Pooja Rao
Pooja is a research scientist working on Science AI and Health AI
Research Areas
Authored Publications
Sort By
Semantic Representation for Agentic Self-Play in Automated Laboratories
Yves Delley
John Coller
Zachary Cutts
Alexander Daryin
Jun Kim
Oscar Donosa
Srinidhi B Seshadri
Tanner Clary
Sandy Klemm
Petar Sirkovic
arXiv (2026)
Preview abstract
This technical report introduces a lab controller system designed as the foundational infrastructure for agentic self-driving laboratories. The core challenge in modern biological research is the immense scale of the search spaces, which are too vast for traditional experimental methods. Our solution is a lab controller that bridges the semantic gap between AI reasoning and the physical wet lab by managing a high-fidelity semantic model, or digital twin, of the laboratory workcell. This live, queryable model provides four crucial services to AI agents:
Introspection: Agents can query the lab's real-time state (e.g., reagent volumes, instrument status) to inform their planning and decision-making, grounding their logic in physical reality. Simulation: Agents can safely test experimental protocols in a time-compressed, virtual environment before physical execution. This is achieved via a discrete accounting engine that validates logistical and scientific feasibility, preventing errors, resource waste, and hardware damage. Trace Persistence: The controller records a complete, multimodal history of every experiment into a research dossier. This dataset is processed by a Wet2Dry pipeline into structured trajectories for training agents via imitation learning and offline reinforcement learning, using advanced techniques to compute rewards from noisy data. Safe Execution: The controller translates agent commands into hardware actions within a multi-layered safety architecture inspired by frameworks like Safe-SDL. This uses Verified Operating Domains (ODDs) to enforce pre-verified boundaries and Control Barrier Functions (CBFs) as real-time safety filters to prevent failures and ensure safe operation.
We have validated these services through case studies demonstrating that the controller is essential for: Robust Debugging: The detailed traces allowed us to diagnose subtle physical-world failures (metamerism) that would have corrupted an AI agent's learning process. Pre-emptive Protocol Verification: The simulation service caught critical scientific and logical errors in agent-generated code before they could cause an experiment to fail. End-to-End Automation: The controller successfully orchestrated a complex, multi-instrument workflow driven entirely by a high-level AI agent command.
Looking forward, this lab controller infrastructure enables a new paradigm of research. It allows for large-scale combinatorial assays that can explore thousands of experimental conditions simultaneously. The resulting data closes the loop on agentic ideation, allowing our AI systems to learn and improve with each experimental cycle, moving beyond simple hill-climbing to sophisticated, multi-variable optimization. By providing the tools to navigate these vast search spaces, the lab controller is a foundational step toward solving grand challenges in biology. Our work is guided by a strong commitment to safety and responsible innovation, incorporating a Human-in-the-Loop (HITL) review portal that uses the digital twin for transparent oversight and an automated biosecurity gateway with state-of-the-art, function-based screening to ensure all synthesized materials are safe by design. This report outlines the what of our system, its capabilities and strategic importance, with further technical details on the how reserved for future publications.
View details
Aptamers as a sandbox for lab-in-the-loop agentic molecular discovery
Zach Cutts
John Coller
Rachel Chen
Hua Tang
Ryder Easterlin
Dina Zverinski
Petar Sirkovic
arXiv preprint arXiv:2610.24806 (2026)
Preview abstract
Despite breakthroughs in AI-driven molecular generation, existing discovery pipelines operate largely in silico, with wet-lab feedback reserved for validation rather than iterative improvement. Whether large language model (LLM)-based reasoning agents can accelerate de novo discovery by iterating with lab data remains unproven.
Here we use protein-binding aptamer design as a sandbox (1-2 week cycles, $1-$3 per candidate) to demonstrate a wet lab in a closed loop with AI systems. We tasked agentic pipelines with developing aptamer binders against α-thrombin and myoglobin across three design-test-improve cycles (∼1,000 candidates per round; ∼4,400 constructs). Statistically significant, bead-corrected on-target hit rates among new candidates rose across rounds from 0.0% in Round 1 to 26.0% in Round 2 and 42.1% in Round 3 on α-thrombin (37.1% excluding literature motif descendants), and from 10.2% in Round 1 to 3.7% in Round 2 and 33.5% in Round 3 on myoglobin. Single-clone bead-binding titrations of eight full-length α-thrombin candidates confirmed concordance with pooled sequencing enrichment (Pearson r=0.90). In these assays, literature-derived motifs engineered by the agents to reduce off-target reactivity in the pooled screen reached apparent affinities of KD=24.6--133 nM, whereas de novo scaffolds showed low-micromolar binding (KD>1 μM).
We also report how our pipelines used myoglobin binding data to exploit the G-quadruplex binder space, diagnose cross-reactivity as on-target specificity dropped from 75.0% to 19.1%, and recover by developing strategies that restored specificity to 72.4%. We demonstrate that empirical lab data is more valuable than existing computational proxies, showing how iterative experimental feedback enables AI agents to diagnose assay artifacts and refine molecular designs across rounds.
