My Research

Fairness in Machine Learning PROCESS

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What is Fairness in the Machine Learning Process?

Coming soon***

Assessing Perceived Fairness in Machine Learning (ML) Process: A Conceptual Framework 

In ML applications, “unfairness” can be caused by bias in the data, curation process, erroneous assumptions, and implicit bias rendered within the algorithmic development process. As ML applications come into broader use, developing fair ML applications is critical. Assessing fairness and developing fair ML applications has become important in the era of Responsible AI in practice in research, industry, and academia. However, a literature survey suggests that fairness in ML is very subjective, and there is no coherent way to describe the fairness of AI/ML processes and applications. To better understand the perception of fairness in the ML process, we conducted virtual focus groups with developers, reviewed prior literature, and integrated notions of justice theory to propose that perceived fairness is a multidimensional concept. In this paper, we will explore the initial outcomes of this effort.

Click on the left image for enlarged view**

Mishra, A., Khazanchi, D. (2023). Assessing Perceived Fairness in Machine Learning (ML) Process: A Conceptual Framework, NeurIPS workshop on Algorithmic Fairness through the Lens of Time, New Orleans


Assessing Perceived Fairness from Machine Learning (ML) Developer's Perspective 


Fairness in machine learning (ML) applications is an important practice for developers in research and industry. In ML applications, unfairness is triggered due to bias in the data, curation process, erroneous assumptions, and implicit bias rendered within the algorithmic development process. As ML applications come into broader use developing fair ML applications is critical. Literature suggests multiple views on how fairness in ML is described from the users perspective and students as future developers. In particular, ML developers have not been the focus of research relating to perceived fairness. This paper reports on a pilot investigation of ML developers perception of fairness. In describing the perception of fairness, the paper performs an exploratory pilot study to assess the attributes of this construct using a systematic focus group of developers. In the focus group, we asked participants to discuss three questions- 1) What are the characteristics of fairness in ML? 2) What factors influence developers belief about the fairness of ML? and 3) What practices and tools are utilized for fairness in ML development? The findings of this exploratory work from the focus group show that to assess fairness developers generally focus on the overall ML application design and development, i.e., business-specific requirements, data collection, pre-processing, in-processing, and post-processing. Thus, we conclude that the procedural aspects of organizational justice theory can explain developers perception of fairness. The findings of this study can be utilized further to assist development teams in integrating fairness in the ML application development lifecycle. It will also motivate ML developers and organizations to develop best practices for assessing the fairness of ML-based applications.

Mishra, A., Khazanchi, D., (2023). Assessing Perceived Fairness from Machine Learning Developer's Perspective arXiv preprint arXiv: 2304.03745. 

Mishra,A.(2022). Perceived Fairness from User's and Developer's Perspective in Machine Learning Systems. The 18th Annual Big XII + MIS Research Symposium, Houston 


Applied ML in Structural Health Monitoring 

Generative AI in Structural Health monitoring

Coming soon***

LLMs in structural health monitoring; simulations

Practicing Responsible AI in Structural Health monitoring

Coming soon***

Working paper: Mishra, A., Vijayvargiya, I., Gangisetti, G., & Khazanchi, D. (2024). Responsible AI Framework in Structural Health Monitoring

Tech stack: Self-supervised Learning (vision transformers), design of experiments, Fairness in the Process, human-in-loop, data collection policy

Weakly Supervised Crack Segmentation Using Crack Attention Networks(CrANET) On Concrete Structures

Crack detection or segmentation on concrete structures is a vital process in structural health monitoring (SHM). Though supervised machine learning techniques have gained tremendous success in this domain, data collection and annotation continue to be challenging. Image data collection is challenging, tedious, and laborious, including accessing representative datasets and manually labeling training data in the SHM domain. According to the literature, there are significant issues with the hand-annotation of image data. To address this gap, we proposed a two-stage weakly supervised learning framework utilizing a novel “crack attention network (CrANET)” with an attention mechanism to detect and segment cracks on images with no human annotations in pixel-level labels. This framework classifies concrete surface images into crack or no-cracks and then uses gradCAM visualization to generate crack segmentation. Professionals and domain experts subsequently evaluate these segmentation results via a human expert validation study. As the literature suggests that weakly supervised learning (WSL) is a limited practice in SHM, this research title will motivate researchers in SHM to research and develop a weakly supervised learning approach processing as state of the art. 


An Investigation into the Advancements of Edge-AI Capabilities for Structural Health Monitoring 

The aim of this study is to investigate the capabilities of edge-AI in the field of structural health monitoring, with a particular emphasis on detecting cracks in concrete bridges. Comprehensive literature suggests that edge-AI approaches have not been utilized in the structural health monitoring domain (SHM). This research work proposed two novel frameworks: an edge-AI framework for the SHM domain and a cloud-edge adaptive intelligence for crack detection (CEAIC) to utilize edge-AI approaches for real-time scenarios. The framework incorporates a novel edge-AI framework, the crowd intelligence approach, and the CrANET framework to perform weakly supervised crack segmentation. Quantization approaches are used to transform the deep learning model into an optimized model to be compatible with edge devices. The edge-AI experiments for the cracks detection task are conducted using Kneron KL520 and Google Coral development board. A responsive website has been developed to demonstrate the real-world implications of the CEAIC framework. This study has the potential to provide a cost-effective and reliable solution for real-time monitoring and assessment of concrete bridge cracks, thereby improving the safety and longevity of bridges. The outcomes of this research endeavor will furnish us with invaluable insights regarding the feasibility and potential advantages of incorporating edge AI into the SHM domain. 


