SCHOOL OF COMPUTATIONAL
AND DATA SCIENCES
Explainable and Interpretable AI
KNOW MOREExplainable and Interpretable AI
Success obtained by deep learning-based architectures in recent years have prompted many neural network models being proposed to solve problems in various domains. Although these models promise very competitive results; they are often perceived as black-box techniques due to their multilayer and nonlinear structure. They are often categorized as non-transparent and their predictions not traceable and explainable by humans. Though these architectures can effectively model very complex problems, they cannot be interpreted. Hence the predictions from these models cannot be explained. Because of this, explain ability and interpretability of deep learning models have received special attention in recent times. There is a lot of on-going research for making models more explainable and interpretable. These approaches primarily seek to understand the internal representations that are formed in the black box model.
RESEARCH SUPERVISOR
Prof. Smitha Rao
Prof. Smitha Rao is a distinguished researcher and an award-winning academician with 25 years of experience. She holds a Ph.D. in Intelligent Agents (Computer Science) and is adept in curriculum development and classroom instruction. During her academic journey, Prof. Rao has held leadership positions as Director, Program Chair, and HOD at well-known institutes. She has also been invited as an international speaker at MIT, Boston, and a Guest speaker and Session chair at several International Conferences.
Prof. Rao is the recipient of the EVUEME Award in WAIN Innovation Challenge, and a firm believer in interdisciplinary research. She has published over 23 research papers in reputed journals like Springer and IEEE.
Prerequisites
- Applicant should have an interest in the areas of Data Science, Data Analytics and Statistics
- Programming in Python/R/MatLab
- Good oral and written skills in English
- Good at working with an interdisciplinary team
Auto Neural Network Architecture
KNOW MOREAuto Neural Network Architecture
Neural Network architectures try to model the underlying relationships in data in a way that mimics the working of the human brain. Neural networks basically comprise of a connection of nodes in various layers. The basic learnable parameters of the Neural Network are weights and biases. Developing neural network models often requires significant architectural engineering. Techniques for automatically discovering the best-performing Neural Network architecture for a given dataset are commonly known as AutoML / AutoDL. It is basically building an optimal Neural Network Structure for a given dataset with minimal human intervention. Various evolutionary and reinforcement algorithms are currently being explored to effectively solve the problem.
RESEARCH SUPERVISOR
Prof. Smitha Rao
Prof. Smitha Rao is a distinguished researcher and an award-winning academician with 25 years of experience. She holds a Ph.D. in Intelligent Agents (Computer Science) and is adept in curriculum development and classroom instruction. During her academic journey, Prof. Rao has held leadership positions as Director, Program Chair, and HOD at well-known institutes. She has also been invited as an international speaker at MIT, Boston, and a Guest speaker and Session chair at several International Conferences.
Prof. Rao is the recipient of the EVUEME Award in WAIN Innovation Challenge, and a firm believer in interdisciplinary research. She has published over 23 research papers in reputed journals like Springer and IEEE.
Prerequisites
- Applicant should have an interest in the areas of Data Science, Data Analytics and Statistics
- Programming in Python/R/MatLab
- Good oral and written skills in English
- Good at working with an interdisciplinary team
Context Extraction for Hierarchical Organization of Videos in the Educational Domain
KNOW MOREContext Extraction for Hierarchical Organization of Videos in the Educational Domain
Studies show that videos are especially important, and are used mostly for review purposes. Videos recorded during in-class teaching and made accessible online are a versatile resource on par with a textbook and the classroom itself. Accessing the content of interest in a long lecture video is not easy because of the lack of an index section to enable quick search. Proposing a framework for automatic topic segmentation can significantly improve the accessibility. Automatic organization and indexing of topics and topic related content with an efficient search feature can help students easily access and review the topic of their choice quickly and efficiently.
RESEARCH SUPERVISOR
Dr. D. C. Kiran
Dr. D. C. Kiran is an illustrious academician with 2 decades of experience. He holds a Ph.D. in Computer Science and Engineering from BITS-Pilani and has held numerous leadership positions at the institute for a decade. His academic journey also highlights his contribution to Presidency University, where he was a part of the founding faculty. He was instrumental in setting up various initiatives and labs, and served as a member of the RAC and DAC. He has also taught and guided communities of working professionals at Wipro Technologies, L&T, CISCO, and Hindustan Zinc Ltd.
