Amit Kumar Jaiswal

amitkumarj441 [at] gmail [dot] com
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I am a third-year PhD student as well as a Marie Curie Researcher 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 a wide range of topics in this area, including existing Quantum-IR frameworks, mathematical formalism behind Quantum Theory; Quantum Probability, and their applications in the study of information retrieval (IR), interactive IR in general. My work is supported by EU Horizon 2020 research under the Marie Skłodowska-Curie grant No. 721321.

Before coming to University of Bedfordshire, I received a Bachelors in Computer Science & Engineering from University Institute of Engineering & Technology, CSJM University, Kanpur. I was previously interned with Kubernetes and OpenShift team under the Google Summer of Code program in the summer of 2017, followed by another software engineering internship with OpenStack team at IBM Mainframe team under the Linux Foundation.

What's new?

  • [Dec 2019] Full paper titled "Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion" has been accepted in ECIR 2020.
  • [Oct 2019] Short paper titled "Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation" has been accepted in FIRE 2019.
  • [Sept 2019] Received DAAD Grant to attend "Autumn School of Information Retrieval and Information Foraging 2019" at Dagstuhl [Slides].
  • [Aug 2019] I have been selected for the Collaborative Research Hackathon organized by the Alan Turing Institute at University of Bristol [Published Report].
  • [June 2019] Paper titled Effects of Foraging in Personalized Content-based Image Recommendation accepted by EARS'19, SIGIR 2019.
  • [Apr 2019] Visiting researcher in Department of Information Engineering, University of Padova, Italy under Prof. Massimo Melucci.
  • [Feb 2019] I served as a Web Chair of CHILites (SIGCHI) 2019
  • [Dec 2018] Presented my first year PhD progress in QUARTZ MTRM at University of Padova, Italy.
  • [Nov 2018] I have been selected for fee waiver scholarship, attending Search Solutions 2018 [Blogpost].
  • [Nov 2018] I have presented my research progress at Signal Media Research. Link to my talk.
  • [Oct 2018] I have been selected for full grant to attend H2O AI Conference happening in London!
  • [Oct 2018] I have been selected to attend Google London Meetup.
  • [Sept 2018] I gave a talk at Bradenburg University of Technology, Cottbus, Germany in QUARTZ Autumn School.
  • [Sept 2018] I gave a talk in FDIA Workshop at ICTIR 2018 on my recent paper titled "Investigating Interactive Information Retrieval via Information Foraging Theory".
  • [Aug 2018] I gave a talk at University of Mannheim, Germany on our recent paper.
  • [June 2018] I gave a talk at the Putteridge Burry at University of Bedfordshire on my progress toward Ph.D. project.
  • [Apr 2018] I joined University of Bedfordshire as a postgrad student. I am a part of IRAC and QUARTZ group where I work on applying quantum theory to interactive search.


Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
Proceedings 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 Frommholz
Proceedings 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. ...

[ Paper ]  [ Slides ]  [ BIBTEX ]

Effects of Foraging in Personalized Content-based Image Recommendation

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
The 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. ...

[ Paper ]  [ Poster ]  [ Slides ]  [ BIBTEX ]

Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases

Amit Kumar Jaiswal, Ivan Panshin, Dimitrij Shulkin, Nagender Aneja, Samuel Abramov
Workshop 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. ...

[ Paper ]  [ Poster ]  [ BIBTEX ]

Identifying pneumonia in chest X-rays: A deep learning approach

Amit Kumar Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna & J. Rodrigues
Journal 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. ...

[ Paper ]  [ Code]  [ BIBTEX ]

Investigating Interactive Information Retrieval via Information Foraging Theory

Amit Kumar Jaiswal
The Eighth BCS-IRSG Symposium on Future Directions in Information Access (FDIA) Workshop, ACM ICTIR, 2018

This research aims to understand the extent in which Information Scent, Information Patch and Information Diet, features of Information Foraging Theory (IFT), address the conceptual issues of information behaviour research by reviewing approaches to information interaction in the context of information seeking and retrieval. As people become more acquainted to using the Web for finding information, they are progressively using it for addressing ever more complex and several major information problems. ...

[ Paper ]  [ BIBTEX ]

Quantum-like Generalization of Complex Word Embedding: A Lightweight Approach for Textual Classification

Amit Kumar Jaiswal, Guilherme Holdack, Ingo Frommholz, Haiming Liu
FGIR, 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. ...

[ Paper ]  [ BIBTEX ]

Parsec: A State Channel for the Internet of Value

Amit Kumar Jaiswal
Ongoing 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. ...

[ Preprint ]  [ Code ]  [ BIBTEX ]

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