View details
UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach
Elzbieta Gruzewska
Sebastien Baur
Matthew Baugh
Sharanya Srinivas
Matthew Thompson
Pramod Rudrapatna
Michael A. Sanchez
Lawrence Z. Cai
Timothy JA Chico
Robert F Storey
Emily Maz
Umesh Telang
Shravya Shetty
Mayank Daswani
arXiv (2025)
Preview abstract
Radar technology presents untapped potential for continuous, contactless, and passive heart rate monitoring via consumer electronics like mobile phones. However the variety of available radar systems and lack of standardization means that a large new paired dataset collection is required for each radar system. This study demonstrates transfer learning between frequency-modulated continuous wave (FMCW) and impulse-radio ultra-wideband (IR-UWB) radar systems, both increasingly integrated into consumer devices. FMCW radar utilizes a continuous chirp, while IR-UWB radar employs short pulses. Our mm-wave FMCW radar operated at 60 GHz with a 5.5 GHz bandwidth (2.7 cm resolution, 3 receiving antennas [Rx]), and our IR-UWB radar at 8 GHz with a 500 MHz bandwidth (30 cm resolution, 2 Rx). Using a novel 2D+1D ResNet architecture we achieved a mean absolute error (MAE) of 0.85 bpm and a mean absolute percentage error (MAPE) of 1.42% for heart rate monitoring with FMCW radar (N=119 participants, an average of 8 hours per participant). This model maintained performance (under 5 MAE/10% MAPE) across various body positions and heart rate ranges, with a 98.9% recall. We then fine-tuned a variant of this model, trained on single-antenna and single-range bin FMCW data, using a small (N=376, avg 6 minutes per participant) IR-UWB dataset. This transfer learning approach yielded a model with MAE 4.1 bpm and MAPE 6.3% (97.5% recall), a 25% MAE reduction over the IR-UWB baseline. This demonstration of transfer learning between radar systems for heart rate monitoring has the potential to accelerate its introduction into existing consumer devices.
View details
Creating an Empirical Dermatology Dataset Through Crowdsourcing With Web Search Advertisements
Abbi Ward
Jimmy Li
Julie Wang
Sriram Lakshminarasimhan
Ashley Carrick
Jay Hartford
Pradeep Kumar S
Renee Wong
Margaret Ann Smith
Dawn Siegel
Steven Lin
Justin Ko
JAMA Network Open (2024)
Preview abstract
Importance: Health datasets from clinical sources do not reflect the breadth and diversity of disease, impacting research, medical education, and artificial intelligence tool development. Assessments of novel crowdsourcing methods to create health datasets are needed.
Objective: To evaluate if web search advertisements (ads) are effective at creating a diverse and representative dermatology image dataset.
Design, Setting, and Participants: This prospective observational survey study, conducted from March to November 2023, used Google Search ads to invite internet users in the US to contribute images of dermatology conditions with demographic and symptom information to the Skin Condition Image Network (SCIN) open access dataset. Ads were displayed against dermatology-related search queries on mobile devices, inviting contributions from adults after a digital informed consent process. Contributions were filtered for image safety and measures were taken to protect privacy. Data analysis occurred January to February 2024.
Exposure: Dermatologist condition labels as well as estimated Fitzpatrick Skin Type (eFST) and estimated Monk Skin Tone (eMST) labels.
Main Outcomes and Measures: The primary metrics of interest were the number, quality, demographic diversity, and distribution of clinical conditions in the crowdsourced contributions. Spearman rank order correlation was used for all correlation analyses, and the χ2 test was used to analyze differences between SCIN contributor demographics and the US census.
Results: In total, 5749 submissions were received, with a median of 22 (14-30) per day. Of these, 5631 (97.9%) were genuine images of dermatological conditions. Among contributors with self-reported demographic information, female contributors (1732 of 2596 contributors [66.7%]) and younger contributors (1329 of 2556 contributors [52.0%] aged <40 years) had a higher representation in the dataset compared with the US population. Of 2614 contributors who reported race and ethnicity, 852 (32.6%) reported a racial or ethnic identity other than White. Dermatologist confidence in assigning a differential diagnosis increased with the number of self-reported demographic and skin-condition–related variables (Spearman R = 0.1537; P < .001). Of 4019 contributions reporting duration since onset, 2170 (54.0%) reported onset within less than 7 days of submission. Of the 2835 contributions that could be assigned a dermatological differential diagnosis, 2523 (89.0%) were allergic, infectious, or inflammatory conditions. eFST and eMST distributions reflected the geographical origin of the dataset.
Conclusions and Relevance: The findings of this survey study suggest that search ads are effective at crowdsourcing dermatology images and could therefore be a useful method to create health datasets. The SCIN dataset bridges important gaps in the availability of images of common, short-duration skin conditions.
View details
