What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Artificial intelligence, Machine learning, Set, Data mining and Hidden Markov model.
His Artificial intelligence study combines topics in areas such as Computer vision and Pattern recognition.
When carried out as part of a general Machine learning research project, his work on Feature is frequently linked to work in Behavioral pattern, therefore connecting diverse disciplines of study.
His Set research incorporates elements of Nonverbal communication, Natural language processing, Video tracking and Ambient intelligence, Human–computer interaction.
In general Data mining study, his work on Data visualization often relates to the realm of GSM, Mobility prediction and Protocol, thereby connecting several areas of interest.
The concepts of his Hidden Markov model study are interwoven with issues in Markov model and Speech processing.
His most cited work include:
- StressSense: detecting stress in unconstrained acoustic environments using smartphones (371 citations)
- The Mobile Data Challenge: Big Data for Mobile Computing Research (352 citations)
- Modeling scenes with local descriptors and latent aspects (338 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Multimedia, Nonverbal communication, Machine learning and World Wide Web are his primary areas of study.
His Artificial intelligence research includes themes of Natural language processing, Computer vision and Pattern recognition.
His Multimedia study incorporates themes from Crowdsourcing, Social media, Session and Personality.
His Nonverbal communication study which covers Human–computer interaction that intersects with Nonverbal behavior.
His study in the field of Unsupervised learning is also linked to topics like Phone.
His Probabilistic logic research integrates issues from Topic model and Data mining.
He most often published in these fields:
- Artificial intelligence (44.12%)
- Multimedia (15.44%)
- Nonverbal communication (15.07%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (44.12%)
- Crowdsourcing (8.46%)
- Ubiquitous computing (7.35%)
In recent papers he was focusing on the following fields of study:
Daniel Gatica-Perez mainly investigates Artificial intelligence, Crowdsourcing, Ubiquitous computing, Social media and Nightlife.
His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition.
His biological study spans a wide range of topics, including Image and Computation.
His Crowdsourcing study also includes fields such as
- Convolutional neural network which connect with Information retrieval,
- Mobile technology most often made with reference to Data science,
- Internet privacy together with Big data.
His work investigates the relationship between Ubiquitous computing and topics such as Computer security that intersect with problems in Statistical model, Probabilistic logic, Anomaly detection and Real-time computing.
His work in Social media addresses issues such as Multimedia, which are connected to fields such as Visual perception, Internship, World Wide Web, Social computing and Urban computing.
Between 2015 and 2021, his most popular works were:
- Anomaly detection in elderly daily behavior in ambient sensing environments (35 citations)
- Anomaly detection in elderly daily behavior in ambient sensing environments (35 citations)
- Hirability in the Wild: Analysis of Online Conversational Video Resumes (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Ubiquitous computing, Social media, Nightlife, Human–computer interaction and Crowdsourcing.
Daniel Gatica-Perez works mostly in the field of Ubiquitous computing, limiting it down to topics relating to Computer security and, in certain cases, Support vector machine, Set and Real-time computing.
His Social media study integrates concerns from other disciplines, such as Multimedia, Advertising, Inference and Categorization.
The Multimedia study combines topics in areas such as Social computing, Scale, Nonverbal communication, Internship and Visual perception.
He has researched Human–computer interaction in several fields, including Probabilistic logic, Anomaly detection and Statistical model.
His study with Crowdsourcing involves better knowledge in World Wide Web.
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