A Comprehensive Study of Augmentation Techniques for Deep-Learning based Speech Emotion Recognition
R. Shankar, A. Kenfack, A. Somayazulu, A. Venkataraman
arXiv:2211.05047 (2023) [link]

Prospective Learning: Back to the Future
Future Learning Collective (>50 authors)
arXiv:2201.07372 (2022) [link]

BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference
S. Ghosal, M. Schatz, A. Venkataraman
Under Revision for Bioinformatics (2024)

Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detection
D. Shama, A. Venkataraman
Under Review for UNSAFE: MICCAI Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (2024)

GAMing the Brain: Investigating the Cross-modal Relationships between Functional Connectivity and Structural Features using Additive Models
A. Kannan, B. Caffo, A. Venkataraman
Under Review for MLCN: MICCAI Workshop on Machine Learning in Clinical Neuroimaging (2024)

A Lesion-aware Edge-based Graph Neural Network for Predicting language ability in Patients with Post-stroke Aphasia
Z. Chen, M. Varkanitsa, P. Ishwar, J. Konrad, M. Betke, S. Kiran, A. Venkataraman
Under Review for MLCN: MICCAI Workshop on Machine Learning in Clinical Neuroimaging (2024)

A Deep Learning Framework to Characterize Noisy Labels in Epileptogenic Zone Localization using Functional Connectivity
N. Nandakumar, D. Hsu, R. Ahmed, A. Venkataraman
ISBI: International Symposium on Biomedical Imaging, pp. 1-5. (2024)[link] [selected for an oral presentation]

Prediction of Lactate Concentrations after Cardiac Surgery Using Machine Learning and Deep Learning Approaches
Y. Kobayashi*, Y. Peng*, B. Bush, E. Yu, Y.-H. Jung, L. Goeddel, G. Whitman, A. Venkataraman+, C.H. Brown+
* Joint first authorship + Joint senior authorship
Frontiers in Medicine, vol. 10, pp. 1-12 (2023)[link]

EPViz: A Flexible and Lightweight Visualizer to Facilitate Predictive Modeling for Multi-channel EEG
D. Currey, J. Craley, D. Hsu, R. Ahmed, A. Venkataraman
PLoS One, vol. 18, no. 2, pp. e0282268 (2023)[link] [software]

A Diffeomorphic Flow-based Variational Framework for Multi-speaker Emotion Conversion
R. Shankar, H.-W. Hsieh, N. Charon, A. Venkataraman 
IEEE Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 39-53 (2023 – Online Aug 2022)[link]

DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization from Resting-State fMRI Connectivity
N. Nandakumar, D. Hsu, R. Ahmed, A. Venkataraman 
IEEE Transactions on Biomedical Engineering, vol. 70, no. 1, pp. 216-227 (2023 – Online July 2022)[link]

DeepSOZ: A Robust Deep model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data
D. Shama, J. Jing, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 14227, pp. 184-194 (2023)[link] [selected for early acceptance (top 14% of submissions)]

GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer’s Disease Severity
S. Wu, A. Venkataraman, S. Ghosal
EMBS: IEEE Conference on Engineering in Medicine and Biology, pp. 1-7 (2023)[link] [selected for an oral presentation]

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds
N.S. D’Souza and A. Venkataraman
IPMI: Information Processing in Medical Imaging, LNCS 13939, pp. 53-65 (2023)[link] [selected for an oral presentation]

A Deep Learning Framework to Localize the Epileptogenic Zone from Dynamic Functional Connectivity Using a Combined Graph Convolutional and Transformer Network
N. Nandakumar, D. Hsu, R. Ahmed, A. Venkataraman
ISBI: International Symposium on Biomedical Imaging, pp. 1-5 (2023)[link]

Adaptive Duration Modification of Speech using Masked Convolutional Networks and Open-Loop Time Warping
R. Shankar and A. Venkataraman
ISCA Speech Synthesis Workshop, pp. 1-5 (2023)[link]

