Shibasish Dasgupta, Phd Email & Phone Number

Kolkata, Wb, In

New York, New York, Us

With two decades of experience in Data & Statistical Sciences following my master's degree, I currently serve as the Associate Director in the AI/ML, Quantitative and Digital Sciences (AQDS) team at Pfizer Research and Development in Mumbai, India. Since joining Pfizer in May 2020, I have played an active & significant role in shaping the "AI/ML Vision for Pfizer R&D Statistics" through regular interactions with senior leadership. I established a cross-functional AI/ML/Quantitative Data Science team, which has been instrumental in supporting multiple global collaborative AI/ML portfolios. This led to the formation of the India Data Science team within the GBDM India Statistics organization, where I was actively involved in the hiring process. I have developed the team's capabilities through global collaborations and have been committed to the professional and personal development of the team members. As a leader in external engagements and talent acquisition for the team, I have represented not only the Data Science/Statistics team but also Pfizer for the past few years as a brand ambassador. I have served on various Scientific Program Committees and Advisory Committees for international conferences and have delivered numerous invited lectures at international scientific events and at various premier universities, institutes, and organizations. I have established the long-term Data Science Summer Internship Program at GBDM India Statistics organization. Each year, I have recruited and mentored summer interns, leveraging my industry and academic connections and networks. I was one of the main organizers for the first ever Chennai AI/ML Symposium jointly hosted by Pfizer and IIT-Madras in September 2023 at IIT-Madras Research Park. One of our Pfizer research projects titled "Application of Statistical Machine Learning in Biomarker Selection" was published in "Scientific Reports," a highly cited journal within the Springer-Nature portfolio, in October 2023.

In

The main agenda of the Board of Studies, Data Science at CMI is to oversee the functioning of the existing MSc Data Science programmee. Another short term objective is to formulate rules to govern a PhD programme in Data Science.

In

Teaching & research in the area of Data & Statistical Sciences. Taught “Multivariate Statistics” as well as "Machine Learning in Survival Analysis" to the master's students in Data Science program at CMI. Though I don't teach at CMI anymore, but I'm still affiliated there.

Kolkata, West Bengal, In

Sharing my experience in framing the syllabus of the master's in data science course and guiding the professors regarding the teaching of the same.

Dearborn, Michigan, Us

Passion for Product & Deep Customer Insight:• Working in the Global Customer Experience (CX) Measurement related projects which comes underthe Intelligent Customer Interactions (ICI). Leading the Business Impact Model development team.• Robust Business Plan delivery with in-depth analyses in revenue opportunity and compliance strategy: Led the Market Demand Model (MDM) development team of Chennai, India in global collaboration with the Data & Modeling team of GDI&A, in Dearborn, MI, USA. • Achieved the $375 million target via Variable Marketing (VM) optimization, ICI and other initiatives such as Europe VM Optimization.• Developed International Operating/Engagement Model for Business & Sales Planning Analytics (BSPA) team -- Delivery of BSPA solutions for the non-US market: -- Led the short-term forecasting team of BSPA for the Big 5 European markets & delivered theresults along with the insights & recommendations to the business. -- Developed the forecasting model for monthly contracts. • Ford India Credit Optimization Project: From data collection, formulation of the business problem to addressing the problem through exploratory data analysis and solving the required optimization problem and then delivering the business recommendations and insights by interacting with the customers/business partners.Capabilities & Management Systems: • Established the "GDI&A India Guest Lecture Series". • Continuously enhancing GDI&A's technical capability & knowledge diversity through recruiting & developing technical talents, learning new tools, knowledge shares, governance forums, MPRs,academic collaborations, participating in internal and external technical workshops & conferences and applying these learning to business assignments and projects.Culture & Values:• Consistently achieving GDI&A's talent development goals, including developing new and existing team members business acumen and technical experience and deepening talent.

In

Teaching, Research, and Consulting in the area of Data & Statistical Sciences at the Interdisciplinary Statistical Research Unit of the Indian Statistical Institute (ISI).

Mobile, Alabama, Us

Teaching, Research and Consulting in the area of Statistical Science at the Department of Mathematics & Statistics.

Oxford, Oh, Us

Teaching, Research and Consulting in the area of Statistical Science at the Department of Information Systems & Analytics, Farmer School of Business.

