About

Vasant Dhar is associated with the New York University Stern School of Business, where he is involved with the Stern Center for Research Computing (SCRC). The center provides computational facilities and services to researchers, including a Slurm HPC cluster, cloud computing resources, data acquisition and storage, research software, and access to the Wharton Research Data System (WRDS). The focus of the center is to support advanced computational research and data analysis through a comprehensive suite of software services, diverse datasets, and scalable storage systems. His work emphasizes facilitating research that leverages high-performance computing, data management, and software tools to advance scholarly inquiry at the Stern School of Business.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Engineering
• Cognitive science
• Philosophy
• Mathematics education
• Mechanical engineering
• Epistemology
• Chemistry

Selected publications

• DBOT: Artificial Intelligence for Systematic Long-Term Investing

  ArXiv.org · 2025-04-08

  preprintOpen access1st authorCorresponding

  Long-term investing was previously seen as requiring human judgment. With the advent of generative artificial intelligence (AI) systems, automated systematic long-term investing is now feasible. In this paper, we present DBOT, a system whose goal is to reason about valuation like Aswath Damodaran, who is a unique expert in the investment arena in terms of having published thousands of valuations on companies in addition to his numerous writings on the topic, which provide ready training data for an AI system. DBOT can value any publicly traded company. DBOT can also be back-tested, making its behavior and performance amenable to scientific inquiry. We compare DBOT to its analytic parent, Damodaran, and highlight the research challenges involved in raising its current capability to that of Damodaran's. Finally, we examine the implications of DBOT-like AI agents for the financial industry, especially how they will impact the role of human analysts in valuation.

  PublisherOA PDFDOI

• Denoising 1P Calcium Imaging of Olfactory Bulb

  Research Square · 2025-11-02

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Data Science in Olfaction

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-10-22 · 1 citations

  preprintOpen accessSenior author

  Abstract Advances in neural sensing technology are making it possible to observe the olfactory process in great detail. In this paper, we conceptualize smell from a Data Science and AI perspective, that relates the properties of odorants to how they are sensed and analyzed in the olfactory system from the nose to the brain. Drawing distinctions to color vision, we argue that smell presents unique measurement challenges, including the complexity of stimuli, the high dimensionality of the sensory apparatus, as well as what constitutes ground truth. In the face of these challenges, we argue for the centrality of odorant-receptor interactions in developing a theory of olfaction. Such a theory is likely to find widespread industrial applications, and enhance our understanding of smell, and in the longer-term, how it relates to other senses and language. As an initial use case of the data, we present results using machine learning-based classification of neural responses to odors as they are recorded in the mouse olfactory bulb with calcium imaging. Our larger objective is to create the equivalent of an “MNIST database for olfaction,” which we call ‘oMNIST,’ so that researchers are able to work from a standard dataset to further the state of the art, similar to how the availability of standard datasets catalyzed research in computer vision.

  PublisherDOI

• Data Science In Olfaction

  arXiv (Cornell University) · 2024-04-08

  preprintOpen accessSenior author

  Advances in neural sensing technology are making it possible to observe the olfactory process in great detail. In this paper, we conceptualize smell from a Data Science and AI perspective, that relates the properties of odorants to how they are sensed and analyzed in the olfactory system from the nose to the brain. Drawing distinctions to color vision, we argue that smell presents unique measurement challenges, including the complexity of stimuli, the high dimensionality of the sensory apparatus, as well as what constitutes ground truth. In the face of these challenges, we argue for the centrality of odorant-receptor interactions in developing a theory of olfaction. Such a theory is likely to find widespread industrial applications, and enhance our understanding of smell, and in the longer-term, how it relates to other senses and language. As an initial use case of the data, we present results using machine learning-based classification of neural responses to odors as they are recorded in the mouse olfactory bulb with calcium imaging.

  PublisherOA PDFDOI

• The Paradigm Shifts in Artificial Intelligence

  Communications of the ACM · 2024 · 32 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Even as we celebrate AI as a technology that will have far-reaching benefits for humanity, trust and alignment remain disconcertingly unaddressed.

  PublisherDOI

• The Paradigm Shifts in Artificial Intelligence

  arXiv (Cornell University) · 2023 · 2 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Kuhn's framework of scientific progress (Kuhn, 1962) provides a useful framing of the paradigm shifts that have occurred in Artificial Intelligence over the last 60 years. The framework is also useful in understanding what is arguably a new paradigm shift in AI, signaled by the emergence of large pre-trained systems such as GPT-3, on which conversational agents such as ChatGPT are based. Such systems make intelligence a commoditized general purpose technology that is configurable to applications. In this paper, I summarize the forces that led to the rise and fall of each paradigm, and discuss the pressing issues and risks associated with the current paradigm shift in AI.

  PublisherOA PDFDOI

• Assumptions Underlying Systems that Support Work Group Collaboration

  CRC Press eBooks · 2020 · 23 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Psychology

  This chapter describes the broad classes of collaborative work support systems discussed in the literature and the ontological primitives they incorporate. Computer-based systems that support managerial work have implicit to them a set of assumptions about those aspects of work being supported that are important, and those that are incidental. The project described in this chapter is a fairly typical type of project involving work group collaboration. The organization in which the system was developed is a large insurance company specializing in “group” benefits. The company is divided into two major functional divisions: insurance and pensions. Most project management systems are based on the Critical Path Method model. The primitives provided by such systems are activities, resources that can be used to carry out the activities, and precedence relationships among the activities. The chapter argues that designers’ ontological assumptions about collaboration influence the primitives of computer-based tools that are designed to support work groups.

  PublisherDOI

• Transforming Finance Into Vision: Concurrent Financial Time Series as Convolutional Nets

  Big Data · 2019-12-01 · 12 citations

  article1st authorCorresponding

  We present a novel representation for multiple synchronized financial time series as images, motivated by deep learning methods in machine vision. The research pursues two related strands of inquiry. The first is to transform concurrent synchronized time series analysis-one that is prevalent in Finance and other domains-into a machine vision problem so that the standard deep learning machinery such as convolutional nets can be applied to the transformed problem. The second line of inquiry pursues the idea of transfer learning, where learning occurs on synthetic simulated data corresponding to a finite set of lead-lag relationships in the concurrent time series, and the learned model is applied out of the box to the application domain, in our case, Finance. The successful application of transfer learning, however, requires that a relationship exists between the simulated and real-world data that the learner is able to discern. This relationship helps to bias the learner toward learning things that will be useful in the application domain. We demonstrate the application of our trained model for identifying data-driven regime shifts in financial time series data. We present an analysis of the results and discuss some of the useful properties of the representation and directions for future research.

  PublisherDOI

• Knowledge-based support systems for long range planning

  Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) · 2018-06-29 · 9 citations

  articleOpen accessSenior author

  Robotics Institute

  PublisherOA PDFDOI

• A Perspective on Natural Language Understanding Capability: An Interview with Sam Bowman

  Big Data · 2017-03-01 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

Frequent coauthors

• Matthias Jarke

  RWTH Aachen University

  10 shared

• Arun Sundararajan

  9 shared

• Hsinchun Chen

  University of Arizona

  8 shared

• Michel Benaroch

  Syracuse University

  6 shared

• Parthasarathy Ranganathan

  Google (United States)

  6 shared

• Albert Croker

  Baruch College

  6 shared

• Foster Provost

  4 shared

• Dashin Chou

  New York University

  4 shared