Fusion Approaches to Predict Post-stroke Aphasia Severity from Multimodal Neuroimaging Data
S. Chennuri, S. Lai, A. Billot, M. Varkanitsa, E.J. Braun, S.Kiran, A. Venkataraman, J. Konrad, P. Ishwar and M. Betke
ICCV Workshop on Computer Vision for Automated Medical Diagnosis, pp. 2644-2653 (2023)[link]

Changes in Functional Connectivity after Transcranial Direct-Current Stimulation: A Connectivity Density Point of View
B. Tang, Y. Zhao, A. Venkataraman, K. Tsapkini, M. Lindquist, J. Pekar, B. Caffo. 
Human Brain Mapping, vol.  44, no. 1, pp. 170-185 (2022)[link]

Federated Learning Enables Big Data for Rare Cancer Boundary Detection
S. Bakas et al. (>50 authors)
Nature Communications, vol. 13, pp. 7346 (2022)[link]

Automated Seizure Activity Tracking and Onset Zone Localization from Scalp EEG using Deep Neural Networks
J. Craley, C. Jouny, E. Johnson, R. Ahmed, D. Hsu, A. Venkataraman
PLoS One, vol. 17, no. 2, pp.  e0264537 (2022)[link]

RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis
N. Nandakumar, K. Manzoor, S. Agarwal, H. Sair, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 13431, pp. 315-325 (2022)[link] [selected for early acceptance (top 13% of submissions)]

SZLoc: A Multi-resolution Architecture for Automated Epileptic Seizure Localization from Scalp EEG
J. Craley, E. Johnson, C. Jouny, D. Hsu, R. Ahmed, A. Venkataraman
MIDL: Medical Imaging with Deep Learning, PMLR 172, pp. 261-281 (2022)[link]

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Propagations and Imaging Biomarkers of Disease
S. Ghosal, Q. Chen, G. Pergola, A.L. Goldman, W. Ulrich, D.R. Weinberger, A. Venkataraman
ICLR: International Conference on Learning Representations, pp. 1-18 (2022)[link]

Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectivity for Multidimensional Clinical Characterizations
N.S. D’Souza, M.B. Nebel, D. Crocetti, J. Robinson, N. Wymbs, S. Mostofsky, A. Venkataraman
NeuroImage, vol. 241, pp. 118388 (2021)
COUNTEN, an AI-Driven Tool for Rapid and Objective Structural Analyses of the Enteric Nervous System
Y. Kobayashi*, A. Bukowski*, S. Das*, N. Wagle, S. Bakshi, M. Saha, J. Kaltschmidt+, A. Venkataraman+, S. Kulkarni+
* Joint first authorship + Joint senior authorship
eNeuro, vol. 8, no. 4, pp. 1-6 (2021)[link]

Automated Eloquent Cortex Localization in Brain Tumor Patients Using Multi-task Graph Neural Networks
N. Nandakumar, K. Manzoor, S. Agarwal, J. Pillai, S. Gujar, H. Sair, A. Venkataraman
Medical Image Analysis, vol. 74, pp. 102203 (2021)[link]

A Generative Discriminative Framework that Integrates Imaging, Genetic, and Diagnosis into Coupled Low Dimensional Space
S. Ghosal, Q. Chen, G. Pergola, A.L. Goldman, W. Ulrich, K.F. Berman, A. Rampino, G. Blasi, L. Fazio, A. Bertolino, D.R. Weinberger, V.S. Mattay, A. Venkataraman
NeuroImage, vol. 238, pp. 118200 (2021)[link]

Neuropsychiatric Disease Classification Using  Functional Connectomics — Results of the Connectomics in NeuroImaging Transfer Learning Challenge
M.D. Schirmer, A. Venkataraman, I. Rekik, M. Kim, S. Mostofsky, M.B. Nebel, K. Rosch, K. Seymour, D. Crocetti, H. Irzan, M. Hutel, S. Ourselin, N. Marlow, A. Melbourne, E. Levchenko, S. Zhou, M. Kunda, H. Lu, N.C. Dvornek, J. Zhuang, G. Pinto, S. Samal, J.L. Bernal-Rusiel, R. Pienaar, A. Wern Chung
Medical Image Analysis, vol. 70, pp. 101972 (2021)[link]

