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Lingam, Mirsayar, van Woesik Recognized as ‘Top Scholars’ by ScholarGPS

Florida Tech faculty members Manasvi Lingam, Mirmilad Mirsayar and Robert van Woesik were named “Top Scholars” by ScholarGPS for their contributions to academia over the last five years. Lingam, who studies astrobiology in the Department of Aerospace, Physics and Space Sciences, was ranked No. 9,562 in the world across all disciplines and nearly 15 million ranked scholars, placing him in the top 0.06% of the platform’s scholars globally. He faired strongly in other areas, including: No. 1,919 (0.1%) among 1.9 million scholars in physical sciences and mathematics No. 491 (0.09%) among 545,000 scholars in physics No. 42 (0.31%) among 13,590 scholars in the specialty area planets ScholarGPS cited Lingam’s strong publication record, the impact of his work and the notable quality of his scholarly contributions. He’s published 50 times since 2020, exploring the possible origins, evolution and future of life in the universe. Mirsayar, who studies aerospace engineering, was ranked No. 35,155 across all disciplines and nearly 15 million ranked scholars, placing him in the top 0.24% of scholars globally. He’s published 11 times between 2020-2023, covering topics such as fracture mechanics and solid mechanics. Other highlights include: No. 6 (0.06%) among 8,601 scholars in fracture mechanics No. 49 (1.7%) among 2,879 scholars in solid mechanics No. 315 (1.8%) among 16,847 scholars in reinforced concrete Van Woesik, who studies coral reef ecology, was ranked No. 58,081 across disciplines, putting him in the top 0.39% of nearly 15 million scholars globally. He’s had 22 publications since 2020, covering topics such as coral bleaching, thermal stress and climate change. Van Woesik, who studies coral reef ecology, was ranked No. 58,081 across disciplines, putting him in the top 0.39% of nearly 15 million scholars globally. He’s had 22 publications since 2020, covering topics such as coral bleaching, thermal stress and climate change. Other highlights include: No. 5,282 (0.32%) among 1.7 million scholars in life sciences No. 336 (0.38%) among 88,930 scholars of ecology and evolutionary biology No. 191 (0.95%) among 19,998 scholars of global change. ScholarGPS uses artificial intelligence and data mining technologies to rank individuals, academic institutions and programs. Scholars are ranked by their number of publications, their citations, their h-index and their ScholarGPS® Ranks, which includes all three metrics. If you're interested in connecting with Manasvi Lingam, Robert van Woesik and Mirmilad Mirsayar- simply contact Adam Lowenstein, Director of Media Communications at Florida Institute of Technology at adam@fit.edu to arrange an interview today.

Climate change research trailblazer elected to prestigious list of AAAS Fellows

University of Delaware professor Rodrigo Vargas has been elected as a fellow of the American Association for the Advancement of Science (AAAS) — one of the largest scientific societies in the world and publisher of the Science family of journals. The new class of AAAS Fellows includes 502 scientists, engineers and innovators across 24 disciplines, who are being honored for their scientifically and socially distinguished achievements. Vargas, professor of ecosystem ecology and environmental change in UD’s College of Agriculture and Natural Resources, is recognized "for distinguished contributions to carbon dynamics across the terrestrial-aquatic interface, development of environmental networks, novel data analysis tools and his leadership in creating a more diverse scientific workforce." Deborah Allen, who retired from UD in 2019 as a professor of biological sciences, was also names as a fellow. She was cited “for transformational contributions to STEM education nationally and internationally, particularly for developments in problem-based learning and faculty development.” Vargas is an ecosystem ecologist who studies how nature-based solutions can help address global environmental change in both terrestrial and coastal ecosystems, Vargas uses a variety of research methods, including data mining, machine learning, remote sensing, measurements of greenhouse gas fluxes and modeling techniques for forecasting applications.

Kelley professor’s M-Score model remains most viable means of predicting corporate fraud

BLOOMINGTON, Ind. — Enhanced oversight over the auditing profession and firms’ financial reporting has led to a proliferation of models to predict financial statement fraud. But one of the first forensic models, the M-Score, devised by an Indiana University Kelley School of Business professor in the late 90s, remains accurate and is the most economically viable for investors to use, according to a forthcoming paper in The Accounting Review — the official journal of the American Accounting Association. The article, “The Costs of Fraud Prediction Errors,” co-authored by M. Daniel Beneish, professor of accounting and the Alva L. Prickett Chair at Kelley, compares seven fraud prediction models with a cost-based measure that nets the benefits of correctly anticipating instances of fraud against the costs borne by incorrectly identifying non-fraud firms as fraudulent. Even though newer fraud models early doubled the success rate of M-Score, which Beneish developed, they did so at the cost of a much larger number of false positives. As a result, the other models are not used in practice by auditors because they are too costly to implement as all flagged firms must be carefully investigated. “I have long known from my experience consulting with Arthur Andersen — for whom my model detected Enron before the debacle — and other public accounting firms, that litigation concerns relating to false positives — firms incorrectly flagged as having fraudulent financial statements — created an unwillingness by auditors’ general counsel to use fraud prediction models in practice,” Beneish said. “My efforts back then to improve the M-Score in the context of auditing failed because I could not increase the model’s success rate without increasing the number of false positives. It seems that the new models cannot either,” he added. Interestingly, as early as 2017 the M-Score flagged Kangmei Pharmaceutical, a Chinese publicly traded company that was involved in financial reporting fraud between 2016 and 2018. Like the Enron scandal in the U.S., the Kangmei Pharmaceutical scandal helped trigger new regulation in China that increased regulatory penalties for financial fraud (effective March 2020) and last November became China’s first successful class-action lawsuit involving corporate fraud. Its chairman was sentenced to 12 years in prison. “The main purpose of our paper is to provide evidence on the costs and benefits of using fraud prediction models, and to show whether using these models is economically viable for auditors, investors and regulators,” Beneish said. “This is important because the traditional measures commonly used in recent research to justify new models are misleading about model performance in fraud samples as the proportion of fraud firms in the population is very small, and as they typically assume that the cost of a false positive and false negatives (missed detections) are equal.” For example, assume that among 10,000 publicly traded firms, there are about 60 fraud firms and 9,940 firms without misreporting. The newer models detected 42 frauds (70% of the total frauds), and incorrectly flagged 3,976 firms (40% of the non-frauds). The latter is too large a number for most decision makers to investigate. “Our evidence that a cost-based assessment of models is preferable to traditional model comparison measures (e.g., area under the curve), should become even more important as efforts by future researchers in the areas of data mining and machine learning intensify,” Beneish said. Patrick Vorst of Maastricht University, assistant professor in financial accounting and accounting & information management, co-authored the paper with Beneish.