Dr. D. C. Kiran has published numerous research papers in national and international journals of repute, and is a Mentor of Change at NITI Aayog.
Semantic Textual Similarity
KNOW MORESemantic Textual Similarity
In the field of Computer Science and Computational Linguistics, measuring semantic textual similarities between words/terms, sentences, paragraphs and documents plays an important role. It also has many applications across several fields such as Biomedical Informatics and Geoinformation. There are several fields where semantic textual similarity can play a direct or indirect role in sentiment analysis, natural language understanding and machine translation. Also, semantic textual similarity can be applied in many areas like short answer grading, duplicate document detection, text clustering, text categorization, topic detection, question and answer sessions, machine translation, text summarization, etc. It helps to improve learning outcomes, by using automated text analysis to judge students’ understanding of high-level concepts from their open-ended written responses typically for homework assignments, practice tests, etc. The AI engine can provide immediate feedback to the student on the basic features of writing.
RESEARCH SUPERVISOR
Dr. N. Mehala
Associate Professor, School of Computational and Data Sciences
Former Faculty with CSIS, BITS Pilani and Nucleus member of the BITS WILPD
Former Associate Professor, PES University
Ph.D. in Computer Science, BITS-Pilani
- M.Tech/M.S (CSE or related domain)
- Desirable:
Programming Skill, Conceptual Thinking
Visible Light Drive Efficient Catalytic Dye Degradation
KNOW MOREVisible Light Drive Efficient Catalytic Dye Degradation
Industrial effluents, laced with textile/specialty dyes, harm marine life when let into water bodies and eventually permeate to land life causing a disturbance in the ecosystem. Modern techniques to address this are advanced oxidation processes and photocatalysis, of which the latter is most sought after because the degradation process produces no secondary pollution. Metal oxide semiconductors (MOS) with a wide band gap (such as TiO2) efficiently degrade dyes under UV irradiation, but are inefficient under solar irradiation (UV constitutes only ~ 4% of solar radiation reaching Earth). Their band gap needs to decrease for visible light photocatalysis. Further, these MOS nanophotocatalysts are not retrievable for reuse.
Industrial employability of nanophotocatalysts requires easy and bulk producibility, non-toxicity, cost-effectiveness, high dye degradation efficiency and most importantly, retrievability of nanophotocatalysts for repeated use. Our research group focusses on the synthesis and application of band gap tailored stable, non-toxic metal oxide/composite nanophotocatalysts for heterojunction photocatalytic oxidation of potential carcinogenic dyes with complete retrievability.
RESEARCH SUPERVISOR
Dr. Anupama A. V.
Project Staff (Psychiatry Dept.), NIMHANS, Bengaluru
Adjunct Faculty, IIT, Gandhinagar
Faculty, KLE’s Lingaraj College, Belagavi
Faculty, Chulalungkorn University, Bangkok
Prerequisites
- M.Sc in Materials Science/Chemistry/Physics/Nanoscience or Nanotechnology/any related field
- M.Tech/B.Tech in Materials Engineering/Metallurgy/Chemical Engineering/Mechanical Engineering/any related field with sound knowledge of chemistry
Desirables
- Experience in synthesis of nanomaterials
- Use of scientific software such as Origin, MatLab etc.
- Prior publication
Hydrogen Evolution by Water Splitting
KNOW MOREHydrogen Evolution by Water Splitting
The ever-growing demand for energy has resulted in the fast depletion of traditional energy sources (petroleum, coal etc.). Moreover, the combustible products of these conventional sources are major contributors to pollution, global warming and the resultant climate change. Renewable, clean and green energy sources such as solar, wind and hydro energy have been the best alternatives; the most promising being hydrogen as a fuel since the product of combustion is pure water.
Splitting water to produce hydrogen can be achieved by electrochemical processes. However, this requires energy. The advanced and most researched water-splitting process is nanocatalysis, which only requires a choice of suitable nanoparticles. Our research team focusses on the development of nanostructures/nanocomposites based on metal oxides, compounds and carbonaceous materials with high physical and chemical stabilities, large scale producibility and retrievability while improving the quantum efficiency of hydrogen evolution.
