Spatial patterns and functional profiles for discovering structure in fMRI data. - Polina Golland; Danial Lashkari; Archana Venkataraman (ACSCC, 2008)

Signal approximation using the bilinear transform. - Archana Venkataraman; Alan V. Oppenheim (ICASSP, 2008)

Exploring functional connectivity in fMRI via clustering. - Archana Venkataraman; Koene R. A. Van Dijk; Randy L. Buckner; Polina Golland (ICASSP, 2009)

Robust feature selection in resting-state fMRI connectivity based on population studies. - Archana Venkataraman; Marek Kubicki; Carl-Fredrik Westin; Polina Golland (CVPR Workshops, 2010)

Joint Generative Model for fMRI/DWI and Its Application to Population Studies. - Archana Venkataraman; Yogesh Rathi; Marek Kubicki; Carl-Fredrik Westin; Polina Golland (MICCAI (1), 2010)

From Brain Connectivity Models to Identifying Foci of a Neurological Disorder. - Archana Venkataraman; Marek Kubicki; Polina Golland (MICCAI (1), 2012)

Joint Modeling of Anatomical and Functional Connectivity for Population Studies. - Archana Venkataraman; Yogesh Rathi; Marek Kubicki; Carl-Fredrik Westin; Polina Golland (IEEE Trans. Medical Imaging, 2012)

Generative models of brain connectivity for population studies. - Archana Venkataraman (2012)

Detecting Epileptic Regions Based on Global Brain Connectivity Patterns. - Andrew Sweet; Archana Venkataraman; Steven M. Stufflebeam; Hesheng Liu; Naoro Tanaka; Joseph R. Madsen; Polina Golland (MICCAI (1), 2013)

From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder. - Archana Venkataraman; Marek Kubicki; Polina Golland (IEEE Trans. Medical Imaging, 2013)

Bayesian Community Detection in the Space of Group-Level Functional Differences. - Archana Venkataraman; Daniel Y.-J. Yang; Kevin A. Pelphrey; James S. Duncan (IEEE Trans. Medical Imaging, 2016)

A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort. - Archana Venkataraman; Nicholas F. Wymbs; Mary Beth Nebel; Stewart Mostofsky (CNI@MICCAI, 2017)

A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models. - Jeff Craley; Emily Johnson 001; Archana Venkataraman (MICCAI (3), 2018)

A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data. - Niharika Shimona D'Souza; Mary Beth Nebel; Nicholas F. Wymbs; Stewart Mostofsky; Archana Venkataraman (MICCAI (3), 2018)

Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields. - Naresh Nandakumar; Niharika Shimona D'Souza; Jeff Craley; Komal Manzoor; Jay J. Pillai; Sachin K. Gujar; Haris I. Sair; Archana Venkataraman (CNI@MICCAI, 2018)

Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging. - Nicha C. Dvornek; Daniel Y.-J. Yang; Archana Venkataraman; Pamela Ventola; Lawrence H. Staib; Kevin A. Pelphrey; James S. Duncan (CoRR, 2018)

VESUS: A Crowd-Annotated Database to Study Emotion Production and Perception in Spoken English. - Jacob Sager; Ravi Shankar; Jacob Reinhold; Archana Venkataraman (INTERSPEECH, 2019)

Automated Emotion Morphing in Speech Based on Diffeomorphic Curve Registration and Highway Networks. - Ravi Shankar; Hsi-Wei Hsieh; Nicolas Charon; Archana Venkataraman (INTERSPEECH, 2019)

A Multi-Speaker Emotion Morphing Model Using Highway Networks and Maximum Likelihood Objective. - Ravi Shankar; Jacob Sager; Archana Venkataraman (INTERSPEECH, 2019)

Weakly Supervised Syllable Segmentation by Vowel-Consonant Peak Classification. - Ravi Shankar; Archana Venkataraman (INTERSPEECH, 2019)

Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG. - Jeff Craley; Emily Johnson 001; Archana Venkataraman (IPMI, 2019)

A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces. - Niharika Shimona D'Souza; Mary Beth Nebel; Nicholas F. Wymbs; Stewart Mostofsky; Archana Venkataraman (IPMI, 2019)

Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks. - Jeff Craley; Emily Johnson 001; Christophe Jouny; Archana Venkataraman (MICCAI (4), 2019)

Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data. - Niharika Shimona D'Souza; Mary Beth Nebel; Nicholas F. Wymbs; Stewart Mostofsky; Archana Venkataraman (MICCAI (3), 2019)

Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework. - Sayan Ghosal; Qiang Chen; Aaron L. Goldman; William Ulrich; Karen Faith Berman; Daniel R. Weinberger; Venkata S. Mattay; Archana Venkataraman (MICCAI (4), 2019)

A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity. - Naresh Nandakumar; Komal Manzoor; Jay J. Pillai; Sachin K. Gujar; Haris I. Sair; Archana Venkataraman (CNI@MICCAI, 2019)

A generative-predictive framework to capture altered brain activity in fMRI and its association with genetic risk: application to Schizophrenia. - Sayan Ghosal; Qiang Chen; Aaron L. Goldman; William Ulrich; Karen Faith Berman; Daniel R. Weinberger; Venkata S. Mattay; Archana Venkataraman (Image Processing, 2019)

Multi-Speaker Emotion Conversion via Latent Variable Regularization and a Chained Encoder-Decoder-Predictor Network. - Ravi Shankar; Hsi-Wei Hsieh; Nicolas Charon; Archana Venkataraman (INTERSPEECH, 2020)

Non-Parallel Emotion Conversion Using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator. - Ravi Shankar; Jacob Sager; Archana Venkataraman (INTERSPEECH, 2020)

A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism. - Niharika Shimona D'Souza; Mary Beth Nebel; Deana Crocetti; Nicholas F. Wymbs; Joshua Robinson; Stewart Mostofsky; Archana Venkataraman (MICCAI (7), 2020)

A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity. - Naresh Nandakumar; Niharika Shimona D'Souza; Komal Manzoor; Jay J. Pillai; Sachin K. Gujar; Haris I. Sair; Archana Venkataraman (MLCN/RNO-AI@MICCAI, 2020)

Neuropsychiatric Disease Classification Using Functional Connectomics - Results of the Connectomics in NeuroImaging Transfer Learning Challenge. - Markus D. Schirmer; Archana Venkataraman; Islem Rekik; Minjeong Kim; Stewart Mostofsky; Mary Beth Nebel; Keri Rosch; Karen Seymour; Deana Crocetti; Hassna Irzan; Michael Hütel; Sébastien Ourselin; et al. (CoRR, 2020)

A joint network optimization framework to predict clinical severity from resting state functional MRI data. - Niharika Shimona D'Souza; Mary Beth Nebel; Nicholas F. Wymbs; Stewart H. Mostofsky; Archana Venkataraman (NeuroImage, 2020)

A Spatio-Temporal Model of Seizure Propagation in Focal Epilepsy. - Jeff Craley; Emily Johnson 001; Archana Venkataraman (IEEE Trans. Medical Imaging, 2020)

Cross-site Epileptic Seizure Detection Using Convolutional Neural Networks. - Danielle Currey; David Hsu; Raheel Ahmed; Archana Venkataraman; Jeff Craley (CISS, 2021)

Predicting Acute Kidney Injury via Interpretable Ensemble Learning and Attention Weighted Convoutional-Recurrent Neural Networks. - Yu-Chung Peng; Niharika Shimona D'Souza; Brian Bush; Charles Brown; Archana Venkataraman (CISS, 2021)

A Multi-scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize the Eloquent Cortex in Brain Tumor Patients. - Naresh Nandakumar; Komal Manzoor; Shruti Agarwal; Jay J. Pillai; Sachin K. Gujar; Haris I. Sair; Archana Venkataraman (IPMI, 2021)

A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes. - Niharika Shimona D'Souza; Mary Beth Nebel; Deana Crocetti; Joshua Robinson; Stewart Mostofsky; Archana Venkataraman (MICCAI (7), 2021)

