8 citations · 44 across the 17 of their papers we have counts for
19 papers
RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
Meghana Kshirsagar, Allen Nie, Ching-An Cheng +5
We introduce RosettaSearch, an inference-time multi-objective optimization approach for backbone conditioned protein sequence design. We use large language models (LLMs) as a gener…
Towards reliable use of artificial intelligence to classify otitis media using otoscopic images: Addressing bias and improving data quality
Yixi Xu, Al-Rahim Habib, Graeme Crossland +8
Ear disease contributes significantly to global hearing loss, with recurrent otitis media being a primary preventable cause in children, impacting development. Artificial intellige…
Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation
Andres Hernandez, Zhongqi Miao, Luisa Vargas +4
The alarming decline in global biodiversity, driven by various factors, underscores the urgent need for large-scale wildlife monitoring. In response, scientists have turned to auto…
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
Shadab Ahamed, Yixi Xu, Ingrid Bloise +5
Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a h…
Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
Zalan Fabian, Zhongqi Miao, Chunyuan Li +10
Due to deteriorating environmental conditions and increasing human activity, conservation efforts directed towards wildlife is crucial. Motion-activated camera traps constitute an…
Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses
Shadab Ahamed, Yixi Xu, Sara Kurkowska +12
This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing literature. While deep learning has been a…
Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data
Mayana Pereira, Meghana Kshirsagar, Sumit Mukherjee +3
Differentially private (DP) synthetic data sets are a solution for sharing data while preserving the privacy of individual data providers. Understanding the effects of utilizing DP…
Navigating the Web of Misinformation: A Framework for Misinformation Domain Detection Using Browser Traffic
Mayana Pereira, Kevin Greene, Nilima Pisharody +3
The proliferation of misinformation and propaganda is a global challenge, with profound effects during major crises such as the COVID-19 pandemic and the Russian invasion of Ukrain…
Poverty rate prediction using multi-modal survey and earth observation data
Simone Fobi, Manuel Cardona, Elliott Collins +5
This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of…
Dwelling Type Classification for Disaster Risk Assessment Using Satellite Imagery
Md Nasir, Tina Sederholm, Anshu Sharma +4
Vulnerability and risk assessment of neighborhoods is essential for effective disaster preparedness. Existing traditional systems, due to dependency on time-consuming and cost-inte…
Retinal Microvasculature as Biomarker for Diabetes and Cardiovascular Diseases
Anusua Trivedi, Jocelyn Desbiens, Ron Gross +3
Purpose: To demonstrate that retinal microvasculature per se is a reliable biomarker for Diabetic Retinopathy (DR) and, by extension, cardiovascular diseases. Methods: Deep Learnin…
An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises
Mayana Pereira, Meghana Kshirsagar, Sumit Mukherjee +2
Diferentially private (DP) synthetic datasets are a powerful approach for training machine learning models while respecting the privacy of individual data providers. The effect of…
Defending Democracy: Using Deep Learning to Identify and Prevent Misinformation
Anusua Trivedi, Alyssa Suhm, Prathamesh Mahankal +7
The rise in online misinformation in recent years threatens democracies by distorting authentic public discourse and causing confusion, fear, and even, in extreme cases, violence.…
Becoming Good at AI for Good
Meghana Kshirsagar, Caleb Robinson, Siyu Yang +13
AI for good (AI4G) projects involve developing and applying artificial intelligence (AI) based solutions to further goals in areas such as sustainability, health, humanitarian aid,…
Height Estimation of Children under Five Years using Depth Images
Anusua Trivedi, Mohit Jain, Nikhil Kumar Gupta +11
Malnutrition is a global health crisis and is the leading cause of death among children under five. Detecting malnutrition requires anthropometric measurements of weight, height, a…
U.S. Broadband Coverage Data Set: A Differentially Private Data Release
Mayana Pereira, Allen Kim, Joshua Allen +3
Broadband connectivity is a key metric in today's economy. In an era of rapid expansion of the digital economy, it directly impacts GDP. Furthermore, with the COVID-19 guidelines o…
Improving Lesion Detection by exploring bias on Skin Lesion dataset
Anusua Trivedi, Sreya Muppalla, Shreyaan Pathak +4
All datasets contain some biases, often unintentional, due to how they were acquired and annotated. These biases distort machine-learning models' performance, creating spurious cor…
Metadata-Based Detection of Child Sexual Abuse Material
Mayana Pereira, Rahul Dodhia, Hyrum Anderson +1
Child Sexual Abuse Media (CSAM) is any visual record of a sexually-explicit activity involving minors. CSAM impacts victims differently from the actual abuse because the distributi…
MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models
Yixi Xu, Sumit Mukherjee, Xiyang Liu +3
Generative machine learning models are being increasingly viewed as a way to share sensitive data between institutions. While there has been work on developing differentially priva…