Vic Anand

Vic Anand

Assistant Professor of Accountancy and John Deere Analytics Teaching Fellowship in Accountancy

  • Email
  • LinkedIn

Contact

206 Wohlers Hall

1206 S. Sixth

Champaign, IL 61820

217-300-3109

vanand@illinois.edu

Vita

Google Scholar

SSRN

Website

Update Your Profile Refresh Your Profile

Listings

Educational Background

  • Ph.D., Accounting, Cornell University, 2014
  • M.S., Accounting, Cornell University, 2013
  • M.B.A., Carnegie Mellon University, 2000
  • S.B., Mechanical Engineering, Massachusetts Institute of Technology, 1995

Positions Held

  • Assistant Professor, Department of Accountancy, University of Illinois at Urbana-Champaign, 2017 to present
  • Assistant Professor of Accounting, Emory University, Goizueta Business School, 2012-2017
  • Senior Analyst, Science Applications International Corporation, 2001-2003
  • Senior Consultant, Deloitte Consulting, 2000-2001
  • Financial Analyst, Ford Motor Company, 1997-1998
  • Manufacturing Engineer, Ford Motor Company, 1995-1997

Recent Publications

  • Anand, V., Balakrishnan, R., & Labro, E. (2018). A Framework for Conducting Numerical Experiments on Cost System Design. Journal of Management Accounting Research.
  • Anand, V., Balakrishnan, R., & Labro, E. (2017). Obtaining Informationally Consistent Decisions When Computing Costs with Limited Information. Production and Operations Management, 26 (2), 211 - 230.  link >

Other Publications

Articles

  • Valero-Cuevas, F., Anand, V., Saxena, A., & Lipson, H. (2007). Beyond Parameter Estimation: Extending Biomechanical Modeling by the Explicit Exploration of Model Topology. IEEE Transactions on Biomedical Engineering, 54 (11), 1951-1964.

Conference Proceedings

  • Anand, V., Lipson, H., & Valero-Cuevas, F. (2005). Blind Inference of Nonlinear Cable Network Topology from Sparse Data. Proceedings of the 2005 Genetic and Evolutionary Computation Conference.
  • Valero-Cuevas, F., Lipson, H., Santos, V., & Anand, V. (2005). Shifting to Population-Based Models and Inferring Model Structure from Data are Two Directions That Will Enhance the Clinical Usefulness of Modeling. Proceedings of the ISB XXth Congress and ASB 29th Annual Meeting.
  • Spears, W., & Anand, V. (1991). A Study of Crossover Operators in Genetic Programming. Sixth International Symposium of Methodologies for Intelligent Systems.

Presentations

  • Anand, V. (2018). Invited presentation. University of Alberta Accounting Research Conference.
  • Anand, V. (2016). Invited presentation. Michigan State University.
  • Anand, V. (2016). Invited presentation. University of Pittsburgh Accounting Seminar.
  • Anand, V. (2014). Invited presentation. Michigan State University.

Working Papers

  • Anand, V. Target Horizons, Uncertainty, and Effort Provision.  link >
  • Anand, V., Webb, A., & Wong, C. Exploring the Consequences of Frequent Feedback about Goal Progress.  link >
  • Anand, V., Bochkay, K., Chychyla, R., & Leone, A. Using Python for Text Analysis in Accounting Research.
  • Anand, V., & Balakrishnan, R. Capacity Planning with Limited Information.
  • Anand, V., Sougiannis, T., Brunner, R., & Ikegwu, K. The Prediction of Profitability Using Machine Learning.  link >

Honors and Awards

  • Arthur Andersen Fellowship in Accountancy, University of Illinois at Urbana-Champaign, Department of Accountancy, 2020 to present
  • John Deer Analytics Teaching Fellowship in Accountancy, University of Illinois at Urbana-Champaign, Department of Accountancy, 2019-2020
  • List of Teachers Ranked as Excellent by Their Students, University of Illinois at Urbana-Champaign, 2018, 2019
  • R.C. Evans Data Analytics Fellow, University of Illinois-Deloitte Foundation Center for Business Analytics, 2017-2019

Current Courses

  • Data Analytics Foundations (ACCY 570) Concepts and foundations underlying data analytics for accounting. Provides fundamental knowledge of how to acquire, organize, synthesize and analyze (possibly large) volumes of data to address questions and problems. After completing this course, students should (1) have a foundational understanding of the techniques underlying data analytics, (2) recognize scenarios and identify appropriate tools for various types of data analysis and (3) use Python and Tableau to perform data analysis.

  • Special Research Problems (ACCY 593) Individual investigations or research projects selected by the students, subject to approval by the graduate adviser and the executive officer of the Department.

Contact

206 Wohlers Hall

1206 S. Sixth

Champaign, IL 61820

217-300-3109

vanand@illinois.edu

Vita

Google Scholar

SSRN

Website

Update Your Profile Refresh Your Profile