Publications

Book Chapters and Volumes

  1. N.S. D’Souza and A. Venkataraman. Network Comparisons for Connectomics. Book chapter under review for Connectomics Analysis, Eds. M.D. Schirmer, A. Wern Chung, T. Arichi, Elsevier Academic Press, In Press, 2022.
  2. M.D. Schirmer, A. Venkataraman, I. Rekik, M. Kim, A. Wern Chung (Eds.). Connectomics in Neuroimaging: MICCAI Workshops. ShenZhen, China, 2019.
  3. A. Venkataraman. Autism Spectrum Disorders: Unbiased Functional Connectomics Provide New Insights into a Multifaceted Neurodevelopmental Disorder. Connectomics: Methods, Mathematical Models and Applications, Eds. B. Munsel, G. Wu, P. Laurienti, Elsevier Academic Press, 2018.
  4. T. Schultz, G. Nedjati-Gilani, A. Venkataraman, L. O’Donnell, E. Panagiotaki (Eds.). Computational Diffusion MRI and Brain Connectivity: MICCAI Workshops. Nagoya, Japan, 2014.

Submitted Papers Under Review or Revision

  1. R. Shankar and A. Venkataraman. Adaptive Rhythm Modification of Speech using Masked Convolutional Networks and Open-Loop Time Warping. Under Review for Interspeech, 2022.
  2. S. Pati et al. (>50 authors). Federated Learning Enables Big Data for Rare Cancer Boundary Detection. Under Review for Nature Medicine, 2022.
  3. D. Currey, J. Craley, D. Hsu, R. Ahmed, A. Venkataraman. EPViz: A Flexible and Lightweight Visualizer to Facilitate Predictive Modeling for Multi-channel EEG. Under Review for Human Brain Mapping, 2022.
  4. N. Nandakumar, D. Hsu, R. Ahmed, A. Venkataraman. DeepSOZ: A Graph Convolutional Network for Automated Seizure Onset Zone Localization from Resting-State fMRI Connectivity. Under Review for IEEE Transactions on Biomedical Engineering, 2022.
  5. R. Shankar, H.-W. Hsieh, N. Charon, A. Venkataraman. A Diffeomorphic Flow-based Variational Framework for Multi-speaker Emotion Conversion. Under Revision for IEEE Trans on Audio, Speech, and Language Processing, 2022.
  6. B. Tang, Y. Zhao, A. Venkataraman, K. Tsapkini, M. Lindquist, J. Pekar, B. Caffo. Changes in Functional Connectivity after Transcranial Direct-Current Stimulation: A Connectivity Density Point of View. Under Rev for Human Brain Mapping, 2022.

Working Papers and Unpublished Preprints

  1. A. Somayazulu*, R. Shankar* and A. Venkataraman. A Comprehensive Study of Augmentation Techniques for Deep-Learning based Speech Emotion Recognition. In Preparation for ICASSP, 2022.
    * Joint first authorship

