Dr. Adarsh Kumar Shukla | Computational Methods | Young Scientist Award

Dr. Bhimrao Ambedkar Government Medical College | India

Dr. Adarsh Kumar Shukla is a motivated and results-driven professional with expertise in Environmental Toxicology, Clinical Bioinformatics, NGS Data Analysis, Pathway Analysis, Computer-Aided Drug Design, Structural Biology, Metabolomics, Computational Biology, and Computational Chemistry. Currently serving as Scientist-B at the Multidisciplinary Research Unit, Dr. Bhimrao Ramji Ambedkar Government Medical College, Kannauj, he has prior experience as a Scientist and Research Associate in Paediatric Cardiac Research at Sri Sathya Sai Sanjeevani Research Foundation, as well as academic and project coordination roles in biotechnology and medicinal plant research. He has coordinated national-level academic events and completed FDPs focused on healthcare innovation. Dr. Shukla has contributed significantly to Health-, Plant-, and Food-Informatics research, presented papers at several conferences, and holds a granted Indian patent for the development of crab-apple-based jelly sheets. His research interests encompass structural bioinformatics, human genomics, and nutritional biotechnology, with strong proficiency in in-silico tools, laboratory instrumentation, and data analysis. Skilled in programming languages such as R, Biopython, and Bioperl, he combines computational and experimental approaches to address complex biomedical challenges.sics.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Shukla, A. K., & Kukshal, P. (2025). Computational simulations aided prioritization of genomic targets for congenital heart disease (CHD) against developmental toxicity. Reproductive Toxicology, 108, 108940.

Shukla, A. K., & Kumar, A. (2025). A chemoinformatics study to prioritization of anticancer orally active lead compounds of pearl millet against adhesion G protein-coupled receptor. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 334, 125960.

Jha, R. K., Shukla, A. K., Kumari, A., & Kumar, A. (2025). Virtual screening of potential orally active anti-bacterial compounds of finger millet. Vegetos, 38(3), 1237–1248.

Jain, S., Shukla, A. K., Deepika, Panwar, S., Kumari, A., Yadav, A. K., & Kumar, A. (2024). Revolutionizing disease treatment through bioengineered probiotics and glucagon‐like peptide 1 (GLP‐1) based strategies: A path towards effective cures. Food Bioengineering, 2024, 1.

Panwar, S., Pal, S., Shukla, A. K., Kumar, A., & Sharma, P. K. (2024). Identification of micronutrient deficiency related miRNA and their targets in Triticum aestivum using bioinformatics approach. Ecological Genetics and Genomics, 31, 100236.

Dr. Yanan Sun | In Memory Computing | Best Researcher Award

Shanghai Jiao Tong University | China

Dr. Y. Sun earned his Ph.D. in Electronic and Computer Engineering from The Hong Kong University of Science and Technology (2015) and his B.S. in Microelectronics from Shanghai Jiao Tong University (2009). He is currently an Associate Professor of Electrical Engineering at Shanghai Jiao Tong University, where he previously served as Assistant Professor. His research focuses on in-memory computing, energy-efficient digital integrated circuits, neuromorphic computing, and AI hardware acceleration. He has published extensively in top-tier journals such as IEEE TCAS-I, TCAD, JSSC, TED, and TGRS, and at leading conferences including DAC, ICCAD, DATE, ISSCC, and ISCAS, with several works nominated for or receiving Best Paper Awards. Notably, he received the First Place Best Paper Award at the IEEE International Conference on Microelectronics (2014) and Best Paper Award nominations at DATE (2020) and ICTA (2023). His impactful contributions span monolithic 3D memory, ReRAM-based neural network acceleration, metastability mitigation in SoCs, and computing-in-memory architectures, demonstrating strong expertise at the intersection of circuit design, architectures, and AI acceleration.

Featured Publications

"Robust Monolithic 3D Carbon-Based Computing-in-SRAM with Variation-Aware Bit-Wise Data-Mapping for High-Performance and Integration Density"

"HyCTor: A Hybrid CNN-Transformer Network Accelerator With Flexible Weight/Output Stationary Dataflow and Multicore Extension"

"Hop-CIM: An all-digital two-level approximate SRAM-CIM macro for high energy-efficient HNN acceleration with data-aware early exit and column-wise partial-sum reuse"

"A Metastability Risk Prediction and Mitigation Technique for Clock-Domain Crossing with Single-Stage Synchronizer in Near-Threshold-Voltage Millivoltage/ Frequency-Domain Network-on-Chip"

"Enabling Low-Power Charge-Domain Nonvolatile Computing-in-Memory (CIM) with Ferroelectric Memcapacitor"