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DBLPI am a Postdoc researcher at University College London (UCL) developing statistical machine learning models for risk stratification of Prostate Cancer. I was a Research Engineer at University of Leeds worked on FinTech AI which spin-out as 4-XTRA Technologies Ltd. I am a final year PhD student as well as Marie Skłodowska-Curie ITN scholar in IRAC at the University of Bedfordshire, advised by Haiming Liu and co-advised by Ingo Frommholz. I research Quantum Information Retrieval. I study the quantum theory evolving behind information retrieval with focus on user-oriented IR based on Information Foraging Theory. My research spans across formal information retrieval (IR) models, mathematical formalism behind Quantum Theory; Quantum Probability, and their applications in the study of information retrieval, interactive IR in general.
Before graduate school, I received a Bachelors in Computer Science & Engineering from UIET, CSJM University. I was also a part of Google Summer of Code program and the Linux Foundation as an intern with Kubernetes, CNCF and Openstack team at the Open Mainframe Project.
What's new?
Publications
Semantic Hilbert Space for Interactive Image Retrieval
Amit Kumar Jaiswal, Haiming Liu, Ingo FrommholzProceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) (ACM ICTIR), 2021
The paper introduces a model for interactive image retrieval utilising the geometrical framework of information retrieval (IR). We tackle the problem of image retrieval based on an expressive user information need in form of a textual-visual query, where a user is attempting to find an image similar to the picture in their mind during querying. The user information need is expressed using guided visual feedback based on Information Foraging which lets the user perception embed within the model via semantic Hilbert space (SHS). This framework is based on the mathematical formalism of quantum probabilities and aims to understand the relationship between user textual and image input, where the image in the input is considered a form of visual feedback. We propose SHS, a quantum-inspired approach where the textual-visual query is regarded analogously to a physical system that allows for modelling different... ...
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Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach
Amit Kumar Jaiswal, Haiming Liu, Ingo FrommholzProceedings of Bridging the Gap between Information Science, Information Retrieval and Data Science (BIRDS) (SIGIR), 2020
Understanding an information forager's actions during interaction is very important to the study of interactive information retrieval. Although information spread in uncertain information space is substantially complex due to the high entanglement of users’ interacting with information objects (text, image, etc.) and vice versa. However, an information forager, in general, accompanies a piece of information (information diet) while searching (or foraging) alternative contents, typically subject to decisive uncertainty. ...
[ Preprint ] [ Slides ] [BIBTEX ] [Video ]
Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion
Amit Kumar Jaiswal, Haiming Liu, Ingo FrommholzProceedings of 42th European Conference on Information Retrieval (ECIR), 2020
Query Auto-completion (QAC) is a prominently used feature in search engines, where user interaction with such explicit feature is facilitated by the possible automatic suggestion of queries based on a prefix typed by the user. Existing QAC models have pursued a little on user interaction and cannot capture a user’s information need (IN) context. ...
[ Paper ] [Slides ] [BIBTEX ]
Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation
Amit Kumar Jaiswal, Haiming Liu, Ingo FrommholzProceedings of the 11th annual meeting of the Forum for Information Retrieval Evaluation (FIRE), 2019
User implicit feedback plays an important role in recommender systems. However, finding implicit features is a tedious task. This paper aims to identify users' preferences through implicit behavioural signals for image recommendation based on the Information Scent Model of Information Foraging Theory. In the first part, we hypothesise that the users' perception is improved with visual cues in the images as behavioural signals that provide users' information scent during information seeking. ...
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Effects of Foraging in Personalized Content-based Image Recommendation
Amit Kumar Jaiswal, Haiming Liu, Ingo FrommholzThe 2nd International Workshop on ExplainAble Recommendation and Search (EARS), SIGIR, 2019
A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the needs of users and provide them with recommendations to personalize their navigation. However, few studies have addressed the question of what drives the users’ attention to specific content within the collection and what influences the selection of interesting items. To this end, we employ the lens of Information Foraging Theory (IFT) to image recommendation to demonstrate how the user could utilize visual bookmarks to locate interesting images. ...
