Publications
Recent Writing
My latest writing on AI/ML is at my personal blog and Medium.
Academia
I spent many years in Academia, here is my CV. Also find my publications on SSRN and Google Scholar.
Coding Projects
My older coding projects are found on my personal project server and Github.
Feature Selection Methods and Feature Selection Curves, RajivShah (Oct 2024)
The honeymoon phase of generative AI is over, LinkedIn (Jun 2024)
GPT-4 Loses: Data Labeling with LLMs, LinkedIn (Oct 2023)
FinBERT beats GPT-4, LinkedIn (Sep 2023)
Lessons earned from Bloomberg GPT, LinkedIn (Aug 2023)
FinBERT beats GPT-3, LinkedIn (Feb 2023)
Reasoning in Large Language Models, Medium (Feb 2023)
Data Science News Sources, Medium (Dec 2022)
Accelerating Document AI, Hugging Face (Nov 2022)
Text style transfer in a spreadsheet using Hugging Face Inference Endpoints, Medium (Nov 2022)
Few shot text classification with SetFit, Medium (Oct 2022)
Getting predictions intervals with conformal inference, Medium (Sep 2022)
Explaining predictions from 🤗 transformer models, Medium (Aug 2022)
Dynamic Adversarial Data Collection, Medium (Aug 2022)
Fine-Tuning for Image Classification using Transformers, Medium (Aug 2022)
The Art of Sprezzatura for Machine Learning: Building Interpretable Models, Medium (Dec 2021)
AI Use Cases for Cyber and Malware Analysts, DataRobot (July 2021)
How MLOps Helps You Dodge The Wrecking Ball of Underspecification, DataRobot (June 2021)
Running Code and Failing Models, DataRobot (Feb 2021)
Using Feature Importance Rank Ensembling (FIRE) for Advanced Feature Selection, DataRobot (Jan 2021)
Predicting Music Genre Based on the Album Cover, DataRobot (Aug 2020)
Improving Model Management in Uncertain Times, DataRobot (Jun 2020)
Using Small Datasets to Build Models, DataRobot (Apr 2020)
AI in Turbulent Times: Navigating Changing Conditions Webinar, DataRobot (Mar 2020)
Stand Up for Best Practices: Misuse of Deep Learning in Nature’s Earthquake Aftershock Paper, Medium (Jun 2019)
Using Machine Learning to Peek Inside the Minds of NFL Coaches, DataRobot (Jan 2019)
Taking Fantasy Football Analytics to the Next Level with Automated Machine Learning, DataRobot (Sep 2018)
Optimization Strategies, Medium (July 2018)
Measure Once, Cut Twice: Moving Towards Iteration in Data Science, DataRobot (Feb 2018)
Using Unlabeled Data to Label Data, Medium (Jan 2018)
Using Google’s Quickdraw to create an MNIST style dataset!, Medium (July 2017)
Using xgbfi for revealing feature interactions, Medium (Aug 2016)
Outlier App, Medium (Jun 2016)
SportVu Analysis, Medium (Apr 2016)
AI/ML Publications
Y Zhao, R Yang, G Chevalier, RC Shah, R Romijnders, Applying deep bidirectional LSTM and mixture density network for basketball trajectory prediction. Optik 158, 266-272
R Shah, R Romijnders, Applying deep learning to basketball trajectories, arXiv preprint arXiv:1608.03793
A Chou, A Torres-Espin, N Kyritsis, JR Huie, S Khatry, R Shah, et al. Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome. Plos one 17 (4), e0265254
B Hodel [R Shah recognized as contributing], Learning to Operate an Excavator via Policy Optimization. Procedia Computer Science Volume 140, 2018, Pages 376-382
Older Academic Publications
Surveillance - Empirical studies on surveillance cameras.
Shah, R. C., & McQuade, Brendan (2016). Surveillance, Security, and Intelligence-Led Policing in Chicago. In (Bennett, Larry; Garner, Roberta and Hague, Euan,eds), Neoliberal Chicago: University of Illinois Press.