Automated Inter-Patient Seizure Detection Using Multichannel Convolutional and Recurrent Neural Networks
J. Craley, C. Jouny, E. Johnson, A. Venkataraman
Journal of Biomedical Signal Processing and Control, vol. 64, pp. 102360 (2021 – Online 2020)[link]

Determining Thresholds for Three Indices of Autoregulation to Identify the Lower Limit of Autoregulation During Cardiac Surgery
X. Liu, K. Akiyoshi, M. Nakano, K. Brady, B. Bush, R. Nandkarni, A. Venkataraman, R.C. Koehler, J.K. Lee, C.W. Hogue, M. Czosnyka, P. Smielewski, C.H. Brown
Journal of Critical Care Medicine, vol. 49, no. 4, pp. 650-660 (2021 – Online 2020)[link]

A Matrix Auto-encoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes
N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 12907, pp. 625-636 (2021)[link]

M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations
N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman
MIDL: Medical Imaging with Deep Learning, PMLR 143, pp. 119-130 (2021)[link] [selected for a long oral presentation]

A Multi-Scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize the Eloquent Cortex in Brain Tumor Patients
N. Nandakumar, K. Manzoor, S. Agarwal, J. Pillai, S. Gujar, H. Sair, A. Venkataraman
IPMI: Information Processing in Medical Imaging, LNCS 12729, pp. 241-252 (2021)[link]

Predicting Acute Kidney Injury via Interpretable Ensemble Learning and Attention Weighted Convolutional-Recurrent Neural Networks
Y. Peng, N.S. D’Souza, B. Bush, C. Brown, A. Venkataraman
CISS: Conference on Information Sciences and Systems, pp. 1-6 (2021)[link]

Cross-Site Epileptic Seizure Detection Using Convolutional Neural Networks
D. Currey, D. Hsu, R. Ahmed, A. Venkataraman, J. Craley
CISS: Conference on Information Sciences and Systems, pp. 1-6 (2021)[link]

G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
S. Ghosal, Q. Chen, G. Pergola, A.L. Goldman, W. Ulrich, K.F. Berman, A. Rampino, G. Blasi, L. Fazio, A. Bertolino, D.R. Weinberger, V.S. Mattay, A. Venkataraman
SPIE Medical Imaging, Image Processing Conference, vol. 11596 (2021)[link] [selected for an oral presentation] [best paper award]

A Joint Network Optimization Framework to Predict Clinical Severity from Resting State fMRI Data
N.S. D’Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman
NeuroImage, vol. 206, pp. 116314 (2020)[link]

A Spatio-Temporal Model of Seizure Propagation in Focal Epilepsy
J. Craley, E. Johnson, A. Venkataraman
IEEE Transactions on Medical Imaging, vol. 39, no. 5, pp. 1404-1418 (2020 – Online 2019)[link]

A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients using Both Static and Dynamic Functional Connectivity
N. Nandakumar, N.S. D’Souza, K. Manzoor, J. Pillai, S. Gujar, S. Agarwal, H. Sair, A. Venkataraman
MLCN: MICCAI Workshop on Machine Learning for Clinical Neuroimaging, LNCS 12449, pp. 34-44 (2020)[link] [selected for an oral presentation] [best paper award]

A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism
N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 12267, pp. 437-447 (2020)[link]

Multispeaker Emotion Conversion via a Chained Encoder-Decoder-Predictor Network and Latent Variable Regularization
R. Shankar, H.-W. Hsieh, N. Charon, A. Venkataraman
Interspeech: Conference of the International Speech Communication Association, pp. 3391-3395 (2020)[link]

Non-parallel Emotion Conversion using a Pair Discrimination Deep-Generative Hybrid Model
R. Shankar, J. Sager, A. Venkataraman
Interspeech: Conference of the International Speech Communication Association, pp. 3396-3400 (2020)[link]

