Biostatistics and Data Science Alumni Profile

Chaoyi Zeng

MS in Biostatistics, 2021

1. Where do you currently work and what are your job responsibilities?

I’m currently a data analyst working for the United Network for Organ Sharing (UNOS). My main responsibility is dealing with various data requests from the transplant community.

2. Why did you choose to study biostatistics and what led you to LSUHSC Biostatistics Program?

After a year of study in Epidemiology, I became highly interested in statistical methods, so I switched to the Biostatistics Program. I’m glad that I made this decision because, on the one hand, I learned systematic knowledge of statistical theories and methods. On the other hand, I received intensive training in using coding and analysis tools. The reason I chose LSUHSC is that the curriculum is well designed, which I believe would satisfy my learning needs while preparing me well for the job market. Plus, the tuition is affordable.

3. What was the most rewarding part of your MS degree?

The most rewarding part is that my MS degree has led me to my current career: a professional data analyst who makes contributions to the organ transplant community/healthcare industry every day. I feel fulfilled and proud each time I look at my signature, which prints the slogan of our organization, ‘Saving Lives Together.’ It’s a long way for someone starting from scratch (since my undergraduate major is English instead of any major related to Statistics or Computer Science), but in the LSUHSC Biostatistics Program, my advisor, Professor Hui-Yi Lin, and I made it happen.

Raven P. Moret

MS in Biostatistics, 2021

1. Where do you currently work and what are your job responsibilities?

I am currently entering into my second year at the Center for Disease Control based in Atlanta, GA. I am serving as a biostatistician and assist on studies to be published in peer-reviewed journals examining provider practice with respect to various diseases. Additionally I am developing protocols for power analysis and sample size within my division as well as course modules for incoming fellows.

2. Why did you choose to study biostatistics and what led you to LSUHSC Biostatistics Program?

After seven years of research I came to enjoy the analytical process of the studies I was conducting. I felt that being able to put my findings into descriptive form to show and explain was very fulfilling after all of my hard work. As a New Orleans native I wanted to stay close to home and was aware of the strong work done at LSUHSC from many of the presentations at the Louisiana Cancer Center Scientific Retreats. I knew I’d be in good hands.

3. What was the most rewarding part of your MS degree?

The most rewarding part of this degree was being able to expand my knowledge from an analytical perspective. The instructors were amazing at providing so many ways to approach and view problems. Also, the technical skillsets I learned through the program, the different software and applications, opened up countless opportunities for me both career and growth wise. Returning to school after so many years was a very intimidating choice but I couldn’t be happier with my decision.

Ondrej Blaha

PhD in Biostatistics, 2019

1. Where do you currently work and what are your job responsibilities?

Currently, I work as a Senior Biostatistician at Stanford University, School of Medicine. I am responsible for running clinical studies under an umbrella of projects that span University’s Pediatric Heart Center, Hospital Medicine, Behavioral and Developmental Studies, and a triplet of concussion studies. A joint effort of University’s departments of neurology, neurosurgery, engineering, center for clinical research, NIH, and PAC-12 are looking into the long-term effects of concussive and sub-concussive impacts on athletes’ brain structure using an in-house developed, censors embedded mouth-guard and in-house developed AI software for evaluating individual events. Currently, my work is mostly in clinical trials and bio-medical sciences in general. I work on everything that is a part of being a senior biostatistician: design of experiments, grant writing, programming, statistical analysis, manuscript writing and editing, etc. That is the fun stuff. I work on developing statistical methodologies as well. I am also responsible for leading and training junior statisticians and preparing training sessions for statistical beginners.

2. Why did you choose to study biostatistics and what led you to LSUHSC Biostatistics Program?

Biostatistics is a marriage between my two most favorite disciplines; mathematics and biology. I could never decide which one I liked better. I loved medicine and biology but always knew that I can influence more and save much more lives through mathematics. It is not an accident that mathematics is called the “queen of the sciences”. However, I missed being in touch with biology throughout my initial years as a mathematician. When I eventually learned about the existence of biostatistics it was clear what career I have to pursue. I learned about the LSUHSC Biostatistics program when Dr. Volaufova was on sabbatical at my Alma Matter in my home country, so you can say it was her who led me there.

