19 citations · 46 across the 14 of their papers we have counts for
14 papers
Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model
Varsha Pendyala, Yiwei Fu, Weizhong Yan +1
Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasting challenging. Recent advan…
Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfiguration
Hoang T. Nguyen, Shaohui Liu, Reetam Sen Biswas +4
The proliferation of distributed energy resources (DERs) in distribution grids enables the active coordination of these assets to reduce costs and enable cleaner operations. Realiz…
RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
Anmol Dwivedi, Ali Tajer, Santiago Paternain +1
Electricity grid's resiliency and climate change strongly impact one another due to an array of technical and policy-related decisions that impact both. This paper introduces a phy…
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand
Yiwei Fu, Nurali Virani, Honggang Wang
Predicting the demand for electricity with uncertainty helps in planning and operation of the grid to provide reliable supply of power to the consumers. Machine learning (ML)-based…
Masked Multi-Step Multivariate Time Series Forecasting with Future Information
Yiwei Fu, Honggang Wang, Nurali Virani
In this paper, we introduce Masked Multi-Step Multivariate Forecasting (MMMF), a novel and general self-supervised learning framework for time series forecasting with known future…
Uncertainty-aware Perception Models for Off-road Autonomous Unmanned Ground Vehicles
Zhaoyuan Yang, Yewteck Tan, Shiraj Sen +4
Off-road autonomous unmanned ground vehicles (UGVs) are being developed for military and commercial use to deliver crucial supplies in remote locations, help with mapping and surve…
Automated Creation and Human-assisted Curation of Computable Scientific Models from Code and Text
Varish Mulwad, Andrew Crapo, Vijay S. Kumar +5
Scientific models hold the key to better understanding and predicting the behavior of complex systems. The most comprehensive manifestation of a scientific model, including crucial…
Adversarial Attacks with Time-Scale Representations
Alberto Santamaria-Pang, Jianwei Qiu, Aritra Chowdhury +4
We propose a novel framework for real-time black-box universal attacks which disrupts activations of early convolutional layers in deep learning models. Our hypothesis is that pert…
Variational Encoder-based Reliable Classification
Chitresh Bhushan, Zhaoyuan Yang, Nurali Virani +1
Machine learning models provide statistically impressive results which might be individually unreliable. To provide reliability, we propose an Epistemic Classifier (EC) that can pr…
Justification-Based Reliability in Machine Learning
Nurali Virani, Naresh Iyer, Zhaoyuan Yang
With the advent of Deep Learning, the field of machine learning (ML) has surpassed human-level performance on diverse classification tasks. At the same time, there is a stark need…
Design of intentional backdoors in sequential models
Zhaoyuan Yang, Naresh Iyer, Johan Reimann +1
Recent work has demonstrated robust mechanisms by which attacks can be orchestrated on machine learning models. In contrast to adversarial examples, backdoor or trojan attacks embe…
Nonlinear Unknown Input and State Estimation Algorithm in Mobile Robots
Pinyao Guo, Hunmin Kim, Nurali Virani +3
This technical report provides the description and the derivation of a novel nonlinear unknown input and state estimation algorithm (NUISE) for mobile robots. The algorithm is desi…
Symbolic Analysis-based Reduced Order Markov Modeling of Time Series Data
Devesh K Jha, Nurali Virani, Jan Reimann +2
This paper presents a technique for reduced-order Markov modeling for compact representation of time-series data. In this work, symbolic dynamics-based tools have been used to infe…
Exploiting Physical Dynamics to Detect Actuator and Sensor Attacks in Mobile Robots
Pinyao Guo, Hunmin Kim, Nurali Virani +3
Mobile robots are cyber-physical systems where the cyberspace and the physical world are strongly coupled. Attacks against mobile robots can transcend cyber defenses and escalate i…