Journal Articles

  1. J. Craley, C. Jouny, E. Johnson, R. Ahmed, D. Hsu, A. Venkataraman. Automated Seizure Activity Tracking and Onset Zone Localization from Scalp EEG using Deep Neural Networks. PLoS One, 17(2): e0264537, 2022.
  2. N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman. Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectivity for Multidimensional Clinical Characterizations. NeuroImage, 241:118388, 2021.
  3. Y. Kobayashi*, A. Bukowski*, S. Das*, N. Wagle, S. Bakshi, M. Saha, J. Kaltschmidt+, A. Venkataraman+, S. Kulkarni+. COUNTEN, an AI-Driven Tool for Rapid and Objective Structural Analyses of the Enteric Nervous System. eNeuro, 8(4):1-6, 2021.
    * Joint first authorship     +Joint senior authorship
  4. N. Nandakumar, K. Manzoor, S. Agarwal, J. Pillai, S. Gujar, H. Sair, A. Venkataraman. Automated Eloquent Cortex Localization in Brain Tumor Patients Using Multi-task Graph Neural Networks. Medical Image Analysis (MedIA), 74:102203, 2021.
  5. 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. A Generative Discriminative Framework that Integrates Imaging, Genetic, and Diagnosis into Coupled Low Dimensional Space. NeuroImage: 238:118200, 2021.
  6. 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. Neuropsychiatric Disease Classification Using Functional Connectomics — Results of the Connectomics in NeuroImaging Transfer Learning Challenge, Medical Image Analysis (MedIA), 70:101972, 2021.
  7. J. Craley, C. Jouny, E. Johnson, A. Venkataraman. Automated Inter-Patient Seizure Detection Using Multichannel Convolutional and Recurrent Neural Networks. Journal of Biomedical Signal Processing and Control, 64:102360, 2021 (Online Access 2020).
  8. 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. Determining Thresholds for Three Indices of Autoregulation to Identify the Lower Limit of Autoregulation During Cardiac Surgery. Journal of Critical Care Medicine, 49(4):650-660, 2021 (Online Access 2020).
  9. N.S. D’Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman. A Joint Network Optimization Framework to Predict Clinical Severity from Resting State fMRI Data. NeuroImage, 206:116314, 2020.
  10. J. Craley, E. Johnson, A. Venkataraman. A Spatio-Temporal Model of Seizure Propagation in Focal Epilepsy. IEEE Transactions on Medical Imaging, 39(5):1404-1418, 2020 (Online Access 2019).
  11. D. Rangaprakash, M.N. Dretsch, A. Venkataraman, J.S. Katz, T.S. Denney Jr., G. Deshpande. Identifying Disease Foci from Static and Dynamic Effective Connectivity Networks: Illustration in Soldiers with Trauma. Human Brain Mapping, 39(1):264-287, 2018.
  12. S. Zhao, D. Rangaprakash, A. Venkataraman, P. Liang, G. Deshpande. Investigating Focal Connectivity Deficits in Alzheimer’s Disease using Directional Brain Networks Derived from Resting-State fMRI. Frontiers on Aging Neuroscience, 9:1-12, 2017.
  13. S. van Noordt, J. Wu, A. Venkataraman, M.J. Larson, M. South, M.J. Crowley. Inter-trial Coherence of Medial Frontal Theta Oscillations Linked to Differential Feedback Processing in High-Functioning Autism. Research in Autism Spectrum Disorders, 37:1-10, 2017.
  14. A. Venkataraman, D. Yang, N. Dvornek, L.H. Staib, J.S. Duncan, K.A. Pelphrey, P. Ventola. Pivotal Response Treatment Prompts a Functional Rewiring of the Brain Among Individuals with Autism Spectrum Disorder. NeuroReport, 27(14):1081-1085, 2016.
  15. 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. Brain Responses to Biological Motion Predict Treatment Outcome in Young Children with Autism. Translational Psychiatry, 6(11):e948 2016.
  16. A. Venkataraman, D. Yang, K.A. Pelphrey, J.S. Duncan. Bayesian Community Detection in the Space of Group-Level Functional Differences. IEEE Transactions Medical Imaging, 35(8):1866-1882, 2016.
  17. A. Venkataraman, J.S. Duncan, D. Yang, K.A. Pelphrey. An Unbiased Bayesian Approach to Functional Connectomics Implicates Social-Communication Networks in Autism. NeuroImage Clinical, 8:356-366, 2015.
  18. A. Venkataraman, M. Kubicki, P. Golland. From Brain Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder. IEEE Transactions on Medical Imaging, 32(11):2078-2098, 2013.
  19. A. Venkataraman, T.J. Whitford, C-F. Westin, P. Golland, M. Kubicki. Whole Brain Resting State Functional Connectivity Abnormalities in Schizophrenia. Schizophrenia Research, 139(1-3):7-12, 2012.
  20. A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin, P. Golland. Joint Modeling of Anatomical and Functional Connectivity for Population Studies. IEEE Transactions on Medical Imaging, 31(2):164-182, 2012.
  21. K.R.A. Van Dijk, T. Hedden, A. Venkataraman, K.C. Evans, S.W. Lazar, R.L. Buckner. Intrinsic Functional Connectivity As a Tool For Human Connectomics: Theory, Properties, and Optimization. J Neurophysiology, 103(1):297-321, 2010.

