Rohit Aggarwal

Professor; David Eccles Emerging Scholar

Department of Operations and Information Systems

Faculty, Tenure Track

Dr. Rohit Aggarwal, a Professor of Information Systems at the University of Utah, specializes in advancing AI systems with applications spanning software development, recruitment, and content creation. His research concentrates on extracting tacit knowledge of human experts and integrating it into AI agents, thereby enhancing their capability to support complex decision-making processes.

His work places a strong emphasis on refining the transparency and explainability of AI models’ decision-making processes. He aims to demystify the inner workings of AI systems, making them more accessible and comprehensible to both experts and lay users alike. This endeavor is crucial for fostering trust, addressing ethical considerations, and promoting responsible AI usage. Through his research, Dr. Aggarwal seeks to develop AI systems that not only provide accurate and reliable results but also offer clear explanations for their decisions.

In addition to his work on explainability, Dr. Aggarwal is involved in developing a single, fine-tuned model that can effectively perform a wide range of tasks within a given domain using only a few-shot prompting technique. This approach eliminates the need to switch between task-specific adapters and layers, streamlining the deployment and utilization of AI models in real-world applications. Creating (synthetic) datasets for instruction tuning AI models using domain-specific tasks and datasets play importat role in this stream of work. Moreover, this initiative plays a crucial role in setting comprehensive benchmarks for evaluating AI systems across multiple dimensions, aiming to standardize performance metrics and ensure robustness in AI-driven solutions.

Another aspect of Dr. Aggarwal’s research involves exploring the application of AI in the realm of advertising, specifically focusing on unique selling propositions (USPs). By leveraging the power of large language models (LLMs) and computer vision (CV) techniques, Dr. Aggarwal and his team are developing innovative methods to extract and analyze USP features from advertisements and landing pages. By understanding the most effective USPs in their industry, companies can refine their advertising campaigns, create more targeted content, crafting compelling narratives around these key differentiators, and ultimately improve their market positioning.

Dr. Aggarwal has published in premium business journals such as Management Science, Information Systems Research, MIS Quarterly, and POM, showcasing his extensive contributions to the academic community. He currently serves as SE at POM.

Former PhD students:

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Education

BE 2000, Dept. of Chemical Engg. & Tech. (DCET), Panjab University, Chandigarh

MBA 2002, Marketing, Management Development Institute (MDI), Gurgaon

Ph.D. 2008, Operations and Management Information Systems, University of Connecticut. Project: Essays on Electronic Word-of-Mouth (eWOM): Effect on Firms and Influence on Venture Capitalists’ Investment Decisions

Honors & Awards

Emerging Scholar Award. University of Utah, 10/2008

Doctoral Dissertation Fellowship Award. University of Connecticut, Storrs, CT, 2008

Teaching

IS 7970-002 Thesis Research-PhD

IS 4460-001 Web Based Applications

IS 6465-001 Web Based Applications

IS 6482-002 Data Mining

IS 7970-001 Thesis Research-PhD

Publications

Rohit Aggarwal, Michael Lee & Vishal Midha (2021). Differential Impact of Content in Online Communication on Heterogeneous Candidates: A Field Study in Technical Recruitment. Information Systems Research. Accepted, 12/2021.

Rohit Aggarwal, Vishal Midha & Nicholas Sullivan (2021). Superlatives and Scope of Improvement in Online Recommendations: Breath of Life or a Kiss of Death?. MIS Quarterly. Published, 05/2021.
https://www.misq.org/skin/frontend/default/misq/pd…

Rohit Aggarwal, Michael Lee, Braxton Osting & Harpreet Singh (2021). Improving Funding Operations of Equity-based Crowdfunding Platforms. Production and Operations Management. Accepted, 05/2021.

Rohit Aggarwal, Vishal Midha & Nicholas Sullivan (2021). Effect of Online Professional Network Recommendations on the Likelihood of Interview: A Field Study. Information Systems Research. Accepted, 05/2021.

Rohit Aggarwal, Vishal Midha & Nicholas Sullivan (2021). The Effect of Gender Expectations and Physical Attractiveness on Discussion of Weakness in Online Professional Recommendations. Information Systems Research. Accepted, 02/2021.

