Domain-Specific Languages for Programmable Cloud Laboratories

My current research contributes to Purdue's Intelligent Codesign Cloud Lab for Non-Equilibrium and Low-Dimensional Materials (ICoN-PCL), an NSF-funded initiative connecting materials research with programmable laboratory infrastructure. I focus on developing a domain-specific language (DSL) that gives experimental protocols a precise, compositional representation.

Using applied category theory, I model equipment and robots through interfaces that describe their observable states and admissible actions, then compose these interfaces into larger laboratory interfaces. The DSL provides an abstraction above device-specific APIs, while equipment models describe state transitions and explicit constraints capture coordination requirements.

The intended workflow lets researchers describe experiments in natural language and use an LLM to translate those descriptions into DSL protocols. A separate checker can then assess type and interface compatibility, execution feasibility within the supplied models, and explicitly encoded safety constraints before device adapters invoke the underlying APIs. These checks establish properties relative to the interfaces, models, and constraints provided; physical execution also requires validated hardware integration and runtime safeguards.

Gaussian Processes for Uncertainty Quantification of Implicit Functions

Many scientific systems exhibit non-smooth responses that behave as implicit functions defined by equilibrium conditions. Rather than modeling the equilibria directly, we proposed reconstructing the underlying energy landscape with a structure-aware GP prior. By incorporating a semi-parametric mean function for energy regularization and conditioning on equilibrium constraints, this approach automatically recovers accurate, multi-modal posterior distributions, enabling safe active learning strategies that efficiently navigate phase transitions.

Adaptable Microarray Platform for High-Throughput Bayesian Optimization

To optimize the enzymatic reaction TMB-HRP-H₂O₂ with multiple additive reagents, we developed a high-throughput BO framework tailored for adaptable microarray platforms. Addressing a mixed discrete-continuous design space characterized by modal transitions and hardware-imposed cardinality constraints, we introduced a slot-based encoding that enforces feasibility by construction. This representation streamlines the optimization of acquisition functions while preserving the surrogate model's capacity to capture the system's inherent non-smooth structure.

Bayesian Optimization for Reactive Sputtering of Superconducting Thin Films

Enhanced Surrogate Modeling for Reactive Sputtering. Benchmarked reactive sputtering meta-learning studies and fixed Gaussian Process Regression implementations that neglected inter-feature correlations. Replaced standard models with SAAS-prior GPs, demonstrating that Attentive Neural Processes (ANP) yield more reliable uncertainty estimates for downstream optimization compared to traditional methods.

Transformer-based Any-step Dynamics Model for Model-based RL

Revisiting Any-step Dynamics Models for MBRL. While reproducing an ICLR paper on Any-step Dynamics Model, I identified a discrepancy between the authors' reported error analysis and their appendix results. I hypothesized that the issue stemmed from the GRU encoder's inability to handle long-horizon dependencies effectively. I built a Transformer-based variant (TADM) that replaces the recurrent backbone with an attention mechanism. The resulting model not only corrected the error scaling behavior but also enabled parallel training , achieving state-of-the-art sample efficiency on MuJoCo benchmarks with significantly lower held-out NLL.

Characterization of LNP Drug Delivery Vehicles by Machine Learning

Physics-Informed LNP Characterization via SAXS. To address the scarcity of realistic Small-Angle X-ray Scattering (SAXS) data for Lipid Nanoparticle (LNP) characterization, we constructed a physics-based simulator that synthesizes high-fidelity scattering profiles from cryo-EM morphologies using FFT approximations. By leveraging this synthetic data to pretrain dual-task deep learning architectures before fine-tuning on limited experimental measurements, we bridged the gap between theoretical physical laws and data-driven correlations. This sim-to-real framework achieved robust generalization (R² > 0.96) on held-out data, establishing a scalable computational proxy that significantly reduces reliance on expensive electron microscopy.

NFC Device Identification Using Deep Learning and RF Fingerprinting

Robust NFC Device Identification via Domain-Adversarial Learning. Standard RF fingerprinting often fails to generalize when reader-tag geometries change, primarily due to position-induced carrier-frequency offsets (CFO) and systematic signal distortions. Instead of treating these variations as random noise, we formulated the problem as a structured domain shift and developed a deep learning pipeline operating directly on raw baseband waveforms. By integrating multi-proxy metric learning strategies, our framework explicitly learns position-invariant representations, achieving 98.4% cross-configuration accuracy and eliminating the need for handcrafted feature extraction.

Perovskite-based Optoelectronic Artificial Synaptic TFT

Physics-Informed Learning for Perovskite-based Neuromorphic Systems. Treating emerging synaptic devices merely as noisy approximations of ideal weights often leads to unstable performance. To bridge the gap between device physics and learning dynamics, we characterized the potentiation-depression dynamics of CsFAMA perovskite TFTs and integrated these hardware constraints directly into the neural network's weight-update equations. We further enhanced this device-aware pipeline by incorporating influence-based relabeling strategies (RDIA-LS) to mitigate the effect of label noise. This co-design approach aligned learning dynamics with hardware behavior, demonstrating superior robustness under extreme noise regimes (up to 80%) and leading to a publication in Nano Energy.