Niraj Jha

Niraj Jha

Princeton University

H-index: 84

North America-United States

About Niraj Jha

Niraj Jha, With an exceptional h-index of 84 and a recent h-index of 37 (since 2020), a distinguished researcher at Princeton University, specializes in the field of Machine learning, Smart healthcare, Computer-aided design, Secure computing, Embedded systems.

His recent articles reflect a diverse array of research interests and contributions to the field:

Im-Promptu: In-Context Composition from Image Prompts

Ctrl: Clustering training losses for label error detection

SECRETS: Subject-efficient clinical randomized controlled trials using synthetic intervention

TAD-SIE: Sample Size Estimation for Clinical Randomized Controlled Trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator

PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare

Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers

EdgeTran: Co-designing transformers for efficient inference on mobile edge platforms

ML-FEED: machine learning framework for efficient exploit detection (extended version)

Niraj Jha Information

University

Position

Professor of Electrical Engineering

Citations(all)

30960

Citations(since 2020)

7018

Cited By

26064

hIndex(all)

84

hIndex(since 2020)

37

i10Index(all)

382

i10Index(since 2020)

123

Email

University Profile Page

Google Scholar

Niraj Jha Skills & Research Interests

Machine learning

Smart healthcare

Computer-aided design

Secure computing

Embedded systems

Top articles of Niraj Jha

Im-Promptu: In-Context Composition from Image Prompts

Advances in Neural Information Processing Systems

2024/2/13

Ctrl: Clustering training losses for label error detection

IEEE Transactions on Artificial Intelligence

2024/2/12

SECRETS: Subject-efficient clinical randomized controlled trials using synthetic intervention

Contemporary Clinical Trials Communications

2024/2/3

TAD-SIE: Sample Size Estimation for Clinical Randomized Controlled Trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator

arXiv preprint arXiv:2401.03693

2024/1/8

PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare

arXiv preprint arXiv:2403.08197

2024/3/13

Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers

arXiv preprint arXiv:2305.17328

2023/5/27

EdgeTran: Co-designing transformers for efficient inference on mobile edge platforms

arXiv preprint arXiv:2303.13745

2023/3/24

ML-FEED: machine learning framework for efficient exploit detection (extended version)

2022

AIMED: AI-Mediated Exploration of Design: An Experience Report

2023/5/9

DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors

arXiv preprint arXiv:2305.05738

2023/5/9

AccelTran: A sparsity-aware accelerator for dynamic inference with transformers

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

2023/2/28

System and method for machine learning assisted security analysis of 5g network connected systems

2023/12/28

SCouT: Synthetic Counterfactuals via Spatiotemporal Transformers for Actionable Healthcare

ACM Transactions on Computing for Healthcare

2023/10/13

System and method for graphical reticulated attack vectors for internet of things aggregate security (gravitas)

2023/10/12

System and method for energy efficient sensors with compression, artificial intelligence, and security

2023/10/10

How MagNet: Machine learning framework for modeling power magnetic material characteristics

IEEE Transactions on Power Electronics

2023/8/28

BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space Exploration

arXiv preprint arXiv:2308.08666

2023/8/16

FlexiBERT: Are current transformer architectures too homogeneous and rigid?

Journal of Artificial Intelligence Research

2023/5/6

REPAIRS: Gaussian Mixture Model-based Completion and Optimization of Partially Specified Systems

ACM Transactions on Embedded Computing Systems

2023/7/24

CODEBench: A neural architecture and hardware accelerator co-design framework

ACM Transactions on Embedded Computing Systems

2023/4/20

See List of Professors in Niraj Jha University(Princeton University)

Co-Authors

academic-engine