Payel Das

According to our database, Payel Das authored at least 93 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units.

Hatem Helal, Jesun Firoz, Jenna A. Bilbrey, Henry Sprueill, Kristina M. Herman, Mario Michael Krell, Tom Murray, Manuel Lopez Roldan, Mike Kraus, Ang Li, Payel Das, Sotiris S. Xantheas, Sutanay Choudhury

J. Chem. Inf. Model., March, 2024

Larimar: Large Language Models with Episodic Memory Control.

Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarath Swaminathan, Sihui Dai, Aurélie C. Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jirí Navrátil, Soham Dan, Pin-Yu Chen

CoRR, 2024

ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation.

Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang

CoRR, 2024

Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints.

Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt, Payel Das, Jie Chen, Wojciech Matusik, Mina Konakovic-Lukovic

CoRR, 2024

Structure-Informed Protein Language Model.

Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang

CoRR, 2024

2023

Physics-enhanced deep surrogates for partial differential equations.

Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson

Nat. Mac. Intell., December, 2023

The incentive gap in data work in the era of large models.

Katy Ilonka Gero, Payel Das, Pierre L. Dognin, Inkit Padhi, Prasanna Sattigeri, Kush R. Varshney

Nat. Mac. Intell., June, 2023

AI Maintenance: A Robustness Perspective.

Pin-Yu Chen, Payel Das

Computer, February, 2023

Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models.

Ioana Baldini, Chhavi Yadav, Payel Das, Kush R. Varshney

CoRR, 2023

Equivariant Few-Shot Learning from Pretrained Models.

Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Rose Driggs-Campbell, Payel Das, Lav R. Varshney

CoRR, 2023

Enhancing Protein Language Models with Structure-based Encoder and Pre-training.

Zuobai Zhang, Minghao Xu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang

CoRR, 2023

Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction.

Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang

CoRR, 2023

Reprogramming Pretrained Language Models for Protein Sequence Representation Learning.

Ria Vinod, Pin-Yu Chen, Payel Das

CoRR, 2023

Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction.

Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Impact of Positional Encoding on Length Generalization in Transformers.

Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das, Siva Reddy

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Equivariant Transfer Learning from Pretrained Models.

Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Driggs Campbell, Payel Das, Lav R. Varshney

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Reprogramming Pretrained Language Models for Antibody Sequence Infilling.

Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das

Proceedings of the International Conference on Machine Learning, 2023

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction.

Minghao Guo, Veronika Thost, Samuel W. Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik

Proceedings of the International Conference on Machine Learning, 2023

Protein Representation Learning by Geometric Structure Pretraining.

Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Direction Aware Positional and Structural Encoding for Directed Graph Neural Networks.

Yonas Sium, Georgios Kollias, Tsuyoshi Idé, Payel Das, Naoki Abe, Aurélie C. Lozano, Qi Li

Proceedings of the IEEE International Conference on Acoustics, 2023

Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models.

Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, Payel Das

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Large-scale chemical language representations capture molecular structure and properties.

Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das

Nat. Mac. Intell., December, 2022

Modified Galerkin method for Volterra-Fredholm-Hammerstein integral equations.

Payel Das, Kapil Kant, B. V. Ratish Kumar

Comput. Appl. Math., September, 2022

Active Sampling of Multiple Sources for Sequential Estimation.

Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das

IEEE Trans. Signal Process., 2022

Explaining Artificial Intelligence Generation and Creativity: Human interpretability for novel ideas and artifacts.

Payel Das, Lav R. Varshney

IEEE Signal Process. Mag., 2022

Optimizing molecules using efficient queries from property evaluations.

Samuel C. Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, Payel Das

Nat. Mach. Intell., 2022

Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators.

Jenna A. Bilbrey, Kristina M. Herman, Henry Sprueill, Sotiris S. Xantheas, Payel Das, Manuel Lopez Roldan, Mike Kraus, Hatem Helal, Sutanay Choudhury

CoRR, 2022

Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions.

Chanakya Ekbote, Moksh Jain, Payel Das, Yoshua Bengio

CoRR, 2022

Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling.

Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das

CoRR, 2022

AlphaFold Distillation for Improved Inverse Protein Folding.

Igor Melnyk, Aurélie C. Lozano, Payel Das, Vijil Chenthamarakshan

CoRR, 2022

SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data.

Ching-Yun Ko, Pin-Yu Chen, Jeet Mohapatra, Payel Das, Luca Daniel

CoRR, 2022

Causal Graphs Underlying Generative Models: Path to Learning with Limited Data.

Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam

CoRR, 2022

GT4SD: Generative Toolkit for Scientific Discovery.

