Category: Research Spotlight

Research article by Dr Tanmoy Chakrabarty published in Physical Review B

Ram Kumar Research Spotlight

A research article by Dr Tanmoy Chakrabarty, Assistant Professor, Physics, SIAS titled CaFe2O(PO4)2: A compound with S=5/2 corner sharing triangular saw-tooth chains has been published in Physical Review B. In this publication, Dr Chakrabarty is one of the two corresponding authors.

Geometric frustration and one-dimensional magnetism boost quantum fluctuations and leads to unusual states of quantum matter. To explore these effects, we used solid state nuclear magnetic resonance (SSNMR), which is a strong local probe to extract the true spin susceptibility and the spin network of a magnetic system.

Here we studied the magnetic behaviour of a well-separated S = 5/2 saw-tooth spin chain compound CaFe2O(PO4)2, which has two different 31P sites in the unit cell. Magnetic susceptibility, heat capacity, and 31P NMR measurements show evidence of strong magnetic frustration (with a frustration parameter ≈ |θCW|/TN) about 70) in CFPO. The major finding of our work came from the NMR measurements which show a broad maximum near 70 K , reflecting the low-dimensional nature and presence of short-range correlations. It is important to note that such a broad maximum feature is not seen in the bulk χ(T) or in heat capacity. Further investigating this broad maximum feature, we conclude that CFPO represents an interesting realization of a frustrated S = 5/2 sawtooth spin-chain.


(a) local environment of P1 and P2 in CFPO.  (b) (T) vs. T at various magnetic fields. (c) Plot of T-dependence of the normalized 31P NMR spectra measured as a function of frequency

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Krea faculty co-author research article published in the Journal of Data Science and Intelligent Systems

Ram Kumar Research Spotlight

A research article titled Modeling Markers for Detection of Psychiatric Disorders Using EEG Signals has been co-authored by Dr Lakshman Varanasi, Assistant Professor, Biological Sciences, SIAS; Varun Viswanathan, Visiting Assistant Professor, Psychology, SIAS; Dr Debasish Mishra, Assistant Professor, Data Science and Information Systems, IFMR GSB; and Steven Chris, Teaching Fellow, Data Science and Information Systems, IFMR GSB. The paper has been published in the Journal of Data Science and Intelligent Systems.

Abstract

The diagnosis of mental (psychiatric) disorders is challenging, and there is a lack of consensus on objective diagnostic criteria that are based on definitive signs that accompany the disorder. There is a need, therefore, to develop objective tools for the examination of these disorders. We present here a novel machine learning (ML) approach that accurately identifies disorders. The approach uses electroencephalography (EEG) signals for diagnosis, which are processed to extract novel region based markers that are found to contain key information about the types of disorders. Subsequently, a support vector machine (SVM) classifier is modeled, integrated with sequential feature (marker) selection (SFS), which identifies optimal and compact marker subsets for disorder detection. The proposed system has been validated using a publicly available dataset. The developed model was benchmarked against existing models and was shown to perform superior to the models it was extensively compared with; it demonstrated a 98.33% accuracy in detecting obsessive-compulsive disorder (OCD). Our findings indicate that an accurate psychiatric diagnosis system can be achieved using EEG signals with significantly fewer, and more interpretable markers. This simpler and transparent approach improves the practicality and trustworthiness of AI/ML-driven diagnostic tools, making them more suitable for real-world clinical integration and understanding by medical professionals.

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Dr Rakesh Sengupta’s paper published in the ACOIT 2025 proceedings

Ram Kumar Research Spotlight

A paper by Dr Rakesh Sengupta, Assistant Professor, Psychology, SIAS, titled ‘Evaluating Continuous-Time Recurrent Neural Networks for State-Dependent EEG Forecasting’ has been published in the proceedings of the 2025 2nd Asian Conference on Intelligent Technologies (ACOIT).

