diff --git a/root/tmva/tmva/BoostedDTBenchmarks.cxx b/root/tmva/tmva/BoostedDTBenchmarks.cxx
new file mode 100644
index 000000000..4e772e85d
--- /dev/null
+++ b/root/tmva/tmva/BoostedDTBenchmarks.cxx
@@ -0,0 +1,179 @@
+/* Authored by Xandru Mifsud (CERN Summer Student) and Lorenzo Moneta (Summer Project Supervisor) */
+
+#include "TSystem.h"
+#include "TTree.h"
+#include "TFile.h"
+
+#include "TMVA/RReader.hxx"
+#include "TMVA/RTensorUtils.hxx"
+#include "TMVA/DataLoader.h"
+#include "TMVA/Factory.h"
+#include "TMVA/MethodBase.h"
+#include "TMVA/Types.h"
+
+#include "benchmark/benchmark.h"
+
+#include "MakeRandomTTree.h"
+
+using namespace TMVA::Experimental;
+using namespace std;
+
+static void BM_TMVA_BDTTraining(benchmark::State &state){
+   // Parameters
+   UInt_t nVars = 4;
+   UInt_t nEvents = 500;
+   Bool_t mem_stats = (state.range(0) == 2000) && (state.range(1) == 10) && (state.range(2) == 1);
+
+   // Memory benchmark data placeholder
+   ProcInfo_t pinfo;
+   Long_t init_mem_res, term_mem_res; init_mem_res = term_mem_res = 0;
+   double mem_res = 0.0;
+
+   // Open output file
+   TString outfileName( "bdt_bench_train_output.root" );
+   TFile* outputFile = TFile::Open(outfileName, "RECREATE");
+
+   // Set up (generate one extra event for testing)
+   TTree *sigTree = genTree("sigTree", nEvents, nVars,0.3, 0.5, 100);
+   TTree *bkgTree = genTree("bkgTree", nEvents, nVars,-0.3, 0.5, 101);
+
+   // Prepare a DataLoader instance, registering the signal and background TTrees
+   auto *dataloader = new TMVA::DataLoader("bdt-bench");
+   dataloader->AddSignalTree(sigTree);
+   dataloader->AddBackgroundTree(bkgTree);
+
+   // Register variables in dataloader, using naming convention for randomly generated TTrees in MakeRandomTTree.h
+   for(UInt_t i = 0; i < nVars; i++){
+      string var_name = "var" + to_string(i);
+      string var_leaflist = var_name + "/F";
+
+      dataloader->AddVariable(var_name.c_str(), 'D');
+   }
+
+   // For each benchmark we specifically ignore this test event such that we exclusively benchmark training.
+   dataloader->PrepareTrainingAndTestTree("",
+                  Form("SplitMode=Block:nTrain_Signal=%i:nTrain_Background=%i:!V", nEvents, nEvents));
+
+   // Benchmarking
+   UInt_t iter_c = 0;
+   for(auto _: state){
+      ROOT::EnableImplicitMT(state.range(2));
+
+      // Create factory instance
+      auto factory = new TMVA::Factory("bdt-bench", outputFile,
+                                    "Silent:!DrawProgressBar:AnalysisType=Classification");
+
+      // Get current memory usage statistics after setup
+      if(mem_stats && iter_c == 0){
+         gSystem->GetProcInfo(&pinfo);
+         init_mem_res = pinfo.fMemResident;
+      }
+      
+      // Construct training options string
+      string opts = "!V:!H:NTrees=" + to_string(state.range(0)) + ":MaxDepth=" + to_string(state.range(1));
+
+      // Train a TMVA method
+      string key = to_string(state.range(0)) + "_" + to_string(state.range(1)) + "_" + to_string(state.range(2));
+      auto method = factory->BookMethod(dataloader, TMVA::Types::kBDT, "BDT_" + key, opts);
+      TMVA::Event::SetIsTraining(kTRUE);
+      method->TrainMethod();
+
+      // Maintain Memory statistics (independent from Google Benchmark)
+      if(mem_stats && iter_c == 0){
+         gSystem->GetProcInfo(&pinfo);
+         term_mem_res = pinfo.fMemResident;
+         mem_res += (double) (term_mem_res - init_mem_res);
+      }
+
+      TMVA::Event::SetIsTraining(kFALSE);
+      method->Data()->DeleteAllResults(TMVA::Types::kTraining, method->GetAnalysisType());
+
+      // Destroy factory entirely
+      factory->DeleteAllMethods();
+      factory->fMethodsMap.clear();
+      delete factory;
+
+      // DEBUG
+      // cout << "[DEBUG] " << key << ": res_mem_init = " << (double) init_mem_res << ", res_mem_term = " << (double) term_mem_res << endl;
+
+      iter_c++;
+   }
+
+   if(mem_stats){
+      mem_res *= iter_c;
+      state.counters["Resident Memory"] = benchmark::Counter(mem_res, benchmark::Counter::kAvgIterations);
+   }
+
+   // Teardown
+   delete sigTree;
+   delete bkgTree;
+
+   outputFile->Close();
+   delete outputFile;
+}
+BENCHMARK(BM_TMVA_BDTTraining)->ArgsProduct({{2000, 1000, 400, 100}, {10, 8, 6, 4, 2}, {1, 4, 8, 16}});
