• Big Data

    Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. We use deep learning to analyze multi-modal Big Data such as genomics or clinical imaging from patients to reveal patterns, trends, and associations with disease. We strive to ultimately translate Big Data from the clinics into useful medical intelligence to impact clinical decisions.

    Precision Medicine

    Precision medicine is an innovative approach to disease prevention and treatment that takes into account individual differences in people’s genes, environments, and lifestyles. We are building precision medicine tools for doctors and their patients to better understand the complex mechanisms underlying disease and to better predict which clinical or surgical interventions will be optimally effective.

    Digital Health

    Digital health is the convergence of the digital and genomic revolutions with health, healthcare, living, and society. Digital health is empowering people to better track, manage, and improve their own and their family's health, live better, more productive lives, and improve society. We develop medical intelligence to power digital health applications in the clinics to improve the precision of medicine for doctors and their patients.

  • We Translate Big Data into Clinical Intelligence!

    Practicing medicine involves first screening patients towards making definitive diagnoses and ultimately therapeutic interventions. We leverage big data and digital health to realize precision medicine for doctors and their patients. We are building the infrastructure to collect, connect, and apply vast amounts of scientific research data and information about our health. Our goal is to understand how individuals respond differently to interventions to help guide more precise and predictive medicine worldwide.


    We develop digital health applications to improve screening for disease. We use data from mobile health apps and medical imaging to monitor large numbers of patients in the clinics. Our goal is to save lives by improving digital surveillance that can alert physicians and their patients earlier about the risks of disease.


    We develop models of medical intelligence to improve diagnosis and management of disease. We leverage deep learning technologies to translate big data into diagnostic and prognostic intelligence. Our goal is to develop medical intelligence models that perform at the level of a specialist physician.


    We mine open data to understand defective patterns of genomics disease signatures. We integrate genomic signatures of drug response to select optimal agents that directly reverse disease genomics. Our goal is to develop more personalized therapeutic options to fight disease.

  • The Search Tag Analyze Resource (STARGEO)

    is an open genomics discovery web platform to annotate digital samples housed within NCBI Gene Expression Omnibus (GEO) and to find robust genomic signatures of disease. We enlist the help of medical students without coding experience to generate genomic signatures of disease which we integrate with other large databases to discover novel biomarkers to diagnose or more optimal therapeutic interventions to treat disease. STARGEO is an NCI funded project through the NIH BD2K NIH initiative to characterize the functional genomics of disease with open data.

  • Our Research Impacts Patients.

    We develop clinical intelligence models from big data that optimally predict disease and appropriate clinical management. We train our models on big data from a variety of sources including public molecular data repositories as well as protected private electronic health information such as clinical imaging . We leverage these models to power intelligent, self-learning predictive clinical applications designed to improve their performance with use over time. We also integrate our molecular models of disease with large databases to find novel biomarkers and novel therapeutics to manage disease.

    Precision screening for skin cancer

    DIGITAL Risk assessment of melanoma

    While melanoma is the number one killer of young adults with cancer, diagnosis is both subjective and imprecise. Overdiagnosis on clinical exam remains a problem with one melanoma on average found for every 30 suspicious skin lesions sent for biopsy. We are using serial imaging to develop a real-time non-invasive approach to improve precision in melanoma screening. We enroll patients in the dermatology clinics with apps that collect images of suspicious lesions on their skin. From this data we develop deep learning models of clinical intelligence to precisely screen for skin cancer from digital images. We build this clinical intelligence back into apps to provide accurate real-time surveillance of suspicious skin lesions for cancer. Our goal is to use digital health applications to save lives through more intelligent and precise screening for skin cancer.

    Precision diagnosis for breast cancer

    Quantitative digital mammography Interpretation

    Over 250,000 women will be diagnosed with breast cancer this year alone, but at least five million will be incorrectly screened by mammography and recalled for additional testing. To realize precision oncology for breast cancer screening, we train, optimize, and validate powerful deep learning algorithms on almost 5 million digital mammograms to more accurately detect clinically significant cancer, reduce anxiety from false positive screening, and decrease morbidity of unnecessary downstream procedures. Improved precision in screening for breast oncology may help tip the balance of routine screening towards greater benefit and less harm for millions of women.

    Precision therapy for brain cancer

    Drug repositioning for Medulloblastoma

    Medulloblastoma is the most common malignant (cancerous) central nervous system tumor in children. It accounts for 15 to 20 percent of all pediatric brain tumors. Medulloblastomas occur most commonly in children between ages of 3 and 8 but can be seen in children and adults of any age. Molecular studies suggest medulloblastoma is not one disease but comprises a collection of at least four distinct molecular subgroups that differ in their clinical severity and response to chemotherapy. We are using our STARGEO genomics discovery platform to refine more precise genomic subtypes of melanoma, and we are looking for personalized therapeutics to make difficult to treat subtypes more amenable to standard chemotherapeutic regimens.

  • Executive Profiles

    We've got the best people running the show!