Rohit Aggarwal, David Kryscynski & Harpreet Singh (2015). Evaluating Venture Technical Competence in VC Investment Decisions. Management Science. Vol. 61. Published, 11/2015.
http://pubsonline.informs.org/doi/abs/10.1287/mnsc…– opens new window

Harpreet Singh, Rohit Aggarwal & Irina Cojuharenco (2015). Strike a Happy Medium: The Effect of IT Knowledge on Venture Capitalists’ Overconfidence in IT Investments. MIS Quarterly. Vol. 39. Published, 10/2015.
http://misq.org/strike-a-happy-medium-the-effect-o…– opens new window

Rohit Aggarwal, David Kryscynski, Vishal Midha & Harpreet Singh (2015). Early to Adopt and Early to Discontinue: the impact of self-perceived and actual IT-knowledge on technology use behaviors of non-IT professionals. Information Systems Research. Published, 03/2015.
http://pubsonline.informs.org/doi/abs/10.1287/isre…– opens new window

Rohit Aggarwal & Harpreet Singh (2013). Differential Influence of Blogs across Different Stages of Decision Making: the Case of Venture Capitalists. MIS Quarterly. Vol. 37. Published, 05/2013.
http://misq.org/differential-influence-of-blogs-ac…– opens new window

Rohit Aggarwal, Ram Gopal, Alok Gupta & Harpreet Singh (2012). Putting Money where Mouths are: the Relation between New Venture Financing and Electronic Word-of-Mouth. Information Systems Research. Vol. 23. Published, 10/2012.
http://pubsonline.informs.org/doi/abs/10.1287/isre…– opens new window

Rohit Aggarwal, Ram Gopal, Ramesh Sankaranarayanan & Param Vir Singh (2012). Blog, Blogger and the Firm: Can Negative Posts Lead to Positive Outcomes. Information Systems Research. Vol. 23. Published, 06/2012.
http://pubsonline.informs.org/doi/abs/10.1287/isre…

Service

Workshop on Information Technologies and Systems. Track Chair: 18th Workshop on Information Technology and Systems (WITS), Paris, France. 12/2008 – 12/2008

Workshop on Information Technologies and Systems. Program Committee: 18th Workshop on Information Technology and Systems (WITS), Paris, France, Dec 13-14 2008. 06/2008 – 12/2008

Capstone Projects Coordinator: This role includes clarifying expectations to students, coordinating with project sponsors, handling information sessions during semesters, meeting students whenever required, and keeping track of the progress of projects. I also actively seek out projects from companies for which students are interested in working with. 08/21/2012 – present. Department service.

Interim Director, Information Systems Programs. 08/21/2012 – 12/09/2012. Department service.

I have been scheduling talks of CEOs and VCs such as Vinod Khosla, and Ram Shriram at the school level. 08/2008 – present. College service.

Operations and Information Systems, Branding Committee. Member, 2008 – present. Department service.

Research Summary

Dr. Rohit Aggarwal, a Professor of Information Systems at the University of Utah, specializes in advancing AI systems with applications spanning software development, recruitment, and content creation. His research concentrates on extracting tacit knowledge of human experts and integrating it into AI agents, thereby enhancing their capability to support complex decision-making processes.

His work places a strong emphasis on refining the transparency and explainability of AI models’ decision-making processes. He aims to demystify the inner workings of AI systems, making them more accessible and comprehensible to both experts and lay users alike. This endeavor is crucial for fostering trust, addressing ethical considerations, and promoting responsible AI usage. Through his research, Dr. Aggarwal seeks to develop AI systems that not only provide accurate and reliable results but also offer clear explanations for their decisions.

In addition to his work on explainability, Dr. Aggarwal is involved in developing a single, fine-tuned model that can effectively perform a wide range of tasks within a given domain using only a few-shot prompting technique. This approach eliminates the need to switch between task-specific adapters and layers, streamlining the deployment and utilization of AI models in real-world applications. Creating (synthetic) datasets for instruction tuning AI models using domain-specific tasks and datasets play importat role in this stream of work. Moreover, this initiative plays a crucial role in setting comprehensive benchmarks for evaluating AI systems across multiple dimensions, aiming to standardize performance metrics and ensure robustness in AI-driven solutions.

Another aspect of Dr. Aggarwal’s research involves exploring the application of AI in the realm of advertising, specifically focusing on unique selling propositions (USPs). By leveraging the power of large language models (LLMs) and computer vision (CV) techniques, Dr. Aggarwal and his team are developing innovative methods to extract and analyze USP features from advertisements and landing pages. By understanding the most effective USPs in their industry, companies can refine their advertising campaigns, create more targeted content, crafting compelling narratives around these key differentiators, and ultimately improve their market positioning.

Dr. Aggarwal has published in premium business journals such as Management Science, Information Systems Research, MIS Quarterly, and POM, showcasing his extensive contributions to the academic community. He currently serves as SE at POM.

Former PhD students: