About the Lab
AI for Science, Grounded in Rigor
AppliedAI-Lab is a research group dedicated to applying AI to high-impact scientific domains — molecular bioinformatics, time series analysis, and materials design — producing rigorous research and open tools that accelerate discovery.
Applying AI Where It Matters Most
We apply state-of-the-art machine learning to three interconnected scientific domains: AI for Bioinformatics, AI for Time Series, and AI for Materials. Each area is driven by real scientific challenges — molecular drug discovery, temporal pattern understanding, and functional material design.
Our approach combines deep learning methodology with domain expertise. We publish at premier AI venues, release production-quality open-source software, and collaborate closely with domain scientists in biology, chemistry, and engineering to ensure our models address genuine scientific needs.
Accelerating Discovery Across Science
We envision a future where generative AI routinely assists scientists in navigating vast chemical spaces, understanding complex temporal signals, and designing materials with precisely targeted properties — shortening discovery cycles from years to months.
Our long-term vision is to be the go-to research lab for AI-driven scientific discovery — a place where the best machine learning meets the hardest domain problems, and where open publication and open-source tooling accelerate progress for everyone.
What Drives Us
Six principles that guide how we think, collaborate, and do science.
Scientific Rigor
We hold our work to the highest standards of reproducibility, transparency, and methodological soundness.
Open Science
We believe knowledge should be shared. We release code, models, and datasets to benefit the broader community.
Bold Curiosity
We encourage researchers to pursue ambitious, unconventional ideas with the freedom to fail and learn.
Collaborative Culture
Our best work happens at the intersection of disciplines. We actively cultivate cross-domain collaboration.
Education First
We invest deeply in mentoring the next generation of AI researchers through hands-on guidance and scholarship.
Responsible Impact
We consider the societal implications of our work from day one and actively research AI safety and fairness.
Our Journey
From a small team with a big idea to a focused research group advancing AI for bioinformatics, time series, and materials.
Lab Founded
AppliedAI-Lab established with a founding team of 4 researchers and a seed grant, with an initial focus on machine learning for scientific applications.
First Major Publication
Published foundational work on graph-based molecular property prediction at NeurIPS, signaling our direction toward AI for bioinformatics.
Bioinformatics Direction Crystallized
Launched the molecular design research thread. First collaboration with a pharmaceutical research institute on structure-based drug discovery.
Time Series Group Established
Dr. Aisha Patel joined to lead a dedicated time series research effort, focusing on forecasting and anomaly detection for industrial sensor data.
Open-Source Release: MolDiff
Released MolDiff, our diffusion-based molecular generation framework, to the community. Adopted by research groups worldwide.
Materials Research Launched
Awarded a research grant to apply AI to functional material design. Dr. Priya Nair joined as Materials Research Lead.
Foundation Models for Time Series
Launched the TSFounder pre-training initiative. First paper on visual representation-based forecasting received an ICML Best Paper Award.
30 Researchers Milestone
Crossed 30 active researchers across three groups. Published 40+ papers. First results from ThermoShield greenhouse materials project.
100+ Publications
Reached 100 cumulative peer-reviewed publications. NeurIPS 2024 paper on scaffold-conditioned diffusion. StealthMat project launched with defense partners.
Join Us in Advancing AI for Science
We welcome PhD candidates, postdoctoral researchers, and industry collaborators passionate about applying AI to bioinformatics, time series, and materials science.
We offer competitive stipends, world-class mentorship, and access to cutting-edge compute infrastructure.