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Mishra, A., Gangisetti, G., & Khazanchi, D. (2024). An Investigation into the Advancements of Edge-AI Capabilities for Structural Health Monitoring"  accepted in IEEE Access

Mishra, A., Gangisetti, G., Khazanchi, D., (2023). Integrating Edge-AI in Structural Health Monitoring Domain arXiv preprint arXiv: 2304.03718 


AI/ML and IT Project Management

A Proposal for Research on the Application of AI/ML in ITPM: Intelligent Project Management  


According to the market research firm Tractica, the global artificial intelligence software market is forecast to grow to 126 billion by 2025. Additionally, the Gartner group predicts that during the same time as much as 80% of the routine work ,  which represents the bulk of human hours spent in today's project management (PM) activities, can be eliminated because of collaboration between humans and smart machines. Today's PM practices rely heavily on human input. However, that is not the optimum use of the human project manager's intuitive, innovative, and creative abilities. Many aspects of a project manager's work could be managed by machines that utilize AI/ML approaches to address nonroutine and predictive tasks. This paper describes IT project management (ITPM) processes and associated tasks and identifies the AI/ML approaches that can support them.


Mishra, A., Khazanchi, D., Tripathi, A. (2022). A Proposal for Research on the Application of AI/ML in ITPM: Intelligent Project Management, International Journal of Information Technology Project Management (IJITPM) 

Edge-AI and Hardware Accelerated Machine Learning

Exploring Bitslicing Architectures for Enabling FHE-assisted Machine Learning 

Homomorphic Encryption (HE) is the ultimate tool for performing secure computations, even in untrusted environments. Application of HE for Deep Learning (DL) inference is an active area of research, given the fact that DL models are often deployed in untrusted environments (e.g. third-party servers) yet inferring on private data. However, existing HE libraries (somewhat (SWHE), leveled (LHE) or fully homomorphic (FHE)) suffer from extensive computational and memory overhead. Few performance optimized high-speed homomorphic libraries are either suffering from certain approximation issues leading to decryption errors or proven to be insecure according to recent published attacks. In this paper, we propose architectural tricks to achieve performance speedup for encrypted DL inference developed with exact HE schemes without any approximation or decryption error in homomorphic computations. The main idea is to apply quantization and suitable data packing in the form of bitslicing to reduce the costly noise handling operation, Bootstrapping while achieving a functionally correct and highly parallel DL pipeline with a moderate memory footprint. Experi-mental evaluation on the MNIST dataset shows a significant (37X) speedup over the non-bitsliced versions of the same architecture. Low memory bandwidths (700MB) of our design pipelines further highlight their promise towards scaling over larger gamut of Edge-AI analytics use cases.

S. Sinha et al., "Exploring Bitslicing Architectures for Enabling FHE-assisted Machine Learning," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022, doi: 10.1109/TCAD.2022.3204909. URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9920696&isnumber=6917053

Soumik Sinha, Sayandeep Saha, Manaar Alam, Ayantika Chatterjee, Anoop Mishra, Deepak Khazanchi and Debdeep Mukhopadhyay, Exploring Bitslicing Architectures for Enabling FHE-assisted Machine Learning, Accepted at CASES: International Conference on Compilers, Architectures, and Synthesis for Embedded Systems, Embedded Systems Week, OCTOBER 07-14, 2022, HYBRID-SHANGHAI; Youtube Link: https://youtu.be/VHsj-XYniHw

Evaluating Visual Search Performance Using Artificial Intelligence

Glaucoma patients experience difficulty with a number of daily activities, including reading, mobility, and driving. Visual search is instrumental to rapidly localizing target objects among competing objects within our environment. While numerous studies have evaluated factors that influence visual search performance, limited evidence is available for how these factors affect visual search in glaucoma patients. Our core hypothesis is that environmental scene context and image characteristics fundamentally influence visual search performance in glaucoma. We propose a conceptual model for evaluating visual search performance in glaucoma patients using artificial intelligence and image processing techniques. Here, the Berlin object in the scene database (BOiS) is used. This database contains 130 high-resolution naturalistic images intended to evaluate visual search performance in a real-world setting. We proposed a model that will improve our understanding of glaucoma-related impairments and their impact on visual search will help to improve the quality of life for a significant portion of the population.

Paper Link: Mishra, A., Anderson, D., Belcher, S., & Khazanchi, D. (2020). Evaluating visual search in glaucoma using deep learning. In 26th Americas Conference on Information Systems, AMCIS 2020. Association for Information Systems. 

