Work with thought leaders and academic experts in Statistics, Probability and Uncertainty

Companies can greatly benefit from working with experts in the field of Statistics, Probability and Uncertainty. These experts can provide valuable insights and analysis to help companies make data-driven decisions, optimize processes, and mitigate risks. By leveraging their expertise, companies can improve forecasting accuracy, identify patterns and trends, and develop predictive models. Whether it's optimizing marketing campaigns, improving supply chain management, or enhancing risk assessment, collaborating with Statistics, Probability and Uncertainty experts can lead to improved business outcomes and competitive advantage.

Researchers on NotedSource with backgrounds in Statistics, Probability and Uncertainty include Tyler Ransom, Edoardo Airoldi, Tim Leung, Athul Prasad, Dr Troy Smith, Olya Skulovich, Do-Yeong KIM--http://www.ajou.ac.kr/abiz/professor/prof-search-popup.do, Dr. Erin Westgate, Ph.D., Hussein Al-Hussein, Brian Bushee, Anindya Ghose, Baris Yoruk, Baidurya Bhattacharya, and Ayse Oktay.

Dr Troy Smith

Scholar with publications in problematic social media use, online behaviours and cybercrime with extensive training and experience in intelligence and analysis for national security
Research Expertise (20)
Cybercrime
Online Behaviours
Internet and Social Media Addiction / Problematic Use
Clinical Psychology
Psychiatry and Mental health
And 15 more
About
Dr. Troy Smith is a distinguished figure in the intersection of cybercrime and online behaviours, with an impressive career in national security spanning over 15 years. His expertise has not only been instrumental in advancing research within these fields but has also significantly contributed to the development of strategic policies and frameworks at both national and international levels. His work is deeply rooted in understanding the complexities of cybercrime, cyberwarfare, and the psychological impacts of social media use, making him a pivotal contributor to initiatives aimed at enhancing digital and space domain collaboration across regions. With a solid foundation in cybercrime and the nuances of online behaviours, Dr. Smith's scholarly output includes a wealth of peer-reviewed articles, book chapters, and presentations that explore the depths of intelligence, cybercrime victimization, terrorist activities in cyberspace, online behaviours and maladaptive social media use landscape. Dr. Smith's role extends beyond research; he is actively involved in shaping the future of cybersecurity education and policy-making. He has played a crucial role in committees focused on developing graduate programs in cybercrime, sector mapping, and establishing national occupational standards. His expertise has also been recognized in international circles, as evidenced by his participation in the Organization of American States Cybersecurity publication and his recent involvement in Working Group 7 of the CYBERSPACE project funded by the European Union. As a contributor to the peer review process for several international journals, including the International Journal of Human-Computer Interaction, BMC Psychiatry, and Cyberpsychology, his insights have propelled forward the discourse on cybersecurity and online behaviour assessment. Dr. Smith's scholarly and professional achievements are further recognized through awards and participation in prestigious programs, such as the Executive MBA program in Spain and the EU Cyber Diplomacy Fellowship Programme. His extensive experience and ongoing contributions to cybersecurity and national security sectors underscore his standing as a leading expert and advocate for international collaboration and policy development in the fight against cybercrime. <br>

