Abhijit Mandal
DirectorData Analytics Lab, Department of Mathematical Sciences
Associate Professor
Mathematical Sciences
Dr. Mandal develops robust statistical methods that focus on eliminating the effect of outliers in noisy data. His background involves robust inference, big data analysis, non-parametric statistics and biostatistics.
Term | Course | Section | Syllabus |
---|---|---|---|
Spring 2025 | DS 6398 - Dissertation I | 25078 | |
Spring 2025 | DS 6399 - Dissertation II | 26308 | |
Spring 2025 | DS 6390 - DS Research Collaborative | 24544 | |
Spring 2025 | DS 6390 - DS Research Collaborative | 26329 | |
Spring 2025 | STAT 6396 - Graduate Research | 25075 | |
Spring 2025 | DS 6474 - Introduction to Data Mining | 26134 | |
Spring 2025 | MATH 5370 - Special Topics | 24044 | |
Spring 2025 | STAT 6370 - Special Topics | 25080 | |
Spring 2025 | STAT 5474 - Statistical Machine Learning I | 23231 | |
Spring 2025 | STAT 5398 - Thesis 1 | 26310 | |
Spring 2025 | RSRC 4033 - Undergraduate Research | 24828 | |
Fall 2024 | DS 6398 - Dissertation I | 15734 | |
Fall 2024 | DS 6399 - Dissertation II | 17750 | |
Fall 2024 | DS 6390 - DS Research Collaborative | 14342 | |
Fall 2024 | STAT 6396 - Graduate Research | 18189 | |
Fall 2024 | DS 6474 - Introduction to Data Mining | 16855 | Syllabus |
Fall 2024 | STAT 6370 - Special Topics | 18195 | |
Fall 2024 | STAT 6370 - Special Topics | 18196 | |
Fall 2024 | STAT 5474 - Statistical Machine Learning I | 13833 | Syllabus |
Fall 2024 | STAT 4329 - Statistical Programming | 14002 | Syllabus |
Fall 2024 | STAT 5329 - Statistical Programming | 12746 | Syllabus |
Fall 2024 | STAT 6329 - Statistical Programming | 14588 | Syllabus |
Fall 2024 | STAT 5399 - Thesis 2 | 17527 | |
Fall 2024 | RSRC 4033 - Undergraduate Research | 11811 |
Term | Course | Section | Syllabus |
---|---|---|---|
Spring 2025 | DS 6390 - DS Research Collaborative | 24544 | |
Spring 2025 | DS 6390 - DS Research Collaborative | 26329 | |
Spring 2025 | STAT 5474 - Statistical Machine Learning I | 23231 | |
Summer 2024 | DS 6399 - Dissertation II | 34101 | |
Spring 2024 | DS 6398 - Dissertation I | 26092 | |
Spring 2024 | DS 6399 - Dissertation II | 27974 | |
Spring 2024 | DS 6390 - DS Research Collaborative | 25277 | |
Spring 2024 | DS 6390 - DS Research Collaborative | 25316 | |
Spring 2024 | STAT 6396 - Graduate Research | 26089 | |
Spring 2024 | STAT 5195 - Graduate Seminar | 22418 | Syllabus |
Spring 2024 | DS 6474 - Introduction to Data Mining | 27761 | Syllabus |
Spring 2024 | STAT 6370 - Special Topics | 26094 | |
Spring 2024 | STAT 5474 - Statistical Machine Learning I | 23675 | Syllabus |
Spring 2024 | STAT 5398 - Thesis 1 | 27977 | |
Fall 2023 | DS 6398 - Dissertation I | 17276 | |
Fall 2023 | DS 6390 - DS Research Collaborative | 15316 | |
Fall 2023 | DS 6381 - Math Found of DS II | 16777 | Syllabus |
Fall 2023 | STAT 5381 - Mathematical Statistics II | 16776 | Syllabus |
Fall 2023 | STAT 4329 - Statistical Programming | 14880 | Syllabus |
Fall 2023 | STAT 5329 - Statistical Programming | 13207 | Syllabus |
Fall 2023 | STAT 6329 - Statistical Programming | 15645 | Syllabus |
