Kedar Hippalgaonkar

Assistant Professor

Nanyang Technological University (NTU)

Singapore

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About

Dr. Kedar Hippalgaonkar is an Assistant Professor at Nanyang Technological University in Singapore. His research areas encompass innovative applications of Generative AI in education, thermoelectric materials, and advanced photonic devices, as evidenced by his recent publications. His work explores topics such as machine learning-enhanced design for photonic applications and optimizing thermoelectric properties in nanostructures.

Articles (26)

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2025

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2022

Collaborators (11)

Simon Rihm

University of Cambridge

UNITED KINGDOM

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Seoin Back

Assistant Professor

Sogang University

SOUTH KOREA

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Patrick Parkinson

Senior Lecturer

The University of Manchester

UNITED KINGDOM

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Ganna Gryn'ova

Associate Professor of Computational Chemistry

University of Birmingham

UNITED KINGDOM

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Yizhong Huang

Nanyang Technological University (NTU)

SINGAPORE

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Jason Hein

Adjunct Professor

University of Bergen

NORWAY

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Alexei Lapkin

University of Cambridge

UNITED KINGDOM

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Tejs Vegge

Professor at Technical University of Denmark

Technical University of Denmark

DENMARK

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D Ian Wilson

University of Cambridge

UNITED KINGDOM

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Wei Wen Wong

Australian National University

AUSTRALIA

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Beatrice W Soh

Institute of Materials Research and Engineering

SINGAPORE

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Kedar Hippalgaonkar

Research Interests

Contact this professor

About

Articles (26)

Bayesian Optimized Design for O-Band Bottom-Up Grown Microring Lasers

Harnessing GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbot’s Impact on Learning

An Eshelby twist to modulate thermal transport

Screening of Coulombic Interactions To Achieve a Higher Power Factor in Conjugated Polymers

Opentrons for automated and high-throughput viscometry

Accelerated chemical science with AI

Few-layer Bi2O2Se: a promising candidate for high-performance near-room-temperature thermoelectric applications

Thermoelectric Property Enhancement of Tellurium Nanowires by Surface Passivation

Stable and Highly Emissive Infrared Yb‐Doped Perovskite Quantum Cutters Engineered by Machine Learning

Oxidation Control to Augment Interfacial Charge Transport in Te‐P3HT Hybrid Materials for High Thermoelectric Performance

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

WyCryst: Wyckoff inorganic crystal generator framework

Harnessing GPT-3.5 for text parsing in solid-state synthesis – case study of ternary chalcogenides

Machine learning based feature engineering for thermoelectric materials by design

Optimization of liquid handling parameters for viscous liquid transfers with pipetting robots, a “sticky situation”

Generative Design and Experimental Validation of Non-Fullerene Acceptors for Photovoltaics

Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization

Ordering‐Dependent Hydrogen Evolution and Oxygen Reduction Electrocatalysis of High‐Entropy Intermetallic Pt4FeCoCuNi

Linking Polaron Signatures to Charge Transport in Doped Thiophene Polymers

Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials

Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics

Automated electrokinetic stretcher for manipulating nanomaterials

Automated pipetting robot for proxy high-throughput viscometry of Newtonian fluids

Diagnosis of doped conjugated polymer films using hyperspectral imaging

Tuning Electronic Structure and Composition of FeNi Nanoalloys for Enhanced Oxygen Evolution Electrocatalysis via a General Synthesis Strategy

Accelerated automated screening of viscous graphene suspensions with various surfactants for optimal electrical conductivity

Collaborators (11)

Simon Rihm

Seoin Back

Patrick Parkinson

Ganna Gryn'ova

Yizhong Huang

Jason Hein

Alexei Lapkin

Tejs Vegge

D Ian Wilson

Wei Wen Wong

Beatrice W Soh

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