RESEARCH SUPERVISOR
Dr. Anupama A. V.
Project Staff (Psychiatry Dept.), NIMHANS, Bengaluru
Adjunct Faculty, IIT, Gandhinagar
Faculty, KLE’s Lingaraj College, Belagavi
Faculty, Chulalungkorn University, Bangkok
Prerequisites
- M.Sc in Materials Science/Chemistry/Physics/Nanoscience or Nanotechnology/any related field
- M.Tech/B.Tech in Materials Engineering/Metallurgy/Chemical Engineering/Mechanical Engineering/any related field with sound knowledge of chemistry
Desirables
- Experience in synthesis of nanomaterials
- Use of scientific software such as Origin, MatLab etc.
- Prior publication
Toxic / Irritant Gas Sensors
KNOW MOREToxic / Irritant Gas Sensors
Various manufacturing / processing industries may generate gases as deliberate products or as undesirable byproducts. Either way, a few of these gases are toxic or irritants, which are life-threatening if not detected immediately upon accidental leakage. A large-scale gas-leakage can result in massive life-loss, or at the least cause long-lasting health issues in survivors. Timely sensing and intervention in case of gas leakage can prevent disaster, which necessitates employment of gas detectors (sensors). Gas sensors normally made of metal oxides, have inherent drawbacks of low sensitivity and selectivity, need high operating temperature, exhibit cross-sensitivity, and are generally costly. In our research group we work on developing metal oxide/nanohybrid structures based versatile gas sensors to address stated drawbacks, while extending the sensor's life span and enhancing efficiency of gas sensing.
Energy band gap tuning, morphological tailoring, heterostructure fabrication with enhanced speed of gas detection, form the core of this research work.
RESEARCH SUPERVISOR
Dr. Anupama A. V.
Project Staff (Psychiatry Dept.), NIMHANS, Bengaluru
Adjunct Faculty, IIT, Gandhinagar
Faculty, KLE’s Lingaraj College, Belagavi
Faculty, Chulalungkorn University, Bangkok
Prerequisites
- M.Sc in Materials Science/Chemistry/Physics/Nanoscience or Nanotechnology/any related field
- M.Tech/B.Tech in Materials Engineering/Metallurgy/Chemical Engineering/Mechanical Engineering/any related field with sound knowledge of chemistry
Desirables
- Experience in synthesis of nanomaterials
- Use of scientific software such as Origin, MatLab etc.
- Prior publication
Fractional Calculus
KNOW MOREFractional Calculus
Leibniz was the first who raised the question “Can the meaning of derivatives with integer order be generalized to derivatives with non-integer orders?” in a mail written to L’Hôpital in 1695. L’Hôpital responded by asking “What if the order will be 1/2?” For which, Leibniz asserted “one day, useful consequences will be drawn”. The first major study of fractional calculus was started by Liouville in 1832. In 1884, Laurent published a paper on the fundamentals of fractional calculus. Fractional calculus generalizes the theory of classical differential and integral calculus to non-integer order. It is also back dated as differential calculus, but there are several reasons that prevented it from becoming as popular as conventional calculus. This is because of its intrinsic complication, the evident self-adequacy of classical calculus, its lack of reasonable geometric or physical interpretation for fractional derivatives. And, hence its applications were delayed in various branches of engineering and science.
RESEARCH SUPERVISOR
Dr. Bijil Prakash
With a Ph.D. in Applied Mathematics and a background in Modelling and Simulation, Dr. Bijil is an expert in this domain and has supervised over 150 student projects.
Dr. Bijil is a former faculty at BITS-Pilani and a sports enthusiast . His interests lie in unique areas including Numerical Solutions of fractional models with wavelets, Home Automation System for Paralyzed People, Image Encryption and Steganography. He has around 11 publications of which 6 are Scopus and 2 are SCI indexed. Knows around 6 languages. An accomplished educational professional with extensive experience and finely honed abilities in teaching and research domain.
Wavelets
KNOW MOREWavelets
Wavelets form a special class of functions which possess the oscillatory behaviour for a small duration and then die out. In comparison to the Fourier Series, we consider dilations and translations of a special type of function in wavelets. Approximation using superposition of functions has existed since the early 1800’s, when Joseph Fourier discovered that he could superpose sines and cosines to represent other functions. However, in wavelet analysis, the scale that we use to look at data plays a special role expansion (using coefficients in a linear combination of the wavelet functions). Data operations can be performed using just the corresponding wavelet coefficients. And if you further choose the best wavelets adapted to your data, or truncate the coefficients below a threshold, your data is sparsely represented.
