11 citations · 11 across the 20 of their papers we have counts for
21 papers
On Evaluating Quantum Kernel Robustness for Low-Resource Cross-Corpus Audio Deepfake Detection
Lisan Al Amin, Lei Zhang, Vandana P. Janeja
Synthetic speech detection is critical for audio security, but performance can degrade when labeled data are scarce and evaluation conditions differ from training. This study exami…
CASC: Causal Adversarial Subspace Clustering for Multivariate Spatiotemporal Data
Francis Ndikum Nji, Vandana Janeja, Jianwu Wang
Deep subspace clustering plays a critical role in applications involving multivariate spatiotemporal data, such as sea ice monitoring, disease spread analysis, and tracking neuro-d…
Linguistically Augmented Audio Speech Data (LinguAS)
Ashley R. Keaton, Zahra Khanjani, Christine Mallinson +1
Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay ahead of the curve. Yet, most…
Multi-Speaker Conversational Audio Deepfake: Taxonomy, Dataset and Pilot Study
Alabi Ahmed, Vandana Janeja, Sanjay Purushotham
The rapid advances in text-to-speech (TTS) technologies have made audio deepfakes increasingly realistic and accessible, raising significant security and trust concerns. While exis…
Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics
Akila Sampath, Vandana Janeja, Jianwu Wang
Quantifying the causal relationship between sea ice thickness and sea surface height (SSH) is essential for understanding the mechanisms driving polar climate dynamics. Conventiona…
Physics-Encoded Inverse Modeling for Arctic Snow Depth Estimation
Akila Sampath, Vandana P. Janeja, Jianwu Wang
Accurate estimation of unobserved quantities in time-varying inverse problems remains challenging when observations are sparse and only indirectly related to the target variable. I…
Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals
Bayu Adhi Tama, Jianwu Wang, Vandana Janeja +1
Accurate subglacial bed topography is essential for ice sheet modeling, yet radar observations are sparse and uneven. We propose a physics-guided residual learning framework that p…
Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data
Francis Ndikum Nji, Vandana Janeja, Jianwu Wang
Deep subspace clustering models are vital for applications such as snowmelt detection, sea ice tracking, crop health monitoring, infectious disease modeling, network load predictio…
B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data
Francis Ndikum Nji, Vandana Janaja, Jianwu Wang
Clustering high-dimensional multivariate spatiotemporal climate data is challenging due to complex temporal dependencies, evolving spatial interactions, and non-stationary dynamics…
Improving Greenland Bed Topography Mapping with Uncertainty-Aware Graph Learning on Sparse Radar Data
Bayu Adhi Tama, Homayra Alam, Mostafa Cham +3
Accurate maps of Greenland's subglacial bed are essential for sea-level projections, but radar observations are sparse and uneven. We introduce GraphTopoNet, a graph-learning frame…
Modeling Heterogeneity across Varying Spatial Extents: Discovering Linkages between Sea Ice Retreat and Ice Shelve Melt in the Antarctic
Maloy Kumar Devnath, Sudip Chakraborty, Vandana P. Janeja
Spatial phenomena often exhibit heterogeneity across spatial extents and in proximity, making them complex to model-especially in dynamic regions like ice shelves and sea ice. In t…
IDRIFTNET: Physics-Driven Spatiotemporal Deep Learning for Iceberg Drift Forecasting
Rohan Putatunda, Sanjay Purushotham, Ratnaksha Lele +1
Drifting icebergs in the polar oceans play a key role in the Earth's climate system, impacting freshwater fluxes into the ocean and regional ecosystems while also posing a challeng…
DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets
Bayu Adhi Tama, Mansa Krishna, Homayra Alam +6
Understanding Greenland's subglacial topography is critical for projecting the future mass loss of the ice sheet and its contribution to global sea-level rise. However, the complex…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies
Tolulope Ale, Nicole-Jeanne Schlegel, Vandana P. Janeja
The focus of our work is improving the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics. The Arctic and Antarctic ice sheets…
Listening for Expert Identified Linguistic Features: Assessment of Audio Deepfake Discernment among Undergraduate Students
Noshaba N. Bhalli, Nehal Naqvi, Chloe Evered +2
This paper evaluates the impact of training undergraduate students to improve their audio deepfake discernment ability by listening for expert-defined linguistic features. Such fea…
Toward Transdisciplinary Approaches to Audio Deepfake Discernment
Vandana P. Janeja, Christine Mallinson
This perspective calls for scholars across disciplines to address the challenge of audio deepfake detection and discernment through an interdisciplinary lens across Artificial Inte…
ALDAS: Audio-Linguistic Data Augmentation for Spoofed Audio Detection
Zahra Khanjani, Christine Mallinson, James Foulds +1
Spoofed audio, i.e. audio that is manipulated or AI-generated deepfake audio, is difficult to detect when only using acoustic features. Some recent innovative work involving AI-spo…
Investigating Causal Cues: Strengthening Spoofed Audio Detection with Human-Discernible Linguistic Features
Zahra Khanjani, Tolulope Ale, Jianwu Wang +3
Several types of spoofed audio, such as mimicry, replay attacks, and deepfakes, have created societal challenges to information integrity. Recently, researchers have worked with so…
Harnessing Feature Clustering For Enhanced Anomaly Detection With Variational Autoencoder And Dynamic Threshold
Tolulope Ale, Nicole-Jeanne Schlegel, Vandana P. Janeja
We introduce an anomaly detection method for multivariate time series data with the aim of identifying critical periods and features influencing extreme climate events like snowmel…
Assessing Annotation Accuracy in Ice Sheets Using Quantitative Metrics
Bayu Adhi Tama, Vandana Janeja, Sanjay Purushotham
The increasing threat of sea level rise due to climate change necessitates a deeper understanding of ice sheet structures. This study addresses the need for accurate ice sheet data…