3. What was the most rewarding part of your PhD degree?

Of receiving my PhD or working towards it? The best part of receiving the degree was the first paycheck. And the knowledge that I now have my dream job and things will only get better from here.  About working towards my degree, it was working with my mentors. Learning about new, unexplored areas of statistics, how to conduct theoretical research, be independent, and also making my program work. You know, the feeling when the program finally runs as intendedJ. LSUHSC has that unique one-on-one approach you can hardly get anywhere else. You can be sure that you will get enough attention and you can always ask questions outside the scope of the classwork. Faculty members were extremely helpful and were always there when I needed them the most. LSUHSC also has unmatched opportunities for collaboration that are readily available. You can work alongside distinguished researchers on various projects of your interest. There is a huge demand and not enough hands to do all the work. It is only up to you if you pick up the call. In the end, these opportunities are what eventually helped me land the best job in the world.

Jacob Maronge

MS in Biostatistics, 2016

1. Where do you currently study and what are your goals after graduating?

I am currently a PhD candidate in biostatistics, which is a specialization within the statistics PhD jointly offered between the departments of statistics and biostatistics at the University of Wisconsin-Madison. I work with Professor Paul Rathouz on outcome-dependent sampling (ODS); the core concept of ODS is how to perform correct estimation and inference when sampling is performed based on observed values of a response, instead of a predictor. Specifically, we are thinking about how to generalize the notion of case-control, studies, which are popular in epidemiology, to non-binary outcomes. We refer to these types of studies as “generalized case-control studies”. My dissertation research revolves around how to design and analyze these types of studies.

During my time at LSUHSC I worked with Professor Zhide Fang on optimal experimental design. The work during my MS was a bit different because it was focused on design using covariates, not outcomes. However, it was particularly useful because I think it gave me a strong understanding of important design concepts.

After graduating, I would like to join a biostatistics department where I can continue to work in biomedical research. My goal is to be a clinical biostatistician and continue to develop methods and useful tools for practitioners.

2. Why did you choose to study biostatistics and what led you to LSUHSC Biostatistics Program?

During my undergraduate degree, I studied both biology and physics. I have always been interested in biology and medicine, but probably had more natural talent in mathematics. Biostatistics gives me the opportunity to use mathematics in a way that feels like it has a more immediate impact. I chose LSUHSC for personal reasons, but it ended up being the perfect place for me at the time.

3. What was the most rewarding part of your MS degree?

My time at LSUHSC was one of the most influential times of my education. The professors I had during my MS still have a huge impact on how I think about statistics now. It was great to be in a place where I felt like I was receiving quality, individualized education. I still keep in touch with many of my instructors from LSUHSC, which is a testament to the effort they put into their students.

The most rewarding part was when I came to Wisconsin, I immediately felt prepared for the coursework and other challenges. Had I not attended LSUHSC, I think I would have struggled much more.

Angela Stodghill

PhD in Biostatistics, 2015

1. Where do you currently work and what are your job responsibilities?

I am a contracted senior statistician for Janssen Pharmaceuticals. My main job duties are designing and planning clinical trials as well as analyzing clinical trial data. In addition, I am working on improving statistical methods used in clinical trials.

2. Why did you choose to study biostatistics and what led you to LSUHSC Biostatistics Program?

Growing up, my parents worked in the medical and pharmaceutical fields. I was planning to be a doctor or a pharmacist. However, I also loved computer programming. More importantly, numbers and the way data can help people in their lives fascinates me. In college, one of my math professors, a statistician working in the pharmaceutical industry, introduced me to the drug development process and the important role a statistician plays in a clinical trial. Therefore, getting a PhD in biostatistics was a no brainer for me. I chose LSUHSC because LSU is a great college to be a part of, and it has spent a huge amount of funding on medical research. In addition, the biostatistics program provides very attractive stipends for PhD students and a lower cost of tuition compared to other state funded schools.

3. What was the most rewarding part of your PhD degree?

During my dissertation process, I learned how to think, research, and solve problems independently. In addition, I learned how to write formally and logically to demonstrate my way of thinking. All these skills help me still in my daily work. Also, the course material that you learn is what will be used in your future job. During my past 4 years of work, I used a majority of the statistical methods I learned in my courses.  Most importantly, I am using SAS to do my daily job which I learned mostly as a PhD student.

