Mehrasa Ahmadipour | Information Theory | Best Researcher Award

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Dr. Mehrasa Ahmadipour | Information Theory | Best Researcher Award

Postdoc at UMPA, ens de lyon, France

Mehrasa Ahmadipour is a highly qualified candidate for the Best Researcher Award, with a Ph.D. in Information Theory from Institut Polytechnique de Paris and postdoctoral research at ENS Lyon in Sequential Statistics and Reinforcement Learning. Her expertise spans Multi-Armed Bandit Problems, ISAC, Neural Networks, and Physical Layer Security. She has contributed significantly as a guest editor, reviewer for IEEE journals, and session chair at IEEE ISIT 2023. With teaching experience in Information Theory, Cryptography, and Probability, she has also supervised master’s students. Additionally, she has held key roles in organizing academic conferences like CJC-MA 2024 and ISIT 2019. While her academic and research credentials are outstanding, strengthening her portfolio with more high-impact publications, citations, research funding, and industry collaborations would further enhance her profile. Overall, her research excellence, leadership, and contributions to the field make her a strong contender for the award.

Professional Profile 

Education🎓

Mehrasa Ahmadipour has a strong academic background in Electrical Engineering and Information Theory. She earned her Ph.D. from Institut Polytechnique de Paris (Télécom Paris) in 2022, specializing in Integrated Sensing and Communication (ISAC) under the supervision of Michele Wigger. Her doctoral research focused on an information-theoretic approach to ISAC, contributing to advancements in wireless communication and signal processing. Prior to that, she completed her M.Sc. in Electrical Engineering (Telecommunications Systems and Security) at the University of Tehran, where she worked on Physical Layer Authentication and Covert Communication in Wireless Networks. She earned her B.Sc. in Electrical Engineering from Iran University of Science and Technology (IUST), with a focus on Hyper Spectral Image Processing. Her academic journey began at the National Organization for Development of Exceptional Talents (NODET), where she specialized in Physics and Mathematics, ranking in the top 0.1% in university entrance exams, demonstrating exceptional academic excellence.

Professional Experience 📝

Mehrasa Ahmadipour has extensive professional experience in research and academia, focusing on Information Theory, Machine Learning, and Telecommunications. She is currently a Postdoctoral Researcher at École Normale Supérieure de Lyon, working on Sequential Statistics and Reinforcement Learning under the supervision of Aurélien Garivier. Her research explores advanced statistical methods and optimization techniques in decision-making processes. Previously, she completed a Master’s internship at Télécom ParisTech, where she applied information-theoretic tools to Machine Learning. Throughout her career, she has contributed to various research areas, including Multi-Armed Bandit Problems, Integrated Sensing and Communication (ISAC), Physical Layer Security, and Covert Communication. In addition to her research, she has played a key role in academia, serving as a session chair at IEEE ISIT 2023, a guest editor for Entropy, and a reviewer for IEEE journals and conferences. Her strong research background, leadership roles, and technical expertise position her as a leading scholar in her field.

Research Interest🔎

Mehrasa Ahmadipour’s research interests lie at the intersection of Information Theory, Machine Learning, and Wireless Communications, with a strong focus on Sequential Statistics and Reinforcement Learning. She is particularly interested in Multi-Armed Bandit Problems, exploring their applications in decision-making, resource allocation, and optimization. Her work in Integrated Sensing and Communication (ISAC) has contributed to advancements in wireless networks, particularly in Multiple Access and Broadcast Channels. She has also conducted research on Physical Layer Security, Covert Communication, and Neural Networks, applying information-theoretic tools to enhance security and efficiency in modern communication systems. Additionally, her research in Machine Learning interpretation using information theory has provided insights into neural network behavior. Through her multidisciplinary expertise, she aims to bridge the gap between statistical learning, security, and telecommunications, making significant contributions to next-generation communication systems and artificial intelligence applications.

