Prasad Kawthekar

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Bharath RamsundarDeep Forest Sciences, DeepChem, previously Computable, previously StanfordVerified email at stanford.edu
Bowen LiuAndreessen Horowitz, insitro, StanfordVerified email at cs.stanford.edu
Joe GomesUniversity of IowaVerified email at uiowa.edu
Christian KästnerCarnegie Mellon University, School of Computer ScienceVerified email at cs.cmu.edu
Pooyan JamshidiAssistant Professor of Computer Science and Engineering, University of South Carolina, ex-GoogleVerified email at cse.sc.edu
Norbert SiegmundProfessor of Computer Science, Leipzig UniversityVerified email at informatik.uni-leipzig.de
Miguel VelezGoogleVerified email at alumni.cmu.edu
Kevin TianAssistant Professor, University of Texas at AustinVerified email at cs.utexas.edu

Prasad Kawthekar

Stanford CS 2018

Verified email at stanford.edu - Homepage


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Retrosynthetic reaction prediction using neural sequence-to-sequence models B Liu, B Ramsundar, P Kawthekar, J Shi, J Gomes, Q Luu Nguyen, S Ho, ... ACS central science 3 (10), 1103-1113, 2017	546	2017
Transfer learning for improving model predictions in highly configurable software P Jamshidi, M Velez, C Kästner, N Siegmund, P Kawthekar 2017 IEEE/ACM 12th International Symposium on Software Engineering for …, 2017	133	2017
Evaluating generative models for text generation P Kawthekar, R Rewari, S Bhooshan URL https://web. stanford. edu/class/archive/cs/cs224n/cs224n 1174, 2017	20	2017
Predicting transcriptional regulatory activities with deep convolutional networks J Paggi, A Lamb, K Tian, I Hsu, PL Cedoz, P Kawthekar BioRxiv, 099879, 2017	7	2017
Sensitivity analysis for building evolving and & adaptive robotic software P Kawthekar, C Kästner Proceedings of the IJCAI Workshop on Autonomous Mobile Service Robots (WSR) 7, 2016	5	2016
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models, ACS Cent Sci. 3 (2017) 1103–1113 B Liu, B Ramsundar, P Kawthekar, J Shi, J Gomes, QL Nguyen, S Ho, ...	4
DART: Deep learning for ART M Dumonal, Y He, P Kawthekar

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