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Olya Skulovich

New York, New York, United States of America
Earth and Environmental Engineering Ph.D. student at Columbia University
Research Expertise (14)
Soil moisture
Land-Atmosphere interaction
climate change
ML
carbon cycle modeling
And 9 more
About
My name is Olya (Ola) Skulovich, I am near completion of my Ph.D. in Earth and Environmental Engineering at Columbia University. During my Ph.D. program, I worked with remote sensing data and utilized machine learning to create long-term consistent soil moisture and vegetation optical depth datasets. In particular, my work included analyzing, regridding, and deseasonalizing remote sensing data from SMAP, SMOS, AMSR-E, and AMSR-2 satellite missions to prepare the data for machine learning. On the methodological side, I developed, tested, and fine-tuned Deep and Convolutional neural networks and built a unique transfer learning training scheme to merge the patched remote sensing data into a consistent dataset. The soil moisture dataset and the corresponding paper (Scientific Data – Nature family journal, https://doi.org/10.1038/s41597-023-02053-x ) have been published. The soil moisture dataset is the only consistent quality dataset available globally, covering 18 years, explicitly targeting soil moisture extremes and anomalies. The vegetation optical depth dataset is the only L-band dataset that spawns back to 2002. After developing the datasets, my research was focused on analyzing trends and variability of soil moisture, including spatiotemporal statistical analysis and identifying regions of different dynamics.  A part of my research was dedicated to modifying and analyzing the process-based carbon cycle model (CARDAMOM). It is a model that simulates carbon fluxes and pools by assimilating data using the Metropolis-Hastings Markov chain Monte Carlo method. My part of the project included developing two new model modules for assimilating solar-induced fluorescence and vegetation optical depth data, including developing model formulation, incorporating the modules in the main model (C and Python), adjusting model uncertainties, likelihood functions, and ecological dynamical constraints, as well as analyzing the updated model’s performance, information content effect, effect on constraining respiration flux and carbon pools. In addition to that, I participated as a collaborator in several research projects investigating the effects of soil moisture and land-atmosphere feedback on European and Siberian droughts and the spatiotemporal relationship between soil moisture dynamics and vegetation productivity.  I presented the results of my research at the American Geophysical Union (AGU) Fall Meetings in 2020, 2022, and 2023, the USMILE Kickoff Meeting 2020, USMILE Meeting 2022, and LEMONTREE Science Meeting: Soil Moisture Stress 2023. I take pride in building a compelling story from scientific findings and enjoy communicating my research to various stakeholders, creating captivating presentations, and engaging public speaking.

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Baidurya Bhattacharya

Computational mechanics, probabilistic risk analysis, statistical inference, Monte Carlo simulations
Research Expertise (44)
computational materials science
probabilistic mechanics
Mechanical Engineering
Industrial and Manufacturing Engineering
Mechanics of Materials
And 39 more
About
Baidurya Bhattacharya is a highly accomplished and respected civil engineer with over 20 years of experience in the field. He was born in Kolkata, India and completed his B.Tech (hons.) in Civil Engineering from the prestigious Indian Institute of Technology Kharagpur in 1991. He then went on to pursue his PhD in Civil Engineering from Johns Hopkins University, which he completed in 1997. After completing his PhD, Bhattacharya started his academic career as a Visiting Professor at the University of Delaware. He then moved on to become an Assistant Professor at the same university, where he taught for several years and mentored numerous students. In 2005, he returned to his alma mater, Indian Institute of Technology Kharagpur, as a Professor in the Department of Civil Engineering. He has been a valuable member of the faculty and has made significant contributions to the department through his research and teaching. Bhattacharya's research interests lie in the areas of structural engineering, earthquake engineering, and soil dynamics. He has published numerous papers in reputable journals and has also presented his work at various international conferences. His research has been recognized and funded by prestigious organizations such as the National Science Foundation and the American Society of Civil Engineers. Aside from his academic career, Bhattacharya is also actively involved in consulting and has worked on various projects in collaboration with government agencies and private firms. He is known for his expertise and has received several awards and honors for his contributions to the field of civil engineering. Bhattacharya is a dedicated educator and mentor, and he continues to inspire and guide young engineers through his teaching and research. His passion for the field and his dedication to his students make him a highly respected figure in the academic community.

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Example Statistics, Probability and Uncertainty projects

How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on Statistics, Probability and Uncertainty?

Optimizing Marketing Campaigns

A Statistics, Probability and Uncertainty expert can analyze customer data, identify target segments, and optimize marketing campaigns. By leveraging statistical models and predictive analytics, companies can improve customer targeting, personalize messaging, and maximize marketing ROI.

Improving Supply Chain Management

By applying statistical analysis and probability models, experts can help companies optimize inventory levels, forecast demand, and improve supply chain efficiency. This can lead to cost savings, reduced stockouts, and improved customer satisfaction.

Enhancing Risk Assessment

Statistics, Probability and Uncertainty experts can develop risk models and conduct scenario analysis to assess and mitigate risks. By quantifying uncertainties and evaluating potential outcomes, companies can make informed decisions, minimize losses, and improve risk management strategies.

Predictive Maintenance

Using statistical techniques and machine learning algorithms, experts can analyze equipment data, identify patterns, and predict maintenance needs. This proactive approach can help companies reduce downtime, optimize maintenance schedules, and extend the lifespan of assets.

Fraud Detection

Statistics, Probability and Uncertainty experts can develop fraud detection models by analyzing patterns and anomalies in transaction data. By leveraging advanced analytics and machine learning, companies can detect fraudulent activities, minimize financial losses, and protect their reputation.