About the Research
The paper explores how short-term brain activity (EEG signals) can be more accurately predicted using lightweight AI models. Such forecasting is critical for real-time neurotechnologies, including Brain-Computer Interfaces (BCIs) and neurofeedback systems. The study benchmarks a Continuous-Time Recurrent Neural Network (CTRNN) against both classical methods and complex deep learning models. It finds that even a compact, highly interpretable CTRNN can effectively capture the non-linear dynamics of human brain activity, offering a competitive alternative to “black-box” AI models in real-time BCI applications.

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Dr Ramadas N publishes a research paper in Physical Review A

Ram Kumar Research Spotlight

Dr Ramadas N, Post-Doctoral Fellow, Physics, Division of Sciences, SIAS, published a research paper titled Minimal decomposition entropy and optimal representations of absolutely maximally entangled states in Physical Review A.

Abstract

Understanding and classifying multipartite entanglement is fundamental to quantum information processing. This work focuses on absolutely maximally entangled (AME) states, a class of highly entangled states characterized by their maximal entanglement across any bipartitions. To analyze and classify AME states, we employ the minimal decomposition entropy, defined as the minimum R\'{e}nyi entropy $S_q$ associated with the state’s decomposition over all local product bases. This quantity identifies the product bases in which the state is maximally localized, thereby yielding optimal representations for analyzing properties of AME states.

The team develops an efficient algorithm for computing the minimal decomposition entropy for finite $q>1$ and compare AME and Haar-random states for ( q = 2 ) and ( q = \infty ) in qubit, qutrit, and ququad systems. For ( q = 2 ), AME states of four qutrits and ququads show lower minimal entropy than generic states, indicating sparser optimal forms. For ( q = \infty ) – related to the geometric measure ofentanglement – AME states exhibit higher entanglement. The algorithm also simplifies known AME states into sparser representations, aiding in distinguishing genuinely quantum AME states from those constructible from classical combinatorial designs. The results advance the classification of AME states and demonstrate the utility of minimal decomposition entropy as both a local unitary invariant and a tool for state simplification.

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Dr Smriti Sharma publishes an article in South Asia: Journal of South Asian Studies

Ram Kumar Research Spotlight

Dr Smriti Sharma, Postdoctoral Fellow, Sociology and Social Anthropology, SIAS, published an article titled Packing Pareshani or Healthcare? The Affective Dimensions of Digitalisation in India’s Health Sector in South Asia: Journal of South Asian Studies.

In this article, author examine how digitalisation in the health sector seeks to standardise care in the name of effective service delivery. Author analyses the affective dimensions of operating a digital portal within the publicly funded health insurance scheme, Ayushman Bharat Yojana, by conceptualising the emic term, pareshani. In Hindi, pareshani encompasses a wide range of meanings, including worry, trouble, tension, helplessness, frustration, distress and exhaustion. Based on fifteen months of ethnographic fieldwork in a private hospital and the public insurer’s headquarters in Haryana, the author show how the intermedial specificity of pareshani undergirds payments and patient treatment. She argues that understanding the affective force of digital portals requires examining how pareshani becomes embedded in intermediaries’ everyday navigation and negotiation of bureaucratic standards and procedures that shape clinical work. More broadly, author reflect on what these affective dynamics reveal about the digital bureaucratic state and its modes of governing healthcare.

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Professor S Sivakumar co-authors a journal article published in Physical Review A

Ram Kumar Research Spotlight

Professor S Sivakumar, Dean – Research, and Professor, Physics, SIAS, has co-authored a journal article titled Generation of large Fock states from coherent states using Kerr interaction and displacement, published in Physical Review A.

Technical Abstract

We discuss a scheme to generate large Fock states. The scheme involves repeatedly applying an experimentally feasible unitary transformation to convert a semiclassical state into a Fock state. The transformation combines Kerr interaction, which is a non-Gaussian operation, and pulsed coherent drives. We identify suitable parameter values (Kerr strength, pulse timings, displacement amplitude) for the physical processes to implement the transformation and generate large Fock states with near-unity fidelity. The feasibility of implementing the scheme in circuit QED architectures is discussed. The method is also suitable for generating Fock states of cavity fields.

