Robert White | particle physics | Best Researcher Award

Dr. Robert White | particle physics | Best Researcher Award

PHD at Charles University in Prague

Rob White is a Postdoctoral Researcher at INFN Sezione di Torino, specializing in the development and characterisation of LGAD sensors. He completed his Ph.D. in Particle Physics at the University of Bristol, focusing on Dark Matter and BSM Physics, and worked on statistical and ML techniques in DQM. Previously, he was a Postdoctoral Researcher at Bristol and earned his M.Phys. in Physics from the University of Manchester. His research includes characterizing FBK EXFLU1 sensors and addressing radiation effects. White has contributed to notable publications on dark matter and Higgs boson decays, reflecting his expertise in advanced particle physics.

Professional Profiles

Publications

Search for dark QCD with emerging jets in proton-proton collisions at \( \sqrt{s} \) = 13 TeV, Publication date:  2024.

Measurement of the production cross section of a Higgs boson with large transverse momentum in its decays to a pair of τ leptons in proton-proton collisions, Publication date:  2024.

Multiplicity and transverse momentum dependence of charge-balance functions in pPb and PbPb collisions at LHC energies, Publication date:  2024.

Constraints on anomalous Higgs boson couplings from its production and decay using the WW channel in proton–proton collisions at \sqrt{s} = 13~\text {TeV}, Publication date:  2024.

Search for long-lived particles decaying in the CMS muon detectors in proton-proton collisions at √𝑠=13  TeV, Publication date:  2024.

Conclusion

Rob White is a promising candidate for the Research for Best Researcher Award, especially given his early achievements in high-impact areas of particle physics. His work on LGAD sensors, involvement in cutting-edge dark matter research, and contributions to significant collaborations like CMS make him a strong contender. However, to further solidify his candidacy, he might focus on developing more independent research initiatives, broadening the impact of his work, and establishing a clear long-term research vision. Overall, White demonstrates great potential for continued excellence and leadership in the field of particle physics.

Jing Hao | High Energy Physics | Best Researcher Award

Dr. Jing Hao | High Energy Physics | Best Researcher Award

China Algorithm Engineer at Beijing, China

Jing Hao is an accomplished algorithm engineer at Baidu Inc. with a Master’s degree in Mechanical Engineering from HuaZhong University of Science and Technology and a Bachelor’s degree from China University of Mining and Technology. Specializing in advanced AI algorithms for visual and image applications, he has designed UAV inspection algorithms for the State Grid and improved defect detection systems. Jing’s academic contributions include pioneering glass surface segmentation methods and publishing in IEEE TCSVT. Passionate about integrating AI in healthcare, he stays updated with the latest technologies and shares insights through his WeChat account, OAOA, with over 130 original posts. High Energy Physics

Professional Profiles

Education

Master’s Degree in Mechanical Engineering – HuaZhong University of Science and Technology Postgraduate Recommendation: September 2020 – June 2022 Weighted Average Score: 89.43 / 100 Rank: 66 / 205 Thesis Title: Research on Visual Assisted System of Engineering Vehicles based on Panoramic Imaging Bachelor’s Degree in Mechanical Engineering – China University of Mining and Technology Graduated with Honors: September 2016 – June 2020 Weighted Average Score: 87.49 / 100 Rank: 22 / 325 Thesis Title: Design of Light Bridge Crane and Remote Consol. High Energy Physics

Work Experience

Baidu Inc., Beijing, China Algorithm Engineer, Visual Video and Image Application Group (July 2022 – Present) Designed UAV inspection algorithms for metal defects in transmission electricity lines of the State Grid and factory safety inspection algorithms. Developed a training scheme based on CAE (Context Autoencoder) self-supervised pre-training algorithm for UAV inspection algorithms. Conducted special data preprocessing for defect data and applied the CAE pretraining algorithm upon the Vision Transformer (ViT) architecture. Released the “Power Vision Large Model Test and Verification Report” with the State Grid Intelligent Research Institute, achieving a 4.25% increase in the AP50 metric. Explored the application of LLM in factory safety inspection, achieving higher accuracy rates using the MiniGPT4 image-text multimodal foundation large model. High Energy Physics

Rewards

National Scholarship (2018) First-class Scholarship of HUST (2021) First-class Scholarship of CUMT (2019) Second-class Scholarship of CUMT (2017) Outstanding Student of CUMT (2018) Excellent Student Leader of Jiangsu Province (2019) High Energy Physics

Publications

  1. T-mamba: Frequency-enhanced gated long-range dependency for tooth 3d cbct segmentation, Publication date: 2024.
  2. Language-aware multiple datasets detection pretraining for DETRs, Publication date: 2024.
  3. GEM: Boost Simple Network for Glass Surface Segmentation via Segment Anything Model and Data Synthesis, Publication date: 2024.
  4. GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation Models, Publication date: 2023.
  5. Simple parameter-free self-attention approximationPublication date: 2023.
  6. A stronger stitching algorithm for fisheye images based on deblurring and registration, Publication date: 2023.
  7. A lightweight and accurate recognition framework for signs of X-ray weld images, Publication date: 2022.
  8. Heterogeneous Generative Knowledge Distillation with Masked Image Modeling
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