AIforSocialGood Symposium at CMU Pittsburgh: https://youtu.be/f9EDTUULtR4

Research presentation: https://digitalcommons.unomaha.edu/srcaf/2020/schedule/179/

Internet-Of-Nano-Things (IoNT)

The Internet of things (IoT) is a network of interconnected devices for exchange of data and information. Since traditional IoT applications are limited by the size of devices, the emergence of nanotechnology has resulted in the development of a new category of connected devices broadly referred to as the Internet of nano things (IoNT).  IoNT includes the miniaturized replacement for traditional IoT sensors for communication, data collection, data transfer, and data processing. IoNT as a concept is still relatively new and has drawn attention from industry, academic researchers, and professional practitioners but needs lot more research in terms development and applications. In this chapter, drawing from the artificial intelligence (AI), IoT, and IoNT literature, we describe the current state of IoNT research, its implications, and propose how artificial intelligence (AI) can be leveraged in future IoNT applications.

Keywords: Internet-of-things, Internet-of-nano-things, artificial intelligence, machine learning

Publication  Link: Mishra, A., Tripathi, A., Khazanchi, D., Hiran, K. K., Vyas, A. K., & Padmanaban, S. (2021). A framework for applying artificial intelligence (AI) with Internet of NanoThings (IoNT). In Machine Learning for Sustainable Development (pp. 1-16). De Gruyter.

Minitrack Link: Mishra, A., Khazanchi, D., & Tripathi, A. (2021, January). Introduction to the Minitrack on Internet of Nano Things (IoNT). In Proceedings of the 54th Hawaii International Conference on System Sciences (p. 4577).   

Research Presentation: https://digitalcommons.unomaha.edu/srcaf/2021/schedule/17/ 

MARIO- Modular Robots for Assistance in Robust and Intelligent Operations

KitchenSimulation.mp4

Intelligent Human-Aware Decision-Making for Semi-Autonomous Human Rehabilitation Assistance Using Modular Robots

Modular Self-reconfigurable Robots (MSRs) are robots that can adapt their shape and mobility while performing their operations. In this research, we consider the locomotion or gait adaptation problem for MSRs in the context of an MSR called MARIO (Modular Robots for Assistance in Robust and Intelligent Operations). The objective of MARIO is to assist patients with spinal cord injury in performing daily living tasks such as fetching out-of-reach objects, e.g., TV remotes, pillows or laundry baskets which leads towards rehabilitation assistance. To address the locomotion problem of MARIO, we propose to use a machine learning-based framework called deep reinforcement learning (DRL) that enables MARIO to autonomously adapt its actions while performing navigation to move towards and reach the object being fetched and bring it back to the human user. We extend the DRL framework with a semi-autonomous decision-making technique called shared autonomy where MARIO intelligently decides to relinquish control to the human when the human wants to intervene to maneuver MARIO during its movements to fetch the object. Our techniques have been validated within an accurately simulated version of MARIO on the Webots robot simulator within daily living environments such as a kitchen and a living room. Our results show that as compared to a reactive controller for MARIO’s motion control, using deep reinforcement learning improves the average time required by MARIO to perform fetching tasks by up to 70 percent when MARIO has to travel longer distances, around 11 meters, to fetch an object, and by up to 85 percent for shorter fetching distances, around 6 meters. Corresponding improvements in average time while integrating shared autonomy with deep reinforcement learning are up to 18 percent when MARIO has to travel longer distances around 11 meters, and by up to 30 percent for shorter fetching distances around 6 meters.

This work was successfully defended in May, 2019 as a Master's Thesis at University of Nebraska at Omaha; Graduate Thesis Advisor: Dr. Prithviraj Dasgupta.; Project WebsiteLink

Dasgupta, P., Mishra, A., Nelson, C., Burnfield, J. (2019) “Towards Intelligent Semi-Autonomous Control of a Modular Robot for Human Mobility Assistance,” 2019 Do Good Robotics Symposium (DGRS'19), October 3-4, 2019, College Park, MD. 

Mishra, A. (2019). Intelligent Human-aware Decision Making for Semi-autonomous Human Rehabilitation Assistance Using Modular Robots (Link: Master’s dissertation, University of Nebraska at Omaha).

Mishra, A, Dasgupta, P., Intelligent & Human-Aware Decision Making for Semi-Autonomous Human Rehabilitation Assistance using Modular Robots, Nebraska Academy of sciences & NASA Nebraska space grant 2018(Poster) [Conference program link]

Mishra, A, Dasgupta, P., Intelligent & Human-Aware Decision Making for Semi-Autonomous Human Rehabilitation Assistance using Modular Robots, Student Research and Creativity Fair, Office of Creative and Research Activity, University of Nebraska at Omaha, 2018(Presentation) (Link)

Mishra, A, "Intelligent and Human-Aware Decision Making for Semi-Autonomous Human Rehabilitation Assistance using Modular Robots", CS Workshop 2018, The University of Nebraska at Omaha  (Link)

User interactions in Stack overflow

Stack overflow data; data source: Google BigQuery dataset( Link). 

Analyzed trends based on programming languages, category of questions, answers & posts, and important topics; 

Classified the potential posts from the overall posts posted by the public. 

Tech stack: Natural language processing, machine learning techniques, and high-performance computing in HCC.

Mentor: Dr. Harvey Siy.