This sparse coding makes wavelets an excellent tool in the field of data compression. Other applied fields that are making use of wavelets include astronomy, acoustics, nuclear engineering, sub-band coding, signal and image processing, neurophysiology, music, magnetic resonance imaging, speech discrimination, optics, fractals, turbulence, earthquake-prediction, radar, human vision, and pure mathematical applications such as solving differential equations.
RESEARCH SUPERVISOR
Dr. Bijil Prakash
With a Ph.D. in Applied Mathematics and a background in Modelling and Simulation, Dr. Bijil is an expert in this domain and has supervised over 150 student projects.
Dr. Bijil is a former faculty at BITS-Pilani and a sports enthusiast . His interests lie in unique areas including Numerical Solutions of fractional models with wavelets, Home Automation System for Paralyzed People, Image Encryption and Steganography. He has around 11 publications of which 6 are Scopus and 2 are SCI indexed. Knows around 6 languages. An accomplished educational professional with extensive experience and finely honed abilities in teaching and research domain.
Prerequisites
- M.Sc in Mathematics/M.Phil in Mathematics.
- Strong Basic Mathematics concepts, Programming Knowledge.
- Candidates who have cleared NET/SLET/GATE shall be given preference.
Combining Data From Different Sources and Making Robust Inferences
KNOW MORECombining Data From Different Sources and Making Robust Inferences
Typically, an organization makes strategic decisions based on various information/ data that exist internally as well as data obtained from other external resources. The Big Data tools/ platforms such as Hadoop, Storm, HPCC Systems etc., help in putting these data together in a form that can be used for analysis. For example, information related to the performance of a product may be pulled from various sources such as proprietary and syndicated surveys (structured data), report/ reviews, field incidence data (repair data), and through unstructured sources such as web blogs, social media etc. In order to get a holistic understanding of the product’s field performance, we need to use the data from different sources differently. The existing data science methods cannot as yet handle combining multiple, heterogeneous sources of data to build a single, accurate model (see “Ten Research Challenge areas in Data Science” by Wing J M published in Harvard Data Science Review, Sep 30, 2020). Bounding the uncertainty of a data model is exacerbated when built from multiple, possibly unrelated data sources. More pragmatically, standardization of data types and data formats could reduce undesired or unnecessary heterogeneity. In this work, we plan to develop Data Science methodologies including data cleansing, standardizing and estimating the desired parameters based on the combined data by minimizing bias induced by the external and internal sources so that organizations can draw robust inferences and take appropriate decisions.
RESEARCH SUPERVISOR
Prof. R. P. Suresh
Prof. R.P. Suresh is an award-winning Data Scientist and highly distinguished expert with over three decades of experience in the industry and academia. He is currently the Principal Director in Supply Chain Analytics at Accenture Applied Intelligence. He is also a consultant with numerous organisations like the Department of Telecommunications (Govt. of India), WIPRO, Kirloskar Cummins, TATA Tea, Hindustan Newspapers and IES Officers Training, among many others.
Prof. Suresh holds a Ph.D. in Statistics and was bestowed the ‘Young Statistician Award’ by the Indian Society of Probability and Statistics. During his academic and professional journey, he wore many hats. He was a faculty at IIM Kozhikode for over a decade and also a researcher at General Motors R&D Centre, India. He has been a visiting faculty and guest lecturer at institutes including Victoria University of Wellington, Indian Institute of Science, and Indian Statistical Institute.
Prof. Suresh is a two-time gold medallist and has received awards including GM India President’s honour. He has refereed over 27 professional journals and contributed to several conferences and trade inventions.
