arXiv subjects

Shreyas Ramachandran

Publications and source records attributed to Shreyas Ramachandran.

4 recordsLinked to original sources

arXiv2025

The World of AI: A Novel Approach to AI Literacy for First-year Engineering Students

This work presents a novel course titled The World of AI designed for first-year undergraduate engineering students with little to no prior exposure to AI. The central problem addressed by this course is that engineering students often lack foundational knowledge of AI and its broader societal implications at the outset of their academic journeys. We believe the way to address this gap is to design and deliver an interdisciplinary course that can a) be accessed by first-year undergraduate engineering students across any domain, b) enable them to understand the basic workings of AI systems sans mathematics, and c) make them appreciate AI's far-reaching implications on our lives. The course was divided into three modules co-delivered by faculty from both engineering and humanities. The planetary module explored AI's dual role as both a catalyst for sustainability and a contributor to environmental challenges. The societal impact module focused on AI biases and concerns around privacy and fairness. Lastly, the workplace module highlighted AI-driven job displacement, emphasizing the importance of adaptation. The novelty of this course lies in its interdisciplinary curriculum design and pedagogical approach, which combines technical instruction with societal discourse. Results revealed that students' comprehension of AI challenges improved across diverse metrics like (a) increased awareness of AI's environmental impact, and (b) efficient corrective solutions for AI fairness. Furthermore, it also indicated the evolution in students' perception of AI's transformative impact on our lives.

cs.CY↗

arXiv2025

Mechanical resonant sensing of spin texture dynamics in a two-dimensional antiferromagnet

The coupling between the spin degrees of freedom and macroscopic mechanical motions, including striction, shearing, and rotation, has attracted wide interest with applications in actuation, transduction, and information processing. Experiments so far have established the mechanical responses to the long-range ordered or isolated single spin states. However, it remains elusive whether mechanical motions can couple to a different type of magnetic structure, the non-collinear spin textures, which exhibit nanoscale spatial variations of spin (domain walls, skyrmions, etc.) and are promising candidates to realize high-speed computing devices. Here, we report the detection of collective spin texture dynamics with nanoelectromechanical resonators made of two-dimensional antiferromagnetic (AFM) MnPS3 with $10^{-9}$ strain sensitivity. By examining radio frequency mechanical oscillations under magnetic fields, new magnetic transitions were identified with sharp dips in resonant frequency. They are attributed to the collective AFM domain wall motions as supported by the analytical modeling of magnetostriction and large-scale spin-dynamics simulations. Additionally, an abnormally large modulation in the mechanical nonlinearity at the transition field infers a fluid-like response due to the ultrafast domain motion. Our work establishes a strong coupling between spin texture and mechanical dynamics, laying the foundation for electromechanical manipulation of spin texture and developing quantum hybrid devices.

cond-mat.mes-hall↗

arXiv2025

AdaptAI: A Personalized Solution to Sense Your Stress, Fix Your Mess, and Boost Productivity

Personalization is a critical yet often overlooked factor in boosting productivity and wellbeing in knowledge-intensive workplaces to better address individual preferences. Existing tools typically offer uniform guidance whether auto-generating email responses or prompting break reminders without accounting for individual behavioral patterns or stress triggers. We introduce AdaptAI, a multimodal AI solution combining egocentric vision and audio, heart and motion activities, and the agentic workflow of Large Language Models LLMs to deliver highly personalized productivity support and context-aware well-being interventions. AdaptAI not only automates peripheral tasks (e.g. drafting succinct document summaries, replying to emails etc.) but also continuously monitors the users unique physiological and situational indicators to dynamically tailor interventions such as micro-break suggestions or exercise prompts, at the exact point of need. In a preliminary study with 15 participants, AdaptAI demonstrated significant improvements in task throughput and user satisfaction by anticipating user stressors and streamlining daily workflows.

cs.HC↗

arXiv2024

Hot Carriers from Intra- and Interband Transitions in Gold-Silver Alloy Nanoparticles

Hot electrons and holes generated from the decay of localized surface plasmons in metallic nanoparticles can be harnessed for applications in solar energy conversion and sensing. In this paper, we study the generation of hot carriers in large spherical gold-silver alloy nanoparticles using a recently developed atomistic modelling approach that combines a solution of Maxwell's equations with large-scale tight-binding simulations. We find that hot-carrier properties depend sensitively on the alloy composition. Specifically, nanoparticles with a large gold fraction produce hot carriers under visible light illumination while nanoparticles with a large silver fraction require higher photon energies to produce hot carriers. Moreover, most hot carriers in nanoparticles with a large gold fraction originate from interband transitions which give rise to energetic holes and "cold" electrons near the Fermi level. Increasing the silver fraction enhances the generation rate of hot carriers from intraband transitions which produce energetic electrons and "cold" holes. These findings demonstrate that alloy composition is a powerful tuning parameter for the design of nanoparticles for applications in solar energy conversion and sensing that require precise control of hot-carrier properties.

cond-mat.mes-hall↗