Ziang Liu | Engineering | Best Researcher Award

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Mr. Ziang Liu | Engineering | Best Researcher Award

Nanjing University | China

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Early Academic Pursuits

Mr. Ziang Liu began his academic journey with distinction at Tianjin University, where he earned his Bachelor of Science in Electronic Engineering. His strong foundation in engineering and mathematics laid the groundwork for advanced research and innovation. Continuing his academic trajectory, he pursued a Master of Science in Electronic Engineering at the prestigious Nanjing University, where he was recognized as an Outstanding Student and awarded the First-class Academic Scholarship.

Professional Endeavors

Ziang has accumulated valuable industry experience through impactful internships. At Meituan Shanghai, he served as an LLMs Evaluation Algorithm Intern, where he designed evaluation schemes and analyzed instruction-following capabilities across large language models such as Qwen, Doubao, ChatGPT 3.5/4, and Llama2-70B.  In another significant role at Alibaba DingTalk in Hangzhou, he worked on the back-end development of Chatmemo, an enterprise AI assistant. There, he implemented knowledge graph subgraph displays and integrated Retrieval-Augmented Generation (RAG), significantly boosting response speed and system performance.

Contributions and Research Focus

Mr. Liu’s core interests revolve around LLMs (Large Language Models), RAG (Retrieval-Augmented Generation), and knowledge graph technologies. He has contributed to the design and optimization of backend systems for intelligent applications in healthcare and enterprise settings. His work on deploying frameworks like Graph RAG and utilizing tools like Redis, MySQL, and Spring Boot has shown practical outcomes in real-world systems, particularly in performance optimization, load balancing, and cache management. His participation in the Nanjing University Intelligent Hospital Project resulted in a custom online medication purchasing system, complete with AI-powered Q&A capabilities and scalable backend infrastructure.

Accolades and Recognition

Ziang Liu’s academic excellence is evident through a remarkable series of accolades earned during both his undergraduate and postgraduate studies. He was honored as the Outstanding Student of Nanjing University in 2023 and received the First-class Academic Scholarship in 2022, recognizing his superior academic performance. His analytical and technical skills were demonstrated through competition achievements, including the Third Prize in the 19th Chinese Graduate Mathematical Modeling Competition (2022) and the Second Prize in the 18th Chinese Electronic Design Competition (2023). Earlier in his academic journey, he was named a Meritorious Winner in the Mathematical Contest in Modeling (MCM) in 2021 and was recognized as an Outstanding Graduate of Tianjin University in 2022. These accomplishments reflect his consistent dedication, innovation, and leadership in engineering and applied mathematics.

Impact and Influence

Ziang Liu’s work has made a tangible impact in both academia and industry. His efforts in improving instruction-following performance in LLMs and optimizing backend systems for enterprise AI applications have proven valuable for real-world implementation. His innovations in intelligent hospital systems demonstrate a commitment to applying advanced AI technologies to enhance societal well-being and operational efficiency.

Legacy and Future Contributions

Poised at the intersection of AI, backend engineering, and applied innovation, Mr. Ziang Liu is emerging as a key contributor to the next generation of AI infrastructure. His hands-on experience with cutting-edge technologies like gRPC, GraphRAG, JWT, and multi-threaded optimization positions him to drive future advancements in AI systems, enterprise platforms, and digital healthcare. With a strong academic record and robust technical expertise, he is well on his way to becoming a leading voice in intelligent systems development.

 

 

Publications

Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification

Authors: Ziang Liu, Kang Fan, Qin Gu, Yaduan Ruan
Journal: Bioengineering
Year: 2025

Studying Multi-Frequency Multilayer Brain Network via Deep Learning for EEG-Based Epilepsy Detection

Authors: Weidong Dang, Dongmei Lv, Linge Rui, Ziang Liu, Guanrong Chen, Zhongke Gao
Journal: IEEE Sensors Journal
Year: 2021

Rishabh Kumar | Computer Science | Best Researcher Award

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Mr. Rishabh Kumar | Computer Science | Best Researcher Award

IIT Bombay | India

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🌱 Early Academic Pursuits

Mr. Rishabh Kumar’s journey in the field of Computer Science and Engineering began with academic brilliance and passion for innovation. He completed his Bachelor of Technology at IIT (ISM) Dhanbad in 2016, where he laid the foundation for his interest in language technologies under the mentorship of Prof. Sukomal Pal. His intellectual trajectory reached new heights when he joined IIT Bombay as a PhD scholar in 2019. There, under the guidance of Prof. Ganesh Ramakrishnan and Prof. Preethi Jyothi, he began pioneering work in Automatic Speech Recognition (ASR) for low-resource Indian languages, including Sanskrit and Hindi — an effort that merges linguistics with machine learning in service of cultural preservation and accessibility.