Predicting Failures Beyond Warranty
KNOW MOREPredicting Failures Beyond Warranty
In the automotive and high-tech industries, it is of interest to predict failures of products and components to help engineers, financial managers and supply chain managers to take appropriate warranty decisions (e.g., extension of warranty), determine warranty reserve fund and stock levels of spare parts, provide alerts to customers, and arrive at bulk replacement or recall decisions. For most components, the manufacturers have the failures data only until the warranty period, and the data that exist beyond warranty period are not very reliable or largely incomplete. Hence the prediction of failures beyond warranty period needs to be based primarily on failure data during warranty period. Some parametric based prediction models (see, e.g., “Weibull Prediction Intervals for a Future Number of Failures by Nordman and Meeker, Technometrics, 2002, Vol 44, p 15-23), Ebrahami E (Reliability Engineering and System Safety, 2009) exist in the literature by estimating the parameters based on the data till the warranty period. However, since typically, only 3-4% of items / parts fail in the warranty period, such parameter estimates may not be reliable.
In this research, it is proposed to develop new methodologies to estimate the failures beyond the warranty by considering a number of variables as well as more sophisticated Machine Learning Methodologies.
Methods: Literature Survey, Data Collection, Data Analysis and Statistics, Simulation and Computation
RESEARCH SUPERVISOR
Prof. R. P. Suresh
Prof. R.P. Suresh is an award-winning Data Scientist and highly distinguished expert with over three decades of experience in the industry and academia. He is currently the Principal Director in Supply Chain Analytics at Accenture Applied Intelligence. He is also a consultant with numerous organisations like the Department of Telecommunications (Govt. of India), WIPRO, Kirloskar Cummins, TATA Tea, Hindustan Newspapers and IES Officers Training, among many others.
Prof. Suresh holds a Ph.D. in Statistics and was bestowed the ‘Young Statistician Award’ by the Indian Society of Probability and Statistics. During his academic and professional journey, he wore many hats. He was a faculty at IIM Kozhikode for over a decade and also a researcher at General Motors R&D Centre, India. He has been a visiting faculty and guest lecturer at institutes including Victoria University of Wellington, Indian Institute of Science, and Indian Statistical Institute.
Prof. Suresh is a two-time gold medallist and has received awards including GM India President’s honour. He has refereed over 27 professional journals and contributed to several conferences and trade inventions.
SCHOOL OF LIBERAL ARTS
AND DESIGN STUDIES
International Economics
KNOW MOREInternational Economics
Empirical study of protectionism Vs. free trade-The world economy has seen a flip flop policy of free trade vs protectionism. Developed countries like the US that have been championing the cause of free trade, especially since 2008, are adopting protectionist barriers, resulting in the US-China Trade War, Brexit etc. On the other hand, countries like China, Vietnam etc, which hitherto were closed economies are opening their economies. It would be worth the while to revisit the free trade vs protectionism debate by conducting a multi-country study over a long period of time to understand the pros and cons, given new issues and situations. Other international economic issues - Unemployment and poverty; inequality; immigration, education, health, economic crisis in various countries; ecological problems; blue economy etc.
RESEARCH SUPERVISOR
Prof. Radhika Lobo
Prof. Radhika Lobo is an award-winning researcher and academician with over 3 decades of experience. She holds a Ph.D. and an M.Phil in Economics and has held leadership positions in academic administration. Prof. Lobo was deputed as an Exchange Faculty at the University of West Georgia and Clayton Business School, in the US. She has also been Visiting Faculty at several B-Schools including, NMIMS, Mumbai; Asia Graduate School of Business, Hyderabad and BIMTECH, Noida.
As a guest speaker at National and International Universities, she has spoken on topics related to the Indian Economy, International Economics and the Financial Crisis.
Prof. Lobo has published a book titled Southeast Asian Crisis: An Economic Analysis, and several research papers across reputed journals.
Pre-requisites
Some basic knowledge of research methodology will help. Rigorous and extensive review of literature will help in identifying the research problem and objectives. It will also help identify the research gap. Making a rough draft of the research proposal will help both the candidate and the supervisor to arrive at the exact topic.
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Gender Economics: Assessment of Gender Sensitive Infrastructure
KNOW MOREGender Economics: Assessment of Gender Sensitive Infrastructure
The finance minister in her recent budget announced that there would be very large outlays towards infrastructure during the Amrit Kaal. It would be very meaningful to conduct a gender analytics to assess if these projects are going to be beneficial to women.