Jennifer Hayden

MS in Biostatistics, 2011

1. Where do you currently work and what are your job responsibilities?

I am a senior research scientist with Humana Healthcare Research. My primary responsibility is to design scientifically valid healthcare studies to answer research questions around clinical and economic trends. Using large ‘real world’ healthcare data sources I develop measures, produce statistical output and interpret and report study results through presentations, abstracts and publications.

2. Why did you choose to study biostatistics and what led you to LSUHSC Biostatistics Program?

By the time I enrolled into the LSUHSC Biostatistics Program I had already enjoyed a long career in healthcare data management which included database development, data maintenance and data monitoring but, I did not have training or skills in statistical data inference. I love working with healthcare data and I knew I wanted to do more with it and could apply the programming skills I already had to a biostatistics degree. I was working in a different department at the LSUHSC School of Public Health when I applied for the program and knew I would get an excellent statistics education from LSUHSC. I personally knew the faculty as colleagues. They are excellent statisticians, well published, and dedicated to helping each of their students succeed; even older and non-traditional students like me.

3. What was the most rewarding part of your MS degree?

Designing and programming the research question which became my Master’s thesis was for me the best future job training I received from the MS program. The faculty challenged me to not be afraid to think independently and I apply these skills daily to develop well designed study protocols which I then translate into SAS code. I really love the work I do and am myself now published in a variety of journals and conferences. The LSUHSC Biostatistics Program changed my life and opened up so many opportunities I would not otherwise have had. I work from home, right here in New Orleans, on huge datasets with hundreds of thousands of observations doing exactly what I envisioned doing all those years ago.

Dr. Hui-Yi Lin’s primary research interest is developing novel statistical methods for genetic studies and omics data analyses, especially interactions of genetic variants for cancers. She also works in big data analysis and machine learning fields. Moreover, Dr. Lin has profound statistical consultation experience with health professionals across multiple disciplines, such as basic science, health behavioral studies, epidemiology studies, and clinical trials. She serves as the Director of the Biostatistics & Bioinformatics Core at the Center for Translational Viral Oncology (CTVO) and as Co-Director of the Data Analytical Unit of the LSUHSC-NO Comprehensive Alcohol-HIV/AIDS Research Center (CARC) at LSUHSC. She is the Principal Investigator (PI) for a multiple-year Department of Defense (DoD) grant for developing polygenic risk scores (PRSs) for prostate cancer. She also served as PI for several NIH grants (such as R21) and as a Co-Investigator for several NIH grants. She has more than 175 peer-reviewed publications in high-impact journals. In addition to her work at LSUHSC, she actively participates in the international prostate cancer genome consortium. Dr. Lin also has extensive working experience at the 2 NCI-designated comprehensive cancer centers: Moffitt Cancer Center and Research Institute and Comprehensive Cancer Center at the University of Alabama at Birmingham (UAB).

# Keywords: Genetic Statistics In Cancers, Omic Data Analysis, Big Data Analysis, Machine Learning, and Clinical Trial  

Dr. Qingzhao Yu serves as the interim Associate Dean at the School of Public Health. Currently, she holds the role of Contact Principal Investigator for two significant NIH grants: An R01 project titled “Interactions between ES-miRNAs and environmental risk factors are responsible for TNBC progression and associated racial health disparities: a novel analysis with multilevel moderation inferences” and an R15 project titled “Trends of disparities in breast cancer progression and health care considering multilevel risk factors.” Dr. Yu has been the Principal Investigator, co-investigator, or biostatistician on numerous grants and projects. She has been authored in more than 130 peer-reviewed publications, one book, and four statistical software packages. Her research primarily focuses on advancing statistical methods for causal inferences and high-dimensional data analysis. These methodologies have found widespread application in uncovering mechanisms underlying observed health disparities, assessing interventions and health policies, as well as optimizing experimental designs. 

# Keywords: Bayesian Method, Machine Learning and Data Mining Techniques, Mediation Analysis, Optimal Experimental Designs, and Spatial Data Analysis.