A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity
N. Nandakumar, K. Manzoor, J. Pillai, S. Gujar, H. Sair, A. Venkataraman
CNI: MICCAI Workshop on Connectomics in Neuroimaging, LNCS 11848, pp. 10-20 (2019)[link] [selected for an oral presentation] [best paper award]

A Multi-Speaker Emotion Morphing Model Using Highway Networks and Maximum Likelihood Objective
R. Shankar, J. Sager, A. Venkataraman
Interspeech: Conference of the International Speech Communication Association, pp. 2848-2852 (2019)[link] [selected for an oral presentation]

VESUS: A Crowd-Annotated Database to Study Emotion Production and Perception in Spoken English
J. Sager, J. Reinhold, R. Shankar, A. Venkataraman
Interspeech: Conference of the International Speech Communication Association, pp. 316-320 (2019)[link] [selected for an oral presentation]

Automated Emotion Morphing in Speech Based on Diffeomorphic Curve Registration and Highway Networks
R. Shankar, H.-W. Hsieh, N. Charon, A. Venkataraman
Interspeech: Conference of the International Speech Communication Association, pp. 4499-4503 (2019)[link]

Weakly Supervised Syllable Segmentation by Vowel-Consonant Peak Classification
R. Shankar and A. Venkataraman
Interspeech: Conference of the International Speech Communication Association, pp. 644-648 (2019)[link]

Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks
J. Craley, C. Jouny, E. Johnson, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 11767, pp. 253-262 (2019)[link] [selected for early acceptance (top 18% of submissions)]

Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework
S. Ghosal, Q. Chen, A.L. Goldman, W. Ulrich, K.F. Berman, D.R. Weinberger, V.S. Mattay, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 11767, pp. 647-655 (2019)[link] [selected for early acceptance (top 18% of submissions)]

Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data
N.S. D’Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 11766, pp. 709-717 (2019)[link]

Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG
J. Craley, E. Johnson, A. Venkataraman
IPMI: Information Processing in Medical Imaging, LNCS 11492, pp. 291-303 (2019)[link] [selected for an oral presentation]

A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces
N.S. D’Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman
IPMI: Information Processing in Medical Imaging, LNCS 11492, pp. 605-616 (2019)[link]

A Generative-Predictive Framework to Capture  Altered Brain Activity in fMRI and its Association with Genetic Risk: Application to Schizophrenia
S. Ghosal, Q. Chen, A.L. Goldman, W. Ulrich, D.R. Weinberger, V.S. Mattay, A. Venkataraman
SPIE Medical Imaging, Image Processing Conference, vol. 10949 (2019)[link]

Identifying Disease Foci from Static and Dynamic Effective Connectivity Networks: Illustration in Soldiers with Trauma
D. Rangaprakash, M.N. Dretsch, A. Venkataraman, J.S. Katz, T.S. Denney Jr., G. Deshpande
Human Brain Mapping, vol. 39, no. 1, pp. 264-287 (2018)[link]

Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields
N. Nandakumar, N.S. D’Souza, J. Craley, K. Manzoor, J. Pillai, S. Gujar, H. Sair, A. Venkataraman
CNI: MICCAI Workshop on Connectomics in Neuroimaging, LNCS 11083: 88-98 (2018)[link] [selected for an oral presentation]

A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data
N.S. D’Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 11072, pp. 163-171 (2018)[link] [selected for early acceptance (top 15% of submissions)]

A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models
J. Craley, E. Johnson, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 11072, pp. 482-489 (2018)[link] [selected for early acceptance (top 15% of submissions)]

Investigating Focal Connectivity Deficits in Alzheimer’s Disease using Directional Brain Networks Derived from Resting-State fMRI
S. Zhao, D. Rangaprakash, A. Venkataraman, P. Liang, G. Deshpande
Frontiers in Aging Neuroscience, vol. 9, pp. 1-12 (2017)[link]

Inter-trial Coherence of Medial Frontal Theta Oscillations Linked to Differential Feedback Processing in High-Functioning Autism
S. van Noordt, J. Wu, A. Venkataraman, M.J. Larson, M. South, M.J. Crowley. 
Research in Autism Spectrum Disorders, vol. 37, pp. 1-10 (2017)[link]

