580 citations · 864 across the 20 of their papers we have counts for
21 papers
Role-Aware Morgan Fingerprints for Reaction Yield Prediction
Chinmay Mirji, Prashant Shekhar, Foram Madiyar +1
Predicting reaction yield from molecular structure and reaction context can cut experimental trial-and-error and speed up condition screening in synthetic chemistry. Recent methods…
Decision-Calibrated Conformal Uncertainty for Pacing Decisions in Streaming Advertising
Prashant Shekhar, Caroline Howard
We develop a decision-calibrated conformal framework for pacing decisions in streaming advertising. Pacing depends on uncertain future inventory, demand pressure, incremental respo…
Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss
Prashant Shekhar, Caroline Howard
Advertising platforms use randomized lift tests to measure incrementality, but privacy-preserving reporting systems degrade the observed signal through match-rate loss, linkability…
PILOT: A Data-Free Continual Learning Approach for Real-Time Semantic Segmentation via Boundary Guidance
Yujing Zhou, Prashant Shekhar, Thomas Yang +1
Real-time semantic segmentation models offer an excellent balance between accuracy and inference speed. However, deploying these models in dynamic real world environments often req…
Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems
Prashant Shekhar, Caroline Howard
Online experiments in ads, recommendation, and member-experience systems are often planned before the dominant interference mechanism is known. A treatment may propagate through bu…
Support-aware offline policy selection for advertising marketplaces
Prashant Shekhar, Caroline Howard
Logged advertising auctions make offline reserve-price evaluation attractive but risky. Replay tables can identify policies with large apparent yield gains, yet they can also hide…
Decision Support for Marketplace Policies under Incomplete Evidence: From Replay to Launch Readiness
Prashant Shekhar, Caroline Howard
Marketplace platforms routinely evaluate pricing and allocation policies using logged observational data, yet strong offline performance does not imply that a policy is safe to dep…
Adversarial-Resilient RF Fingerprinting: A CNN-GAN Framework for Rogue Transmitter Detection
Raju Dhakal, Prashant Shekhar, Laxima Niure Kandel
Radio Frequency Fingerprinting (RFF) has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the s…
Advancing Practical Homomorphic Encryption for Federated Learning: Theoretical Guarantees and Efficiency Optimizations
Ren-Yi Huang, Dumindu Samaraweera, Prashant Shekhar +1
Federated Learning (FL) enables collaborative model training while preserving data privacy by keeping raw data locally stored on client devices, preventing access from other client…
Secure Distributed Learning for CAVs: Defending Against Gradient Leakage with Leveled Homomorphic Encryption
Muhammad Ali Najjar, Ren-Yi Huang, Dumindu Samaraweera +1
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine lear…
Cross-Model Transferability of Adversarial Patches in Real-time Segmentation for Autonomous Driving
Prashant Shekhar, Bidur Devkota, Dumindu Samaraweera +2
Adversarial attacks pose a significant threat to deep learning models, particularly in safety-critical applications like healthcare and autonomous driving. Recently, patch based at…
Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading
Siddharth S. Parida, Supratik Bose, Megan Butcher +2
The computationally expensive estimation of engineering demand parameters (EDPs) via finite element (FE) models, while considering earthquake and parameter uncertainty limits the u…
A Forward Backward Greedy approach for Sparse Multiscale Learning
Prashant Shekhar, Abani Patra
Multiscale Models are known to be successful in uncovering and analyzing the structures in data at different resolutions. In the current work we propose a feature driven Reproducin…
Object shape error modelling and simulation during early design stage by morphing Gaussian Random Fields
Manoj Babu, Pasquale Franciosa, Prashanth Shekar +1
Geometric and dimensional variations in objects are caused by inevitable uncertainties in manufacturing processes and often lead to product quality issues. Failing to model the eff…
ALPS: A Unified Framework for Modeling Time Series of Land Ice Changes
Prashant Shekhar, Beata Csatho, Tony Schenk +2
Modeling time series is a research focus in cryospheric sciences because of the complexity and multiscale nature of events of interest. Highly non-uniform sampling of measurements…
Hierarchical regularization networks for sparsification based learning on noisy datasets
Prashant Shekhar, Abani Patra
We propose a hierarchical learning strategy aimed at generating sparse representations and associated models for large noisy datasets. The hierarchy follows from approximation spac…
Hierarchical Data Reduction and Learning
Prashant Shekhar, Abani Patra
This paper describes a hierarchical learning strategy for generating sparse representations of multivariate datasets. The hierarchy arises from approximation spaces considered at s…
Momentum-resolved Electron Energy Loss Spectroscopy for Mapping the Photonic Density of States
Prashant Shekhar, Marek Malac, Vaibhav Gaind +3
Strong nanoscale light-matter interaction is often accompanied by ultra-confined photonic modes and large momentum polaritons existing far beyond the light cone. A direct probe of…
Controlling evanescent waves using silicon photonic all-dielectric metamaterials for dense integration
Saman Jahani, Sangsik Kim, Jonathan Atkinson +11
Ultra-compact, densely integrated optical components manufactured on a CMOS-foundry platform are highly desirable for optical information processing and electronic-photonic co-inte…
Strong Coupling in Hyperbolic Metamaterials
Prashant Shekhar, Zubin Jacob
Nanoscale light-matter interaction in the weak-coupling regime has been achieved with unique hyperbolic metamaterial modes possessing a high density of states. Here, we show strong…
Hyperbolic metamaterials: fundamentals and applications
Prashant Shekhar, Jonathan Atkinson, Zubin Jacob
Metamaterials are nano-engineered media with designed properties beyond those available in nature with applications in all aspects of materials science. In particular, metamaterial…