Stony Brook University Computer Science

Working with big data, researchers study the science, engineering, and technology behind producing and maintaining large data streams. The areas of big data that are studied at SBU incorporate a variety of industries including finance, medical, and science as well as governmental implications of big data. Based on the theory that the big data phenomenon is driven by massive amounts of data in need of powerful, scalable algorithms, researchers face the challenge to control errors and sampling processes.

Computer Vision is an AI field that enables machines to interpret and understand visual data from images and videos, similar to human vision. It involves using algorithms and models to perform tasks like image recognition, video analysis, facial recognition, object detection, scene reconstruction, image restoration, and augmented reality. This technology is applied in various sectors including autonomous vehicles, security, healthcare, retail, and agriculture, among others. By processing visual information, computer vision systems can identify patterns, objects, and activities, facilitating automated decision-making and actions based on visual inputs. Advances in AI, especially deep learning, have significantly enhanced computer vision capabilities, making it integral to numerous modern technological applications.

Building reliable, high-performance, secure, and energy-friendly software through state-of-the-art language techniques, including formalizing software expectations and semantics, systematic testing, and code verification.

Because it deals with people as well as computational systems, as a knowledge area HCI demands consideration of cultural, social, organizational, cognitive and perceptual issues. Consequently it draws on a variety of disciplinary traditions, including psychology, ergonomics, computer science, graphic and product design, anthropology and engineering. Faculty, students, and researchers focused on parallel computing, architecture, network protocols, wireless and mobile computing, sensor networks and embedded systems.

Informational assurance involves research and educational activities that actively address the development of trustworthy information systems and the quality of the information stored on information systems and networks. Researchers work closely with security and computer architecture faculty. Security researchers at SBU lead one of the top cybersecurity centers in the northeastern United States. The group’s research areas include: language-based security, detection and mitigation of vulnerabilities, trust management, assurance and vulnerability analysis, intrusion detection, storage security, security monitoring and regulatory compliance, and authentication.

Information management is concerned with the capture, digitization, representation, organization, transformation of algorithms for efficient and effective access. Researchers explore intelligent information systems that include theoretical and systems work in the areas of databases, stream processing, cloud computing, data integration, and data mining. Products focus on building secure systems, distributed and multimedia databases, social network analysis and ranking data.

With the advances in computing, networking, and sensing technologies, physical systems have never been more tightly integrated with our daily lives. Internet of Things (IoT) is a state-of-the-art technology connecting the pervasively available devices and humans. We are envisioning a system, where everyone can interact with physical environments through highly connected and readily available devices. Some of active research areas include, building dynamic models for environmental monitoring, designing a software system that can outlive the hardware platform through mobile agent systems, and developing quantitative model checking techniques that can connect the gap between the cyber systems and physical systems.

Many computing applications that are used today would not be possible without networks. The research interests of the networking group encompasses network measurement, network architectures and protocols, the design and implementation of applications, and systems as well as network performance analysis. Taking an analytical and experimental approach, areas of networking research include storage area networking, energy systems, mobility, social networks, wireless, and resource allocation. Research in this area includes wireless and sensor networks focused on security, architecture and hardware, access control, signal processing, and data dissemination.

Neuromorphic computing is a field of computer science that aims to study computing systems that emulate the functionality of biological neural networks in the human brain. The goal of neuromorphic computing is to create hardware and software systems that can perform complex cognitive tasks more efficiently and effectively than traditional computing systems.

One of the key components of neuromorphic computing is the spiking neural network (SNN), which is a type of artificial neural network that is inspired by the biological processes that occur in the human brain. Unlike traditional artificial neural networks, which are based on continuous signals, spiking neural networks transmit information in the form of discrete spikes or pulses, which more closely resemble the way neurons in the brain communicate with one another.

Spiking neural networks have several advantages over traditional neural networks, including greater power efficiency, faster processing times, and the ability to process temporal information more effectively. This makes them particularly well-suited for applications in areas such as robotics, image and speech recognition, and autonomous systems.

The development of brain-inspired spiking neural networks is an active area of research within neuromorphic computing. We are working to conduct research on new algorithms and architectures that are more closely aligned with the biological processes that occur in the brain. These brain-inspired systems have the potential to revolutionize computing by enabling the creation of more efficient and intelligent machines that can perform complex tasks with greater accuracy and speed.

The ultimate goal of NLP is to develop algorithms that make computing machines understand human language, whether spoken or written. It is a discipline whose scope encompasses computer science, mathematics, and linguistics. As we surround ourselves with ever increasing variety of personal media, the importance of having computers process human language becomes paramount: everything from correcting typos to summarizing articles is now being heavily reinforced by techniques in NLP.

Operating systems are a system positioned in between hardware and software. While handling resource allocation for other applications and for itself, they provide a homogeneous virtual environment for other applications where hardware specific details are abstracted away. With the wide use of IoT technologies, there is a wide spectrum of Operating Systems, from a tiny one that are compiled together with applications for embedded systems to a more complex one that can provide a virtual environment to other operating systems running on the same hardware.

Programming languages are the medium through which programmers precisely describe concepts, formulate algorithms, and reason about solutions. Programming and software researchers are dedicated to developing software and tools that minimize software errors and improve the human aspects of computing. Focused on end-user programming and software engineering, research thrusts include language design, syntax analysis, programming constructs, open-source, and logic programming.

Quantum computing is an efficient way of solving problems using quantum physics. Unlike classical computers that rely on the bi-state of bits, quantum computers use quantum bits that can be in a superposed state. Manipulating the superposed quantum states, some problems can be solved faster than classical computers. With the recent advances in the quantum computing technologies, classical computer can no longer simulate quantum computers (quantum supremacy). Quantum cryptography, molecule simulation, SAT problems, solving linear systems of equations are some of the application areas of quantum computing.

Hardware and software infrastructure is collected called “computer systems”. Computer systems span the sub-disciplines of operating systems, parallel and distributed systems, communications networks, and architecture. Systems Fundamentals covers the hardware and the the low-level software that interacts with that hardware.