Area of research
Biomedical Engineering · Materials Chemistry
Research interest
Research interests include Computer science, Chemistry, Nanotechnology, Process engineering, Artificial intelligence, and Retrosynthetic analysis.
Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules
Developing Pharmaceutically Relevant Pd-Catalyzed C–N Coupling Reactivity Models Leveraging High-Throughput Experimentation
Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis
Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions
Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back
Machine learning for predicting the viscosity of binary liquid mixtures
Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure
Opportunities for Machine Learning and Artificial Intelligence to Advance Synthetic Drug Substance Process Development
Physics‐informed Transfer Learning for Out‐of‐sample Vapor Pressure Predictions
Generative models for molecular discovery: Recent advances and challenges
Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform
Machine-Learning-Guided Discovery of Electrochemical Reactions
Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids
Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach
The Open Reaction Database
Toward Machine Learning-Enhanced High-Throughput Experimentation
Ready, Set, Flow! Automated Continuous Synthesis and Optimization
Automated Chemical Reaction Extraction from Scientific Literature
Microfluidic electrochemistry for single-electron transfer redox-neutral reactions
Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis
Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors
Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening
Evaluating and clustering retrosynthesis pathways with learned strategy
Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning
Continuous Production of Five Active Pharmaceutical Ingredients in Flexible Plug-and-Play Modules: A Demonstration Campaign
A Multifunctional Microfluidic Platform for High‐Throughput Experimentation of Electroorganic Chemistry
Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning
Multitask prediction of site selectivity in aromatic C–H functionalization reactions
Analyzing Learned Molecular Representations for Property Prediction
A robotic platform for flow synthesis of organic compounds informed by AI planning