Shah, R. C., & Braithwaite, J. (2012). Spread Too Thin: Analyzing the Effectiveness of the Chicago Camera Network on Crime. Police Practice and Research: An International Journal
Shah, R.C. (2010). Effectiveness of Red Light Cameras in Chicago: An Exploratory Analysis
Open Standards
Open standards are publicly available specifications that offer a wealth of economic and technological benefits. Governments around the world are considering mandating open standards, especially in the area of document formats.
Shah, R.C., Kesan, J. P., & Kennis A. Lessons for Government Adoption of Open Standards: A Case Study of the Massachusetts Policy (2008). Journal of Information Technology & Politics 5(4), 387-398. A publicly available draft is at SSRN.
Shah, R. C., & Kesan, J. P. (2009). Running Code as Part of an Open Standards Policy. First Monday 6(1).
Shah, R.C., & Kesan, J.P. (2012). Lost in Translation: Interoperability Issues for Open Standards. I/S: A Journal of Law and Policy for the Information Society 8(1), 113-141.
Shah, R. C., & Kesan, J. P. (draft). An Empirical Study of Open Standards. (A revised version won Best Paper Award for E-Government Track at HICSS 41)
Role of Defaults
Defaults are pre-selected options chosen by the manufacturer or the software developer. Users tend to defer to these pre-selected options. Policymakers can take advantage of this deference in setting defaults.
Shah, R. C., & Kesan, J. P. (2008). Setting Online Policy With Software Defaults. Information, Communication, and Society 11(7), 989-1007.
Shah, R.C., & Sandvig, C. (2008). Defaults as De Facto Regulation: The Case of Wireless Access Points. Information, Communication and Society, 11(1), 25-46.
Kesan, J. P., & Shah, R.C. (2006). Setting Software Defaults: Perspectives from Law, Computer Science and Behavioral Economics. Notre Dame Law Review, 82(2), 583-634.
How Software (and Architecture) Affects Users
This work has largely focused on identifying features or characteristics of code that have significance in regulating behavior. These characteristics are manipulable and are considered governance characteristics, because of their ability to differentially influence behavior. This work has led us to focus on open standards and defaults.
Shah, R. C., & Kesan, J. P. (2007). How Architecture Regulates. Journal of Architectural and Planning Research, 24(4), 350-359.
Shah, R. C., & Kesan, J. P. (2003). Manipulating the Governance Characteristics of Code. Info, 5(4), 3-9.
Development of Software
This work focuses on the development of software with an emphasis on the role of several institutions including universities, firms, consortia, and the open source movement is examined. For each institution, the analysis examines their internal processes and norms that affect the development process. The analysis also examines how each institution emphasizes different social and technical attributes that are embedded in code.
Shah, R. C., & Kesan, J. P. (2009). Recipes for Cookies: How Institutions Shape Communication Technologies. New Media & Society, 11(3), 315-336.
Shah, R. C., & Kesan, J. P. (2005). Nurturing Software: How Societal Institutions Shape the Development of Software. Communications of the ACM, 40(9), 80-85.
Kesan, J. P., & Shah, R. C. (2004). Deconstructing Code. Yale Journal of Law & Technology, 6, 277-389.
Shah, R. C., & Kesan, J. P. (2003). Incorporating Societal Concerns into Communication Technologies. IEEE Technology and Society Magazine, 22(2), 28-33.
How Government Can Shape Software
Government can influence the development of code in many ways. This section focuses on the government’s regulatory power, fiscal power, and the ability to influence intellectual property rights.
Kesan, J. P., & Shah, R. C. (2005). Shaping Code. Harvard Journal of Law & Technology, 18(2), 319-399.
History of the Internet
The Internet's origins date back to the 1960s with government funded research into computer networks. This work traces the history and implications of shifting control over the Internet to the private sector, a process called privatization.
Shah, R. C., & Kesan, J. P. (2007). The Privatization of the Internet's Backbone Network. Journal of Broadcasting and Electronic Media, 51(1), 93-109.
Kesan, J. P., & Shah, R. C. (2001). Fool Us Once Shame on You - Fool Us Twice Shame on Us: What We Can Learn from the Privatizations of the Internet Backbone Network and the Domain Name System. Washington University Law Quarterly, 79(1), 89-220.