M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations. - Niharika Shimona D'Souza; Mary Beth Nebel; Deana Crocetti; Joshua Robinson; Stewart Mostofsky; Archana Venkataraman (MIDL, 2021)

G-MIND: an end-to-end multimodal imaging-genetics framework for biomarker identification and disease classification. - Sayan Ghosal; Qiang Chen; Giulio Pergola; Aaron L. Goldman; William Ulrich; Karen Faith Berman; Giuseppe Blasi; Leonardo Fazio; Antonio Rampino; Alessandro Bertolino; Daniel R. Weinberger; Venkata S. Mattay; et al. (Image Processing, 2021)

Automated inter-patient seizure detection using multichannel Convolutional and Recurrent Neural Networks. - Jeff Craley; Emily Johnson 001; Christophe Jouny; Archana Venkataraman (Biomed. Signal Process. Control., 2021)

A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes. - Niharika Shimona D'Souza; Mary Beth Nebel; Deana Crocetti; Nicholas F. Wymbs; Joshua Robinson; Stewart Mostofsky; Archana Venkataraman (CoRR, 2021)

A Deep-Bayesian Framework for Adaptive Speech Duration Modification. - Ravi Shankar; Archana Venkataraman (CoRR, 2021)

Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks. - Naresh Nandakumar; Komal Manzoor; Shruti Agarwal; Jay J. Pillai; Sachin K. Gujar; Haris I. Sair; Archana Venkataraman (Medical Image Anal., 2021)

Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge. - Markus D. Schirmer; Archana Venkataraman; Islem Rekik; Minjeong Kim; Stewart H. Mostofsky; Mary Beth Nebel; Keri Rosch; Karen Seymour; Deana Crocetti; Hassna Irzan; Michael Hütel; Sébastien Ourselin; et al. (Medical Image Anal., 2021)

Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations. - Niharika Shimona D'Souza; Mary Beth Nebel; Deana Crocetti; Joshua Robinson; Nicholas F. Wymbs; Stewart H. Mostofsky; Archana Venkataraman (NeuroImage, 2021)

A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space. - Sayan Ghosal; Qiang Chen; Giulio Pergola; Aaron L. Goldman; William Ulrich; Karen Faith Berman; Giuseppe Blasi; Leonardo Fazio; Antonio Rampino; Alessandro Bertolino; Daniel R. Weinberger; Venkata S. Mattay; et al. (NeuroImage, 2021)

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease. - Sayan Ghosal; Qiang Chen; Giulio Pergola; Aaron L. Goldman; William Ulrich; Daniel R. Weinberger; Archana Venkataraman (ICLR, 2022)

RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis. - Naresh Nandakumar; Komal Manzoor; Shruti Agarwal; Haris I. Sair; Archana Venkataraman (MICCAI (1), 2022)

SZLoc: A Multi-resolution Architecture for Automated Epileptic Seizure Localization from Scalp EEG. - Jeff Craley; Emily Johnson; Christophe Jouny; David Hsu; Raheel Ahmed; Archana Venkataraman (MIDL, 2022)

Prospective Learning: Back to the Future. - Joshua T. Vogelstein; Timothy D. Verstynen; Konrad P. Kording; Leyla Isik; John W. Krakauer; Ralph Etienne-Cummings; Elizabeth L. Ogburn; Carey E. Priebe; Randal C. Burns; Kwame S. Kutten; James J. Knierim; James B. Potash; et al. (CoRR, 2022)

A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition. - Ravi Shankar; Abdouh Harouna Kenfack; Arjun Somayazulu; Archana Venkataraman (CoRR, 2022)

Prospective Learning: Principled Extrapolation to the Future. - Ashwin De Silva; Rahul Ramesh; Lyle H. Ungar; Marshall G. Hussain Shuler; Noah J. Cowan; Michael L. Platt; Chen Li; Leyla Isik; Seung-Eon Roh; Adam S. Charles; Archana Venkataraman; Brian Caffo; et al. (CoLLAs, 2023)

GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity. - Sarah Wu; Archana Venkataraman; Sayan Ghosal (EMBC, 2023)

Fusion Approaches to Predict Post-stroke Aphasia Severity from Multimodal Neuroimaging Data. - Saurav Chennuri; Sha Lai; Anne Billot; Maria Varkanitsa; Emily J. Braun; Swathi Kiran; Archana Venkataraman; Janusz Konrad; Prakash Ishwar; Margrit Betke (ICCV (Workshops), 2023)

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds. - Niharika Shimona D'Souza; Archana Venkataraman (IPMI, 2023)

A Deep Learning Framework to Localize the Epileptogenic Zone from Dynamic Functional Connectivity Using a Combined Graph Convolutional and Transformer Network. - Naresh Nandakumar; David Hsu; Raheel Ahmed; Archana Venkataraman (ISBI, 2023)

DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data. - Deeksha M. Shama; Jiasen Jing; Archana Venkataraman (MICCAI (8), 2023)

Adaptive Duration Modification of Speech using Masked Convolutional Networks and Open-Loop Time Warping. - Ravi Shankar; Archana Venkataraman (SSW, 2023)

A Diffeomorphic Flow-Based Variational Framework for Multi-Speaker Emotion Conversion. - Ravi Shankar; Hsi-Wei Hsieh; Nicolas Charon; Archana Venkataraman (IEEE ACM Trans. Audio Speech Lang. Process., 2023)

DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization From Resting-State fMRI Connectivity. - Naresh Nandakumar; David Hsu; Raheel Ahmed; Archana Venkataraman (IEEE Trans. Biomed. Eng., 2023)

A Deep Learning Framework To Characterize Noisy Labels In Epileptogenic Zone Localization Using Functional Connectivity. - Naresh Nandakumar; David Hsu; Raheel Ahmed; Archana Venkataraman (ISBI, 2024)

A Lesion-Aware Edge-Based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia. - Zijian Chen; Maria Varkanitsa; Prakash Ishwar; Janusz Konrad; Margrit Betke; Swathi Kiran; Archana Venkataraman (MLCN@MICCAI, 2024)

QID2: An Image-Conditioned Diffusion Model for Q-Space Up-Sampling of DWI Data. - Zijian Chen; Jueqi Wang; Archana Venkataraman (CDMRI@MICCAI, 2024)

GAMing the Brain: Investigating the Cross-Modal Relationships Between Functional Connectivity and Structural Features Using Generalized Additive Models. - Arunkumar Kannan; Brian Caffo; Archana Venkataraman (MLCN@MICCAI, 2024)

Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detection. - Deeksha M. Shama; Archana Venkataraman (UNSURE@MICCAI, 2024)

Re-ENACT: Reinforcement Learning for Emotional Speech Generation using Actor-Critic Strategy. - Ravi Shankar; Archana Venkataraman (CoRR, 2024)

BUNDL: Bayesian Uncertainty-aware Deep Learning with Noisy training Labels for Seizure Detection in EEG. - Deeksha M. Shama; Archana Venkataraman (CoRR, 2024)

Editorial for the Special Issue on the 2022 Medical Imaging with Deep Learning Conference. - Shadi Albarqouni; Christian F. Baumgartner; Qi Dou 001; Ender Konukoglu; Bjoern H. Menze; Archana Venkataraman (Medical Image Anal., 2024)

BiSCoT: Behavior-Informed Subgroup-Consistent Connectome Template for Interpretable Brain Network Analysis. - Zijian Chen; Stefen Beeler-Duden; Sophie Lawson; Zachary J. Jacokes; John Darrell Van Horn; Kevin A. Pelphrey; Archana Venkataraman (MICCAI (12), 2025)

LLM-Powered Cross-Modal Alignment for Explainable Seizure Detection from EEG. - Maryam Riazi; Deeksha M. Shama; Archana Venkataraman (MICCAI (14), 2025)

Learning Explainable Imaging-Genetics Associations Related to a Neurological Disorder. - Jueqi Wang; Zachary J. Jacokes; John Darrell Van Horn; Michael C. Schatz; Kevin A. Pelphrey; Archana Venkataraman (MICCAI (11), 2025)