Matteo Manica, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Jannis Born, Dean Clarke, Yves Gaetan Nana Teukam, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang-Han Hsu, Federico Zipoli, Oliver Schilter, Giorgio Giannone, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith

CoRR, 2022

Learning Geometrically Disentangled Representations of Protein Folding Simulations.

N. Joseph Tatro, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan, Rongjie Lai

CoRR, 2022

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework.

Vijil Chenthamarakshan, Samuel C. Hoffman, C. David Owen, Petra Lukacik, Claire Strain-Damerell, Daren Fearon, Tika R. Malla, Anthony Tumber, Christopher J. Schofield, Helen M. E. Duyvesteyn, Wanwisa Dejnirattisai, Loic Carrique, Thomas S. Walter, Gavin R. Screaton, Tetiana Matviiuk, Aleksandra Mojsilovic, Jason Crain, Martin A. Walsh, David I. Stuart, Payel Das

CoRR, 2022

Cloud-Based Real-Time Molecular Screening Platform with MolFormer.

Brian Belgodere, Vijil Chenthamarakshan, Payel Das, Pierre L. Dognin, Toby Kurien, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, Richard A. Young

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning.

Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Biological Sequence Design with GFlowNets.

Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 2022

Data-Efficient Graph Grammar Learning for Molecular Generation.

Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik

Proceedings of the Tenth International Conference on Learning Representations, 2022

Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations.

Yair Schiff, Vijil Chenthamarakshan, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das

Proceedings of the IEEE International Conference on Acoustics, 2022

Knowledge Graph Generation From Text.

Igor Melnyk, Pierre L. Dognin, Payel Das

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Fourier Representations for Black-Box Optimization over Categorical Variables.

Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model.

Samuel C. Hoffman, Vijil Chenthamarakshan, Dmitry Yu. Zubarev, Daniel P. Sanders, Payel Das

CoRR, 2021

Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination.

Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das

CoRR, 2021

Benchmarking deep generative models for diverse antibody sequence design.

Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurélie C. Lozano

CoRR, 2021

Physics-enhanced deep surrogates for PDEs.

Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson

CoRR, 2021

Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention.

Kahini Wadhawan, Payel Das, Barbara A. Han, Ilya R. Fischhoff, Adrian C. Castellanos, Arvind Varsani, Kush R. Varshney

CoRR, 2021

Do Large Scale Molecular Language Representations Capture Important Structural Information?

Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das

CoRR, 2021

Gi and Pal Scores: Deep Neural Network Generalization Statistics.

Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen

CoRR, 2021

Towards creativity characterization of generative models via group-based subset scanning.

Celia Cintas, Payel Das, Brian Quanz, Skyler Speakman, Victor Akinwande, Pin-Yu Chen

CoRR, 2021

Predicting Deep Neural Network Generalization with Perturbation Response Curves.

Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Best Arm Identification in Contaminated Stochastic Bandits.

Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Active Binary Classification of Random Fields.

Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das

Proceedings of the IEEE International Symposium on Information Theory, 2021

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design.

Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen

Proceedings of the 38th International Conference on Machine Learning, 2021

Active Estimation From Multimodal Data.

Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das

Proceedings of the IEEE International Conference on Acoustics, 2021

ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models.

Pierre L. Dognin, Inkit Padhi, Igor Melnyk, Payel Das

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Self-Progressing Robust Training.

Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Reprogramming Language Models for Molecular Representation Learning.

Ria Vinod, Pin-Yu Chen, Payel Das

CoRR, 2020

Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics.

Yair Schiff, Vijil Chenthamarakshan, Karthikeyan Natesan Ramamurthy, Payel Das

CoRR, 2020

Explaining Chemical Toxicity using Missing Features.

Kar Wai Lim, Bhanushee Sharma, Payel Das, Vijil Chenthamarakshan, Jonathan S. Dordick

CoRR, 2020

Active learning of deep surrogates for PDEs: Application to metasurface design.

Raphaël Pestourie, Youssef Mroueh, Thanh V. Nguyen, Payel Das, Steven G. Johnson

CoRR, 2020

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics.

Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu S. Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cícero Nogueira dos Santos, Pin-Yu Chen, Yi Yan Yang, Jeremy Tan, James Hedrick, Jason Crain, Aleksandra Mojsilovic

CoRR, 2020

Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models.

Vijil Chenthamarakshan, Payel Das, Inkit Padhi, Hendrik Strobelt, Kar Wai Lim, Benjamin Hoover, Samuel C. Hoffman, Aleksandra Mojsilovic

CoRR, 2020

Optimizing Mode Connectivity via Neuron Alignment.

N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Decentralized Parallel Algorithm for Training Generative Adversarial Nets.

Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models.

Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Combinatorial Black-Box Optimization with Expert Advice.

Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios, Payel Das, Samuel C. Hoffman, Troy David Loeffler, Subramanian Sankaranarayanan

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Toward a neuro-inspired creative decoder.

Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness.

Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin

Proceedings of the 8th International Conference on Learning Representations, 2020

Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets.

Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang

Proceedings of the 8th International Conference on Learning Representations, 2020

Improving Efficiency in Large-Scale Decentralized Distributed Training.

Wei Zhang, Xiaodong Cui, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, Youssef Mroueh, Alper Buyuktosunoglu, Payel Das, David S. Kung, Michael Picheny

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

DualTKB: A Dual Learning Bridge between Text and Knowledge Base.

Pierre L. Dognin, Igor Melnyk, Inkit Padhi, Cícero Nogueira dos Santos, Payel Das

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Learning Implicit Text Generation via Feature Matching.

Inkit Padhi, Pierre L. Dognin, Ke Bai, Cícero Nogueira dos Santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Projection and multi projection methods for nonlinear integral equations on the half-line.

Nilofar Nahid, Payel Das, Gnaneshwar Nelakanti

J. Comput. Appl. Math., 2019

Decentralized Parallel Algorithm for Training Generative Adversarial Nets.

Mingrui Liu, Youssef Mroueh, Wei Zhang, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das

CoRR, 2019

Toward A Neuro-inspired Creative Decoder.

Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn

CoRR, 2019

Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection.

Tom Sercu, Sebastian Gehrmann, Hendrik Strobelt, Payel Das, Inkit Padhi, Cícero Nogueira dos Santos, Kahini Wadhawan, Vijil Chenthamarakshan

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Superconvergence of Iterated Galerkin Method for a Class of Nonlinear Fredholm Integral Equations.

Payel Das, Nilofar Nahid, Gnaneshwar Nelakanti

Proceedings of the Recent Advances in Intelligent Information Systems and Applied Mathematics, 2019

2018

Discrete Legendre spectral Galerkin method for Urysohn integral equations.

Payel Das, Gnaneshwar Nelakanti, Guangqing Long

Int. J. Comput. Math., 2018

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences.

Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cícero Nogueira dos Santos, Matthew Riemer, Inkit Padhi, Vijil Chenthamarakshan, Aleksandra Mojsilovic

CoRR, 2018

Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives.

Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Neurology-as-a-Service for the Developing World.

Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels

CoRR, 2017

Automated brain state identification using graph embedding.

Hongyuan You, Adam Liska, Nathan Russell, Payel Das

Proceedings of the 2017 International Workshop on Pattern Recognition in Neuroimaging, 2017

2016

Legendre Spectral Projection Methods for Fredholm-Hammerstein Integral Equations.

Payel Das, Mitali Madhumita Sahani, Gnaneshwar Nelakanti, Guangqing Long

J. Sci. Comput., 2016

Erratum to: Discrete Legendre spectral projection methods for Fredholm-Hammerstein integral equations [J. Comput. Appl. Math 278 (2015) 293-305].

Payel Das, Gnaneshwar Nelakanti, Guangqing Long

J. Comput. Appl. Math., 2016

Corrigendum to: "Convergence analysis of discrete legendre spectral projection methods for hammerstein integral equations of mixed type" Applied Mathematics and Computation Volume 265, 15 August 2015, Pages 574-601.

Payel Das, Gnaneshwar Nelakanti

Appl. Math. Comput., 2016

2015

Discrete Legendre spectral projection methods for Fredholm-Hammerstein integral equations.

Payel Das, Gnaneshwar Nelakanti, Guangqing Long

J. Comput. Appl. Math., 2015

Convergence analysis of discrete legendre spectral projection methods for hammerstein integral equations of mixed type.

Payel Das, Gnaneshwar Nelakanti

Appl. Math. Comput., 2015

2014

Comparative study of metamodelling techniques in building energy simulation: Guidelines for practitioners.

Liesje Van Gelder, Payel Das, Hans Janssen, Staf Roels

Simul. Model. Pract. Theory, 2014

Legendre spectral projection methods for Urysohn integral equations.

Payel Das, Mitali Madhumita Sahani, Gnaneshwar Nelakanti

J. Comput. Appl. Math., 2014

2011

Modeling mutations of influenza virus with IBM Blue Gene.

Zhen Xia, Payel Das, Tien Huynh, Ajay K. Royyuru, Ruhong Zhou

IBM J. Res. Dev., 2011

2009

Free energy simulations reveal a double mutant avian H5N1 virus hemagglutinin with altered receptor binding specificity.

Payel Das, Jingyuan Li, Ajay K. Royyuru, Ruhong Zhou

J. Comput. Chem., 2009

2006

Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction.

Payel Das, Mark Moll, Hernán Stamati, Lydia E. Kavraki, Cecilia Clementi

Proc. Natl. Acad. Sci. USA, 2006