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Svetlana Segarceanu
Kepler-Rominfo SA
Bucuresti, Romania

Svetlana Segarceanu received her PhD from the Polytechnic University of Bucharest and has a master’s degree in mathematical analysis from the University of Bucharest. She currently works in research and development (R&D) at Beia Consult International, mainly in the fields of environmental sound recognition and natural language processing (NLP).

Svetlana has worked as a programmer and researcher in computer science since 1983, including work on database management, computer-aided instruction (CAI), and speech signal applications at the Computer Research Institute in Bucharest, Romania, as well as the development of several modules for different applications using PL/I, Java, C, C++, the .NET environment, and SAS. In the development of the European Satisfaction Index System (ESIS), she was responsible for raw data processing and classification procedures, 2D and 3D data visualization procedures, and the development of a graphical editor for structural equations. She also developed an interface for installing and configuring sensors used in aviation applications, adding a new function to the existing installation module and a module for testing the transmission of messages from sensors.

Svetlana has authored more than 30 papers and has participated in several conferences, including the International Conference on Speech and Computer (SPECOM) and Telecommunication, Informatics, Energy and Management (TIEM). She is an active contributor at the Annual Symposium of the Institute of Solid Mechanics and the Commission of Acoustics Session, and has managed or participated in multiple international projects over the last 15 years.

She has been a reviewer for CR since 1992.


The AI tech-stack model
Tsaih R., Chang H., Hsu C., Yen D. Communications of the ACM 6669-77, 2023.  Type: Article

Artificial intelligence (AI) utilization in enterprises is rapidly expanding, and organizations have to keep up with AI’s pace of growth. Once customers start to benefit from smart solutions such as predictive schemes or recommendation syste...


 A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data
Murray J., Philipson P. Computational Statistics & Data Analysis 170(1): 1-15, 2022.  Type: Article

Many longitudinal clinical studies involve the repeated periodic measurement of continuous responses over a period to detect any changes that might occur in the condition of the individual, such as events of interest like survival after 90 days. I...


Unifying logical and statistical AI with Markov logic
Domingos P., Lowd D. Communications of the ACM 62(7): 74-83, 2019.  Type: Article

A Markov logic network (MLN) is a probabilistic logic that endows the first-order logic with a degree of uncertainty by applying Markov networks. It represents an active area of research, introduced in the early 2000s by the very autho...


Environmental audio scene and sound event recognition for autonomous surveillance: a survey and comparative studies
Chandrakala S., Jayalakshmi S. ACM Computing Surveys 52(3): 1-34, 2019.  Type: Article

As my colleagues and I define in a previous paper, “environmental sound recognition (AESR) is a relatively new discipline of computer science destined to extend the field of speech-based applications, or the study of music so...


UCF’s 30-year REU site in computer vision
Lobo N., Shah M. Communications of the ACM 62(1): 31-34, 2019.  Type: Article

The article describes the University of Central Florida’s (UCF’s) history and position within the Research Experiences for Undergraduates (REUs) program. REUs are research programs in the US for undergraduates study...


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