Shreenidhi Bharadwaj
Currently teaching:
Intelligent Systems and Robotics
Computer Vision
Shreenidhi Bharadwaj is an educator, data enthusiast and an analytics practitioner. Currently, as M&A COE lead, he advises Private Equity/Venture Capital firms and C-level Executives on value creation, post-close synergies along with data & analytics(BI/AI) strategies focused on business outcomes. In his previous executive leadership role at Syndigo, Bharadwaj led the data strategy, data science, next-gen platform and M&A integrations. His expertise revolves around driving innovation, standardization, development & operationalizing machine learning models, data engineering at scale using on premise & cloud platforms, effective data visualizations, model-driven design & algorithmic thinking. He was elected to the Global Standards Architecture Board at GS1, where he worked with global industry leaders to develop standards, road maps, governance & compliance requirements relating to food services, healthcare, retail, supply chain & CPG/FMCG verticals. His experience spans multiple verticals such as Healthcare, Retail, MarTech, AdTech, EdTech, FinTech, Telecom and public safety with companies operating as startups to fortune 100. Bharadwaj is a graduate of the MS Analytics program from The University of Chicago and BE, Electronics and Communications from Manipal Institute of Technology, India. His interests include Data Engineering, Intelligent Systems and Robotics, Machine Learning at scale, Data Visualization & Knowledge Engineering
Alianna Maren
Currently teaching:
Natural Language Processing
Artificial Intelligence and Deep Learning
Dr. Alianna J. Maren is a researcher and thought-leader in predictive analytics, with four patents, ranging from predictive analytics to knowledge discovery to sensor fusion (the latter was named as Patent of the Week by the New York Times shortly after it was awarded). Her expertise ranges from statistical thermodynamics to model patterns and rapidly-changing systems to text analytics in knowledge discovery to neural networks. As cofounder and chief scientist with EagleForce Associates (later Viziant Corporation), she was key in supporting DIA's panels on knowledge discovery and predictive analytics. Her innovative work at Viziant led to three of her four patent awards and two rounds of investment funding. As senior scientist with Accurate Automation Corporation, Maren's innovations in neural networks, sensor fusion, and system identification were the basis for receiving over $3.4M in federally-funded SBIR and STTR contracts. She introduced and taught the first course in Knowledge Discovery at Georgetown University, and also Cloud Computing at both George Mason and Marymount Universities Her first book, the “Handbook of Neural Computing Applications” (Academic, 1990), became a landmark in the emerging neural network arena. Maren holds a Ph.D. in physical chemistry from Arizona State University and a bachelor's degree in mathematics from the University of North Dakota.
Tom Miller is faculty director of the data science program at Northwestern University. He started with the program when it was called predictive analytics and for the past ten years has been responsible for growing the curriculum, introducing specializations, and designing numerous distance learning courses. During the 2020-21 academic year, he will be teaching natural language processing, knowledge engineering, and unsupervised learning methods. Tom is the author of six textbooks about data science published by Pearson Education. He also owns Research Publishers LLC, a California company that, in addition to publishing books and periodicals, provides research and consulting, measurement services, event forecasting, and natural language processing solutions. Most recently, Tom has been developing a new journal, Data Science Quarterly, promoting data science as a discipline and showing its relevance to social and political discourse. The online version is available at https://www.data-science-quarterly.com.
Nethra Sambamoorthi
Currently teaching:
Artificial Intelligence and Deep Learning
Nethra Sambamoorthi is an adviser for CRMportals, a global analytics consulting company. In addition to delivering client solutions, he specializes in building best practices in data capture, designing innovative solutions and creating thought leadership in consumer dynamics and marketing intelligence. Sambamoorthi has produced many of the best practices in predictive modeling across multiple industries—credit cards, auto insurance, investment services, retail, CPG and pharmaceutical marketing. He previously was an entrepreneur, focusing on developing and delivering real-time email marketing systems and ROI trends. He has also worked with XLmarketing Corporation, Acxiom Corporation, Schering Plough Corporation, American Express and Prudential, using marketing analytics to address regulatory aspects while optimizing for return on investment and for consumer engagement. Sambamoorthi received a PhD from the University of Pittsburgh, specializing in applied multivariate statistics, and an MA in mathematics and an MSc in statistics from the University of Madras.
Dr. Jennifer Sleeman has over 11 years of machine learning experience and over 21 years of software engineering experience, in both academic and government/industry settings. Her research is primarily centered around generative models, natural language processing, semantic representation and deep learning. Sleeman earned her Ph.D. in Computer Science from the University of Maryland, Baltimore County (UMBC) working under the expert direction of Dr. Tim Finin and Dr. Milton Halem in 2017. Her early work was published in AI Magazine in 2015. She formalized how one would apply generative topic models for graphs and introduced this work at the International Semantic Web conference in 2015. Following her novel contribution of generative topic modeling for graphs, she used generative topic models to discover cross-domain influence and relatedness among scientific research papers. Her Ph.D. thesis adapted the theory of data assimilation, which is based on theoretical approaches for temporal integration of physical observations with dynamic simulation models, and applied it to a large, multi-sourced, scientific text collection. This methodology provided a new approach for multi-source data integration and trend prediction using an innovative method for filtering noise and accounting for missing model data. Her work was awarded a Microsoft AI for Earth resource grant in 2017 and 2018 and also won the best paper award in the Semantic Web for Social Good Workshop presented at International Semantic Web Conference in 2018. Sleeman was recognized as a top data scientist in the Washington D.C. area in 2017 (FemTech) and was recognized as a 2019 CSEE Rising Star. She also teaches Introduction to Artificial Intelligence at the University of Maryland, Baltimore County (UMBC).
Syamala Srinivasan
Currently teaching:
Natural Language Processing
Artificial Intelligence and Deep Learning
Syamala Srinivasan has been a faculty of the Data Science program at Northwestern University since 2013. She teaches courses in Deep Learning, NLP, Machine Learning and Modeling. She has over 35 years of industrial and academic experience in data science. She has applied data science methods to solve business problems. Currently, Syamala is the Chief Analytics Officer at Tada Cognitive solutions, Inc. She is integrating AIML business solutions with the Tada platform. Prior to Tada, she worked for the US Army in predictive maintenance of ground vehicles, for 4 years. Before that, she worked at Caterpillar Inc., in various technical and management positions for 22 years. She started and developed the Department of Analytics and was the director of analytics before retiring from Caterpillar. She focused on developing and deploying analytical solutions to complex business problems in the entire value chain of product development, manufacturing, supply chain, marketing, sales, and human resources. She developed innovative techniques when solving complex business problems and received two U.S. patents. One of those patents was used to spin off a new startup company, called Akoya. It was one of the first Data Science companies. Akoya was then integrated into Birla soft, part of Birla Technologies Inc.
Syamala has a Ph.D. and M.S. in Statistics from Colorado State University. She also received a M.S. and a B.S in Mathematics from India.
Her prior academic appointments include National Louis University, Bradley University, Chicago State University, Northern Illinois University, and Colorado State University. She has been teaching 401, 410, 422, 450, 453 and 458 at Northwestern University.