Peer-Reviewed Conference Papers

  1. N. Nandakumar, K. Manzoor, S. Agarwal, H. Sair, A. Venkataraman. RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis. To Appear in Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022. [Acceptance Rate ≈ 30%] Selected for Early Acceptance (Top 13% of Submissions)
  2. J. Craley, E. Johnson, C. Jouny, D. Hsu, R. Ahmed, A. Venkataraman. SZLoc: A Multi-resolution Architecture fo Automated Epileptic Seizure Localization from Scalp EEG. To Appear in Medical Imaging with Deep Learning (MIDL), 2022.
  3. S. Ghosal, Q. Chen, G. Pergola, A.L. Goldman, W. Ulrich, D.R. Weinberger, A. Venkataraman. A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Propagations and Imaging Biomarkers of Disease.  In Proc. ICLR: International Conference on Learning Representations, pp. 1-18, 2022.
    [Acceptance Rate ≈ 30%]
  4. N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman. A Matrix Auto-encoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes. In Proc. MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 12907:625-636, 2021. [Acceptance Rate ≈ 30%]
  5. N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman. M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations. In Proc. MIDL: Medical Imaging with Deep Learning, PMLR:1-12, 2021. Selected for a Long Oral Pres (<15% of Papers)
  6. N. Nandakumar, K. Manzoor, S. Agarwal, J. Pillai, S. Gujar, H. Sair, A. Venkataraman. A Multi-Scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize The Eloquent Cortex in Brain Tumor Patients. In Proc. IPMI: Information Processing in Medical Imaging, LNCS 12729:241-252, 2021. [Acceptance Rate ≈ 30%]
  7. Y. Peng, N.S. D’Souza, B. Bush, C. Brown, A. Venkataraman. Predicting Acute Kidney Injury via Interpretable Ensemble Learning and Attention Weighted Convoutional–Recurrent Neural Networks. In Proc. Conference on Information Sciences and Systems (CISS), pp. 1-6, 2021.
  8. D. Currey, D. Hsu, R. Ahmed, A. Venkataraman, J. Craley. Cross-Site Epileptic Seizure Detection Using Convolutional Neural Networks. In Proc. Conf on Information Sciences and Systems (CISS), pp. 1-6, 2021.
  9. 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. G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification. In Proc. SPIE, vol.11596, 2021. Selected an Oral Presentation (<15% of Papers) – Best Paper Award
  10. N. Nandakumar, N.S. D’Souza, K. Manzoor, J. Pillai, S. Gujar, S. Agarwal, H. Sair, A. Venkataraman. A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients using Both Static and Dynamic Functional Connectivity. In Proc. MLCN: MICCAI Workshop on Machine Learning for Clinical Neuroimaging, LNCS 12449:34-44, 2020.
    Selected for an Oral Presentation – Best Paper Award
  11. N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman. A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism. In Proc. MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 12267:437-447, 2020. [Acceptance Rate ≈ 30%]
  12. R. Shankar, H.-W. Hsieh, N. Charon, A. Venkataraman. Multispeaker Emotion Conversion via a Chained Encoder-Decoder-Predictor Network and Latent Variable Regularization. In Proc. Interspeech: Conference of the International Speech Communication Association, 3391-3395, 2020.
  13. R. Shankar, J. Sager, A. Venkataraman. Non-parallel Emotion Conversion using a Pair Discrimination Deep-Generative Hybrid Model. In Proc. Interspeech: Conf of the International Speech Communication Association, 3396-3400, 2020.
  14. N. Nandakumar, K. Manzoor, J. Pillai, S. Gujar, H. Sair, A. Venkataraman. A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity. In Proc. CNI: MICCAI Workshop on Connectomics in Neuroimaging, LNCS 11848:10-20, 2019.
    Selected for an Oral Presentation (<25% of Papers) – Best Paper Award
  15. R. Shankar, J. Sager, A. Venkataraman. A Multi-Speaker Emotion Morphing Model Using Highway Networks and Maximum Likelihood Objective. In Proc. Interspeech: Conference of the International Speech Communication Association, 2848-2852, 2019. Selected for an Oral Presentation (<20% of Papers)
  16. J. Sager, J. Reinhold, R. Shankar, A. Venkataraman. VESUS: A Crowd-Annotated Database to Study Emotion Production and Perception in Spoken English. In Proc. Interspeech: Conf of the International Speech Communication Association, 316-320, 2019. Selected for an Oral Presentation (<20% of Papers)
  17. R. Shankar, H.-W. Hsieh, N. Charon, A. Venkataraman}. Automated Emotion Morphing in Speech Based on Diffeomorphic Curve Registration and Highway Networks. In Proc. Interspeech: Conference of the International Speech Communication Association, 4499-4503, 2019.
  18. R. Shankar and A. Venkataraman. Weakly Supervised Syllable Segmentation by Vowel-Consonant Peak Classification. In Proc. Interspeech: Conf of the Intl Speech Communication Association, 644-648, 2019.
