mathematics

Nilanjan Chatterjee

Nilanjan Chatterjee is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Nilanjan Chatterjee rather than just read about it. In short: Nilanjan Chatterjee is an Indian-American statistician who is the Bloomberg Distinguished Professor of Biostatistics and Genetic Epidemiology at Johns Hopkins University. He also has appointments in the Department of Biostatistics in the Bloomberg School of Public Health and in the Department of Oncology in the Sidney Kimmel Comprehensive Cancer Center.

Key takeaways

Nilanjan Chatterjee belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Nilanjan Chatterjee to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Nilanjan Chatterjee from memory before moving on to harder problems.

Reference excerpt

Nilanjan Chatterjee is an Indian-American statistician who is the Bloomberg Distinguished Professor of Biostatistics and Genetic Epidemiology at Johns Hopkins University. He also has appointments in the Department of Biostatistics in the Bloomberg School of Public Health and in the Department of Oncology in the Sidney Kimmel Comprehensive Cancer Center. He was formerly the chief of the Biostatistics Branch of the National Cancer Institute's Division of Cancer Epidemiology and Genetics.

Biography Chatterjee was born in Kolkata, India, to parents Aditya Nath and Pranati Chatterjee. He attended Ballygunge Government High School and Saint Xavier's College. He received his bachelor's degree in statistics from the Indian Statistical Institute in 1993, and his master's in 1995. Chatterjee earned his PhD in Statistics from the University of Washington, Seattle in 1999. His PhD thesis, titled "Semiparametric Inference Based on Estimating Equations in Regression Models for Two-Phase Outcome-Dependent Sampling," was advised by Norman Breslow and Jon A. Wellner. Chatterjee joined the National Cancer Institute (NCI) as a postdoctoral fellow in the biostatistics branch of the Division of Cancer Epidemiology and Genetics (DCEG) in 1999, and became a tenure-track investigator in 2001 and a senior investigator in 2004. He served as chief of the biostatistics branch from 2008 until 2015. He remains a Special Volunteer at the NCI.

Research Chatterjee is known for his work in quantitative genetics, cancer research, big data, statistical methodology, genomics, gene-environment interaction, genetic association and genome-wide association studies. Chatterjee developed a model for predicting breast cancer risk that combines individual-level data on risk factors such as smoking and age with genetic variations associated with higher risk of breast cancer. He led a large collaboration of experts from the Johns Hopkins School of Medicine, the Bloomberg School of Public Health, and the Whiting School of Engineering that used big data and machine learning to develop tools for autism risk prediction and intervention. Chatterjee has participated in the Johns Hopkins Individualized Health Initiative (Hopkins inHealth), which aims to improve individual and population health outcomes through innovative collaborations among experts in biomedical and data science. Chatterjee's contributions to the initiative involved research on using genetic markers for risk prediction models as well as their applications to personalized medicine and public health interventions. Through his quantitative studies of gene-environment and gene-gene interactions, Chatterjee has also made fundamental contributions to theoretical and methodological approaches in epidemiology and biostatistics. Using statistics from genome-wide association studies to appraise the number of DNA variations that contribute to different physical traits and diseases, Chatterjee and colleagues developed a method for estimating the number of individual samples needed in order to identify genetic bases of traits, such as height or body mass index (BMI), or diseases, such as diabetes or bipolar disorder. This method can be used for designing genetic studies and understanding genetic risk prediction.

Awards Fellow of the American Statistical Association (2008) Mortimer Spiegelman Award (2010) George W. Snedecor Award (2011) COPSS Presidents' Award (2011) Gertrude Cox Award (2011) Elected Member of the American Epidemiologic Society (2012)

Publications Chatterjee has more than 40,000 citations in Google Scholar and an h-index of 98.

Pubmed citations Google Scholar citations Highly Cited Articles (more than 1000 citations)

2010 with EK Speliotes, CJ Willer, SI Berndt, KL Monda, G Thorleifsson, AU Jackson, et al, Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index, in: Nature Genetics. Vol. 42, nº 11; 937-948. 2010 with HL Allen, K Estrada, G Lettre, SI Bernd, MN Weedon, F Rivadeneira, et al, Hundreds of variants clustered in genomic loci and biological pathways affect human height, in: Nature. Vol. 467, nº 7317; 832-838. 2007 with DJ Hunter, P Kraft, KB Jacobs, DG Cox, M Yeager, SE Hankinson, A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer, in: Nature Genetics. Vol. 39, nº 7; 870-874. 2007 with M Yeager, N Orr, RB Hayes, KB Jacobs, P Kraft, S Wacholder, et al, Genome-wide association study of prostate cancer identifies a second risk locus at 8q24, in: Nature Genetics. Vol. 39, nº 5; 645-649. 2008 with G Thomas, KB Jacobs, M Yeager, P Kraft, S Wacholder, N Orr, K Yu, et al, Multiple loci identified in a genome-wide association study of prostate cancer, in: Nature Genetics. Vol. 40, nº 3; 310-315.

References

Illustrations

Worked examples

Example 1 — a first encounter with Nilanjan Chatterjee

Start with the simplest possible case. Write down what Nilanjan Chatterjee claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Nilanjan Chatterjee before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Nilanjan Chatterjee ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Nilanjan Chatterjee

In research: Nilanjan Chatterjee appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Nilanjan Chatterjee in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Nilanjan Chatterjee is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1970s births, 20th-century Indian mathematicians, 20th-century statisticians, so understanding it makes those chapters shorter.
In everyday life: Look for Nilanjan Chatterjee outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Nilanjan Chatterjee in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Nilanjan Chatterjee means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Nilanjan Chatterjee out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Nilanjan Chatterjee in simple terms?

Why does Nilanjan Chatterjee matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Nilanjan Chatterjee?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Nilanjan Chatterjee.