Dr Tanmoy Bhattacharya | Computational Modeling | Best Researcher Award

Dr Tanmoy Bhattacharya | ย External Professor | Best Researcher Award๐Ÿ†

External Professor at Santa Fe Institute,United States๐ŸŽ“

Tanmoy Bhattacharya is a distinguished scientist and researcher, currently holding the position of Scientist 5 and Laboratory Fellow at Los Alamos National Laboratory (LANL) and serving as an External Professor at the Santa Fe Institute. With a career spanning over three decades, Bhattacharya has made significant contributions to physics, computational biology, and microbiology. He is renowned for his interdisciplinary research, leadership in scientific collaborations, and innovations in computational tools that have had a lasting impact on the scientific community.

Professional Profileย 

๐Ÿง‘โ€๐ŸŽ“Education๐ŸŽ“

Tanmoy Bhattacharya’s academic journey began at the prestigious Indian Institute of Technology (IIT) Kharagpur, where he earned his B.Sc. in Physics in 1982, followed by an M.Sc. in Physics in 1984 under the guidance of Prof. Debabrata Basu. He then pursued his Ph.D. in Physics at the Tata Institute of Fundamental Research in Bombay, India, completing his dissertation on “Tree Unitarity Breakdown in Spontaneously Broken N=1 Supergravity Theories and Phenomenology of a Superlight Gravitino” in 1989 under the mentorship of Prof. Probir Roy.

๐Ÿ’ผWork Experience

Bhattacharya’s professional career began with post-doctoral fellowships at Brookhaven National Laboratory, Centre de Energie Atomique in Saclay, and Los Alamos National Laboratory (LANL) between 1989 and 1995. He transitioned to a staff role at LANL in 1995 and has been a significant contributor to the laboratory ever since. His roles have evolved from Limited Term Staff Member to Scientist 5 and Laboratory Fellow, reflecting his growing expertise and leadership within the institution. Additionally, he served as a Professor at the Santa Fe Institute from 2006 to 2017 and continues to contribute as an External Professor.

๐Ÿ› ๏ธSkills

Tanmoy Bhattacharya possesses a wide range of skills, including expertise in theoretical physics, computational biology, and microbiology. His technical skills extend to programming and software development, having contributed to the creation of tools like hyperTeX, the hyperref LaTeX package, and the development of the Apache webserver. His ability to lead large-scale research collaborations and his contributions to computational methods in high-energy physics and lattice quantum chromodynamics demonstrate his proficiency in both scientific research and technical innovation.

๐Ÿ†Awards and Honors

Bhattacharya has been the recipient of numerous prestigious awards throughout his career. Some of his notable honors include the Los Alamos Distinguished Performance Award (1999, 2022), the Duke CHAVI-ID Outstanding Contributions Award (2015), and recognition as a Highly Cited Researcher by Clarivate Analytics in multiple years (2016, 2018, 2019, 2020). In 2020, he was named a Los Alamos Laboratory Fellow, a testament to his exceptional contributions to the scientific community. Most recently, in 2023, he was recognized among the top scientists in Biology and Biochemistry by research.com and was part of the LANL team that won an R&D 100 award for the โ€œCANDLEโ€ project.

ย Membership ๐Ÿ›๏ธ

Tanmoy Bhattacharya is a member of the American Physical Society, actively participating in divisions such as Computational Physics and Particles and Fields. He has held leadership roles in the US Lattice Quantum Chromodynamics (USQCD) collaboration, contributing to the strategic direction of high-energy physics. Additionally, he moderates the hep-lat arXiv and is involved in the International Society of Genetic Genealogy.

Research Focus ๐Ÿ”ฌ

Bhattacharya’s research focuses primarily on theoretical physics, computational biology, and microbiology. His work in lattice quantum chromodynamics (LQCD) has been pivotal in understanding fundamental particles and forces. In the field of computational biology, he has made significant contributions to HIV research and genetic analysis, as evidenced by his work with the HIV Genetics and HIV Database teams at LANL. His interdisciplinary approach allows him to tackle complex problems at the intersection of physics, biology, and computer science, making his research both innovative and impactful across multiple fields.