  19. J. Craley, C. Jouny, E. Johnson, A. Venkataraman. Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks. In Proc. MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 11767:253-262, 2019. [Acceptance Rate ≈ 30%] Selected for Early Acceptance (Top 18% of Submissions)
  20. S. Ghosal, Q. Chen, A.L. Goldman, W. Ulrich, K.F. Berman, D.R. Weinberger, V.S. Mattay, A. Venkataraman. Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 11767:647-655, 2019. [Acceptance Rate ≈ 30%]
    Selected for Early Acceptance (Top 18% of Submissions)
  21. N.S. D’Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman. Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 11766:709-717, 2019. [Acceptance Rate ≈ 30%]
  22. J. Craley, E. Johnson, A. Venkataraman. Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG. In Proc. IPMI: Information Processing in Medical Imaging, LNCS 11492:291-303, 2019. [Acceptance Rate ≈ 30%]
    Selected for an Oral Presentation (<30% of Papers)
  23. N.S. D’Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman. A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces. In Proc. IPMI: Information Processing in Medical Imaging, LNCS 11492:605-616, 2019. [Acceptance Rate ≈ 30%]
  24. S. Ghosal, Q. Chen, A.L. Goldman, W. Ulrich, D.R. Weinberger, V.S. Mattay, A. Venkataraman. A Generative-Predictive Framework to Capture Altered Brain Activity in fMRI and its Association with Genetic Risk: Application to Schizophrenia. SPIE Medical Imaging, vol. 10949, 2019.
  25. N. Nandakumar, N.S. D’Souza, J. Craley, K. Manzoor, J. Pillai, S. Gujar, H. Sair, A. Venkataraman. Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields. In Proc. CNI: MICCAI Workshop on Connectomics in Neuroimaging, LNCS 11083:88-98, 2018.
    Selected for an Oral Presentation (<25% of Papers)
  26. N.S. D’Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman. A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data. In Proc. MICCAI: Intl Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 11072:163-171, 2018. [Acceptance Rate ≈ 30%]
    Selected for Early Acceptance (Top 15% of Submissions)
  27. J. Craley, E. Johnson, A. Venkataraman. A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 11072:482-489, 2018. [Acceptance Rate ≈ 30%]
    Selected for Early Acceptance (Top 15% of Submissions)
  28. A. Venkataraman, N. Wymbs, M.B. Nebel, S. Mostofsky. A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort. In Proc CNI: MICCAI Workshop on Connectomics in Neuroimaging, LNCS 10511, pp. 60-69 2017.
    Selected for an Oral Presentation (<20% of Papers)
  29. N.C. Dvornek, D. Yang, A. Venkataraman, P. Ventola, L.H. Staib, K.A. Pelphrey, J.S. Duncan. Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging. In Proc. MICCAI Workshop on Multimodal Learning for Clin Dec Support, pp. 1-8, 2016. Selected for an Oral Presentation
  30. A. Venkataraman, D. Yang, K.A. Pelphrey, J.S. Duncan. Community Detection in the Space of Functional Abnormalities Reveals both Heightened and Reduced Brain Synchrony in Autism. In Proc. BAMBI: Bayesian and Graphical Models for Biomedical Imaging, pp. 1-12, 2015. Selected for an Oral Presentation
  31. A. Sweet*, A. Venkataraman*, S.M. Stufflebeam, H. Liu, N. Tanaka, P. Golland. Detecting Epileptic Regions Based on Global Brain Connectivity Patterns. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 8149:98-105, 2013. [Acceptance Rate ≈ 30%] Selected for an Oral Presentation (<10% of Papers)
    *Joint first authorship (equal contributions)
  32. A. Venkataraman, M. Kubicki, P. Golland. From Brain Connectivity Models to Identifying Foci of a Neurological Disorder. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 7510:697-704, 2012. [Acceptance Rate ≈ 30%]
    Selected for an Oral Presentation (<10% of Papers)
  33. A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin, P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6361:191-199, 2010. [Acceptance Rate ≈ 30%]
    Selected for an Oral Presentation (<10% of Papers)
  34. A. Venkataraman, M. Kubicki, C-F. Westin, P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis: 63-70, 2010.
  35. A. Venkataraman, K.R.A Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. In Proc. ICASSP: IEEE Conference on Acoustics, Speech and Signal Proc, 441-444, 2009.
  36. P. Golland, D. Lashkari, A. Venkataraman. Spatial Patterns and Functional Profiles for Discovering Structure in fMRI Data. In Proc. Asilomar Conf on Signals, Systems and Computers, 1402-1409, 2008.
  37. A. Venkataraman, A.V. Oppenheim, Signal Approximation using the Bilinear Transform, In Proc. ICASSP: IEEE Conference on Acoustics, Speech and Signal Processing, 3729-3732, 2008.

