Matthew Lanham
Clinical Associate Professor of Management
Quantitative Methods
Professor Lanham is a member of the Quantitative Methods Area in Purdue University's Daniels School of Business. He coordinates and teaches Daniels' Data Mining, Predictive Analytics, Using R for Analytics, and Industry Practicum courses, as well as interfacing these activities with Purdue's Krenicki Center for Business Analytics & Machine Learning serving as its Associate Director of Student Engagements. Please visit MatthewALanham.com for work his students have done with industry partners – you just might be persuaded to connect with him to scope out a project yourself. Also, feel free to read about and connect with the other wonderful faculty here in Daniels whom are also mentoring Experiential Learning opportunities with companies. Boiler up!
Lessons from A Memorable Coach
Reflecting on the enduring legacy of fictional Coach Norman Dale from the movie Hoosiers, Matthew Lanham, Daniels School professor, explores how his leadership style inspired a career in coaching beyond sports, emphasizing teamwork, fundamentals, and personal growth over personal success.
- Lanham to serve on SAS’ Faculty Advisory Board
Matthew Lanham, a clinical associate professor in quantitative methods at Purdue University’s Mitch Daniels School of Business, has been invited to serve on SAS’ Faculty Advisory Board. - It’s Time to do Good, and not Grow Weary
A key way to think of Data for Good is anything that challenges you to exhort your time and resources on problems that touch the heart and to help people grow and make better decisions, says Matthew Lanham. - Doing Good with Data
Doing Good with Data - Lanham elected to INFORMS Analytics Certification Board
Matthew Lanham, clinical assistant professor and academic director of the Business Analytics and Information Management master’s program (MSBAIM) at Purdue University’s Mitchell E. Daniels, Jr. School of Business, has been elected to serve on the INFORMS Analytics Certification Board for a three-year term. - Krannert honors two faculty members with KATE Awards
The school recently recognized two winners of the Krannert Alumni Teaching Excellence, or KATE, Awards, which are selected by a committee of four faculty members and five graduating seniors. - Preparing Students for Post-Pandemic Success
Krannert's Academic Director for the MS BAIM program, Prof. Matthew Lanham speaks with Inside Higher Ed on how universities can help their students become more competitive and enhance their own degree offerings. - MS BAIM Students Win Best Student Paper Award at MWDSI Conference
For the second year in a row, a team of students from Krannert's MS Business Analytics and Information Management (MS BAIM) program won Best Student Paper at the Midwest Decisions Sciences Institute (MWDSI) conference. - SAS honors Krannert’s Matthew Lanham with 2019 Distinguished Professor Award
A persistent analytics talent gap creates big opportunities for people who can wield analytics to help organizations make better decisions. Innovative analytics users and students who are rushing to fill that gap — and those who teach them — were honored this week at the SAS Global Forum, including Matthew Lanham of the Purdue University Krannert School of Management.
2024 Salgo-Noren Outstanding Master’s Teaching Award
2023 Scholarship of Teaching and Learning (SoTL) Fellowship
2023 UPS George D. Smith Prize
2022 Dr. Charlene Sullivan Transformative Impact Award
2021 Krannert Alumni Teaching Excellence (KATE) Award
2019 Krannert School of Management Transformative Impact Award
2019 Purdue University Summer Teaching Innovation Award
2019 SAS Distinguished Professor Award
2018 Purdue University Summer Teaching Innovation Award
2018 Purdue University Teaching for Tomorrow Fellowship
2017 Purdue University Teaching for Tomorrow Fellowship
Contact
lanhamm@purdue.edu
Phone: (765) 494-4419
Office: YONG 937
Quick links
Area(s) of Expertise
Artificial Intelligence, Business Analytics, Data Analytics, Data Mining, Decision Sciences, Experiential Learning, Information Technology, Machine Learning