|Paul (Pawel) Lewicki|
October 7, 1953 |
|Alma mater||University of Warsaw|
|Known for||Cognitive Research on Nonconscious Acquisition of Knowledge
Founder of StatSoft
Paul (Pawel) Lewicki is a cognitive scientist, an entrepreneur, and investor. He was a professor of cognitive psychology at the University of Tulsa from 1984 through 2011, where he established the Nonconscious Information Processing Laboratory. After leaving the University in 2011, he assumed the full-time role of CEO of StatSoft, a multinational analytics software company that he founded and where he was a majority shareholder. On March 24, 2014, StatSoft was acquired by Dell, and he assumed an executive position at Dell to help with the process of integrating StatSoft with Dell.
Research in cognitive psychology
As a cognitive scientist he is best known for his research on nonconscious information processing and self-perpetuation, where he demonstrated that procedural knowledge is created via nonconscious acquisition of information about complex covariations between events or features, and that nonconsciously, individuals can acquire very complex knowledge structures based on highly-multidimensional patterns of data.
Lewicki's research on the self-perpetuating development of encoding dispositions has demonstrated that accidentally acquired (and even very slight) cognitive preferences or other encoding/interpretive dispositions can gradually develop and strengthen in a self-perpetuating manner. Specifically, common encoding biases may convert ambiguous information into subjective experience of encountering — in fact nonexistent — evidence that supports pre-existing interpretive schemata of the individual, thus strengthening those schemata in a self-perpetuating manner. This mechanism may contribute to the development of [personality trait]s and individual preferences, it may facilitate learning, but it can also lead to self-perpetuating development of dysfunctional biases, phobias, aversions and other symptoms of personality disorders.
Cognitive mining versus predictive data mining
Lewicki together with fellow cognitive researcher Thomas Hill were among the first to publish evidence that advanced expertise acquired by humans via experience, involves the acquisition (learning) and utilization of repeatable patterns in data that are structurally more complex than what humans can verbalize or intuitively experience, because they involve high-order interactions between multiple variables. The human consciousness usually cannot handle more than only third-order interactions.
The logical implication of this evidence is the recognition of the limitations of traditional statistical analysis methods as knowledge discovery tools, because they rely on testing hypotheses that first have to be explicitly formulated by researchers. Instead, not unlike modern predictive data mining and predictive analytics techniques and algorithms, nonconcscious acquisition of knowledge is the result of the application of pattern recognition algorithms against large numbers of exemplars of complex and high-dimensional sensory inputs without any or very few preexisting assumptions and hypotheses. Once such repeatable patterns are detected (learned), this knowledge can be used like predictive models to anticipate future events at a useful level of accuracy, even if the models are too complex to advance the scientific understanding of the underlying data before further research is conducted.
The latter approach became popular and rapidly adopted by the corporate world as so-called “predictive data mining” starting in the late 1990s and StatSoft’s STATISTICA Data Miner is now one of the widely used enterprise-level software systems for data mining and predictive analytics.
Lewicki began to write data analysis software for personal computers around 1984. He found the mainframe data analysis software difficult to use and needed a tool to analyze his research data. He gave this software away for free to colleagues, who provided positive feedback and this led him to incorporate StatSoft in 1984. StatSoft then expanded into enterprise-scale predictive analytics software and, according to the company website, is now an international analytic solution software provider with more than 1,000,000 users and 30 offices worldwide.
In a recent interview, Lewicki emphasized that StatSoft’s corporate culture remains focused on long-term research and altruism rather than profits: "Our mission is to create value and to make the world a better place, and analytics contribute directly to that. Our employees' biggest reward is to see that what we're doing works and is making a difference." 
Lewicki is the co-author (with Thomas Hill) of Statistics: Methods and Applications (also freely available in electronic format as the StatSoft Electronic Statistics Textbook.) is a popular resource on statistics and predictive modeling on the web with over 375,000 links from other websites worldwide (according to Google Analytics).
At StatSoft, Lewicki (with Thomas Hill) also directed research sponsored by the Electric Power Research Institute of Palo Alto (EPRI), to demonstrate the effectiveness of pattern recognition and predictive modeling algorithms for the optimization of combustion processes, to reduce harmful emissions from fossil fuel plants. The results of this research were published as EPRI Report number 1016494, and were also presented at the 2008 Meeting of the Asia-Pacific Partnership on Clean Development and Climate. Lewicki has communicated to EPRI that because of the relevance of this work for environmentally sustainable power generation, this technology will be released to the public domain as soon as the proper regulatory infrastructure is established by the EPA and DOE.
Support for European Countries in crisis
In the fall of 2012, Lewicki led the “Free Enterprise Software for Struggling European Economies” initiative, where StatSoft is installing free enterprise systems to companies in Greece, Portugal, and Spain to boost their productivity and competitiveness, and to facilitate their recovery. Lewicki has directed an open letter to fellow CEOs of US software companies urging them to join the initiative.
- Evatt, Robert.StatSoft helps others find patterns within data. Tulsa World (accessed August 14, 2011)
- Bloomberg. Company Overview of StatSoft, Inc. (accessed November 10, 2012
- Dell Press Release March 24, 2014. Dell Acquires StatSoft to Bolster Portfolio of Big Data Solutions (accessed March 24, 2014
- Lewicki, Paul, Hill, Thomas, & Czyzewska, Maria (1992). Nonconscious acquisition of information. American Psychologist, 47, 796-801
- Lewicki, Paul (1986). Nonconscious social information processing. New York: Academic Press
- Nisbet, Robert; Elder, John; Miner, Gary (2009). Handbook of Statistical Analysis & Data Mining Applications, Academic Press/Elsevier, ISBN 978-0-12-374765-5
- Miner, Gary; Elder, John; Hill, Thomas; Nisbet, Robert; Delen, Dursun; Fast, Andrew (2012); Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, Academic Press/Elsevier, ISBN 978-0-123869791
- Karl Rexer, Heather Allen, & Paul Gearan (2011); 2011 Data Miner Survey Summary, presented at Predictive Analytics World, Oct. 2011.
- Beck-Round, Carol (2007). The Data Wiz. Oklahoma Magazine, March 6–7
- Hill, Thomas; Lewicki, Paul (2006). Statistics: Methods and applications. A comprehensive reference for science, industry, and data mining. StatSoft Inc. ISBN 1-884233-59-7
- Electric Power Research Institute (EPRI) (2008), Statistical Use of Existing DCS Data for Process Optimization EPRI, Palo Alto, CA. (report# 1016494)
- Hill, Thomas (2008). Process Optimization through the Application of Data-Driven (Data Mining) Methods to Historical Process Data. Asia-Pacific Partnership on Clean Development and Climate
- StatSoft Press Release: StatSoft to Aid Struggling European Economies With Free Enterprise Analytics Software (accessed November 3, 2011)
- Adame, Jaime (2012).From Tulsa, With Love (& Logic): Gift from local company an effort to jumpstart European economy. Urban Tulsa, November 14, 2012. (accessed November 14, 2012)
- Federal Aviation Administration Registry.. Airmen Registry Database. (accessed April 2, 2013)