Lijiya Chacko

Ms. Lijiya Chacko is a PhD candidate and a teaching assistant at the Department of Humanities and Social Sciences at IIT Bombay. She completed undergraduate studies in psychology at the University of Calicut and postgraduation in neural and cognitive sciences at the University of Hyderabad. Her doctoral research examines how attentional priority is shaped by multiple influences, including emotional salience, reward associations, and prior experience.

Prior to her doctoral studies, Lijiya gained substantial experience in experimental cognitive science through research positions at Cognitive Behavioural Neuroscience Laboratory, IIT Bombay, and the Action Control and Cognition Lab at the University of Hyderabad. She has also contributed to research training initiatives, including conducting workshops on experimental methods and eye-tracking applications at the Annual Conference of Cognitive Sciences, 2024. She was awarded the International Brain Research Organization’s Neuroscience Training Grant to attend Cognitive Neuroimaging Skills Training in Cambridge, held at the MRC Cognition and Brain Sciences Unit, University of Cambridge, UK, in September 2025.

As a Fulbright-Nehru Doctoral Research Fellow, Lijiya is examining the behavioral and neural mechanisms of statistically learned spatial attentional biases, with particular emphasis on whether such biases operate automatically and persist over time, or whether they remain flexible and responsive to current goals. Through her research, she aims to contribute to a deeper understanding of how past experiences shape attention, with implications for cognitive theory and real-world adaptive behavior. She enjoys music, books and movies in her leisure time.

Sujata Singh

Dr. Sujata Singh is a technical analyst at Customized Energy Solutions Pvt. Ltd., an energy sector-based consulting firm, in association with Indian Energy Storage Alliance (IESA). She obtained her Ph.D. degree from the Indian Institute of Science, Bangalore. Her doctoral research focused on studying the microstructure-property correlation of High Entropy Alloy-Carbon Nanotubes composites. She received her master’s degree in metallurgical and materials engineering from the Indian Institute of Technology, Kharagpur, and bachelor’s degree from Birsa Institute of Technology, Sindri, Dhanbad.

Dr. Singh’s research interest is in the study of electrochemical behavior of advanced materials, understanding underlying electrochemistry in protective coatings/thin films, utilizing nanomaterials to achieve enhanced properties, electron microscopy, microstructure-property correlation, and failure analysis with applications in the field of corrosion and Lithium-Ion Battery. She has published several research articles in reputed international journals and received the Senior Research Fellowship issued by the Ministry of Human Resource and Development, Government of India. She also received an award for the best all-round performance in 2013.

As a Fulbright-Nehru Postdoctoral Research fellow, Dr. Singh is developing alternate battery technologies that enable sustainable, cost-effective, and reliable electrochemical energy storage. The focus of the study is to investigate new electrode architecture, exploring methods to mitigate issues such as capacity fading, mechanical degradation, dendrite formation and cyclability in Sodium-Antimony Intermetallic Battery. The project advances the field’s understanding of this promising energy storage technology.

Pijush Ghosh

Prof. Pijush Ghosh is a professor in the Department of Applied Mechanics and Biomedical Engineering at Indian Institute of Technology Madras. His research group focuses on design and fabrication of stimuli responsive soft actuators. Solvent and light are the two major stimuli that his group is working on. He applies molecular dynamics simulations to investigate the underlying mechanism involved in solvent triggered actuation and polymer-solvent interaction. His group also works on developing polymer-clay and polymer-concrete interfaces for different civil engineering projects. Mechanical investigation of cluster crystals is another area of his research interest.

Prof. Ghosh received his undergraduate degree in civil engineering from Bengal Engineering College, Shibpur Howrah, MTech from IIT Kanpur and Ph.D. from North Dakota State University. He did a brief postdoc at Johns Hopkins University. He worked for about three years in URS corporation before joining IIT Madras in 2011.

Besides research and teaching, Prof. Ghosh takes a deep interest in rural education. He has started an initiative called ‘Teach to Learn’ (www.teachtolearn.co.in)  about 10 years back, which focus on connecting the premier institute of the country with rural schools applying different education models.

G.L. Samuel

Dr. G.L. Samuel is a distinguished Professor in the Department of Mechanical Engineering at the Indian Institute of Technology, Madras, where he leads the Centre of Excellence for Advanced Laser Material Processing and Surface Engineering. He completed his PhD at IIT Madras in 2001 and pursued Post-Doctoral Fellowship at Kyungpook National University, South Korea, before joining the faculty at IIT Madras in 2005.

With expertise in Micro Manufacturing, Advanced Laser Material Processing, and Metrology, Dr. Samuel teaches a range of courses at both undergraduate and postgraduate levels including Automation in Manufacturing, Artificial Intelligence, Micro Manufacturing Technology, Machining and Metrology, as well as Modelling and Simulation.

Dr. Samuel has a strong publication record and holds patents in various areas of manufacturing. He has organized continuing education programmes, workshops, and international conferences, fostering academic development and collaborations with universities worldwide, including Texas A&M University.

As a part of the Fulbright-Nehru Academic and Professional Excellence fellowship, Dr. Samuel will conduct research at Texas A&M, focusing on developing digital twins for smart factory entities. The project aims to create a virtual representation that mimics the behaviour of the physical machines, enabling real-time monitoring, analysis, and optimization of all the processes in an autonomous factory.