Award and Honor🏆

Mehrasa Ahmadipour has received several prestigious awards and honors for her academic excellence and research achievements. She ranked in the top 0.1% of all participants in the university entrance exam (Concours) in 2010, demonstrating exceptional academic ability. Later, in 2016, she ranked in the top 1% of all participants in the university entrance exam for the master’s program, further solidifying her position as a top-tier student in Electrical Engineering. Her research contributions in Information Theory, Reinforcement Learning, and Wireless Communications have earned her recognition in the academic community, including invitations to serve as a guest editor for Entropy and as a session chair at IEEE ISIT 2023. Additionally, she has been actively involved in reviewing for leading IEEE journals and conferences, contributing to the advancement of knowledge in her field. Her outstanding academic record, research impact, and leadership roles highlight her as a distinguished scholar.

Research Skill🔬

Mehrasa Ahmadipour possesses a diverse set of research skills in Information Theory, Machine Learning, and Wireless Communications. She is highly proficient in Sequential Statistics, Reinforcement Learning, and Multi-Armed Bandit Problems, with expertise in designing and analyzing optimization algorithms for decision-making processes. Her work on Integrated Sensing and Communication (ISAC) demonstrates her ability to apply information-theoretic approaches to modern wireless networks, particularly in Multiple Access and Broadcast Channels. Additionally, she has strong skills in Physical Layer Security, Covert Communication, and Neural Network Interpretation, utilizing advanced mathematical modeling and probabilistic methods. She is also an experienced reviewer and editor for leading IEEE journals, demonstrating her ability to critically evaluate cutting-edge research. Her technical skills include proficiency in MATLAB, Simulink, Python, and C++, enabling her to implement and validate complex theoretical models. Her strong analytical thinking, problem-solving abilities, and interdisciplinary expertise make her a highly skilled researcher.

Conclusion💡

Mehrasa Ahmadipour is a highly qualified and competitive candidate for the Best Researcher Award, given her strong research background, postdoctoral contributions, peer-reviewing roles, and teaching experience. However, to strengthen the nomination, focusing on high-impact publications, citation impact, research funding, and industrial collaborations would further solidify her case. If her publication and citation metrics are strong, she would be an excellent choice for this award.

Publications Top Noted✍️

  • Title: An information-theoretic approach to joint sensing and communication
    Authors: M. Ahmadipour, M. Kobayashi, M. Wigger, G. Caire
    Year: 2022
    Citations: 109

  • Title: Joint sensing and communication over memoryless broadcast channels
    Authors: M. Ahmadipour, M. Wigger, M. Kobayashi
    Year: 2021
    Citations: 32

  • Title: An information-theoretic approach to collaborative integrated sensing and communication for two-transmitter systems
    Authors: M. Ahmadipour, M. Wigger
    Year: 2023
    Citations: 18

  • Title: Strong converses for memoryless bi-static ISAC
    Authors: M. Ahmadipour, M. Wigger, S. Shamai
    Year: 2023
    Citations: 13

  • Title: Coding for sensing: An improved scheme for integrated sensing and communication over MACs
    Authors: M. Ahmadipour, M. Wigger, M. Kobayashi
    Year: 2022
    Citations: 13

  • Title: Integrated communication and receiver sensing with security constraints on message and state
    Authors: M. Ahmadipour, M. Wigger, S. Shamai
    Year: 2023
    Citations: 11

  • Title: Covert communication over a compound discrete memoryless channel
    Authors: M. Ahmadipour, S. Salehkalaibar, M.H. Yassaee, V.Y.F. Tan
    Year: 2019
    Citations: 10

  • Title: State masking over a two-state compound channel
    Authors: S. Salehkalaibar, M.H. Yassaee, V.Y.F. Tan, M. Ahmadipour
    Year: 2021
    Citations: 3

  • Title: Strong Converse for Bi-Static ISAC with Two Detection-Error Exponents
    Authors: M. Ahmadipour, M. Wigger, S. Shamai
    Year: 2024
    Citations: 2

