World's Best Scientists 2026 revealed!
Luca Daniel

Luca Daniel

D-Index & Metrics

Engineering and Technology

D-Index
36
Citations
6021
World Ranking
8646
National Ranking
2400

Overview

Luca Daniel is affiliated with MIT in the United States and focuses primarily on engineering disciplines, with a specialization in electrical and electronic engineering as well as artificial intelligence and automotive engineering. Their research contributions also extend into statistical and nonlinear physics and radiology, nuclear medicine, and imaging.

The scientist's work covers various topics, including:

  • Optimal Power Flow Distribution
  • Model Reduction and Neural Networks
  • Power System Optimization and Stability
  • Adversarial Robustness in Machine Learning
  • Advanced MRI Techniques and Applications
  • Probabilistic and Robust Engineering Design
  • Electrical and Bioimpedance Tomography

Major recent publications demonstrate a focus on computational methods, electrical field applications, and robustness in machine learning models:

  • Fast and Accurate Tensor Completion With Total Variation Regularized Tensor Trains, 2020, IEEE Transactions on Image Processing
  • Metasurface Matching Layers for Enhanced Electric Field Penetration Into the Human Body, 2020, IEEE Access
  • Towards Certificated Model Robustness Against Weight Perturbations, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Matching Layer Design for Far-Field and Near-Field Penetration Into a Multilayered Lossy Media, 2022, IEEE Antennas and Propagation Magazine
  • Comparison and Analysis of Algorithms for Coordinated EV Charging to Reduce Power Grid Impact, 2024, IEEE Open Journal of Vehicular Technology

Frequent collaborators in their research include:

  • Samuel Chevalier
  • José E. Cruz Serrallés
  • Tsui-Wei Weng
  • Ilias I. Giannakopoulos
  • Riccardo Lattanzi

Luca Daniel has published notably in a number of venues, with repeated contributions to:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Power Systems
  • IEEE Access
  • IEEE Antennas and Propagation Magazine

Their work exemplifies an interdisciplinary approach combining engineering principles with the development of advanced computational techniques, focusing on both theoretical and applied aspects of electrical engineering and machine learning.

Best Publications

  • Towards Fast Computation of Certified Robustness for ReLU Networks

    Tsui-Wei Weng;Huan Zhang;Hongge Chen;Zhao Song

  • A multiparameter moment-matching model-reduction approach for generating geometrically parameterized interconnect performance models

    L. Daniel;Ong Chin Siong;L.S. Chay;Kwok Hong Lee

  • Efficient Neural Network Robustness Certification with General Activation Functions

    Huan Zhang;Tsui-Wei Weng;Pin-Yu Chen;Cho-Jui Hsieh

  • Guaranteed passive balancing transformations for model order reduction

    J.R. Phillips;L. Daniel;L.M. Silveira

  • Guaranteed passive balancing transformations for model order reduction

    Joel Phillips;Luca Daniel;L. Miguel Silveira

  • Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

    Tsui-Wei Weng;Huan Zhang;Pin-Yu Chen;Jinfeng Yi

  • Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos

    Zheng Zhang;T. A. El-Moselhy;I. M. Elfadel;L. Daniel

  • Efficient Neural Network Robustness Certification with General Activation Functions

    Huan Zhang;Tsui-Wei Weng;Pin-Yu Chen;Cho-Jui Hsieh

  • Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

    Tsui-Wei Weng;Huan Zhang;Pin-Yu Chen;Jinfeng Yi

  • Towards Fast Computation of Certified Robustness for ReLU Networks

    Tsui-Wei Weng;Huan Zhang;Hongge Chen;Zhao Song

  • Design of microfabricated inductors

    L. Daniel;C.R. Sullivan;S.R. Sanders

  • A Quasi-Convex Optimization Approach to Parameterized Model Order Reduction

    Kin Cheong Sou;A. Megretski;L. Daniel

  • CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks.

    Akhilan Boopathy;Tsui-Wei Weng;Pin-Yu Chen;Sijia Liu

  • Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition

    Zheng Zhang;Xiu Yang;Ivan V. Oseledets;George Em Karniadakis

  • Stable FFT-JVIE solvers for fast analysis of highly inhomogeneous dielectric objects

    Athanasios G. Polimeridis;Jorge Fernandez Villena;Luca Daniel;Jacob K. White

  • A Piecewise-Linear Moment-Matching Approach to Parameterized Model-Order Reduction for Highly Nonlinear Systems

    B.N. Bond;L. Daniel

  • Modeling and Simulation of Vanadium Dioxide Relaxation Oscillators

    Paolo Maffezzoni;Luca Daniel;Nikhil Shukla;Suman Datta

  • Parameterized model order reduction of nonlinear dynamical systems

    B. Bond;L. Daniel

  • The ultimate signal-to-noise ratio in realistic body models.

    Bastien Guérin;Jorge F. Villena;Athanasios G. Polimeridis;Elfar Adalsteinsson

  • Stable Reduced Models for Nonlinear Descriptor Systems Through Piecewise-Linear Approximation and Projection

    B.N. Bond;L. Daniel

  • Compact Modeling of Nonlinear Analog Circuits Using System Identification via Semidefinite Programming and Incremental Stability Certification

    Bradley N Bond;Zohaib Mahmood;Yan Li;Ranko Sredojevic

  • Big-Data Tensor Recovery for High-Dimensional Uncertainty Quantification of Process Variations

    Zheng Zhang;Tsui-Wei Weng;Luca Daniel

  • Guaranteed stable projection-based model reduction for indefinite and unstable linear systems

    Bradley N. Bond;Luca Daniel

  • Reduced-Order Models for Electromagnetic Scattering Problems

    Amit Hochman;Jorge Fernandez Villena;Athanasios G. Polimeridis;Luis Miguel Silveira

  • Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations

    Jeet Mohapatra;Tsui-Wei Weng;Pin-Yu Chen;Sijia Liu

  • POPQORN: Quantifying Robustness of Recurrent Neural Networks

    Ching-Yun Ko;Zhaoyang Lyu;Lily Weng;Luca Daniel

Frequent Co-Authors

Pin-Yu Chen
Pin-Yu Chen IBM (United States)
Sijia Liu
Sijia Liu Michigan State University
Huan Zhang
Huan Zhang University of California, Los Angeles
Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Lawrence L. Wald
Lawrence L. Wald Harvard University
Giuseppe Carlo Calafiore
Giuseppe Carlo Calafiore Polytechnic University of Turin
Alberto Sangiovanni-Vincentelli
Alberto Sangiovanni-Vincentelli University of California, Berkeley
Suman Datta
Suman Datta Georgia Institute of Technology

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