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Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
Amit Kumar Jaiswal, Ivan Panshin, Dimitrij Shulkin, Nagender Aneja, Samuel AbramovWorkshop Towards Causal, Explainable and Universal Medical Visual Diagnosis, CVPR, 2019
Pathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans. The examination process of such lymph node which encompasses metastasized cancer cells is histopathologically organized. However, the task of finding metastatic tissues is gradual which is often challenging. In this work, we present our deep convolutional neural network based model validated on PatchCamelyon (PCam) benchmark dataset for fundamental machine learning research in histopathology diagnosis. ...
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Identifying pneumonia in chest X-rays: A deep learning approach
Amit Kumar Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna & J. RodriguesJournal of Measurement, 2019
The rich collection of annotated datasets piloted the robustness of deep learning techniques to effectuate the implementation of diverse medical imaging tasks. Over 15% of deaths include children under age five are caused by pneumonia globally. In this study, we describe our deep learning based approach for the identification and localization of pneumonia in Chest X-rays (CXRs) images. Researchers usually employ CXRs for the diagnostic imaging study. Several factors such as positioning of the patient and depth of inspiration can change the appearance of the chest X-ray, complicating interpretation further. ...
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Quantum-like Generalization of Complex Word Embedding: A Lightweight Approach for Textual Classification
Amit Kumar Jaiswal, Guilherme Holdack, Ingo Frommholz, Haiming LiuFGIR, LWDA, 2018
In this paper, we present an extension, and an evaluation, to existing Quantum like approaches of word embedding for IR tasks that (1) improves complex features detection of word use (e.g., syntax and semantics), (2) enhances how this method extends these aforementioned uses across linguistic contexts (i.e., to model lexical ambiguity) - specifically Question Classification -, and (3) reduces computational resources needed for training and operating Quantum based neural networks, when confronted with existing models. ...
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Parsec: A State Channel for the Internet of Value
Amit Kumar JaiswalOngoing Work, 2017
We propose Parsec, a web-scale State channel for the Internet of Value to exterminate the consensus bottleneck in Blockchain by leveraging a network of state channels which enable to robustly transfer value off-chain. It acts as an infrastructure layer developed on top of Ethereum Blockchain, as a network protocol which allows coherent routing and interlocking channel transfers for trade-off between parties. A web-scale solution for state channels is implemented to enable a layer of value transfer to the internet. Existing network protocol on State Channels include Raiden for Ethereum and Lightning Network for Bitcoin. ...
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Mentoring
PostgreSQL, Google Code-In (Oct'18-Dec'18)
Zineng Tang, Currently at UNC Chapel Hill
OpenWISP, Google Code-In (Nov'17-Jan'18)
Talks
- [Oct 2022] Delivered a talk on Neural Ordinary Differential Equations (Neural ODEs) at University College London, United Kingdom.
- [Aug 2019] I gave a talk on Machine Learning for Protein Folding at Jean Golding Institute, University of Bristol, UK.
- [May 2019] I gave an invited talk at UNIPD Department of Information Engineering, University of Padova, Italy.
- [Dec 2017] I gave an invited talk on Cloud native computing for NFV at Open Source Networking Days, National Informatics Centre, Delhi.
- [Dec 2017] I gave an invited talk on Cloud Native Design/Refactoring across Kubernetes at the Kubernetes Contributor Summit at KubeCon+CloudNativeCon Austin, TX, US.
- [Nov 2017] I gave a talk on Big Iron in Little Clouds, or How I learned to make a better OpenStack cloud on z/VM at OpenStack Sydney Summit, Australia.
- [Nov 2017] I gave an invited talk on Hyperledger at BlockchainDevelopers Meetup.
- [June 2017] I gave an invited talk on Ethereum Blockchain at PyDelhi Meetup, India.
- [Mar 2017] I gave a talk on Web Development with Rust at Rust Hack Day, HBTU, Kanpur, India.
- [Jan 2017] I gave an invited talk on Diving into Docker: Developing a Darn Fast, Repeatable Workflow at WikiToLearn Conference, Jaipur, India.
