Dr. Brian Jalaian's Website
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  • Jalaian AI Lab
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  • News
    • AI Trustworthiness and Risk Assessment for Challenged Contexts (ATRACC)
  • Publications
    • On Accelerating Edge AI: Optimizing Resource-Constrained Environments
    • Neuro-Symbolic Integration for Open Set Recognition in Network Intrusion Detection
    • Uncertainty-Quantified Neurosymbolic AI for Open Set Recognition in Network Intrusion Detection
    • Mitigating Large Vision-Language Model Hallucination at Post-hoc via Multi-agent System
    • A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System
    • A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI
    • Neurosymbolic AI in Cybersecurity: Bridging Pattern Recognition and Symbolic Reasoning
    • Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo
    • Reducing classifier overconfidence against adversaries through graph algorithms
    • Improving Object Detection Robustness against Natural Perturbations through Synthetic Data Augmentation
    • Shedding Light on Darkness: Enhancing Object Detection Robustness with Synthetic Perturbations for Real-world Challenges
    • Enhancing object detection robustness: A synthetic and natural perturbation approach
    • Enhancing Resilience in Mobile Edge Computing Under Processing Uncertainty
    • Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications
    • The Methodological Pitfall of Dataset-Driven Research on Deep Learning: An IoT Example
    • Maximizing Energy Efficiency With Channel Uncertainty Under Mutual Interference
    • EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
    • URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference Methods
    • Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
    • Runtime Monitoring of Deep Neural Networks Using Top-Down Context Models Inspired by Predictive Processing and Dual Process Theory
    • Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks
    • Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
    • Task Offloading with Uncertain Processing Cycles
    • Improving Differential Evolution through Bayesian Hyperparameter Optimization
    • Achieving Real-Time Spectrum Sharing in 5G Underlay Coexistence with Channel Uncertainty
    • AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
    • Computational Intelligence in Uncertainty Quantification for Learning Controland Differential Games
    • EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
    • Fountain Coding for Information Protection in Tactical Networks
    • Graph neural networks with adaptive frequency response filter
    • Homology as an Adversarial Attack Indicator
    • Machine learning raw network traffic detection
    • Maximize Spectrum Efficiency in Underlay Coexistence With Channel Uncertainty
    • Minimizing AoI in a 5G-Based IoT Network Under Varying Channel Conditions
    • On DoF Conservation in MIMO Interference Cancellation based on Signal Strength in the Eigenspace
    • Re-orienting Toward the Science of the Artificial: Engineering AI Systems
    • Toward Safe Decision-Making via Uncertainty Quantification in Machine Learning
    • Trinity: Trust, Resilience and Interpretability of Machine Learning Models
    • Adversarial Distillation of Bayesian Neural Networks
    • Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
    • Better call surrogates: A hybrid evolutionary algorithm for hyperparameter optimization
    • Context: Separating the forest and the trees—Wavelet contextual conditioning for AI
    • Exploring performance trade-offs in tactical edge networks
    • Generalized bayesian posterior expectation distillation for deep neural networks
    • On uncertainty and robustness in large-scale intelligent data fusion systems
    • Ursabench: Comprehensive benchmarking of approximate bayesian inference methods for deep neural networks
    • A Real-Time Solution for Underlay Coexistence with Channel Uncertainty
    • Optimal Power Control with Channel Uncertainty in Ad Hoc Networks
    • Are Graph Neural Networks Miscalibrated?
    • An example preprint / working paper
    • Attribution-based confidence metric for deep neural networks
    • Attribution-driven causal analysis for detection of adversarial examples
    • Coping uncertainty in coexistence via exploitation of interference threshold violation
    • Design and Development of Interactive Intelligent Medical Agent
    • Intelligent search, rescue, and disaster recovery via internet of things
    • Kronos: A 5G scheduler for AoI minimization under dynamic channel conditions
    • The Case for Robust Adaptation: Autonomic Resource Management is a Vulnerability
    • To cancel or not to cancel: Exploiting interference signal strength in the eigenspace for efficient MIMO DoF utilization
    • Uncertain context: Uncertainty quantification in machine learning
    • Uncertainty Quantification in Internet of Battlefield Things
    • A survey of feature space reduction methods for context aware processing in IoBT networks
    • Detecting adversarial examples using data manifolds
    • Evaluating LoRaWAN-based IoT devices for the tactical military environment
    • Investigating LoRa for the internet of battlefield things: a cyber perspective
    • On Stream-Centric Learning for Internet of Battlefield Things
    • Power management for Internet of Things
    • Programmable control plane for mission critical wireless networks
    • Reaping the benefits of dynamic TDD in massive MIMO
    • Reasoning about Complex and Uncertain Worlds in Physical, Social, and Virtual Realms: A Report of the Army Science Planning and Strategy Meetings Held in Fiscal Year 2018 at the US Army Research Laboratory
    • Steganographic Internet of Things: Graph Topology Timing Channels
    • Technical Report Coping Uncertainty in Coexistence via Exploitation of Interference Threshold Violation
    • A generalized optimization framework for control plane in tactical wireless networking
    • Affective Computing of Constitutional States for Human Information Interaction
    • Impact of full duplex scheduling on end-to-end throughput in multi-hop wireless networks
    • On 3D autonomous delivery systems: Design and development
    • On the integration of SIC and MIMO DoF for interference cancellation in wireless networks
    • An Online Admission Control Algorithm for Dynamic Traffic in Underlay Coexistence Paradigm
    • Modeling and optimization for programmable unified control plane in heterogeneous wireless networks
    • An example journal article
    • An optimal scheduling framework for concurrent transmissions in wireless cognitive radio networks
    • Biologically inspired artificial endocrine system for human computer interaction
    • Cooperative spectrum sensing in cognitive wireless sensor networks
    • Dynamic Spectrum Access Algorithm Based on Game Theory in Cognitive Radio Networks
    • ERDT: Energy-efficient reliable decision transmission for intelligent cooperative spectrum sensing in industrial IoT
    • Harmonizing SIC and MIMO DoF interference cancellation for efficient network-wide resource allocation
    • PaperIO: A 3D Interface towards the Internet of Embedded Paper-Craft
    • An example conference paper
    • Location Aware CR-MAC: A multi-channel cross layered PHY-MAC protocol for cognitive radio ad hoc networks
    • Analysis of Sonogram Images of Thyroid Gland Based on Wavelet Transform
    • Analysis of Sonographic Images of Breast
  • Teaching
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PyTorch

Oct 26, 2023 · 1 min read
Go to Project Site

PyTorch is a Python package that provides tensor computation (like NumPy) with strong GPU acceleration.

Last updated on May 19, 2024
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Brian Jalaian, Ph.D.
Authors
Brian Jalaian, Ph.D.
Associate Professor
My research focuses on developing safe, robust, and reliable AI systems, with emphasis on large language models and foundational AI technologies.

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