Texas A&M University College Station - Statistics
Research Scientist | Statistician
Pharmaceuticals
Amir
Nikooienejad, Ph.D.
Carmel, Indiana
I am currently a research scientist in Diabetes group at Eli Lilly and Company working on early phase clinical trials.
During my Ph.D. years, I developed significant expertise in various statistical topics including: High Dimensional Variable/Feature Selection, Bayesian Hierarchical Models, Bayesian Data Analysis, Survival Data Analysis, Linear Regression, Logistic Regression, Exploratory Data Analysis, etc. while exploiting R, Rcpp, C++ and Parallel Programming tools in clusters to implement relevant methodologies.
I am a hard worker, fast learner, enthusiast in learning new materials and an experienced computer programmer. I consider those as my strong capabilities which help me perform at my best in accomplishing any assigned job. My expertise in computer programming is rooted back to my engineering years where I got my Bachelor’s and Master’s degree in Electrical Engineering/Signal processing.
Postdoctoral Research Fellow, Department of Statistics
Amir worked at Texas A&M University as a Postdoctoral Research Fellow, Department of Statistics
Research Scientist | Clinical Statistician
Amir worked at Eli Lilly and Company as a Research Scientist | Clinical Statistician
Statistics Intern in Oncology
I worked as statistics intern at Lilly Corporate Center in the GSS group under the oncology department, working on missing data recovery in real world evidence data.
I used Bayesian multiple imputation technique to perform missing data recovery which enhanced the performance of survival data analysis compared to traditional methods.
Course Instructor
Instructor for the undergraduate course, STAT 301, in the Department of Statistics in TAMU. This course covers various topics in statistics from introduction to statistics to test of hypothesis, sampling methods, inference and one way ANOVA.
Graduate Research Assistant
Amir worked at Texas A&M University - Statistics Department as a Graduate Research Assistant
Graduate Research Assistant
During the collaboration I had with MDA, I worked on an application of Bayesian variable selection method for binary outcome in cancer genomic data. This type of problems are in the category of high or ultra high dimensional settings where the traditional methods seem impractical. We exploit non-local prior for regression coefficients to solve this problem in the context of generalized linear model. This shows improvement in precision of classifier as well as reduced error in estimating regression coefficients.
Doctor of Philosophy (Ph.D.)
Statistics
Master of Science (MSc)
Electrical and Electronics Engineering
Postdoctoral Research Fellow, Department of Statistics
Bachelor of Science (BSc)
Electrical and Electronics Engineering
Vice President
Treasurer
urn:li:fs_education:(ACoAAA5g0AEBoxGfqs1X0oE5X1aUKRBygFckIaI,213505281)
President
Vice President
Treasurer
urn:li:fs_education:(ACoAAA5g0AEBoxGfqs1X0oE5X1aUKRBygFckIaI,213505281)
President
Member
Vice President
Treasurer
urn:li:fs_education:(ACoAAA5g0AEBoxGfqs1X0oE5X1aUKRBygFckIaI,213505281)
President
Member
Vice President
Treasurer
urn:li:fs_education:(ACoAAA5g0AEBoxGfqs1X0oE5X1aUKRBygFckIaI,213505281)
President
Member
Vice President
Treasurer
urn:li:fs_education:(ACoAAA5g0AEBoxGfqs1X0oE5X1aUKRBygFckIaI,213505281)
President
Member