← Leaderboards
Zongqi Xia
University Of Pittsburgh At Pittsburgh
$6,092,494
Attributed
$6,092,494
Total exposure
3
Grants
3
Lead (contact PI)
Attributed= this PI's even-split share of every grant they're on (the fair, additive number). Exposure = full size of all those grants. They are the sole PI on all grants (the two match).
Funding over time
peak $1.1M · FY2012–25$2M$1.5M$1M$500K$0
'12
'13
'14
'15
'16
'17
'18
'19
'20
'21
'22
'23
'24
'25
Funding mix
By agency
NIH$6,092,494 · 3
By mechanism
R01$5,135,882 · 2
K08$956,612 · 1
Top collaborators
No co-investigators on record.
Most similar at University Of Pittsburgh At Pittsburgh
Same institution · by research overlap
- Daniel B. Campbell$2,402,655
- Carlos Cruchaga$41,433,350
- Douglas Jay Perkins$17,165,936
- Ava M. Puccio$3,798,500
- David Alan Greenberg$16,853,059
Others in their field
Top investigators on “Cohort”
- David R. Weir · University Of Michigan At Ann Arbor$406,814,098
- George R Seage · Abt Associates, Inc.$297,656,361
- Eric Jeffrey Topol · Cleveland Clinic Lerner Col/Med-Cwru$242,146,554
- Glenda E Gray · Wits Health Consortium (Pty), Ltd$227,738,417
- Lawrence Corey · Fred Hutchinson Cancer Center$227,738,417
- James Dennis Neaton · Northwestern University$203,539,316
Research focus
CohortNervous System DisorderMultiple SclerosisHealthcare SystemsPharmaceutical PreparationsPhenotypeNeurologicCostRegistriesRelapseDisabilityCollaborationsElectronic Health RecordMultiple Sclerosis PatientClinical ManagementNational Institute Of Neurological Disorders And StrokeMissionSocioeconomicsPredictive ModelingGenomicsPreventEnsureIndividualized MedicineEffectiveness
Grant awards (17)
Leveraging electronic health records to optimize treatment selection and response in multiple sclerosis$560,643
R01 · FY2025 · NS · contact PI
Leveraging electronic health records to optimize treatment selection and response in multiple sclerosis$595,341
R01 · FY2024 · NS · contact PI
Real-world impact of the COVID-19 pandemic in people with multiple sclerosis$386,508
R01 · FY2024 · NS · contact PI
Leveraging electronic health records to optimize treatment selection and response in multiple sclerosis$678,220
R01 · FY2023 · NS · contact PI
Real-world impact of the COVID-19 pandemic in people with multiple sclerosis$398,445
R01 · FY2023 · NS · contact PI
Real-world impact of the COVID-19 pandemic in people with multiple sclerosis$410,128
R01 · FY2022 · NS · contact PI
Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response$407,317
R01 · FY2020 · NS · contact PI
Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response$340,616
R01 · FY2020 · NS · contact PI
Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response$298,872
R01 · FY2019 · NS · contact PI
Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response$344,656
R01 · FY2018 · NS · contact PI
Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response$344,715
R01 · FY2017 · NS · contact PI
Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response$370,421
R01 · FY2016 · NS · contact PI
Leveraging genetics and environment to predict presymptomatic multiple sclerosis$182,468
K08 · FY2016 · NS · contact PI
Leveraging genetics and environment to predict presymptomatic multiple sclerosis$193,536
K08 · FY2015 · NS · contact PI
Leveraging genetics and environment to predict presymptomatic multiple sclerosis$193,536
K08 · FY2014 · NS · contact PI
Leveraging genetics and environment to predict presymptomatic multiple sclerosis$193,536
K08 · FY2013 · NS · contact PI
Leveraging genetics and environment to predict presymptomatic multiple sclerosis$193,536
K08 · FY2012 · NS · contact PI