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Senior Technical Product Manager & Researcher, AI/ML

The University of Texas at Austin

• Own product vision and roadmap for a multi-stage deep-learning pipeline that processes whole-slide pathology images, predicts 8+ molecular biomarkers, and delivers >95% accurate pediatric brain tumor diagnosis and subtyping across several major cancer types.
• Lead 4 software engineers to ship sequential production-grade models (tile extractor, patch classifier, slide aggregator, biomarker regressor), cutting end-to-end inference time from 18 minutes to under 3 minutes per slide while boosting AUC from 0.89 to 0.97.
• Established weekly clinician feedback loops with pathologists, driving 6 major iteration cycles that reduced critical false-negative rates by 68% and achieved 92% oncologist-reported trust score.
• Translated complex neuropathology requirements into PRDs and acceptance criteria; aligned pediatric pathologists and engineering stakeholders, positioning the product for IRB approval and future clinical trial integration.

Graduate Teaching Assistant, AI/ML

The University of Texas at Austin

• Conducting office hours and tutoring sessions with students to ensure their success in AI/ML courses.
• Grading assignments and providing constructive feedback, helping students understand their strengths and areas for improvement.
• Collaboarting with faculty to improve course materials, integrating the latest research and instructional methods.
• Mentoring students and fostoring an inclusive learning environment, encouraging curiosity, critical thinking, and a passion for AI/ML.

Consulting Technical Product Manager, AI/ML (MBA Capstone)

Workday

Contributed to Workday's ML/AI product roadmap by identifying scalable solutions that reduce costs, mitigate enterprise risk, and enhance the user experience for 70M+ users globally.

• Shaped Workday’s global AI/ML product strategy by identifying and prioritizing high-impact solutions that reduced cost, lowered enterprise risk, and improved UX for 70M+ end users worldwide.
• Built and delivered a comprehensive market map of 50+ AI vendors; surfaced 8 strategic partnerships that aligned directly with Workday’s platform goals and unlocked multi-year co-development opportunities.
• Led a 6-month cross-functional research initiative with design, engineering, and exec leadership; drove iterative prioritization, synthesized insights into executive artifacts, and presented recommendations to customers and internal stakeholders, substantially increasing buy-in and accelerating Workday’s public AI roadmap.

Senior Technical Product Manager, AI/ML

UW Health

Built and scaled healthcare IT products (Best Buy Health RPM, Apple Health, Epic Systems, Azure OpenAI, UiPath RPA, Stryker, Vocera, MacOS, GenAI, AI/ML) to reduce clinician burnout, costs, enterprise risk, and improve the quality of healthcare.

• Owned and shipped a 50+ use-case GenAI roadmap on Epic EHR; successfully launched three production Azure OpenAI agents (medication reconciliation summaries, discharge instructions, patient message triage) now embedded in daily workflows of thousands of clinicians across the health system.
• Achieved sustained >90% auto-accept rates and saved >42,000 clinician hours in the first year alone by automating repetitive, high-volume, low-risk documentation tasks that previously consumed up to 40% of nursing shift time.
• Built and productionized a real-time Databricks feedback pipeline that ingested usage telemetry and qualitative signals, cutting iteration cycles from months to days and directly supplying continuous prompt, retrieval, and UX optimization.
• Led technical integrations of GenAI agents with Epic, Best Buy Health RPM, and UiPath RPA platforms while implementing rigorous regulatory compliance and guardrails.

Co-Founder of CODAmarket & Lead Technical Product Manager

CODAworx

Built and scaled a SaaS eCommerce platform that connects art buyers and commissioners with creators, redefining how artists expand their reach and monetize their artwork.