Dr. Donald Mercante‘s research interests include design and analysis of experiments, multivariate permutation methods, and statistical models for correlated data. He is the principal investigator for the NIH-funded Data Management and Analysis Core for the LSU Superfund Research Center and the Co-Director of the Biostatistics and Epidemiology Research Design Core for the NIH-funded LaCATS Center grant at LSU-HSC. 

# Keywords: Clinical Trials, Study Design, Permutation Methods, and Correlated Data

Dr. Zhide Fang’s research interests include (1) developing novel statistical methodologies for analyzing high-throughput, large-scale genomic, epigenomic metagenomics data; (2)  developing efficient algorithms for Big Data; (3) developing methods and algorithms for constructing model-based experimental designs for the development of statistical theory.

# Keywords: Gene Expression, DNA Methylation, Microbiome, Big Data, and Machine Learning

Dr. Evrim Oral’s statistical methodology research involves several different areas. She specifically focuses on proposing robust estimation in generalized linear models (GLM) and survey sampling. She also develops novel estimators that utilize randomized response techniques (RRT) for sensitive topics. Her methodology research also includes proposing new estimation techniques and statistical tests for data subject to limit of detection (LOD). She does research on several other areas of survey sampling as well, such as ranked set sampling and assessing the effects of unit nonresponse and attrition in health-related studies. Her collaborative research interests are diverse. She has been working as a Co-investigator on several NIH-funded projects such as Quality of Life in Prostate Cancer Project (QPCaP) and The Women and Their Children’s Health (WaTCH). She worked for the LSU Health Care Services Division for many years and utilized big data from electronic health records (EHR) to improve LSU patients’ outcomes. She has been providing statistical consulting and supporting research in many departments and schools of LSUHSC, such as OB/GYN, Otolaryngology, Emergency Department, and School of Nursing. She is currently doing research with the Comprehensive Alcohol-HIV/AIDS Research Center (CARC) researchers and evaluating the effects of alcohol use in people with HIV.

# Keywords: Generalized Linear Models, Robust Statistics, Survey Methodology, Surveying Sensitive Topics, and Level of Detection

Dr. Anand Paul’s research interests are centered on the development of advanced mathematical and statistical models for Artificial Intelligence (AI) and Machine Learning (ML) systems. A key focus of my work is on exploring and formulating novel machine learning algorithms, utilizing techniques such as Markov Decision Processes and Hidden Markov Models to develop robust intelligence and knowledge discovery frameworks. Through these efforts, I aim to enhance the performance, reliability, and interpretability of AI systems.  I have experience in working with intelligent agents (reinforcement learning), using attention mechanisms in Natural Language Processing, and feature selections and dimensionality reduction techniques. My current research is focused on developing efficient mathematical models for gradient descent in ML systems, and Bayesian frame for selective quantization. Through these endeavors, I continuously seek to bridge theoretical advancements with practical applications, contributing to the evolution of resilient and intelligent AI systems. In LSUHSC I have started to work with Orthopedic data to provide insight into various intersections of AI with healthcare, aiming to improve diagnostic accuracy and patient outcomes.

# Keywords: Evolving Intelligence, Machine Learning, Mathematical Modeling, Statistical Analysis, Knowledge Discovery

Dr. Joonha Chang focuses on the methodological development of advanced algorithms. His specialties include latent class clustering, non-homogeneous continuous-time Markov chains, optimization, and signal processing algorithms. Dr. Chang aims to develop and refine methodologies to advance medical research and healthcare. His research projects include: (1) Latent classification of multi-modal non-homogeneous continuous-time Markov chains, (2) Integer programming formulation for clinical scheduling, and (3) Signal processing mobile typing patterns to passively detect intoxication.

# Keywords: Latent Class Clustering, Markov Chain, Integer Programming, Optimization, Signal Processing

Dr. Siyi Chen’s research focuses on developing causal inference techniques (e.g. Mendelian Randomization) to identify biomarkers that reveal the genetic basis of diseases (such as Alzheimer’s Disease). In bioinformatics, I am interested in integrating multi-omics data. Meanwhile, I also collaborate with investigators from various disciplines to amplify the impact of my work.

# Keywords: Causal Inference, Alzheimer’s Disease, Multi-omics, and Bioinformatics