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Sergey A. Kryazhimskiy
University Of California, San Diego
$1,995,807
Attributed
$1,995,807
Total exposure
2
Grants
2
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 $404.9K · FY2020–25$500K$375K$250K$125K$0
'20
'21
'22
'23
'24
'25
Funding mix
By agency
NIH$1,995,807 · 2
By mechanism
R01$1,203,671 · 1
R35$792,136 · 1
Top collaborators
No co-investigators on record.
Most similar at University Of California, San Diego
Same institution · by research overlap
- Bradley S Moore$17,879,881
- Paul R Jensen$5,509,244
- Lena Gerwick$2,120,964
- Amro M Hamdoun$4,577,740
- Michael D Burkart$14,832,373
Others in their field
Other Emerging Leaders on “Whole Genome”
- Scott Topper · Broad Institute, Inc.$40,042,399
- Badri N Vardarajan · University Of Miami School Of Medicine$27,396,438
- Patricia Shifflett · Westat, Inc.$16,650,520
- Brian William Kunkle · University Of Miami School Of Medicine$15,408,032
- Leigh A Johnson · University Of North Texas Hlth Sci Ctr$14,236,023
- Hemali Phatnani · New York University School Of Medicine$13,932,565
Research focus
Whole GenomeMutationPathogenic BacteriaVirusSaccharomyces CerevisiaeGenotypeMicrobialNatural SelectionsPhenotypeStructureYeastsDrug ResistanceGeneticFitnessAffectMalignant NeoplasmsEvolutionDrug ToleranceInduced MutationInsertional MutagenesisGenetic EngineeringDependenceInsertion MutationInterest
Grant awards (6)
Empirical and Theoretical Studies of Adaptive Mutations in Microbes and Their Evolutionary Consequences$387,247
R35 · FY2025 · GM · contact PI
Empirical and Theoretical Studies of Adaptive Mutations in Microbes and Their Evolutionary Consequences$404,889
R35 · FY2024 · GM · contact PI
Characterizing variation in the local structure of fitness landscapes to assess the predictability of evolution$304,772
R01 · FY2023 · GM · contact PI
Characterizing variation in the local structure of fitness landscapes to assess the predictability of evolution$305,307
R01 · FY2022 · GM · contact PI
Characterizing variation in the local structure of fitness landscapes to assess the predictability of evolution$305,436
R01 · FY2021 · GM · contact PI
Characterizing variation in the local structure of fitness landscapes to assess the predictability of evolution$288,156
R01 · FY2020 · GM · contact PI