• Owned full product lifecycle for a two-sided eCommerce marketplace connecting artists with commissioners and buyers; led an 8-person cross-functional team from vision through scaled revenue growth.
• Doubled customer retention by designing and shipping self-service onboarding, in-app guidance, and personalized outreach flows informed by cohort analysis and NPS feedback.
• Redesigned core inventory, RFP, and transaction workflows using A/B testing and real-time analytics; reduced fulfillment tension by 40%+ and drove material GMV uplift.
• Established product-led growth discipline (analytics instrumentation, funnel optimization, experimentation cadence) that solidified CODAworx as the category-leading platform for public art commissions.

Technical Product Manager & Researcher

UW-Madison School of Medicine and Public Health

• Owned product strategy and roadmap for regenerative cardiac tissue therapies; defined clinical success metrics and prioritized features that turned research breakthroughs into pre-clinical assets ready for FDA pathway and commercialization.
• Shipped data and analytics products (custom Python/R, instrumentation pipelines) that cut experimental processing time 63%, boosted reproducibility scores 41%, and became the standard workflow cited by the American Heart Association and 30+ institutions.
• Led discovery with cardiologists and heart-failure patients; translated unmet needs into prioritized requirements that increased therapeutic efficacy 2.4× in pre-clinical models.
• Built a go-to-market narrative, setting the stage for grant and industry funding by linking technical milestones directly to patient outcomes and an $800M+ addressable market.

Assistant Manager

Sendik's Food Market

• Led store operations and team coordination across grocery, dairy, and frozen departments, improving stock availability and shelf compliance.
• Implemented inventory control strategies that reduced shrink and spoilage by identifying ordering inefficiencies and optimizing backstock flow.
• Trained and supervised grocery staff, enhancing customer service consistency and reducing labor turnover through team development initiatives (e.g. training courses).
• Collaborated with regional vendors and corporate buyers to execute seasonal promotions and streamline product assortments, driving category sales.

Senior Patrol Leader

Boy Scouts of America

• Elected as the highest-ranking youth leader, overseeing the entire troop’s operations of 20+ scouts, including planning, delegation, and execution of troop activities and meetings.
• Coordinated and supervised the Patrol Leaders’ Council (PLC), setting agendas and facilitating decision-making for troop-wide events, training, and progression.
• Led troop-wide communications, representing youth interests in collaboration with adult leaders to align programming with the BSA’s values and goals.
• Developed and coached emerging youth leaders, cultivating a culture of responsibility, initiative, and servant leadership across all patrols.

Assistant Manager

Grasch Foods Inc.

• Led store operations and team coordination across grocery, dairy, and frozen departments, improving stock availability and shelf compliance.
• Implemented inventory control strategies that reduced shrink and spoilage by identifying ordering inefficiencies and optimizing backstock flow.
• Trained and supervised grocery staff, enhancing customer service consistency and reducing labor turnover through team development initiatives (e.g. training courses).
• Collaborated with regional vendors and corporate buyers to execute seasonal promotions and streamline product assortments, driving category sales.

Patrol Leader

Boy Scouts of America

• Led a patrol of 10+ scouts, planning and facilitating weekly meetings, campouts, and advancement activities to ensure engagement and progression of skills.
• Served as the primary liaison between the troop leadership and patrol members, communicating responsibilities and resolving conflicts to promote team building.
• Organized patrol duties during outings, ensuring the execution of meal prep, campsite setup, and safety practices.
• Mentored younger scouts, fostering leadership, accountability, and personal growth through peer instruction and engagement.

Volunteer

Ronald McDonald House Charities

Volunteer

Boy Scouts of America

Volunteer

SSM Health

Volunteer

Madison Area Technical College

Volunteer

National Alliance on Mental Illness

Eagle Scout

National Eagle Scout Association

Volunteer

UW Health

Member

Mensa International

CTO Professional Program

The Massachusetts Institute of Technology

Information Technology

• Academic Merit Scholarship

Master of Business Administration (MBA)

Carnegie Mellon University

Healthcare, Management Science and Engineering

• David A. Tepper Academic Merit Scholarship
• Software Engineering Graduate Certificate
• Swartz Center for Entrepreneurship
• Dean's List

