Human - Machine Interaction

Our Research Themes and Areas of Focus

Our Research Areas

Areas and Focus

Our Research Areas

Design and Interaction

Natural language systems with cultural nuance; multimodal interfaces; adaptive accessibility; affective computing

Data Science and Impact

Bias detection/mitigation; privacy-preserving methods; health equity; educational innovation; environmental monitoring

Robotics and Embodiment

Assistive technologies; collaborative manufacturing; social robotics;  safety in human-robot teams

Ethics and Governance

Fairness auditing; accountability frameworks; policy research; inclusive AI design; environmental sustainability

Foundational Theory

Human-AI trust calibration; cultural factors in AI design; autonomy vs. assistance trade-offs; explanability-performance balance

Impact Areas

From Research to Real Solutions

Healthcare and Accessibility

Extending quality diagnostics and personalized care to rural and underserved populations through equitable AI systems, while preserving privacy, patient autonomy, and provider autonomy.

Education and Opportunity

Creating adaptive learning systems that support diverse learning styles, identify at-risk students responsibly, and expand quality education access to remote communities.

Environmental Sustainability

Using data science and robotics to optimize renewable energy, predict climate impacts, monitor biodiversity, and support sustainable agriculture, while minimizing AI’s environmental footprint.

Labor and Economic Justice

Researching how automation affects workers and designing AI systems that augment human capabilities, preserve dignified livelihoods, and protect vulnerable populations from displacement.

Social Justice and Democratic Participation

Developing tools that make algorithmic decisions transparent, contestable, and accountable, informing evidence-based policy while guarding against surveillance and manipulation.

Research Methodologies

Research That Listens, Learns, and Transforms

In this institution, we work together with a variety of stakeholders to understand the impact of and ways to address responsible integration of AI in human life. Through transdisciplinary methods, stakeholders are brought together in dialogue to talk about challenges and opportunities for sustainable solutions in various fields, including health, mental well-being, education, farming, or social justice.

Transdisciplinary research means to engage in participatory and action-oriented methods towards a better future.

Employing stakeholder analysis
⁠Drawing up problem statements and research agendas with various stakeholders. ⁠
Collecting and analyzing relevant data together ⁠
Conducting scenario and outcome mapping sessions
⁠Inquiring into philosophical and ethical questions
Conducting frame reflections
⁠Searching for policy- program, or innovation related opportunities

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Members

Prof. Siba Kumar Udgata

Professor, School of Computer & Information Sciences, University of Hyderabad

Prof. Siba Kumar Udgata is a Professor at the School of Computer and Information Sciences, University of Hyderabad, and heads the Centre for Modelling, Simulation and Design (CMSD), a high-performance computing facility at the university. He holds a Master’s and PhD in Computer Science with specialisation in mobile computing and wireless communication, and his primary research interests span wireless communication, wireless sensor networks, IoT, intelligent algorithms, and soft computing. He was a United Nations Fellow and worked as a research fellow at the United Nations University / International Institute for Software Technology (UNU/IIST), Macau in 2001. He serves as Principal Investigator on multiple Government of India funded research projects focused on sensor network applications and swarm intelligence techniques, and has over 2,700 citations with more than 161 publications to his credit.

Prof. Kaushik Das Sharma

Professor, Department of Applied Physics, University of Calcutta

Prof. Kaushik Das Sharma is a Professor and Head of the Electrical Engineering Section, Department of Applied Physics, University of Calcutta. He holds B.Tech. and M.Tech. degrees in Electrical Engineering from the University of Calcutta — receiving both the Kanodia Research Scholarship in 2002 and the University Gold Medal in 2004 — and a PhD from Jadavpur University. His key research interests span fuzzy control, stochastic optimisation, machine learning, robotics, and computational biology. He serves as Editor of IEEE Transactions on Vehicular Technology and Engineering Applications of Artificial Intelligence (Elsevier), and is an Editorial Board Member of Nature Scientific Reports. He co-authored the Springer book Intelligent Control: A Stochastic Optimization Based Adaptive Fuzzy Approach (2018), and was invited as a Visiting Teacher-Researcher Fellow at the University of Paris-Est Créteil, France in 2019.