    Dexter Hadley,

    MD, PhD

    Principal Investigator

    Dr. Hadley focused on genomics and computational biology during his medical education at Penn. His early work focused identifying genomic risk factors for pediatric neuropsychiatric disease such as autism and ADHD. After completing medical school and a surgical internship, he led a translational bioinformatics team at the Center For Applied Genomics at CHoP where his research contributed to an ongoing precision medicine clinical trial for ADHD. He trained in pathology at Stanford where he mined the open data for biomarkers of severe dengue hemorrhagic fever infection. His research now focuses on digital health to generate, annotate and ultimately reason over big data stores to improve the precision of medicine across the disease spectrum.

    Maria Wei,

    MD, PhD

    Clinical/Scientific Collaborator

    Dr. Wei is a dermatologist who specializes in melanoma and disorders of pigmentation (acquired and inherited). She is Director of the UCSF Melanoma Surveillance clinic that sees individuals with melanoma, or those at high risk for developing melanoma such as those with a family history of melanoma, positive genetic testing showing a risk of melanoma, or those with many moles or large congenital moles. Dr. Wei leads a research laboratory that studies melanoma and melanocyte biology and how melanomas become resistant to treatment. The laboratory also studies why there is a gender disparity in melanoma outcomes, men having worse outcomes, and in addition is investigating novel biomarkers for melanoma.

    Simone Stalling,

    MD, PhD

    Clinical Collaborator

    Dr. Stalling is a dermatologist in private practice. Her background is in bioengineering from Penn where she developed artificial tissues during medical school. Dr. Stalling is actively involved in assessing the impact of digital health in dermatology practice and she regularly reviews mobile applications and new technology in her clinical practice.

  • Trainee Profiles

    We've got a top notch team behind the production!

    Jordan Spatz, PhD

    Post-doctoral fellow

    Dr. Spatz is a medical student at UCSF with a focus on practicing medicine and biomedical scientific inquiry in space. Dr. Spatz trained at MIT in bioastronautics and worked for Northrop Grumman to design aeronautical transportation. Dr. Spatz is mining the open data with STARGEO to find novel approaches to treat refractory breast cancer.

    James Pan

    Pre-doctoral student

    James is a neurosurgery bound medical student at Stanford University. He studied design at Carnigie Mellon University and performed research in neurosurgery. James' interests lie in brain cancer and he is actively using STARGEO to mine the open data to reposition drugs for medulloblastoma.

    Abhishek Bhattacharya 

    Pre-doctoral student

    Abhishek is anundergraduate honors student at UCSB majoring in Computer Science and Biology. He is a member of the prestigious College of Creative Studies at his campus which recognizes a select group of students for their excellence in academics and research. In the last few years, he has worked with leading scientists at Stanford, UCSF and UCSB to uncover the potential of big data in computational biology. He is an avid computer programmer and software engineer, and his interests lie in deep learning precision interpretation of medical imaging for the clinics. He is involved with a number of ongoing projects in skin and breast cancer quantitative image analysis.

  • We need superstars to help us do awesome work!

    We are looking for passionate and motivated candidates who think Big Data can change everything about the delivery of medicine and healthcare. We work closely with clinicians to test hypotheses that we formulate in the laboratory. To apply for any of these positions, please send your CV, a brief statement of research interests, and contact information for three references in one PDF document to dexter.hadley@ucsf.edu.

    Postdoctoral Fellows

    Our laboratory is particularly geared to recruiting combined MD, PhD candidates who seek to make a translational impact in the clinic. We are ideally looking for candidates who have already earned a MD and/or PhD degree with a strong background in bioinformatics, computational biology, biostatistics, and genomics. A great publication record is not required, but a strong desire to publish is. Strong problem-solving skills, creative thinking, and the ability to build new software tools as needed are required. Applicants must possess good communication skills and be fluent in both spoken and written English. A background in molecular biology or medicine or pharmacology will be a strong plus. Prior experience with genetic, genomic, clinical imaging, biomarker discovery, drug repositioning is recommended but not mandatory. Familiarity with machine learning, text-mining, knowledge representation, and high performance parallel computing platforms and building web and mobile applications is a definite plus.

    Medical Students

    We love med students and offer ample opportunities for them to get involved with our translational research. No coding required! We have medical students using STARGEO to estimate robust signatures of disease genomics from which we propose novel biomarkers or drug repositioning strategies for disease. We have medical students in the clinics that collect patient data with apps that feed our self-learning models of clinical intelligence. As a medical student, we will help to devise a computational project that aligns with you clinical interests. Your expected effort will be variable and contingent on your clinical commitments. We will provide you with many opportunities to publish your clinical and / or basic science research in peer-reviewed journals, and we encourage medical students to present their work at medical/scientefic conferences around the world. Finally. we offer you ample opportunities for mentoring and professional networking that may illustrate alternative careers in medicine for you to pursue that drive innovation in health care and embrace translational research.

    Predoctoral Students

    We are looking for graduate rotation students to commit 50% of their time working on our research projects. Rotating students will be provided space in our shared office-space in Mission Hall. You will meet with the PI on a bi-weekly basis. You will interact with our Post-doctoral Fellows and graduate students. You are expected to attend our weekly laboratory meetings where your will present your rotation work. You are strongly encouraged to submit a paper on your work, even though it is just a rotation projectWe will expose you to a variety of computational frameworks and techniques that include:

    • Python for open-source machine learning
    • R for open-source statistical software
    • Postgres for open-source relational database management..
  • Contact Us

    Don't be afraid to reach out. You + us = awesome.

    550 16th St
    San Francisco CA 94158