Conference Abstracts

  1. D. Currey, J. Craley, D. Hsu, R. Ahmed, A. Venkataraman. EPViz: A Flexible and Lightweight Visualizer to Facilitate Predictive Modeling for Multi-channel EEG. American Epilepsy Society Annual Meeting, 2021.
  2. J. Craley, C. Jouny, E. Johnson, Raheel Ahmed, David Hsu, A. Venkataraman. GraphTrack: Automated Seizure Detection and Tracking in Scalp EEG Recordings. American Epilepsy Society Annual Meeting, 2021.
  3. S. Ghosal, Qiang Chen, Giulio Pergola, Daniel Weinberger, A. Venkataraman. An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification. Asilomar Invited Session: From Neural Networks to Neural Systems: Using AI to Decode the Brain in Health and Disease, 2020.
  4. N.S. D’Souza, M.B. Nebel, N. Wymbs, S. Mostofsky, A. Venkataraman. A Joint Network Optimization Framework to Predict Clinical Severity from Resting-State Functional MRI Data. Second International Conference on Medical Imaging and Case Reports, 2019. Invited Abstract and Presentation
  5. N.S. D’Souza, M.B. Nebel, N. Wymbs, S. Mostofsky, A. Venkataraman. A Joint Network Optimization Framework to Predict Clinical Severity from Resting-State Functional Connectomics. Flux Congress, 2019.
  6. A. Venkataraman, N.S. D’Souza, M.B. Nebel, N. Wymbs, S. Mostofsky. Predicting Behavior from Resting-State fMRI Connectivity. SAND9: Stat Analysis of Neuronal Data, 2019. Selected for a Young Investigator Spotlight Presentation
  7. N.S. D’Souza, M.B. Nebel, N. Wymbs, S. Mostofsky, A. Venkataraman. A Generative-Discriminative Basis Learning Framework to Predict Autism Spectrum Disorder Severity. ISBI: International Symposium on Biomedical Imaging, 2018.
  8. N. Nandakumar, N.S. D’Souza, H. Sair, A. Venkataraman. A Modified K-Means Algorithm for Resting State FMRI Analysis of Brain Tumor Patients, As Validated by Language Localization. ISBI: Intl Symposium on Biomedical Imaging, 2018.
  9. J. Craley, E. Johnson, A. Venkataraman. Robust Seizure Detection Using Coupled Hidden Markov Models. ISBI: Intl Symposium on Biomedical Imaging, 2018.
  10. A. Venkataraman, J.S. Duncan, D. Yang, K.A. Pelphrey. Abnormal Functional Communities in Autism. IMFAR: Intl Meeting For Autism Research, 2016. Selected for an Oral Presentation (<5% of Abstracts)
  11. D. Rangaprakash, G. Deshpande, A. Venkataraman, J.S. Katz, T.S. Denney, M.N. Dretsch. Identifying Foci of Brain Disorders from Effective Connectivity Networks, ISMRM, 2016. Selected for an Honorable Mention
  12. A. Venkataraman, J.S. Duncan, D. Yang, K.A. Pelphrey. An Unbiased Bayesian Approach to Functional Connectomics Implicates Social-Communication Networks in Autism. In Proc. ISBI: International Symposium on Biomedical Imaging, 2015. Invited Abstract and Presentation
  13. S. Zhao, A. Venkataraman, P. Liang, G. Deshpande. Investigating the Role of Brain Stem in Alzheimer’s Disease using Directional Brain Networks derived from Resting State fMRI, ISMRM, 2015.
  14. A. Venkataraman, M. Kubicki, P. Golland. From Brain Connectivity Models to Identifying Foci of a Neurological Disorder. 3rd Biennial Conference on Resting State Brain Connectivity, Sept 2012.
  15. A. Venkataraman, K.R.A Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering, OHBM, June 2009.

Dissertations

  1. J. Craley. Novel Graphical Model and Neural Network Frameworks for Automated Seizure Detection, Tracking, and Localization in Focal Epilepsy. PhD Thesis. Johns Hopkins University, Baltimore MD, 2021.
  2. N.S. D’Souza. Blending Generative Models with Deep Learning for Multidimensional Phenotypic Prediction from Brain Connectivity Data. PhD Thesis. Johns Hopkins University, Baltimore MD, 2021.
  3. R. Nandkarni. Examination of the Association Between Arterial Blood Pressure Below the Lower Limit of Autoregulation and Acute Kidney Injury After Cardiac Surgery. MSE Thesis. Johns Hopkins University, Baltimore MD, 2019.
  4. A. Venkataraman. Generative Models of Brain Connectivity for Population Studies. PhD Thesis. Massachusetts Institute of Technology, Cambridge MA, 2012.
  5. A. Venkataraman. Signal Approximation Using the Bilinear Transform. MEng Thesis. Massachusetts Institute of Technology, Cambridge MA, 2007.