisite density. This ensures that the design can withstand the distributed aerodynamic loads and alleviates numerical performance issues that could potentially hinder convergence of the optimizer. This is the first procedure to co‑optimize topology, movable supports, and a graded skin while respecting a minimum eigenfrequency; the scheme remains fully differentiable and requires no mode‑tracking. We present several numerical examples that showcase the benefits of simultaneous consideration of mass, frequency, and aerodynamic constraints in designing airfoil structures with variable support locations.

  • A Systematic Investigation of Interior Point Methods for Aerodynamic Shape Optimization

    SSRN Electronic Journal · 2025-01-01

    preprintOpen accessSenior author
  • Simulações de computador revelam como a roda foi inventada, 6 mil anos atrás

    2025-06-13

    article1st authorCorresponding
  • Towards Crash-Tolerant Rotorcraft Design via Topology Optimization

    2025-05-20

    article

    We present our ongoing efforts towards the development of crash-tolerant rotorcraft airframe structures through topology optimization, with the goal of enhancing energy absorption and occupant survival during vertical impact events. A high strain rate explicit dynamics solver has been developed, fully accelerated on GPUs, to enable rapid and accurate simulation of impact events critical to crashworthiness evaluation. In parallel, we have built a scalable three-dimensional topology optimization framework that enforces stiffness, weight, and frequency constraints simultaneously, driving structurally efficient and vibration-resistant designs. Benchmarking results demonstrate significant GPU-enabled speedups, facilitating high-fidelity crash simulations and large-scale optimization at practical turnaround times. This work establishes a computational foundation for future integration of crash-centric objectives and constraints into the optimization framework.

  • How was the wheel invented? Computer simulations reveal the unlikely birth of a world-changing technology nearly 6,000 years ago

    2025-06-11

    article1st authorCorresponding
  • Routes to STEM: toward making science education more accessible and inclusive

    Race Ethnicity and Education · 2025-03-07 · 1 citations

    articleOpen access1st authorCorresponding
  • A systematic investigation of interior point methods for aerodynamic shape optimization

    Aerospace Science and Technology · 2025-05-09 · 2 citations

    articleSenior authorCorresponding
  • Multi-Physics Three-Dimensional Component Placement and Routing Optimization Using Geometric Projection

    Journal of Mechanical Design · 2024-01-12 · 6 citations

    articleSenior author

    Abstract This article presents a novel three-dimensional topology optimization framework developed for 3D spatial packaging of interconnected systems using a geometric projection method (GPM). The proposed gradient-based topology optimization method simultaneously optimizes the locations and orientations of system components (or devices) and lengths, diameters, and trajectories of interconnects to reduce the overall system volume within the prescribed 3D design domain. The optimization is subject to geometric and physics-based constraints dictated by various system specifications, suited for a wide range of transportation (aerospace or automotive), heating, ventilation, air-conditioning, and refrigeration, and other complex system applications. The system components and interconnects are represented using 3D parametric shapes such as cubes, cuboids, and cylinders. These objects are then projected onto a three-dimensional finite element mesh using the geometric projection method. Sensitivities are calculated for the objective function (bounding box volume) with various geometric and physics-based (thermal and hydraulic) constraints. Several case studies were performed with different component counts, interconnection topologies, and system boundary conditions and are presented to exhibit the capabilities of the proposed 3D multi-physics spatial packaging optimization framework.

  • How to Build a Modular Low-Cost Telemetered Datalogger

    SSRN Electronic Journal · 2024-01-01

    preprintOpen accessSenior author
  • Optimal Design of eVTOLs for Urban Mobility using Analytical Target Cascading (ATC)

    2024-01-04 · 8 citations

    article

    Over the past few decades, multidisciplinary design optimization (MDO) techniques have shown great potential in generating optimal designs for complex system of systems. Monolithic MDO methods that formulate the design problem as a single optimization problem are effective, but present challenges in coordination at an organizational level. On the other hand, distributed MDO methods decompose the design problem into different optimization problems and hence offer more modularity and flexibility for organizational implementations. In this article, one such distributed MDO method, Analytical Target Cascading (ATC), is investigated as a candidate for the design of electric Vertical Take Off and Landing aircraft (eVTOL). Design of eVTOLs for urban mobility has been a subject of immense interest over the past decade. eVTOLs offer many advantages over conventional modes of urban transport such as being environmentally friendly, utilization of vertical space for transport, and competitive cost of transportation. Most current efforts for eVTOL design are in relatively early stages. Hence, distributed MDO methods that can effectively consider complex interactions between different subsystems and disciplines can help support design efforts for eVTOLs. In this study, ATC is implemented to optimize the total cost per flight for a simple mission, involving take-off to a set altitude, cruising at constant velocity for a range of 50-150 km, and landing, all while carrying a given payload. The key design parameters that are optimized as a part of this study are the mass of aircraft and individual subsystems, cruise velocity, wingspan, and radius of the propeller. Furthermore, proximity of the resulting optimal solution using ATC and monolithic MDO methods is demonstrated. General observations are also articulated regarding potential computational advantages, such as parallelism and tailored solution algorithms, as well as organizational considerations, such as distributed iterative subproblem formulation refinement conducted by human subject matter experts and team coordination.

Recent grants

Frequent coauthors

  • Satya R. T. Peddada

    University of Illinois Urbana-Champaign

    18 shared
  • James T. Allison

    University of Illinois Urbana-Champaign

    17 shared
  • Anurag Bhattacharyya

    SRI International

    15 shared
  • Ziliang Kang

    13 shared
  • Prateek Ranjan

    University of Illinois System

    10 shared
  • Joaquim R. R. A. Martins

    University of Michigan–Ann Arbor

    10 shared
  • Lawrence Zeidner

    RTX (United States)

    9 shared
  • Diab W. Abueidda

    7 shared

Labs

  • Neurocognition of Language LabPI

Awards & honors

  • Essell Young Investigator Award (2003-2005)
  • American Speech, Language & Hearing Association award for St…
  • American Speech, Language & Hearing Association award for St…
  • Teachers College, Columbia University Vice President's Grant…
  • Teachers College, Columbia University Provost's Investment F…
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