Master of Science (MSc)

The University of Texas at Austin

Artificial Intelligence and Applied Mathematics

• Academic Merit Scholarship
• Dean's List

Bachelor of Science (BSc)

UW-Madison School of Medicine and Public Health

Genetics

• Albert J. & Adelaide E. Riker Academic Merit Scholarship
• Spring Forward Academic Merit Scholarship
• William F. Vilas Academic Merit Scholarship
• Qualtrics Academic Merit Scholarship
• Distinctive Scholastic Achievement
• Regenerative Medicine Certificate
• Graduated in 3 Years
• Dean's List

Master of Science (MSc)

The University of Colorado, Boulder, School of Engineering

Computational Data Science

• Academic Merit Scholarship
• Dean's List

K-12, High School Diploma

Elmbrook Schools

General Education

• AP Scholar with Distinction
• Honor Roll

Eagle Scout

Boy Scouts of America

Academic Merit Scholarship

Carnegie Mellon University

Academic Merit Scholarship

The Massachusetts Institute of Technology

Academic Merit Scholarship

The University of Colorado, Boulder

Academic Merit Scholarship

University of Texas at Austin

Albert J. & Adelaide E. Riker Academic Merit Scholarship

The University of Wisconsin, Madison

Spring Forward Academic Merit Scholarship

The University of Wisconsin, Madison

William F. Vilas Academic Merit Scholarship

The University of Wisconsin, Madison

Qualtrics Academic Merit Scholarship

The University of Wisconsin, Madison

Distinctive Scholastic Achievement

The University of Wisconsin, Madison

Dean's List

Carnegie Mellon University

Dean's List

The University of Colorado, Boulder

Dean's List

The University of Texas at Austin

Dean's List

The University of Wisconsin, Madison

AP Scholar with Distinction

Elmbrook Schools

Honor Roll

Elmbrook Schools

1st Place

National Federation of Music Clubs

Exemplary Soloist

National Federation of Music Clubs

1st Place

Wisconsin Music Teacher's Association

Exemplary Soloist

Wisconsin Music Teacher's Association

1st Place

Milwaukee Music Teacher's Association

1st Place

Wisconsin State Music Association

Prompt Engineering GPT-4 to Answer Patient Inquiries: A Real-Time Implementation in the Electronic Health Record across Provider Clinics

UW-Health, Department of Medical Informatics

As LLMs evolve, the role of prompt engineering has become critical to successful deployment. The University of Wisconsin Health system is part of an Epic Systems Corp (Epic) initiative to use GPT-4 to provide healthcare providers with draft responses to patients' messages to clinicians. This study examined a pre-post design comparing crowd-sourced manual prompts to a novel semi-automated prompt design approach. We hypothesized that using a semi-automated approach to prompt engineering would increase the usability of the generated output in providers responding to patient inquiries.

Healthcare Cybersecurity in the Modern Age

Carnegie Mellon University, Department of Computer Science

Protecting patient health information is a critical priority in modern healthcare. Electronic health records (EHRs), medical devices, and digital health apps have greatly expanded the volume of sensitive data, making healthcare a prime target for cyberattacks. In the United States alone, 2023 saw 725 healthcare data breaches reported to federal authorities, exposing over 133 million patient records. Such breaches not only undermine patient privacy but also erode the trust essential to effective care. Ensuring the security of patient data requires addressing conventional cybersecurity threats—from ransomware to insider misuse—while also leveraging cutting-edge technologies. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to enhance security by detecting threats and preserving privacy. This survey provides a comprehensive review of cybersecurity as it pertains to patient data protection. We focus on the U.S. regulatory environment, including laws like HIPAA (Health Insurance Portability and Accountability Act), HITECH (Health Information Technology for Economic and Clinical Health Act), and the 21st Century Cures Act, which establish strict rules for safeguarding health data. We explore key domains of patient data security—EHR security, biometric data protection, federated learning for health data, intelligent threat detection systems, data anonymization to prevent re-identification, and secure data-sharing protocols. For each area, we examine traditional challenges (e.g., ransomware attacks, insider threats, data breaches) and highlight AI-driven solutions. We also analyze important ML techniques such as anomaly detection, adversarial machine learning, and privacy-preserving ML, discussing how they apply to healthcare security. Throughout, we address legal and ethical considerations for patient data security in the U.S., ensuring that technical measures align with regulatory requirements and patient rights. The goal is to provide a structured academic overview of the state of cybersecurity for patient data and how AI/ML innovations are helping protect healthcare information in an evolving threat landscape.