Sat Gupta | Mathematics | Best Researcher Award

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Dr. Sat Gupta | Mathematics | Best Researcher Award

Professor at UNC Greensboro, United States

Dr. Sat Narain Gupta, a Professor of Statistics at the University of North Carolina at Greensboro, is a distinguished researcher specializing in sampling techniques, time series analysis, and biostatistics. Holding dual Ph.D. degrees in Statistics and Mathematics, he has made significant contributions to statistical theory and interdisciplinary research. A Fellow of the American Statistical Association, he has received numerous accolades, including the UNCG Undergraduate Research Mentoring Award (2024) and the Lifetime Achievement Award from the India Association of Statistics and Reliability (2023). He has secured multiple NSF grants, led statistical research initiatives, and served as Editor-in-Chief for the Journal of Statistical Theory and Practice. With extensive academic leadership experience, including department head and graduate program director roles, he has also mentored students at various levels. His impact on statistical education, research, and professional service makes him a strong candidate for the Best Researcher Award, though continued large-scale funded research could further solidify his case.

Professional Profile 

Education

Dr. Sat Narain Gupta holds dual Ph.D. degrees in Statistics and Mathematics, reflecting his deep expertise in theoretical and applied statistical research. He earned his Ph.D. in Statistics from a prestigious institution, where he specialized in advanced sampling techniques, time series analysis, and biostatistics. His second Ph.D. in Mathematics further strengthened his analytical foundation, allowing him to bridge the gap between statistical theory and mathematical applications. Throughout his academic journey, Dr. Gupta has pursued rigorous research, contributing significantly to statistical methodologies and interdisciplinary studies. His educational background has not only shaped his research but also positioned him as a respected mentor and educator. As a Professor of Statistics at the University of North Carolina at Greensboro, his advanced training continues to influence his teaching and mentoring, guiding students and researchers in statistical sciences. His dual expertise uniquely qualifies him for leading statistical research initiatives and educational advancements.

Professional Experience

Dr. Sat Narain Gupta is a distinguished Professor of Statistics at the University of North Carolina at Greensboro, where he has been instrumental in advancing statistical research and education. With a strong academic foundation in statistics and mathematics, he has extensive experience in teaching, mentoring, and developing innovative statistical methodologies. His professional career spans decades of contributions to theoretical and applied statistics, including sampling techniques, time series analysis, and biostatistics. Dr. Gupta has actively collaborated with researchers across disciplines, applying statistical models to solve complex real-world problems. He has also supervised numerous graduate students, guiding them in cutting-edge statistical research. Beyond academia, he has served as a consultant for various organizations, leveraging his expertise to enhance data-driven decision-making. His commitment to research, education, and interdisciplinary collaboration has solidified his reputation as a leading figure in statistical sciences, making significant contributions to both academia and industry.

Research Interest

Dr. Sat Narain Gupta’s research interests lie at the intersection of theoretical and applied statistics, focusing on areas such as sampling techniques, time series analysis, biostatistics, and statistical inference. He is particularly interested in developing innovative statistical methodologies that enhance data analysis across diverse fields, including healthcare, economics, and environmental sciences. His work delves into robust estimation techniques, predictive modeling, and the application of statistical algorithms to large and complex datasets. Dr. Gupta is also deeply engaged in interdisciplinary research, collaborating with experts from various domains to apply statistical tools for solving real-world challenges. His contributions have been widely recognized in academic circles, with numerous publications in high-impact journals. He continues to explore emerging trends in machine learning and data science, integrating statistical principles to improve model accuracy and reliability. Through his research, Dr. Gupta aims to advance statistical knowledge and its practical applications in decision-making processes.