A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort
A. Venkataraman, N. Wymbs, M.B. Nebel, S. Mostofsky
CNI: MICCAI Workshop on Connectomics in NeuroImaging, LNCS 10511, pp. 60-69 (2017)[link] [selected for an oral presentation]

Pivotal Response Treatment Prompts a Functional Rewiring of the Brain Among Individuals with Autism Spectrum Disorder
A. Venkataraman, D. Yang, N. Dvornek, L.H. Staib, J.S. Duncan, K.A. Pelphrey, P. Ventola
NeuroReport, vol. 27, no. 14, pp. 1081-1085 (2016)[link]

Brain Responses to Biological Motion Predict Treatment Outcome in Young Children with Autism
D. Yang, K.A. Pelphrey, D.G. Sukhodolsky, M.J. Crowley, E. Dayan, N. Dvornek, A. Venkataraman, J.S Duncan, L.H. Staib, P. Ventola
Translational Psychiatry, vol. 6, no. 11, pp. E948 (2016)[link]

Bayesian Community Detection in the Space of Group-Level Functional Differences
A. Venkataraman, D. Yang, K.A. Pelphrey, J.S. Duncan
IEEE Transactions Medical Imaging, vol. 35, no. 8, pp. 1866-1882 (2016)[link]

Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging
N.C. Dvornek, D. Yang, A. Venkataraman, P. Ventola, L.H. Staib, K.A. Pelphrey, J.S. Duncan
MICCAI Workshop on Multimodal Learning for Clinical Decision Support, pp. 1-8 (2016)[link] [selected for an oral presentation]

An Unbiased Bayesian Approach to Functional Connectomics Implicates Social-Communication Networks in Autism
A. Venkataraman, J.S. Duncan, D. Yang, K.A. Pelphrey
NeuroImage: Clinical, vol. 8, pp. 356-366 (2015)[link]

Community Detection in the Space of Functional Abnormalities Reveals both Heightened and Reduced Brain Synchrony in Autism
A. Venkataraman, D. Yang, K.A. Pelphrey, J.S. Duncan
BAMBI: MICCAI Workshop on Bayesian and Graphical Models for Biomedical Imaging, pp. 1-12 (2015)[link] [selected for an oral presentationn]

From Brain Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder
A. Venkataraman, M. Kubicki, P. Golland
IEEE Transactions on Medical Imaging, vol. 32, no. 11, pp. 2078-2098 (2013)[link]

Detecting Epileptic Regions Based on Global Brain Connectivity Patterns
A. Sweet*, A. Venkataraman*,  S.M. Stufflebeam, H. Liu, N. Tanaka, P. Golland
* Joint first authorship (equal contribution)
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 8149, pp. 98-105 (2013)[link] [selected for an oral presentation]

Whole Brain Resting State Functional Connectivity Abnormalities in Schizophrenia
A. Venkataraman, T.J. Whitford, C-F. Westin, P. Golland, M. Kubicki
Schizophrenia Research, vol. 139, no. 1-3, pp. 7-12 (2012)[link]

Joint Modeling of Anatomical and Functional Connectivity for Population Studies
A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin, P. Golland
IEEE Transactions on Medical Imaging, vol. 31, no. 2, pp. 164-182 (2012)[link]

From Brain Connectivity Models to Identifying Foci of a Neurological Disorder
A. Venkataraman, M. Kubicki, P. Golland
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 7510, pp. 697-704 (2012)[link][selected for an oral presentation]

Intrinsic Functional Connectivity As a Tool For Human Connectomics: Theory, Properties, and Optimization
K.R.A. Van Dijk, T. Hedden, A. Venkataraman, K.C. Evans, S.W. Lazar, R.L. Buckner.
Journal of Neurophysiology, vol. 103, no. 1, pp. 297-321 (2010)[link]

Joint Generative Model for fMRI/DWI and its Application to Population Studies
A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin, P. Golland
MICCAI: Medical Image Computing and Computer Assisted Intervention,  LNCS 6361, pp. 191-199 (2010)[link] [selected for an oral presentation]

Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies
A. Venkataraman, M. Kubicki, C-F. Westin, P. Golland
MMBIA: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 63-70 (2010)[link]

Exploring Functional Connectivity in fMRI via Clustering
A. Venkataraman, K.R.A Van Dijk, R.L. Buckner, P. Golland
ICASSP: IEEE Conference on Acoustics, Speech and Signal Processing, pp. 441-444 (2009)

Spatial Patterns and Functional Profiles for Discovering Structure in fMRI Data
P. Golland, D. Lashkari, A. Venkataraman
Asilomar Conference on Signals, Systems and Computers, pp. 1402-1409 (2008)[link] [invited contribution]

Signal Approximation using the Bilinear Transform
A. Venkataraman, A.V. Oppenheim
ICASSP: IEEE Conference on Acoustics, Speech and Signal Processing, pp. 3729-3732 (2008)[link]

Network Comparisons and their Applications in Connectomics
N.S. D’Souza, A. Venkataraman
Connectomics Analysis, M.D. Schirmer, A. Wern Chung, T. Arichi (Eds).  Elsevier Academic Press (2023)

Connectomics in NeuroImaging
M.D. Schirmer, A. Venkataraman, I. Rekik, M. Kim, A. Wern Chung (Eds)
MICCAI Workshops, ShenZhen, China, Elsevier Academic Press (2019)

Autism Spectrum Disorders: Unbiased Functional Connectomics Provide New Insights into a Multifaceted Neurodevelopmental Disorder
A. Venkataraman
Connectomics: Methods, Mathematical Models and Applications, B. Munsel, G. Wu, P. Laurienti (Eds). Elsevier Academic Press (2018)

Computational Diffusion MRI and Brain Connectivity
T. Schultz, G. Nedjati-Gilani, A. Venkataraman, L. O’Donnell, E. Panagiotaki (Eds)
MICCAI Workshops, Nagoya, Japan, Elsevier Academic Press (2014)

A Novel Bayesian Framework for Temporal Seizure Detection from EEG given Noisy and Uncertain Training Labels
D. Shama, A. Venkataraman
AI for Epilepsy Meeting (2024)[selected for an oral presentation]

A Biologically Inspired Model to Integrate Multi-modal Imaging and Whole Genome Sequencing Data
S. Ghosal, Qiang Chen, Giulio Pergola, D. Weinberger, A. Venkataraman
OHBM: Organization of Human Brain Mapping Annual Meeting (2023)[selected for an oral presentation (<5% of abstracts)][merit award]

EPViz: A Flexible and Lightweight Visualizer to Facilitate Predictive Modeling for Multi-channel EEG
D. Currey, J. Craley, D. Hsu, R. Ahmed, A. Venkataraman
American Epilepsy Society Annual Meeting (2021)

GraphTrack: Automated Seizure Detection and Tracking in Scalp EEG Recordings
J. Craley, C. Jouny, E. Johnson, Raheel Ahmed, David Hsu, A. Venkataraman
American Epilepsy Society Annual Meeting (2021)

An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
S. Ghosal, Q. Chen, G. Pergola, D. Weinberger, A. Venkataraman
Asilomar Invited Session: From Neural Networks to Neural Systems: Using AI to Decode the Brain in Health and Disease (2020)

A Joint Network Optimization Framework to Predict Clinical Severity from Resting-State Functional MRI Data
N.S. D’Souza, M.B. Nebel, N. Wymbs, S. Mostofsky, A. Venkataraman
International Conference on Medical Imaging and Case Reports (2019)

A Joint Network Optimization Framework to Predict Clinical Severity from Resting-State Functional Connectomics
N.S. D’Souza, M.B. Nebel, N. Wymbs, S. Mostofsky, A. Venkataraman
Flux Society Congress (2019)

Predicting Behavior from Resting-State fMRI Connectivity
A. Venkataraman, N.S. D’Souza, M.B. Nebel, N. Wymbs, S. Mostofsky
SAND9: Statistical Analysis of Neuronal Data (2019)[selected for a young investigator spotlight presentation]

