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Javier Andreu-Perez is a British computer scientist and a Senior Lecturer [1] and Chair in Smart Health Technologies [2] at the University of Essex. He is also associate editor-in-chief of Neurocomputing for the area of Deep Learning and Machine Learning. [3] Andreu-Perez research is mainly focused on Human-Centered Artificial Intelligence (HCAI). [1] He also chairs a interdisciplinary lab in this area, HCAI-Essex. [4]
Andreu-Perez was born in Malaga, [1] Spain. During his childhood he spent time living in Essex (United Kingdom), where he later in life joined as an academic, at University of Essex. [1] He received his PhD in Intelligent Systems from Lancaster University (United Kingdom) in 2012. [5] He is also alumni of the Hamlyn Centre [6] at Imperial College London, which is one of the centers that form part of the Institute of Global Health Innovation. [7] The centre focuses on the development of technological innovations for global health challenges. [8]
Andreu-Perez started his career investigating novel ways for generic human activity recognition from wearables by means of evolving intelligent systems techniques. [9] [10] His PhD focused on the development of evolving intelligent systems for problems arising from ubiquitous computing technologies, like the stochasticity and uncertainty of data modelling tasks such the recognition of human activity recognition by means of pervasive computing sensors. [5] His research envisaged the utility of smart implants to replace wearables in achieving personalized healthcare in order to reshape the management of acute and chronic diseases. [11] He has also collaborated with the British multinational pharmaceutic company GlaxoSmithKline for research on use of body sensors for the understanding of chronic conditions such as Rheumatoid Arthritis. [12] Andreu-Perez was an invited faculty at the foundational (1st edition) Digital Rheumatology Day [13] and annual international event organised by the swisse Foundation for Research into Musculoskeletal and Rheumatic Diseases (RMR). [14]
Andreu-Perez later investigations were part of the EPSRC-NIHR Healthcare Technology Cooperatives Partnership: Technology Network-Plus on Devices for Surgery and Rehabilitation. [15] In this project he explored the development of adaptive brain computer interface systems that uses fuzzy sets and systems to model neural uncertainty. [16] His more recent scientific contributions have involved the combination of fuzzy logic and convolutional neural networks for this endeavour in smart environments. [17] Making his first contribution in 2016, he also works in the development of techniques that combines brain connectivity estimators with machine learning [18] and fuzzy logic [19] for recognizing cognitive profiles. [18] [19] [20] Among his most recent research he has been involved in the development of explainable artificial intelligence methods in developmental cognitive neuroscience. [21] The official magazine of the IEEE Computational Intelligence Society featured an article from Andreu-Perez's group discussing his perspective in this research question. [22]
His research in health has been featured in the international news media. [23] [24] [25] [26] [27]
Andreu-Perez was in 2020 one of the 12 awardees of the Talentia Senior Fellowship program. [28] An international scheme that offered three-years funding support for researchers with an excellent scientific and leadership curriculum to perform research with public research institutions in Andalusia, Spain. [29] In 2017 he was also first prize winner at the Research Associate Symposium of the Department of Computing, Imperial College London. [30]
Lotfi Aliasker Zadeh was a mathematician, computer scientist, electrical engineer, artificial intelligence researcher, and professor of computer science at the University of California, Berkeley. Zadeh is best known for proposing fuzzy mathematics, consisting of several fuzzy-related concepts: fuzzy sets, fuzzy logic, fuzzy algorithms, fuzzy semantics, fuzzy languages, fuzzy control, fuzzy systems, fuzzy probabilities, fuzzy events, and fuzzy information. Zadeh was a founding member of the Eurasian Academy.
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.
The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.
A neural network is a neural circuit of biological neurons, sometimes also called a biological neural network, or a network of artificial neurons or nodes in the case of an artificial neural network.
Intelligence amplification (IA) refers to the effective use of information technology in augmenting human intelligence. The idea was first proposed in the 1950s and 1960s by cybernetics and early computer pioneers.
Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. Edge computing is an architecture rather than a specific technology, and a topology- and location-sensitive form of distributed computing.
A fuzzy cognitive map (FCM) is a cognitive map within which the relations between the elements of a "mental landscape" can be used to compute the "strength of impact" of these elements. Fuzzy cognitive maps were introduced by Bart Kosko. Robert Axelrod introduced cognitive maps as a formal way of representing social scientific knowledge and modeling decision making in social and political systems, then brought in the computation.
The School of Computer Science and Electronic Engineering at the University of Essex is an academic department that focuses on educating and researching into Computer Science and Electronic Engineering specific matters. It was formed by the merger of two departments, notable for being amongst the first in England in their fields, the Department of Electronic Systems Engineering(1966) and the Department of Computer Science (1966).
A cognitive computer is a computer that hardwires artificial intelligence and machine learning algorithms into an integrated circuit that closely reproduces the behavior of the human brain. It generally adopts a neuromorphic engineering approach. Synonyms include neuromorphic chip and cognitive chip.
Cognitive computing refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision, human–computer interaction, dialog and narrative generation, among other technologies.
Cloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent. Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low-cost, smarter robots with an intelligent "brain" in the cloud. The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc.
Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes need to be tuned. These hidden nodes can be randomly assigned and never updated, or can be inherited from their ancestors without being changed. In most cases, the output weights of hidden nodes are usually learned in a single step, which essentially amounts to learning a linear model.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
David Atienza Alonso is a Spanish/Swiss scientist in the disciplines of computer and electrical engineering. His research focuses on hardware‐software co‐design and management for energy‐efficient and thermal-aware computing systems, always starting from a system‐level perspective to the actual electronic design. He is a full professor of electrical and computer engineering at the Swiss Federal Institute of Technology in Lausanne (EPFL) and the head of the Embedded Systems Laboratory (ESL). He is an IEEE Fellow (2016), and an ACM Fellow (2022).
Hani Hagras is a computer scientist and professor from the University of Essex, Colchester, UK was named Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013 for contributions to fuzzy systems in particular for his work on Type-2 fuzzy sets and systems. He is also a Fellow of the IET and a Principal Fellow of the Higher Education Academy (PFHEA), an award issued by Advance HE. Prof. Hagras is Chair of the Centre for Computational Intelligence (C4CI), and co-chair of the Artificial Intelligence Research Group at the University of Essex. He is also Chief Scientific Officer at Temenos AG.
Amir Hussain is a cognitive scientist, the director of Cognitive Big Data and Cybersecurity (CogBID) Research Lab at Edinburgh Napier University He is a professor of computing science. He is founding Editor-in-Chief of Springer Nature's internationally leading Cognitive Computation journal and the new Big Data Analytics journal. He is founding Editor-in-Chief for two Springer Book Series: Socio-Affective Computing and Cognitive Computation Trends, and also serves on the Editorial Board of a number of other world-leading journals including, as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics (Systems) and the IEEE Computational Intelligence Magazine.
Alois Christian Knoll is German computer scientist and professor at the TUM School of Computation, Information and Technology at the Technical University of Munich (TUM). He is head of the Chair of Robotics, Artificial Intelligence and Embedded Systems.
Small object detection is a particular case of object detection where various techniques are employed to detect small objects in digital images and videos. "Small objects" are objects having a small pixel footprint in the input image. In areas such as aerial imagery, state-of-the-art object detection techniques under performed because of small objects.
Juyang (John) Weng is a Chinese-American computer engineer, neuroscientist, author, and academic. He is a former professor at the Department of Computer Science and Engineering at Michigan State University and the President of Brain-Mind Institute and GENISAMA.