Award and Honor

Dr. Sat Narain Gupta has received numerous awards and honors in recognition of his outstanding contributions to the field of statistics and data science. His accolades include prestigious research excellence awards, best paper awards in international conferences, and recognition from esteemed statistical organizations. He has been honored for his significant advancements in statistical methodologies, particularly in sampling techniques, time series analysis, and biostatistics. Dr. Gupta has also received fellowships from renowned academic institutions, acknowledging his dedication to innovative research and education. His contributions to interdisciplinary collaborations have earned him accolades from various scientific communities. In addition to his research achievements, he has been recognized for his mentorship and leadership in academia, guiding students and researchers toward excellence. Through his work, Dr. Gupta has made a lasting impact on the field, and his awards serve as a testament to his dedication, expertise, and influence in statistical research and applications.

Research Skill

Dr. Sat Narain Gupta possesses exceptional research skills in statistical analysis, data modeling, and computational techniques. His expertise spans diverse areas, including sampling methods, time series analysis, biostatistics, and predictive modeling. He is proficient in applying advanced statistical tools and programming languages such as R, Python, and SAS to analyze complex datasets. His strong analytical thinking enables him to develop innovative methodologies for real-world problems in various domains, including healthcare, finance, and environmental sciences. Dr. Gupta excels in designing rigorous research studies, interpreting data with precision, and deriving meaningful insights that contribute to scientific advancements. His ability to integrate theoretical knowledge with practical applications has led to numerous impactful publications and collaborations. Additionally, his expertise in statistical software and machine learning techniques enhances his ability to work on interdisciplinary projects. His research skills reflect his commitment to excellence, innovation, and the advancement of statistical science in modern applications.

Conclusion

Dr. Sat Narain Gupta is an outstanding candidate for the Best Researcher Award. His long-standing contributions to statistical research, mentorship, and leadership make him highly deserving. His fellowships, awards, and research funding demonstrate an exceptional career in advancing the field of statistics. While some areas like high-impact publications and industry collaborations could be further developed, his academic impact and leadership in statistical sciences are exemplary.

Publications Top Noted

  • Title: Nurses’ presenteeism and its effects on self-reported quality of care and costs
    Authors: SA Letvak, CJ Ruhm, SN Gupta
    Year: 2012
    Citations: 461

  • Title: Estimation of sensitivity level of personal interview survey questions
    Authors: S Gupta, B Gupta, S Singh
    Year: 2002
    Citations: 247

  • Title: On improvement in estimating the population mean in simple random sampling
    Authors: S Gupta, J Shabbir
    Year: 2008
    Citations: 204

  • Title: Pressure ulcers: factors contributing to their development in the OR
    Authors: D Engels, M Austin, L McNichol, J Fencl, S Gupta, H Kazi
    Year: 2016
    Citations: 166

  • Title: Mean and sensitivity estimation in optional randomized response models
    Authors: S Gupta, J Shabbir, S Sehra
    Year: 2010
    Citations: 138

  • Title: On estimating finite population mean in simple and stratified random sampling
    Authors: J Shabbir, S Gupta
    Year: 2010
    Citations: 105

  • Title: Variance estimation in simple random sampling using auxiliary information
    Authors: S Gupta, J Shabbir
    Year: 2008
    Citations: 100

  • Title: Predictors of Student Success in Entry-Level Undergraduate Mathematics Courses
    Authors: S Gupta, DE Harris, NM Carrier, P Caron
    Year: 2006
    Citations: 94

  • Title: Estimation of the mean of a sensitive variable in the presence of auxiliary information
    Authors: S Gupta, J Shabbir, R Sousa, P Corte-Real
    Year: 2012
    Citations: 89

  • Title: Differences in health, productivity and quality of care in younger and older nurses
    Authors: S Letvak, C Ruhm, S Gupta
    Year: 2013
    Citations: 88

Mohammad Shifat-E-Rabbi | Mathematical Modeling | Best Researcher Award

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Dr. Mohammad Shifat-E-Rabbi | Mathematical Modeling | Best Researcher Award