Seattle, Wa, Us

Fred Hutchinson Cancer Research Center (FHCRC), home of three Nobel laureates, is an independent, nonprofit research institution dedicated to the development and advancement of biomedical research to eliminate cancer and other potentially fatal diseases. Recognized internationally for its pioneering work in bone-marrow transplantation, the Center’s five scientific divisions collaborate to form a unique environment for conducting basic and applied science. The Hutchinson Center, in collaboration with its clinical and research partners, the University of Washington and Seattle Children’s, is the only National Cancer Institute-designated comprehensive cancer center in the Pacific Northwest. My main focus of research at Fred Hutchinson Cancer Research Center was on the statistical methods for selection and evaluation of biomarker. I have worked on several bio-statistical methodology projects during my stay at Fred Hutch. Summary of my research projects at Fred Hutch: 1. When evaluating principal surrogate biomarkers in vaccine trials, missingness in potential outcomes requires prediction using auxiliary variables and/or augmented study design with a close-out placebo vaccination (CPV) component. The estimated likelihood approach, which separates the estimation of biomarker distribution from the maximization of the estimated likelihood, has often been adopted. Here, we develop a likelihood-based approach that jointly estimates the two parts and describe the methods for selecting auxiliary variables as risk predictors and/or biomarker predictors.2. Development of a Bayesian predictive approach for designing biomarker validation studies. Classical (non-Bayesian) approach for study design relies on parameter estimates from pilot data. Ignoring variability in parameter estimates can result in under-powered study design. I have investigated an alternative approach based on Bayesian predictive power.

Gainesville, Florida, Us

Research, Teaching & Consulting in the area of Statistical Sciences.I have received PhD in Statistics from the University of Florida, Gainesville in August, 2013. I have pursued my doctoral research under the joint supervision of Prof. Malay Ghosh (adviser) and Prof. Kshitij Khare (co-adviser). My Ph.D dissertation topic was: High Dimensional Inference and Variable Selection.I have been a Teaching Assistant and also served as the solo instructor of some of the core undergraduate/graduate level courses in the Statistics Department at University of Florida. In doing so, I gained valuable experience in designing the course and lecture notes, giving lectures, preparing quizzes, homework assignments and exams, and last but not the least, communicating with more than 200 students and with my teaching assistants. Teaching so many students at a time taught me how to deal with lots of people and increased my leadership as well as interpersonal relationship skills. This ability will help me in collaborating and communicating with the people at any organization. My teaching experience at the University of Florida are as follows: 1. Teaching Instructor (Spring 2010 - Fall 2011) Teaching instructor for the courses Introduction to the Practice of Statistics II (huge classes of more than 200 students each!), Introduction to Probability/Fundamentals of Probability (this course is intended for undergraduate as well as graduate students from different disciplines) and Engineering Statistics.2. Teaching Assistant (Fall 2007 - Fall 2009, Spring 2012 - Summer 2013) Teaching assistant for undergraduate and graduate courses in Statistics, including Introduction tothe Practice of Statistics I and II, Engineering Statistics, Non-parametric Statistical Methods,Fundamentals of Probability and Statistical Methods for Research II.

Pune, Maharashtra, In

Here I have worked in the Statistical Learning and Data Mining group called "Analytics Centre of Excellence." I was Involved in various projects, one of which is named "Snippet Tagging Using Text Mining." This was a unique text classification problem posed by Gridstone Research which has a powerful and flexible analysis and modeling platform that helps investment professionals make better investment decisions. I've also used hierarchical multi-class classification using different statistical machine learning techniques like Support Vector Machines (SVMs), Naive Bayes and Hidden Markov Models in various predictive analytics projects and one of our biggest clients was SPSS.

Mumbai, Maharashtra, In

Here I worked in the "Business Analytics R&D group." I Pursued my M.Phil. research in collaboration with TATA Research Development and Design Centre (TRDDC) in the mathematical and statistical aspects of Support Vector Machines (SVMs), its related areas and its application in the classification problems. Also explored the possible applications of this newly and fast emerging area in finance, medical science and business. I was also Involved in a Customer Relationship Management (CRM) project to predict customer churn by different modeling tools like Logistic Regression, SVMs, Decision Trees and survival data mining.My other works at TRDDC are the following:1. Variable Selection Methods: Principal Component Analysis, Correlation and Information Theory. 2. Inventum: A Predictive Analytic Modeling of Workers Compensation Classification (a project about two-class classification problem). 3. Choosing Multiple Parameters for SVM: Automatic parameter tuning of SVM (based on the theory of minimizing the upper bound of the generalization error of a classification problem). 4. Automated Rail Weight Identification System: A project on multi-class classification problem. 5. Feature selection for SVM using Automatic Tuning. 6. Explored a new area for very large SVM training using Core Vector Machines (CVMs), its theory and applications.

Maharashtra, In

I was a teaching assistant at the Department of Statistics and Center for Advanced Studies in Statistics for graduate courses in Statistics, including Mathematical Analysis and Data Mining while pursuing my Master of Philosophy (MPhil) research on "Application of Support Vector Machines in Classification Problems" in collaboration with TATA Research Development and Design Center (TRDDC), Pune, India under the joint supervision of Prof. M.B. Rajarshi, Retired Professor, Department of Statistics and Center for Advanced Studies in Statistics, Savitribai Phule Pune University, India and Dr. Aniruddha Pant, Founder & CEO, Algo Analytics, Pune, India.

Doctor Of Philosophy (Phd), Statistics

Post Doctoral Research Fellow, Biostatistics & Machine Learning

Master Of Philosophy (Mphil), Statistics & Machine Learning

Master Of Science - Ms, Statistics

Bachelor Of Science - Bs, Statistics