Cross Country assessment of economic opportunities for women in developing countries-It is now well documented that gender disparities have led to slower economic growth and development. The gender gap in terms of labor force participation, in terms of leadership in the corporate or political arena, in terms of wages, in terms of opportunities etc is quite stark in developing countries. Also, developing countries especially those in Asia, share several economic and cultural similarities. A comparative look into the gendered laws for women’s empowerment, gender budgeting, other efforts to help women scale up and their impact will help understand the specific efforts towards bridging this gap and possibly make policy suggestions to emulate these practices, if the same are worth the while.
Other topics under this area could include- assessment of profitability with gender diversity; Feminist Economics-a theoretical study to understand various schools of thought; Education and gender aspects, unpaid care work and its implications on the economy etc.
RESEARCH SUPERVISOR
Prof. Radhika Lobo
Prof. Radhika Lobo is an award-winning researcher and academician with over 3 decades of experience. She holds a Ph.D. and an M.Phil in Economics and has held leadership positions in academic administration. Prof. Lobo was deputed as an Exchange Faculty at the University of West Georgia and Clayton Business School, in the US. She has also been Visiting Faculty at several B-Schools including, NMIMS, Mumbai; Asia Graduate School of Business, Hyderabad and BIMTECH, Noida.
As a guest speaker at National and International Universities, she has spoken on topics related to the Indian Economy, International Economics and the Financial Crisis.
Prof. Lobo has published a book titled Southeast Asian Crisis: An Economic Analysis, and several research papers across reputed journals.
Pre-requisites
Some basic knowledge of research methodology will help. Rigorous and extensive review of literature will help in identifying the research problem and objectives. It will also help identify the research gap. Making a rough draft of the research proposal will help both the candidate and the supervisor to arrive at the exact topic.
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Indian Economy: Assessment of Foreign Market Entry Options for Small Businesses in Rural India
KNOW MOREIndian Economy: Assessment of Foreign Market Entry Options for Small Businesses in Rural India
Foreign Direct Investment is a form of capital that was secured mainly by large firms producing goods or offering services that were internationally accepted. However, in recent times, small businesses entry into the international arena has opened with options like strategic alliance, joint ventures, licensing and franchising becoming available. In fact, small businesses located in rural India too seem to benefit from the same in terms of visibility, volume of business and profitability. These firms may also be offering greater employment opportunities to people in the geographical area. A study of such firms could provide a case in point to the government to direct its policy efforts towards encouraging such investment and opportunities.
Other research areas- Banking and financial sector reforms, fiscal and monetary sector, education
RESEARCH SUPERVISOR
Prof. Radhika Lobo
Prof. Radhika Lobo is an award-winning researcher and academician with over 3 decades of experience. She holds a Ph.D. and an M.Phil in Economics and has held leadership positions in academic administration. Prof. Lobo was deputed as an Exchange Faculty at the University of West Georgia and Clayton Business School, in the US. She has also been Visiting Faculty at several B-Schools including, NMIMS, Mumbai; Asia Graduate School of Business, Hyderabad and BIMTECH, Noida.
As a guest speaker at National and International Universities, she has spoken on topics related to the Indian Economy, International Economics and the Financial Crisis.
Prof. Lobo has published a book titled Southeast Asian Crisis: An Economic Analysis, and several research papers across reputed journals.
Pre-requisites
Some basic knowledge of research methodology will help. Rigorous and extensive review of literature will help in identifying the research problem and objectives. It will also help identify the research gap. Making a rough draft of the research proposal will help both the candidate and the supervisor to arrive at the exact topic.
APPLY NOWThe Relation Between Perceptual Grouping and Visual Attention
KNOW MOREThe Relation Between Perceptual Grouping and Visual Attention
The visual world is complex and presents a lot of stimulation at any point of time. Hence, selection of relevant stimuli and inhibition or ignoring of irrelevant stimuli is very important. Human capacity to process that information is limited. The factors that facilitate or hinder attention are many. I am specifically interested in the role of perceptual grouping in visual selection. Extensive research has been done in the area but the questions of whether perceptual grouping itself requires attention or what kinds of grouping facilitate attention is still not resolved. I hope to explore this relation through computer-based behavioral experiments.
RESEARCH SUPERVISOR
Dr. Setu Havanur
Ph.D. in Psychology, University of Birmingham
Pre-requisites
Mainly Computer based experiments conducted
- Master’s degree essential
Desirable
- Familiarity with cognition and related courses at master’s level.