A Generative-Discriminative Basis Learning Framework to Predict Autism Spectrum Disorder Severity
N.S. D’Souza, M.B. Nebel, N. Wymbs, S. Mostofsky, A. Venkataraman
ISBI: International Symposium on Biomedical Imaging (2018)

A Modified K-Means Algorithm for Resting State FMRI Analysis of Brain Tumor Patients, As Validated by Language Localization
N. Nandakumar, N.S. D’Souza, H. Sair, A. Venkataraman
ISBI: International Symposium on Biomedical Imaging (2018)

Robust Seizure Detection Using Coupled Hidden Markov Models
J. Craley, E. Johnson, A. Venkataraman
ISBI: International Symposium on Biomedical Imaging (2018)

Abnormal Functional Communities in Autism
A. Venkataraman, J.S. Duncan, D. Yang, K.A. Pelphrey
IMFAR: International Meeting For Autism Research (2016)[selected for an oral presentation (<5% of abstracts)]

Identifying Foci of Brain Disorders from Effective Connectivity Networks
D. Rangaprakash, G. Deshpande, A. Venkataraman, J.S. Katz, T.S. Denney, M.N. Dretsch
ISMRM: International Society for Magnetic Resonance in Medicine (2016)[honorable mention]

An Unbiased Bayesian Approach to Functional Connectomics Implicates Social-Communication Networks in Autism
A. Venkataraman, J.S. Duncan, D. Yang, K.A. Pelphrey
ISBI: International Symposium on Biomedical Imaging (2015)

Investigating the Role of Brain Stem in Alzheimer’s Disease using Directional Brain Networks derived from Resting State fMRI
S. Zhao, A. Venkataraman, P. Liang, G. Deshpande
ISMRM: International Society for Magnetic Resonance in Medicine (2015)

From Brain Connectivity Models to Identifying Foci of a Neurological Disorder
A. Venkataraman, M. Kubicki, P. Golland
3rd Biennial Conference on Resting State Brain Connectivity (2012)

Exploring Functional Connectivity in fMRI via Clustering
A. Venkataraman, K.R.A Van Dijk, R.L. Buckner, P. Golland
OHBM: Organization of Human Brain Mapping Annual Meeting (2009)

Interpretable Machine Learning and Deep Learning Frameworks for Predictive Analytics and Biomarker Discovery from Multimodal Imaging Genetics Data
S. Ghosal
PhD Thesis. Johns Hopkins University, Baltimore MD (2023).

Manipulating Emotions: Generative Modeling of Prosody for Emotional Speech Synthesis 
R. Shankar
PhD Thesis. Johns Hopkins University, Baltimore MD (2023).

Graph Based Deep Learning Models for Analysis of Resting State fMRI with Applications in Localization and Dynamic Connectivity Analysis 
N. Nandakumar
PhD Thesis. Johns Hopkins University, Baltimore MD (2023).

Novel Graphical Model and Neural Network Frameworks for Automated Seizure Detection, Tracking, and Localization in Focal Epilepsy 
J. Craley
PhD Thesis. Johns Hopkins University, Baltimore MD (2021).

Blending Generative Models with Deep Learning for Multidimensional Phenotypic Prediction from Brain Connectivity Data
N.S. D’Souza
PhD Thesis. Johns Hopkins University, Baltimore MD (2021)

Examination of the Association Between Arterial Blood Pressure Below the Lower Limit of Autoregulation and Acute Kidney Injury After Cardiac Surgery
R. Nandkarni
MSE Thesis. Johns Hopkins University, Baltimore MD (2019)

Generative Models of Brain Connectivity for Population Studies
A. Venkataraman
PhD Thesis. Massachusetts Institute of Technology, Cambridge MA (2012)

Signal Approximation Using the Bilinear Transform
A. Venkataraman
MEng Thesis. Massachusetts Institute of Technology, Cambridge MA (2007)

archanav@bu.edu
617-353-2811