Suggestions
Emeli Dral
Co-founder and CTO Evidently AI - Machine Learning Instructor w/100K+ students
Professional Background
Emeli Dral is a distinguished professional in the field of machine learning and data science, whose expertise is transformed into impactful tools for machine learning teams through her role as Co-founder and Chief Technology Officer (CTO) at Evidently AI. With a robust focus on developing solutions that enable teams to analyze and monitor model behavior in production, Emeli plays a crucial role in empowering organizations to harness the full potential of machine learning technologies. Her passion for bridging the gap between complex algorithms and practical applications makes her a key figure in the industry.
In addition to her CTO responsibilities, Emeli is passionate about education and knowledge dissemination. A co-author of a highly-regarded Coursera specialization that has attracted more than 100,000 students, she is a fervent advocate for mentoring the next generation of data scientists. Emeli also co-founded Data Mining in Action, an initiative that has become the largest offline program in data science across the Commonwealth of Independent States (CIS). Her commitment to expanding access to data science education reflects her belief in its transformative power.
Emeli's extensive experience includes leading over 50 applied machine learning projects across various sectors, including e-commerce, telecommunications, and industrial manufacturing. Her versatility is complemented by her role as an invited speaker at numerous prestigious conferences, where she shares her invaluable insights into applied machine learning.
Education and Achievements
Emeli's academic credentials lay a strong foundation for her professional success. She obtained both her Bachelor's and Master's degrees in Applied Mathematics and Informatics from RUDN University, achieving a remarkable GPA of 4.9/5.0 for her Bachelor's and a perfect GPA of 5.0/5.0 for her Master's, both with distinction. Further enhancing her knowledge, she also pursued a Master's degree in Computer Science at the esteemed Yandex School of Data Analysis. These rigorous academic achievements underscore Emeli's dedication to her field and her commitment to excellence.
Throughout her career, Emeli has held key academic positions, including serving as a Visiting Lecturer at the Graduate School of Management at St. Petersburg State University and at HARBOUR.SPACE, as well as at the Yandex School of Data Analysis. Her role as a Senior Lecturer at the renowned Moscow Institute of Physics and Technology (MIPT) illustrates her expertise and her dedication to nurturing future talents in the tech landscape.
Achievements
Emeli Dral is not only recognized for her professional and educational accomplishments but also as a thought leader in machine learning. She has graced the stage as an invited speaker at prestigious conferences such as the Data Workshop Club Conference in Warsaw (2019), the Future of Aluminium Conference in Warsaw (2019), the Toronto Machine Learning Summit (2020), DataNatives in Berlin (2020), and OpenTalks.AI in Moscow (2021). These speaking engagements highlight her position as an influential figure in the industry and demonstrate her willingness to share her knowledge with peers.
Her previous roles as Co-founder and Chief Data Scientist at Mechanica AI, as well as Chief Data Scientist at Yandex Data Factory and Head of Predictive Analysis Group at Yandex Data Factory, showcase her ability to innovate and lead within fast-paced environments. Emeli's experience as a Data Scientist at Yandex, one of the most reputable tech companies, along with her earlier role as a Software Developer at Rambler, has equipped her with a wealth of industry knowledge and practical skills that propel her forward in her current ventures.
Through her multifaceted career, Emeli Dral exemplifies what it means to be a leader in the data science community. Her ability to seamlessly integrate theoretical knowledge with practical applications has positioned her at the forefront of the industry, making significant contributions to both academia and applied machine learning practices.