Non-technical summary

The simple harmonic oscillator is a ubiquitous model in physics, describing everything from swinging pendulums to vibrating molecules. In the quantum world, these microscopic oscillators are restricted to specific, equally spaced energy levels—effectively forming a “ladder” of energy. An electromagnetic field confined in a cavity behaves exactly like a harmonic oscillator. But how do we force this field to climb the ladder and reach a specific, high-energy rung of our choice? Our work presents a new scheme that significantly outperforms known schemes for generating large photon states. Combining nonlinearity (where the output is not strictly proportional to the input) with displacement operations (essentially, “kicking” the oscillator in phase space), we can guide the system to much higher energy levels than previously possible. The paper discusses the mechanism behind this controlled ascent.

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Dr Junaid Iqbal co-authors a paper published in Evidence Based HRM: A Global Forum for Empirical Scholarship

Ram Kumar Research Spotlight

A research paper titled Impact of Psychological Contract Fulfillment on Frontline Employees In-role and Extra-Role Job Performance: Role of Employee Empowerment, co-authored by Dr Junaid Iqbal, Post-Doctoral Fellow, IFMR GSB has recently been published in the Evidence Based HRM: A Global Forum for Empirical Scholarship (Emerald Publishing), ABDC-B-ranked journal.

Brief: In this study, the authors examine the influence of transactional and relational psychological contracts on the in-role and extra-role performance of frontline banking employees, grounded in the principles of social exchange theory. Specifically, they analyse how different forms of perceived employer–employee obligations shape employees’ task-related and discretionary behaviors. Furthermore, they explore the mediating role of employee empowerment in strengthening the link between psychological contracts and performance outcomes. By doing so, the study provides a comprehensive understanding of how psychological contract fulfillment translates into enhanced work performance within the banking sector.

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Dr Suhail Ahmad co-authors a paper published in Current Alzheimer Research (Bentham Sciences)

Ram Kumar Research Spotlight

A research paper co-authored by Dr Suhail Ahmad, Post-Doctoral Fellow, Psychology, SIAS has recently been published in Current Alzheimer Research (Bentham Sciences), with an impact factor of 1.9 and major indexing in Scopus, SCIE, and PubMed. The paper is titled Early Diagnosis of Alzheimer’s: Machine Learning Analysis Leveraging Structural MRI.

Brief: This longitudinal study investigated whether structural MRI–based surface-based morphometry (SBM) of subcortical brain regions can support early detection of Alzheimer’s disease. Using ADNI data, morphological changes (cortical thickness, sulcal depth, and gyrification index) were tracked over 6 months to 3 years in individuals with mild cognitive impairment (MCI) who later progressed to Alzheimer’s, compared with healthy controls. Significant progressive atrophy—especially in cortical thickness—was identified. Machine learning models trained on these features showed improving performance over time, achieving the highest accuracy near the point of diagnosis. The findings suggest that SBM-derived subcortical atrophy patterns, particularly cortical thickness, serve as sensitive biomarkers and, when combined with machine learning, provide a scalable framework for early Alzheimer’s prediction.

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Dr Junaid Iqbal co-authors a research paper published in FIIB Business Review (Sage Publication)

Ram Kumar Research Spotlight

Dr Junaid Iqbal, Post-Doctoral Fellow, IFMR GSB has co-authored a research paper titled Cognitive Diversity, Team Efficacy and Team Learning: A Triadic Model for Enhancing Team Performance published in FIIB Business Review (Sage Publication), an ABDC-B, ranked journal with an impact factor of 2.8.

Brief

In this study we investigated how cognitive diversity influences team performance within India’s banking sector, an industry undergoing rapid digital transformation while operating within traditionally hierarchical organisational structures. Drawing on social cognitive theory, we develop a triadic framework in which team efficacy mediates the relationship between cognitive diversity and team performance, and team learning moderates the link between team efficacy and performance outcomes. The findings extend social cognitive theory by identifying team efficacy as the core psychological mechanism through which diverse cognitive perspectives translate into coordinated action, and by highlighting team learning as a key boundary condition, particularly in high power-distance environments. From a practical standpoint, the study underscores the importance for banking organisations to intentionally foster collective efficacy and institutionalise continuous learning practices in order to fully realize the performance advantages of cognitive diversity.

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