- Familiarity or experience with designing experiments using E-prime, MatLab, Python or similar software.
Perceptual Grouping and Counting
KNOW MOREPerceptual Grouping and Counting
Research on counting has shown the existence of two processes: (i) subitization (for counting to four items) which is automatic and is not affected by stimulus manipulations; (ii) enumeration (counting item-by-item when the number to be counted is greater than four). Thus, individuation seems to facilitate faster enumeration. Perceptual grouping (based on some principles) makes it difficult to individuate but some studies have shown that grouping facilitates counting. I am interested in probing these questions in detail to understand what visual properties are involved in counting small and big numbers.
RESEARCH SUPERVISOR
Dr. Setu Havanur
Ph.D. in Psychology, University of Birmingham
Pre-requisites
Mainly Computer based experiments conducted
- Master’s degree essential
Desirable
- Familiarity with cognition and related courses at master’s level.
- Familiarity or experience with designing experiments using E-prime, MatLab, Python or similar software.
Perceptual and Visual Working Memory
KNOW MOREPerceptual and Visual Working Memory
Visual working memory (VWM) is a limited capacity storage of working memory responsible for maintaining visual information briefly for the purpose of ongoing task. Some studies have shown that perceptual grouping can increase the capacity of this storage. I am interested in exploring this further to understand the types of grouping that facilitate VWM and whether there is a limit to this expansion of storage.
These three threads of research, although asking different questions and using different stimuli and experiments, will contribute to the understanding of nature of visual properties and how they are related to attentional processes.
RESEARCH SUPERVISOR
Dr. Setu Havanur
Ph.D. in Psychology, University of Birmingham
Pre-requisites
Mainly Computer based experiments conducted
- Master’s degree essential
Desirable
- Familiarity with cognition and related courses at master’s level.
- Familiarity or experience with designing experiments using E-prime, MatLab, Python or similar software.
Sociology / Anthropology of Marriage and Kinship
KNOW MORESociology / Anthropology of Marriage and Kinship
Marriage and kinship have been foundational to the disciplines of Sociology and Anthropology, and a key gateway to understand societies. Key theoretical interventions in kinship studies, especially influenced by feminist approaches have ‘denaturalised’ kinship towards more processual and performative dimensions of relatedness. These interventions continue to inform our knowledge about social hierarchies, intimacies and personhood. Relatedness, broadly conceived, is also central to movements for social and policy changes, to understand other social constellations or identities based on gender, ethnicity, class, or caste.
RESEARCH SUPERVISOR
Prof. Ranjana Raghunathan
Ranjana Raghunathan is an Assistant Professor in Sociology/Anthropology at Vidyashilp University. Her research has spanned the Anthropology of Kinship & Relatedness, Phenomenology of Religion, Everyday Life, and Migration & Diaspora Studies. Ranjana has conducted ethnographic field research in Mumbai, Singapore and Malaysia exploring women’s lived experiences of home, belonging and intimacies. She received her PhD from the National University of Singapore (NUS), where she also taught courses on Asian Studies, Popular cultures, South Asian diasporas, Marriage and Kinship, and Human-Environment interactions. Her doctoral thesis won the Ground-breaking Subject Matter Accolade at the International Convention of Asia Scholars Book Prize 2021 and the Best PhD Dissertation in South Asian Studies award in NUS for 2020.
Pre-requisites
A draft research proposal which outlines a tentative research problem and literature review. Some exposure at master’s level to humanities or social sciences.
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Migration and South Asian Diasporas
KNOW MOREMigration and South Asian Diasporas
Migration and Diaspora studies is a rich inter-disciplinary field, which now contributes to several policy discussions, and more nuanced understanding of the histories of south Asian Diasporas, lived experiences of migrants in diverse and multicultural contexts, and the ways in which gender, ethnicity and class intersect with the broader trends. Rootedness or being settled have often been conceptualized as the norm of human societies, and migration as an aberration or disruption. However, migration and mobility have been central to human existence since the days of early civilizations. In the ‘modern’ contexts with a greater emphasis on borders, migration is a highly complex and layered phenomenon. South Asian diaspora is among the world’s largest and widespread, and it continues to grow exponentially. The rise of refugees from war-torn regions, have brought an urgent focus on the experiences of migration and borders. Additionally, ‘domestic’ migration has come to typify the experiences of millions of people in Asian contexts.