Assistant Professor at North South University, Bangladesh

Dr. Mohammad Shifat-E-Rabbi is an Assistant Professor in the Department of Electrical and Computer Engineering at North South University, Bangladesh. He earned his Ph.D. in Biomedical Engineering from the University of Virginia, where his dissertation, “Transport Generative Models in Pattern Analysis and Recognition,” focused on developing mathematical and computational frameworks for artificial intelligence and machine learning. Dr. Shifat-E-Rabbi’s research interests include applied mathematics, machine learning, image informatics, computational biology, and pattern recognition. He has contributed to various publications, such as “End-to-End Signal Classification in Signed Cumulative Distribution Transform Space” in IEEE Transactions on Pattern Analysis and Machine Intelligence. At North South University, he teaches courses in Artificial Intelligence, Machine Learning, and programming languages. His academic journey began with a B.Sc. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology.

Professional Profile 

  • Google Scholar
  • Scopus Profile
  • ORCID Profile

Education

Dr. Mohammad Shifat-E-Rabbi’s educational journey began at Rangpur Zilla School and Rangpur Cadet College in Bangladesh. He earned his B.Sc. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology (BUET) in 2015. He then pursued his Ph.D. in Biomedical Engineering at the University of Virginia (UVa), USA, focusing on Pattern Analysis and Recognition within the Imaging and Data Science Laboratory. His dissertation, titled “Transport Generative Models in Pattern Analysis and Recognition,” centered on developing mathematical and computational frameworks for artificial intelligence and machine learning. During his doctoral studies, Dr. Shifat-E-Rabbi served as a research assistant under the supervision of Prof. Gustavo Rohde.

Professional Experience

Dr. Mohammad Shifat-E-Rabbi is an Assistant Professor in the Department of Electrical and Computer Engineering at North South University, Bangladesh. He earned his Ph.D. in Biomedical Engineering from the University of Virginia, USA, where he specialized in Pattern Analysis and Recognition within the Imaging and Data Science Laboratory. During his doctoral studies, Dr. Shifat-E-Rabbi served as a research assistant under the supervision of Prof. Gustavo Rohde. Prior to his Ph.D., he completed his B.Sc. in Electrical and Electronic Engineering at the Bangladesh University of Engineering and Technology (BUET) in 2015. At BUET, he was involved in the Digital Signal Processing research lab. Dr. Shifat-E-Rabbi’s research interests encompass applied mathematics, machine learning, image informatics, computational biology, and pattern recognition. In his current role, he teaches courses in Artificial Intelligence, Machine Learning, and programming languages. His academic journey began at Rangpur Zilla School and Rangpur Cadet College in Bangladesh.

Research Interest

Dr. Mohammad Shifat-E-Rabbi’s research interests encompass applied mathematics, machine learning, image informatics, computational biology, and pattern recognition. He has contributed to the development of the Radon Signed Cumulative Distribution Transform (R-CDT) and its applications in classifying signed images. Additionally, he has worked on predictive modeling of hematoma expansion in intracerebral hemorrhage patients and the real-time intelligent classification of COVID-19 and thrombosis through massive image-based analysis of platelet aggregates. Dr. Shifat-E-Rabbi has also explored transport-based morphometry for analyzing nuclear structures in digital pathology images across various cancers. His work aims to bridge theoretical advancements with practical applications, enhancing the understanding and analysis of complex biological and medical data.

Award and Honor

Dr. Mohammad Shifat-E-Rabbi has been recognized for his significant contributions to the fields of artificial intelligence and machine learning. His collaborative research on “End-to-End Signal Classification in Signed Cumulative Distribution Transform Space” was published in the prestigious IEEE Transactions on Pattern Analysis and Machine Intelligence. This work, conducted alongside colleagues from the University of Virginia, received support from esteemed institutions such as the National Institutes of Health and the Office of Naval Research, underscoring its impact and importance.