+
+static void BM_TMVA_BDTTesting(benchmark::State &state){
+   // Parameters
+   UInt_t nVars = 4;
+   UInt_t nEvents = 500;
+   Bool_t mem_stats = (state.range(0) == 2000) && (state.range(1) == 10) && (state.range(2) == 1);
+
+   // Memory benchmark data placeholder
+   ProcInfo_t pinfo;
+   Long_t init_mem_res, term_mem_res; init_mem_res = term_mem_res = 0;
+   double mem_res = 0.0;
+
+   // Open output file
+   TString outfileName( "bdt_bench_test_output.root" );
+   TFile* outputFile = TFile::Open(outfileName, "RECREATE");
+
+   // Set up
+   auto inputFile = new TFile("bdt_bench_test_input.root","RECREATE");
+   TTree *testTree = genTree("testTree", nEvents, nVars,0.3, 0.5, 102, false);
+   testTree->Write();
+   delete testTree;
+   inputFile->Close();
+   delete inputFile;
+
+   ROOT::RDataFrame testDF("testTree","bdt_bench_test_input.root");
+   auto testTensor = AsTensor<Float_t>(testDF);
+
+   // Benchmarking
+   UInt_t iter_c = 0;
+   for(auto _: state){
+      ROOT::EnableImplicitMT(state.range(2));
+
+      // Test a TMVA method via RReader
+      string key = to_string(state.range(0)) + "_" + to_string(state.range(1)) + "_" + to_string(state.range(2));
+
+      // Get current memory usage statistics after setup
+      if(mem_stats && iter_c == 0){
+         gSystem->GetProcInfo(&pinfo);
+         init_mem_res = pinfo.fMemResident;
+      }
+
+      RReader model("./bdt-bench/weights/bdt-bench_BDT_" + key + ".weights.xml");
+      model.Compute(testTensor);
+
+      // Maintain Memory statistics (independent from Google Benchmark)
+      if(mem_stats && iter_c == 0){
+         gSystem->GetProcInfo(&pinfo);
+         term_mem_res = pinfo.fMemResident;
+         mem_res += (double) (term_mem_res - init_mem_res);
+      }
+
+      iter_c++;
+   }
+
+   if(mem_stats){
+      mem_res *= iter_c;
+      state.counters["Resident Memory"] = benchmark::Counter(mem_res, benchmark::Counter::kAvgIterations);
+   }
+
+   // Teardown
+   outputFile->Close();
+}
+BENCHMARK(BM_TMVA_BDTTesting)->ArgsProduct({{2000, 1000, 400, 100}, {10, 8, 6, 4, 2}, {1, 4, 8, 16}});
+
+BENCHMARK_MAIN();
diff --git a/root/tmva/tmva/CMakeLists.txt b/root/tmva/tmva/CMakeLists.txt
index a55abf4fc..e57484a32 100644
--- a/root/tmva/tmva/CMakeLists.txt
+++ b/root/tmva/tmva/CMakeLists.txt
@@ -3,6 +3,11 @@ if(ROOT_tmva_FOUND)
       CrossValidationBenchmarks.cxx
       LABEL short
       LIBRARIES Core Tree MathCore TMVA)
+
+   RB_ADD_GBENCHMARK(BoostedDTBenchmarks
+      BoostedDTBenchmarks.cxx
+      LABEL short
+      LIBRARIES Core Tree TreePlayer MathCore RIO XMLIO ROOTDataFrame TMVA)
 endif()
 
 if(ROOT_tmva_FOUND AND ROOT_tmva-cpu_FOUND AND ROOT_imt_FOUND)
diff --git a/root/tmva/tmva/MakeRandomTTree.h b/root/tmva/tmva/MakeRandomTTree.h
new file mode 100644
index 000000000..59b234834
--- /dev/null
+++ b/root/tmva/tmva/MakeRandomTTree.h
@@ -0,0 +1,41 @@
+#include "TRandom3.h"
+#include "TTree.h"
+
+// Utility function for generating a random TTree with Gaussian float data, for the specified number of points and vars
+TTree* genTree(std::string name, UInt_t nPoints, const UInt_t nVars, Double_t offset, Double_t scale = 0.3, UInt_t seed = 100,
+               bool evtCol = true){
+   // Initialisation
+   TRandom3 rng(seed);
+   Float_t vars[nVars]; for(auto& var: vars){ var = 0.0;}
+   UInt_t id = 0;
+
+   // Create new TTree instance
+   auto data = new TTree(name.c_str(),name.c_str());
+
+   // Add a branch corresponding to each variable
+   for(UInt_t i = 0; i < nVars; i++){
+      std::string var_name = "var" + std::to_string(i);
+      std::string var_leaflist = var_name + "/F";
+
+      data->Branch(var_name.c_str(), vars + i, var_leaflist.c_str());
+   }
+
+   // And add a branch for the (unique) Event identifier
+   if(evtCol){
+      data->Branch("EventNumber", &id, "EventNumber/I");
+   }
+
+   // Populate TTree instance with Gaussian data
+   for(UInt_t j = 0; j < nPoints; j++){
+      for(UInt_t i = 0; i < nVars; i++){
+         vars[i] = rng.Gaus(offset, scale);
+      }
+
+      data->Fill();
+      id++;
+   }
+
+   // Important: Disconnects the tree from the memory locations of vars[i]
+   data->ResetBranchAddresses();
+   return data;
+}
\ No newline at end of file