👨‍💼 Professional Endeavors

With roles spanning startups to tech giants, Mr. Kumar has applied his research to real-world systems. At Samsung R&D, Bangalore, during his 2023–2024 internship, he developed advanced ASR models for Hindi and spearheaded cross-lingual proper noun recognition using Large Language Models (LLMs). His early stints as Technical Head at Gartley618 Technologies and Lead Developer at Pocketin demonstrate his versatility across full-stack development, iOS applications, and blockchain-based platforms. Additionally, his research internship at Wrig Nanosystems involved Android-based biomedical device integration, showcasing his interdisciplinary reach.

🧠 Contributions and Research Focus

Mr. Kumar’s work is particularly focused on ASR for underrepresented languages, speech-text alignment, and LLM-based improvements in speech technologies. His innovations include:

🔹 Developing Vāgyojaka, a Sanskrit ASR annotation and post-editing tool
🔹 Creating a SpeechQC Agent, a natural language–driven framework for speech dataset validation
🔹 Building ASR pipelines for Indian Parliament (SansadTV)
🔹 Advancing BharatGen Hindi ASR systems

His contributions are documented across top-tier conferences and journals, including ACL, EMNLP, INTERSPEECH, and CSL, with several first-author papers that blend linguistic knowledge and computational innovation.

🏆 Accolades and Recognition

Mr. Kumar’s excellence has been widely recognized. He received the Microsoft Research India Travel Grant to attend INTERSPEECH 2022 and has won prestigious competitions like the State Android App Contest by Jharkhand Government and the National Multilingual Theater Competition. His early achievements include multiple Math Olympiad prizes, an NTSE Level II qualification, and distinctions in Macmillan Olympiads by the University of New South Wales. These accolades reflect his lifelong dedication to problem-solving, innovation, and excellence.

🌍 Impact and Influence

Rishabh Kumar’s research has directly impacted India’s speech technology ecosystem, contributing essential tools and models for building inclusive, vernacular AI systems. His work supports broader national initiatives such as Bhashini and aligns with global goals of linguistic equity in AI. By building publicly usable ASR tools, datasets, and systems for resource-poor languages, he is democratizing access to technology for millions of Indian users.

🔭 Legacy and Future Contributions

As Mr. Kumar approaches the culmination of his PhD, his trajectory signals an exciting future. His legacy lies in fusing computational prowess with cultural sensitivity — bringing Indian linguistic diversity to the forefront of AI innovation. Whether in academia, industry, or open-source collaborations, he is poised to continue shaping the next generation of multilingual ASR systems, LLM-based speech understanding, and resource-efficient AI tools. His work will inspire young researchers to explore the intersection of language, society, and technology.

Publications

📝 Linguistically Informed Automatic Speech Recognition in Sanskrit
 Author: Rishabh Kumar (assumed from your context)
 Journal: Computer Speech & Language (CSL)
 Year: 2025

📝 Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using LLMs
 Author: Rishabh Kumar (assumed)
 Conference: EMNLP (Findings of the 2024 Conference on Empirical Methods in Natural Language Processing)
 Year: 2024

📝 Linguistically Informed Post-processing for ASR Error Correction in Sanskrit
 Author: Rishabh Kumar
 Conference: INTERSPEECH
 Year: 2022

📝 Vāgyojaka: An Annotating and A Post-Editing Tool for Automatic Speech Recognition
 Author: Rishabh Kumar
 Conference: INTERSPEECH (Show and Tell)
 Year: 2022

📝 Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights
 Author: Rishabh Kumar
 Conference: ACL (Findings of the Association for Computational Linguistics)
 Year: 2021