| Use of machine learning for behavioral distinction of autism and ADHD M Duda, R Ma, N Haber, DP Wall Translational psychiatry 6 (2), e732-e732, 2016 | 245 | 2016 |
| Flexible neural representation for physics prediction D Mrowca, C Zhuang, E Wang, N Haber, LF Fei-Fei, J Tenenbaum, ... Advances in neural information processing systems 31, 2018 | 234 | 2018 |
| Threedworld: A platform for interactive multi-modal physical simulation C Gan, J Schwartz, S Alter, D Mrowca, M Schrimpf, J Traer, J De Freitas, ... arXiv preprint arXiv:2007.04954, 2020 | 194 | 2020 |
| Effect of wearable digital intervention for improving socialization in children with autism spectrum disorder: a randomized clinical trial C Voss, J Schwartz, J Daniels, A Kline, N Haber, P Washington, Q Tariq, ... JAMA pediatrics 173 (5), 446-454, 2019 | 150 | 2019 |
| Learning to play with intrinsically-motivated, self-aware agents N Haber, D Mrowca, S Wang, LF Fei-Fei, DL Yamins Advances in neural information processing systems 31, 2018 | 123 | 2018 |
| SuperpowerGlass: a wearable aid for the at-home therapy of children with autism P Washington, C Voss, A Kline, N Haber, J Daniels, A Fazel, T De, ... Proceedings of the ACM on interactive, mobile, wearable and ubiquitous …, 2017 | 107 | 2017 |
| SuperpowerGlass: a wearable aid for the at-home therapy of children with autism P Washington, C Voss, A Kline, N Haber, J Daniels, A Fazel, T De, ... Proceedings of the ACM on interactive, mobile, wearable and ubiquitous …, 2017 | 107 | 2017 |
| A wearable social interaction aid for children with autism P Washington, C Voss, N Haber, S Tanaka, J Daniels, C Feinstein, ... Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors …, 2016 | 94 | 2016 |
| Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism S Levy, M Duda, N Haber, DP Wall Molecular autism 8, 1-17, 2017 | 91 | 2017 |
| Crowdsourced validation of a machine-learning classification system for autism and ADHD M Duda, N Haber, J Daniels, DP Wall Translational psychiatry 7 (5), e1133-e1133, 2017 | 83 | 2017 |
| Data-driven diagnostics and the potential of mobile artificial intelligence for digital therapeutic phenotyping in computational psychiatry P Washington, N Park, P Srivastava, C Voss, A Kline, M Varma, Q Tariq, ... Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 5 (8), 759-769, 2020 | 82 | 2020 |
| Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism J Daniels, JN Schwartz, C Voss, N Haber, A Fazel, A Kline, P Washington, ... NPJ digital medicine 1 (1), 32, 2018 | 74 | 2018 |
| Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism J Daniels, JN Schwartz, C Voss, N Haber, A Fazel, A Kline, P Washington, ... NPJ digital medicine 1 (1), 32, 2018 | 74 | 2018 |
| Superpower glass: delivering unobtrusive real-time social cues in wearable systems C Voss, P Washington, N Haber, A Kline, J Daniels, A Fazel, T De, ... Proceedings of the 2016 ACM International Joint Conference on Pervasive and …, 2016 | 73 | 2016 |
| Feasibility testing of a wearable behavioral aid for social learning in children with autism J Daniels, N Haber, C Voss, J Schwartz, S Tamura, A Fazel, A Kline, ... Applied clinical informatics 9 (01), 129-140, 2018 | 72 | 2018 |
| Guess What? Towards Understanding Autism from Structured Video Using Facial Affect H Kalantarian, P Washington, J Schwartz, J Daniels, N Haber, DP Wall Journal of healthcare informatics research 3, 43-66, 2019 | 55 | 2019 |
| A gamified mobile system for crowdsourcing video for autism research H Kalantarian, P Washington, J Schwartz, J Daniels, N Haber, D Wall 2018 IEEE international conference on healthcare informatics (ICHI), 350-352, 2018 | 52 | 2018 |
| Active world model learning with progress curiosity K Kim, M Sano, J De Freitas, N Haber, D Yamins International conference on machine learning, 5306-5315, 2020 | 48 | 2020 |
| Precision telemedicine through crowdsourced machine learning: testing variability of crowd workers for video-based autism feature recognition P Washington, E Leblanc, K Dunlap, Y Penev, A Kline, K Paskov, MW Sun, ... Journal of personalized medicine 10 (3), 86, 2020 | 42 | 2020 |
| Feature selection and dimension reduction of social autism data P Washington, KM Paskov, H Kalantarian, N Stockham, C Voss, A Kline, ... Pacific Symposium on Biocomputing 2020, 707-718, 2019 | 42 | 2019 |