Research Skill

Dr. Mohammad Shifat-E-Rabbi possesses a robust set of research skills that bridge applied mathematics, machine learning, and computational biology. His expertise includes developing mathematical models and computational frameworks, notably in pattern recognition and image informatics. Dr. Shifat-E-Rabbi has contributed to the advancement of the Radon Cumulative Distribution Transform (R-CDT), enhancing image classification techniques. His collaborative work on “End-to-End Signal Classification in Signed Cumulative Distribution Transform Space” exemplifies his ability to integrate theoretical concepts with practical applications, leading to more efficient signal classification methods. His research portfolio demonstrates proficiency in handling complex datasets, developing innovative algorithms, and applying interdisciplinary approaches to solve real-world problems. Dr. Shifat-E-Rabbi’s commitment to advancing artificial intelligence and machine learning is evident through his scholarly publications and ongoing projects.

Conclusion

If the researcher has made significant contributions through innovation, publications, and demonstrated impact, they would be a strong candidate for the Best Researcher Award. However, if the research is still in its early stages or lacks broader validation, additional work on practical applications, benchmarking, and interdisciplinary collaborations could further strengthen their case.

Publications Top Noted

  • Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19

    • Authors: M. Nishikawa, H. Kanno, Y. Zhou, T.H. Xiao, T. Suzuki, Y. Ibayashi, J. Harmon, M. Shifat-E-Rabbi, et al.
    • Published in: Nature Communications
    • Year: 2021
    • Citations: 71

  • Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning

    • Authors: S. Kundu, B.G. Ashinsky, M. Bouhrara, E.B. Dam, S. Demehri, M. Shifat-E-Rabbi, et al.
    • Published in: Proceedings of the National Academy of Sciences
    • Year: 2020
    • Citations: 57

  • Cell image classification: a comparative overview

    • Authors: M. Shifat-E-Rabbi, X. Yin, C.E. Fitzgerald, G.K. Rohde
    • Published in: Cytometry Part A
    • Year: 2020
    • Citations: 39

  • Radon cumulative distribution transform subspace modeling for image classification

    • Authors: M. Shifat-E-Rabbi, X. Yin, A.H.M. Rubaiyat, S. Li, S. Kolouri, A. Aldroubi, G.K. Rohde
    • Published in: Journal of Mathematical Imaging and Vision
    • Year: 2021
    • Citations: 28

  • PREHEAT: Precision heart rate monitoring from intense motion artifact corrupted PPG signals using constrained RLS and wavelets

    • Authors: M.S. Islam, M. Shifat-E-Rabbi, A.M.A. Dobaie, M.K. Hasan
    • Published in: Biomedical Signal Processing and Control
    • Year: 2017
    • Citations: 26

  • Blind Deconvolution of Ultrasound Images Using ℓp\ell_p-Norm-Constrained Block-Based Damped Variable Step-Size Multichannel LMS Algorithm

    • Authors: M.K. Hasan, M. Shifat-E-Rabbi, S.Y. Lee
    • Published in: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
    • Year: 2016
    • Citations: 12

  • Local sliced Wasserstein feature sets for illumination invariant face recognition

    • Authors: Y. Zhuang, S. Li, M. Shifat-E-Rabbi, X. Yin, A.H.M. Rubaiyat, G.K. Rohde
    • Published in: Pattern Recognition
    • Year: 2025
    • Citations: 10

  • End-to-end signal classification in signed cumulative distribution transform space

    • Authors: A.H.M. Rubaiyat, S. Li, X. Yin, M. Shifat-E-Rabbi, Y. Zhuang, G.K. Rohde
    • Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence
    • Year: 2024
    • Citations: 9

  • Nearest Subspace Search in The Signed Cumulative Distribution Transform Space for 1D Signal Classification

    • Authors: A.H.M. Rubaiyat, M. Shifat-E-Rabbi, Y. Zhuang, S. Li, G.K. Rohde
    • Published in: IEEE International Conference on Acoustics, Speech and Signal Processing
    • Year: 2022
    • Citations: 9

  • Speckle tracking and speckle content based composite strain imaging for solid and fluid filled lesions

    • Authors: M. Shifat-E-Rabbi, M.K. Hasan
    • Published in: Ultrasonics
    • Year: 2017
    • Citations: 9