A Machine Learning Approach Toward the Detection of Lung Cancer

The University of Texas at Austin, Department of Computer Science

Lung cancer remains the leading cause of cancer-related mortality worldwide, yet early detection significantly improves patient survival rates. This project investigates whether a machine learning model can estimate lung cancer risk based on a brief survey capturing key demographic, lifestyle, and symptom-related factors. Using a public synthetic dataset of approximately 300 individuals with 15 features (including age, gender, smoking history, and binary indicators for various symptoms and habits), we developed and evaluated two classifiers: logistic regression and random forest. Model development involved data preprocessing, feature engineering, and hyperparameter tuning via cross-validation. On a held-out test set, both models achieved high performance, with accuracy ranging from 82% to 89% and ROC AUC scores near 0.95. Analysis revealed that respiratory symptoms and alcohol consumption were among the strongest predictors of lung cancer risk, consistent with epidemiological evidence. To enhance interpretability, we applied SHAP (SHapley Additive Explanations) to assess feature contributions for individual predictions, confirming that the models' decision patterns aligned with clinical capabilities. While the dataset and scope limit direct clinical application, the findings highlight the potential of lightweight, survey-based predictive tools for early lung cancer risk screening.

The Evolution of Biological Machine Learning

Carnegie Mellon University, Department of Computer Science

Biological Machine Learning (BioML) has evolved over the past several years, and new models have dramatically advanced genomics, proteomics, metabolomics, systems biology, drug discovery, and clinical diagnostics. This survey provides an in-depth overview of BioML models developed, focusing on research breakthroughs and performance benchmarks. We categorize approaches by learning paradigm (supervised, unsupervised, transfer, self-supervised, and generative models) and by application domain. In genomics, deep learning—especially transformer-based architectures—now analyzes DNA regulatory code and variant effects with remarkable accuracy. In proteomics, research breakthroughs like AlphaFold have achieved near-experimental accuracy in protein 3D structural prediction, while large protein language models learn functional properties from sequence data. Metabolomics and systems biology are leveraging AI to integrate high-dimensional -omics data, improving the classification of diseases. Drug discovery has utilized these technologies for molecular property prediction, de-novo drug design, and protein--ligand docking, reaching state-of-the-art results (e.g. diffusion models that outperform traditional virtual screening). In clinical diagnostics, these models rival expert accuracy in detecting diseases from genomic and imaging data. We discuss and compare leading models in each domain, highlight their performance improvements over prior methods, and organize them into a coherent taxonomy; as a part of this, limitations are examined, along with future directions.

Rates of Transient Hypoparathyroidism Post-Thyroidectomy - It is All in the Definition

UW-Health, Department of Surgery

Transient hypoparathyroidism is the most common complication after total thyroidectomy, but unfortunately there is no single accepted definition for this postoperative complication. The lack of a universal definition makes it impossible to compare outcomes across institutions or registries. Although institution specific incidences can be reported to patients in select cases, this is not unanimously applicable. This is problematic because patient education and expectations regarding the true incidence of this common complication are unclear which creates deficiencies in the consent process. As a result of the variability in the definition of postoperative transient hypoparathyroidism, we see inconsistent treatment protocols, potentially delayed hospital discharges and increased healthcare costs. It is difficult to establish best practices when we are not all measuring the same outcome.

Epigenetic Priming of Human Pluripotent Stem Cell-Derived Cardiac Progenitor Cells Accelerates Cardiomyocyte Maturation

Oxford Academic

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) exhibit a fetal phenotype that limits in vitro and therapeutic applications. Strategies to promote cardiomyocyte maturation have focused interventions on differentiated hPSC-CMs, but this study tests priming of early cardiac progenitor cells (CPCs) with polyinosinic-polycytidylic acid (pIC) to accelerate cardiomyocyte maturation. CPCs were differentiated from hPSCs using a monolayer differentiation protocol with defined small molecule Wnt temporal modulation, and pIC was added during the formation of early CPCs. pIC priming did not alter the expression of cell surface markers for CPCs, expression of common cardiac transcription factors, or final purity of differentiated hPSC-CMs. However, CPC differentiation in basal medium revealed that pIC priming resulted in hPSC-CMs with enhanced maturity manifested by increased cell size, greater contractility, faster electrical upstrokes, increased oxidative metabolism, and more mature sarcomeric structure and composition. To investigate the mechanisms of CPC priming, RNAseq revealed that cardiac progenitor-stage pIC modulated early Notch signaling and cardiomyogenic transcriptional programs. Chromatin immunoprecipitation of CPCs showed that pIC treatment increased deposition of the H3K9ac activating epigenetic mark at core promoters of cardiac myofilament genes and the Notch ligand, JAG1. Inhibition of Notch signaling blocked the effects of pIC on differentiation and cardiomyocyte maturation. Furthermore, primed CPCs showed more robust formation of hPSC-CMs grafts when transplanted to the NSGW mouse kidney capsule. Overall, epigenetic modulation of CPCs with pIC accelerates cardiomyocyte maturation enabling basic research applications and potential therapeutic uses.

Abstract 17323: Polyinosinic-Polycytidylic Acid Primes Cardiac Progenitors From Human Induced Pluripotent Stem Cells for Enhanced Cell Therapy and Cardiomyocyte Maturation

American Heart Association

Cardiomyocytes derived from human induced pluripotent stem cells (hiPS-CMs) hold promise for disease modeling, drug discovery, and therapy, but the challenge remains to create mature cardiomyocytes like those found in the adult heart. While groups have increased the maturity of hiPS-CMs in extended culture with electrical, metabolic, and mechanical stimulation, we hypothesized that epigenetic modulation during the formation of cardiac progenitors (hiPS-CPCs) could enhance their capacity to form mature CMs. We found that priming with the innate immune agonist polyinosinic-polycytidylic acid (pIC) decreased cardiac lineage-HDAC expression during the formation of hiPS-CPCs in defined small molecule monolayer differentiation. While both untreated and primed day 5 hiPS-CPCs contained equivalent >80% purity of KDR+PDGRF alpha + CPC populations, gene expression studies using RNAseq demonstrated that pIC priming enhanced the early cardiogenic and Notch signaling programs. When both groups were differentiated in basal media, primed hiPS-CPCs gave rise to more mature cardiomyocytes based on larger cell size, increased optical action potential upstroke velocity, greater oxidative metabolism, enhanced sarcomere maturation, and upregulated transcriptional markers of CM maturation including cTnI, cardiac actin, and alpha MHC. These maturation effects of pIC treatment were blocked by the Notch inhibitor DAPT. Most impressively, primed hiPS-CPCs improved survival as well as myocardial systolic/diastolic function in a mouse model of myocardial infarction.

CODAmarket

An e-commerce marketplace for public artists and enthusiasts.

• E-Commerce
• Commissioned Art

Gallify

An AR/VR art marketplace built for digital creators and enthusiasts.

• Augmented Reality
• Computer Vision
• Blockchain
• E-Commerce