Summer 2023 | STAT 5399 - Thesis 2 | 36138 | |
Spring 2023 | STAT 5195 - Graduate Seminar | 22641 | Syllabus |
Spring 2023 | STAT 6195 - Graduate Seminar | 27548 | Syllabus |
Spring 2023 | STAT 1380 - Statistical Literacy | 27395 | Syllabus |
Spring 2023 | STAT 5399 - Thesis 2 | 24107 | |
Fall 2022 | MATH 4399 - Indiv Studies in Mathematics | 16177 | |
Fall 2022 | DS 5380 - Math Found of DS I | 18789 | Syllabus |
Fall 2022 | DS 6380 - Math Found of DS I | 20423 | Syllabus |
Fall 2022 | STAT 4329 - Statistical Programming | 19390 | Syllabus |
Fall 2022 | STAT 5329 - Statistical Programming | 16078 | Syllabus |
Fall 2022 | STAT 6329 - Statistical Programming | 20479 | Syllabus |
Fall 2022 | STAT 5398 - Thesis 1 | 16099 | |
Fall 2022 | RSRC 4033 - Undergraduate Research | 14107 | |
Fall 2021 | DS 5380 - Math Found of DS I | 18564 | Syllabus |
Fall 2021 | STAT 5380 - Mathematical Statistics I | 11502 | Syllabus |
Fall 2021 | STAT 4329 - Statistical Programming | 20148 | Syllabus |
Fall 2021 | STAT 5329 - Statistical Programming | 16027 | Syllabus |
Spring 2021 | STAT 5396 - Graduate Research | 29447 | |
Spring 2021 | STAT 4329 - Statistical Programming | 28138 | Syllabus |
Spring 2021 | STAT 5329 - Statistical Programming | 25671 | Syllabus |
Fall 2020 | STAT 3320 - Probability and Statistics | 18790 | Syllabus |
Term | Course | Section | Evaluation |
---|---|---|---|
Spring 2024 | STAT 5195 - Graduate Seminar | 22418 | Evaluation |
Spring 2024 | DS 6474 - Introduction to Data Mining | 27761 | Evaluation |
Spring 2024 | STAT 5474 - Statistical Machine Learning I | 23675 | Evaluation |
Fall 2023 | DS 6381 - Math Found of DS II | 16777 | Evaluation |
Fall 2023 | STAT 5381 - Mathematical Statistics II | 16776 | Evaluation |
Fall 2023 | STAT 4329 - Statistical Programming | 14880 | Evaluation |
Fall 2023 | STAT 5329 - Statistical Programming | 13207 | Evaluation |
Fall 2023 | STAT 6329 - Statistical Programming | 15645 | Evaluation |
Spring 2023 | STAT 5195 - Graduate Seminar | 22641 | Evaluation |
Spring 2023 | STAT 1380 - Statistical Literacy | 27395 | Evaluation |
Fall 2022 | DS 5380 - Math Found of DS I | 18789 | Evaluation |
Fall 2022 | DS 6380 - Math Found of DS I | 20423 | Evaluation |
Fall 2022 | STAT 4329 - Statistical Programming | 19390 | Evaluation |
Fall 2022 | STAT 5329 - Statistical Programming | 16078 | Evaluation |
Fall 2022 | STAT 6329 - Statistical Programming | 20479 | Evaluation |
Fall 2021 | DS 5380 - Math Found of DS I | 18564 | Evaluation |
Fall 2021 | STAT 5380 - Mathematical Statistics I | 11502 | Evaluation |
Fall 2021 | STAT 4329 - Statistical Programming | 20148 | Evaluation |
Fall 2021 | STAT 5329 - Statistical Programming | 16027 | Evaluation |
Spring 2021 | STAT 4329 - Statistical Programming | 28138 | Evaluation |
Spring 2021 | STAT 5329 - Statistical Programming | 25671 | Evaluation |
Fall 2020 | STAT 3320 - Probability and Statistics | 18790 | Evaluation |
Dr. Mandal develops robust statistical methods that focus on eliminating the effect of outliers in noisy data. His background involves robust inference, big data analysis, non-parametric statistics and biostatistics.