๐Ÿ“–Publications :ย 

  1. High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
    ๐Ÿ—“๏ธ 2024 | ๐Ÿ“ฐ Scientific Reports | ๐Ÿง ๐Ÿ“ˆ
  2. The pion-nucleon sigma term from Lattice QCD
    ๐Ÿ—“๏ธ 2024 | ๐Ÿ“ฐ Proceedings of Science | ๐Ÿ’ฅ๐Ÿ”ฌ
  3. Control variates for lattice field theory
    ๐Ÿ—“๏ธ 2024 | ๐Ÿ“ฐ Physical Review D | ๐Ÿ“Š๐Ÿงฎ
  4. Prevention efficacy of the broadly neutralizing antibody VRC01 depends on HIV-1 envelope sequence features
    ๐Ÿ—“๏ธ 2024 | ๐Ÿ“ฐ Proceedings of the National Academy of Sciences of the United States of America | ๐Ÿฆ ๐Ÿ’‰
  5. Nucleon isovector axial form factors
    ๐Ÿ—“๏ธ 2024 | ๐Ÿ“ฐ Physical Review D | โš›๏ธ๐Ÿ“
  6. Deep learning uncertainty quantification for clinical text classification
    ๐Ÿ—“๏ธ 2024 | ๐Ÿ“ฐ Journal of Biomedical Informatics | ๐Ÿค–๐Ÿ“š
  7. Confronting the axial-vector form factor from lattice QCD with MINERvA antineutrino-proton data
    ๐Ÿ—“๏ธ 2023 | ๐Ÿ“ฐ Physical Review D | ๐Ÿงช๐Ÿ”ฌ
  8. Quark chromoelectric dipole moment operator on the lattice
    ๐Ÿ—“๏ธ 2023 | ๐Ÿ“ฐ Physical Review D | โš›๏ธโš™๏ธ
  9. Electroweak box diagram contribution for pion and kaon decay from lattice QCD
    ๐Ÿ—“๏ธ 2023 | ๐Ÿ“ฐ Physical Review D | ๐Ÿ“ฆ๐Ÿ”‹
  10. nEDM from the theta-term and chromoEDM operators
    ๐Ÿ—“๏ธ 2023 | ๐Ÿ“ฐ Proceedings of Science | ๐Ÿงฒ๐Ÿ“

Arijit De | Computational Modeling | Best Researcher Award

 

 

 

Mr Arijit De | Computational Modeling | Best Researcher Awardย 

Senior Research Fellow at ย Jadavpur University ,Kolkata, India

Arijit De is a seasoned Machine Learning Engineer with over 6 years of expertise in the field. ๐ŸŒŸ His skills span data science, Python programming, and SQL, adeptly applied in projects using PyTorch, TensorFlow, and OpenCV. Arijit has a strong background in Computer Vision and Natural Language Processing, contributing to end-to-end ML solutions. Currently, he leads deep learning pipeline development at mVizn Pte. Ltd., focusing on semantic segmentation of 3D point clouds. His career includes impactful roles at TCS and as a TCS Research Fellow at Jadavpur University, where he developed innovative ML solutions for healthcare and data quality enhancement projects.

Profile:

Scopusย 

๐Ÿ“š Education:

Arijit De has pursued an extensive academic journey culminating in a pending PhD from Jadavpur University, focusing on cutting-edge research in Machine Learning. He holds an M.Tech in Computer Science & Engineering and a B.Tech from Techno India, Kolkata, showcasing his academic prowess with impressive GPAs. Arijit has augmented his academic achievements with certifications such as Deep Learning and TensorFlow from Deeplearning.ai, underscoring his commitment to staying at the forefront of technological advancements in AI. His academic and certification credentials solidify his expertise in applying theoretical knowledge to practical ML solutions, driving innovation in the field.

 

๐Ÿ‘จโ€๐Ÿซ Professional Experience

As a Machine Learning Engineer at mVizn Pte. Ltd., Arijit spearheads the development of DL pipelines for semantic segmentation, optimizing data processing and deploying models in web applications. His tenure at TCS Research Fellow focused on Alzheimer’s disease classification and brain tumor detection using advanced ML techniques.

 

Skills and Technologies

Arijit’s proficiency extends across Python, Java, C++, and SQL, alongside technologies such as PyTorch, TensorFlow, and OpenCV. He leverages cloud platforms like Microsoft Azure and possesses theoretical expertise in Deep Learning, Computer Vision, and NLP.

 

 

Research focus:

Arijit De’s research focus is likely centered around the application of machine learning (ML) and data science techniques to solve real-world problems. Specifically, his interests may include:

  1. Machine Learning Development: Arijit has extensive experience in developing end-to-end ML solutions using frameworks like PyTorch, TensorFlow, and Scikit-Learn. His research may involve advancing ML algorithms, improving model performance, and exploring novel applications of ML in various domains.
  2. Computer Vision: Given his proficiency in OpenCV and experience in Computer Vision principles, Arijit may be researching topics related to image and video analysis, object detection and recognition, and image processing techniques using ML.
  3. Natural Language Processing (NLP): With skills in NLTK and likely other NLP tools, Arijit may be interested in research related to text analysis, sentiment analysis, language modeling, and other NLP applications.
  4. Data Analysis and Visualization: Arijit’s expertise in Python and SQL for data analysis and visualization suggests he may also be involved in research focused on deriving insights from large datasets, exploratory data analysis, and developing visualization techniques to communicate complex data.
  5. Cloud Computing and Deployment: Knowledge of deploying ML applications on cloud platforms indicates research interest in scalable and distributed ML systems, cloud-native ML architectures, and optimizing ML models for deployment in cloud environments.
  6. Project Management and Risk Management: Arijit’s background in planning, estimation, and risk management of projects suggests a practical focus on applying ML and data science methodologies in industry settings, ensuring project success and mitigating risks.

Citations:

Citations: 42 ๐Ÿ“‘

Documents: 7 ๐Ÿ“„

h-index: 2๐Ÿ“ˆ

 

Publication Top Notes: