Verint Da Vinci Research

At Verint, our team of data and AI scientists are constantly at work to make customer engagement better around the globe through innovation and research. Our researchers hold hundreds of patents, and regularly publish their findings in leading industry and scholarly publications. Here are some of scientists’ latest work.

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Towards Awareness of Human Relational Strategies in Virtual Agents

This paper investigates how humans apply relational strategies to virtual agents and chatbots compared to human agents in a customer service environment.

Authors: Ian Beaver, Cynthia Freeman, Abdullah Mueen

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An Adaptive Deep Clustering Pipeline to Inform Text Labeling at Scale

Learn how Verint researchers created a flexible and scalable clustering pipeline that integrates the fine-tuning of language models, a high performing k-NN library, and community detection techniques to help analysts quickly surface and organize relevant user intentions from conversational texts.

Authors: Xinyu Chen, Ian Beaver

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Is AI at Human Parity Yet? A Case Study on Speech Recognition

What does it even mean for an AI system to reach human parity? How is progress towards that goal being measured? This article focuses on the current state of speech recognition and the recent developments in benchmarking and measuring performance of AI models built for speech processing.

Author: Ian Beaver


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The Success of Conversational AI and the AI Challenge it Reveals

A massive increase in conversational artificial intelligence research and advancement in recent years has enabled systems to produce rich and varied turns in human-like conversations that often rely on crowd worker opinions as the primary measurement of success. The challenge, though, is that evaluation strategies need to mature alongside AI systems that are mature in more “human” tasks that involve creativity and variation,

Author: Ian Beaver

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Timevae: A Variational Auto-Encoder for Multivariate Time Series Generation

Recent work in synthetic data generation in the time-series domain has focused on the use of Generative Adversarial Networks. In this paper, we propose a novel architecture for synthetically generating time-series data with the use of Variational Auto-Encoders (VAEs).

Authors: Abhyuday Desai, Cynthia Freeman, Zuhui Wang, Ian Beaver

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Automated Conversation Review to Surface Virtual Assistant Misunderstandings: Reducing Cost and Increasing Privacy

With the rise of Intelligent Virtual Assistants (IVAs), there is a necessary rise in human effort to identify conversations containing misunderstood user inputs. Here, we present a scalable system for automated conversation review that can identify potential miscommunications and provide IVA designers with suggested actions to fix errors in IVA understanding, prioritizes areas of language model repair, and automates the review of conversations where desired.

Authors: Ian Beaver, Abdullah Mueen

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Experimental Comparison and Survey of 12-Time Series Anomaly Detection Algorithms

There’s a plethora of AI anomaly detection methods across domains, which can be a burden for AI developers. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps.

Authors: Cynthia Freeman, Jonathan Merriman, Ian Beaver, Abdullah Mueen

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Fine-Tuning Language Models for Semi-Supervised Text Mining

In this paper, we present an empirical study of a pipeline for semi-supervised text clustering tasks. Our proposed method utilizes a small number of labeled samples to fine-tune pre-trained language models. This fine-tuning step adapts the language models to produce task-specific contextualized representations, improving the performance of downstream text clustering tasks.

Authors: Xinyu Chen, Ian Beaver, Cynthia Freeman

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Meet Our Researchers

Ian Beaver, PhD has worked on topics surrounding human-computer interactions such as gesture recognition, user preference learning, and communication with multi-modal intelligent virtual assistants since 2005. He has authored nearly 50 patents within the field of human language technology and regularly serves as a PC member in many top AI and NLP conferences as well as a columnist for AAAI’s AI Magazine. Ian is Chief Scientist of Da Vinci AI and Analytics at Verint where he leads teams to optimize human productivity in contact centers and improve customer self-service experiences by way of automation and augmentation. See Ian’s published research.

Xinyu Chen is a research scientist at Verint Systems Inc and a PhD student at Washington State University. His interests are in the field of Natural Language Processing, Parallel Computing, and Graph Theory. His research includes integrating the power of graph theories, parallel computing, and machine learning techniques to solve real-world natural language problems. See Xinyu’s published research.

Cynthia Freeman, PhD is a research scientist at Verint. She holds a BS in mathematics from Gonzaga University, a MS in applied mathematics from the University of Washington, and a PhD in computer science from the University of New Mexico. Her research includes machine learning applications, time series analysis, such as anomaly detection methods, and statistical analysis of contact center data. See Cynthia’s publications.

Grant Anderson is a Research Scientist at Verint and part-time PhD student at Edinburgh Napier University (ENU) in Scotland. He is currently researching applications of large language models and generative AI in contact centers as part of the Da Vinci AI Research group. Prior to this he obtained his BS in Games Development from ENU, where his honors project focused on evolving neural networks for control of vehicles in games. See Grant’s published research.

Serkan Kefel, PhD is a Lead Research Scientist at Verint in the Da Vinci AI Research group. Serkan’s research focuses on statistical and machine learning based modelling of time-series forecasting problems in various workforce optimization and management areas as well as noise and outlier handling in such series. His PhD work focused on the application of computer vision and image processing for cancer detection and research. See Serkan’s published research.

Karni Gilon, Director of Analytics, has an MSC from Weizmann Institute in Mathematics and Computer Science and an executive MBA from Tel Aviv University. She served in leading roles in several prominent technology fields, including natural language processing  in Citi Innovation Lab and distributed network switching at Broadcom. She studied data science and natural language processing at the Hebrew University of Jerusalem, with several publications. See Karni’s published research.

Kostya Varakin, an Advanced Data Scientist, has more than 15 years of experience in software development and natural language processing, Kostya is passionate about creating innovative solutions that address global challenges. As the pond constructor and founder of CO2SaaS, he leads a team that builds algae ponds that sequester carbon dioxide. As an NLP researcher at Verint, he works on developing state-of-the-art technologies that enable natural and intuitive interactions between humans and machines. Previously, he co-founded and served as the CTO of Sherlock Garden, a company that applied NLP to regulatory compliance, and SQream Technologies, which optimized database query performance using parallel computing. See Kostya’s published research.

Samuel (Shmuel) Londner is an experienced NLP researcher. Graduating from the Technion with a B.Sc in Electrical Engineering, Shmuel has extensively researched machine learning and deep learning algorithms across various domains, especially in NLP. In parallel with his work at Verint, he is studying hybrid OCR algorithms at Tel Aviv University, bridging the gap between computer vision and natural language processing. See Shmuel’s published research.

José R. Benkí, PhD is Research Science Director at Verint. He received his BSEE and BA from the University of Texas at Austin, and his PhD in Linguistics from the University of Massachusetts at Amherst. Prior to joining Verint, José was on the research and teaching faculty at the Institute for Social Research at the University of Michigan, Michigan State University, and the Ohio State University. He has peer-reviewed publications in survey methodology, speech science, and bilingualism, and is a member of the American Association for Public Opinion Research (AAPOR). See José’s published research.