Functional programming has been gaining significant popularity in recent years, especially in the field of computer science. It is a programming paradigm that focuses on writing programs by composing, manipulating, and transforming functions, rather than using statements to change program state. This approach has influenced modern computer science research in various ways, leading to new advancements and techniques that have revolutionized the industry.

One of the main influences of functional programming on modern computer science research is its emphasis on the use of immutable data and pure functions. Immutable data refers to variables that cannot be changed once they are initialized, and pure functions are those that do not produce any side effects. These concepts have created a new way of thinking about program design and have led to the development of techniques such as data immutability, lazy evaluation, and shared memory concurrency.

Data immutability is a technique that ensures data remains unchanged once it is defined. This approach has many benefits, including preventing data races and promoting thread safety in multi-threaded applications. This problem has become increasingly relevant with the rise of parallel programming and the need to optimize programs for efficient execution on modern hardware.

Lazy evaluation, also known as deferred evaluation, is a method where expressions are evaluated only when their results are needed. This technique allows for the delayed execution of operations, which can improve program performance and resource utilization. It also enables the creation of infinite data structures, which have become essential in modern Big Data applications.

Shared memory concurrency is a programming paradigm that allows multiple threads to access shared data concurrently. It ensures that access to data is synchronized, preventing data races and ensuring thread safety. This concept has been widely adopted in modern programming languages, such as Java and C++, to take advantage of multi-core architectures and parallel processing.

Another significant influence of functional programming on modern computer science research is its emphasis on higher-order functions. These are functions that can take other functions as arguments or return functions as values. Higher-order functions allow for the creation of reusable code, which is a key aspect of functional programming. They also facilitate the creation of domain-specific languages and provide a more concise and expressive way of writing code.

For instance, take the concept of map, a higher-order function that applies a given function to each element in a list, producing a new list with the results. This function has become a staple in functional programming and is also present in many mainstream programming languages. Its implementation not only provides a more concise way of transforming data but also allows for better code reusability.

Moreover, functional programming has also influenced areas such as artificial intelligence and machine learning research. The emphasis on pure functions and immutable data has led to the development of techniques such as deep learning and neural networks, which have had a significant impact on these fields. These techniques use pure functions to process data, making them more reliable and easier to scale.

In conclusion, functional programming has had a profound influence on modern computer science research. Its focus on using pure functions, immutable data, and higher-order functions has led to the development of new techniques and approaches that have greatly advanced the industry. With the rise of parallel and distributed computing, the emphasis on these concepts will only continue to grow, making functional programming a crucial aspect of modern computer science research.