RESEARCH SUPERVISOR
Prof. Ranjana Raghunathan
Ranjana Raghunathan is an Assistant Professor in Sociology/Anthropology at Vidyashilp University. Her research has spanned the Anthropology of Kinship & Relatedness, Phenomenology of Religion, Everyday Life, and Migration & Diaspora Studies. Ranjana has conducted ethnographic field research in Mumbai, Singapore and Malaysia exploring women’s lived experiences of home, belonging and intimacies. She received her PhD from the National University of Singapore (NUS), where she also taught courses on Asian Studies, Popular cultures, South Asian diasporas, Marriage and Kinship, and Human-Environment interactions. Her doctoral thesis won the Ground-breaking Subject Matter Accolade at the International Convention of Asia Scholars Book Prize 2021 and the Best PhD Dissertation in South Asian Studies award in NUS for 2020.
Pre-requisites
A draft research proposal which outlines a tentative research problem and literature review. Some exposure at master’s level to humanities or social sciences.
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Phenomenology of Religion
KNOW MOREPhenomenology of Religion
The significance of religion has not waned due to modernisation, secularisation and globalisation processes, instead it continues to grow, as evident from the increase in religion-driven conflicts and nationalisms around the world. While the monothetic or Judeo-Christian conceptions of religion as a category have been challenged, a broader emphasis on text-based and dominant forms of religions have been central to conversations on religion. The phenomenology of religion has brought a focus on the intimate, personal and experiential dimensions of religions, which provides exciting pathways to explore social hierarchies and embodiments in everyday life. Moreover, vernacular and indigenous practices in everyday life provide new understandings of other social processes such as urbanisation and migration.
RESEARCH SUPERVISOR
Prof. Ranjana Raghunathan
Ranjana Raghunathan is an Assistant Professor in Sociology/Anthropology at Vidyashilp University. Her research has spanned the Anthropology of Kinship & Relatedness, Phenomenology of Religion, Everyday Life, and Migration & Diaspora Studies. Ranjana has conducted ethnographic field research in Mumbai, Singapore and Malaysia exploring women’s lived experiences of home, belonging and intimacies. She received her PhD from the National University of Singapore (NUS), where she also taught courses on Asian Studies, Popular cultures, South Asian diasporas, Marriage and Kinship, and Human-Environment interactions. Her doctoral thesis won the Ground-breaking Subject Matter Accolade at the International Convention of Asia Scholars Book Prize 2021 and the Best PhD Dissertation in South Asian Studies award in NUS for 2020.
Pre-requisites
A draft research proposal which outlines a tentative research problem and literature review. Some exposure at master’s level to humanities or social sciences.
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Urban Studies
KNOW MOREUrban Studies
Bangalore is the third most populous city of India, with a population of over ten million. It exploded into an urban agglomeration after the Information Technology boom at the turn of the millennium. However, academic research about the city have approached it mainly as a case study for globalization, unchecked development and urban problems. I seek to understand and map the processes of urbanization through entanglements of gender, religion and inequalities, and how they inflect the experiences of diverse individuals and groups in the city.
RESEARCH SUPERVISOR
Prof. Ranjana Raghunathan
Ranjana Raghunathan is an Assistant Professor in Sociology/Anthropology at Vidyashilp University. Her research has spanned the Anthropology of Kinship & Relatedness, Phenomenology of Religion, Everyday Life, and Migration & Diaspora Studies. Ranjana has conducted ethnographic field research in Mumbai, Singapore and Malaysia exploring women’s lived experiences of home, belonging and intimacies. She received her PhD from the National University of Singapore (NUS), where she also taught courses on Asian Studies, Popular cultures, South Asian diasporas, Marriage and Kinship, and Human-Environment interactions. Her doctoral thesis won the Ground-breaking Subject Matter Accolade at the International Convention of Asia Scholars Book Prize 2021 and the Best PhD Dissertation in South Asian Studies award in NUS for 2020.
Pre-requisites
A draft research proposal which outlines a tentative research problem and literature review. Some exposure at master’s level to humanities or social sciences.
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