# magnetic_reconnection_classifier.py
# Scientific Computation Research Center (SCOREC) — RPI
# Classifying rare magnetic reconnection events in space weather data

import torch
import torch.nn as nn
from torch.amp import autocast  # bfloat16 mixed-precision — 1.4x speedup

class ReconnectionCNN(nn.Module):
    """
    8.1M parameter classifier for NASA Heliophysics simulation data.
    Training loss: 0.94 → 0.34 via residual connections + batch norm.
    Mixed-precision: 28% faster training, 22% less GPU memory.
    """
    def __init__(self, in_channels=3):  # Psi, B-field, Current
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(in_channels, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        )
        self.residual = self._make_residual_block(64)
        self.head = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(64, 2),
        )

    def forward(self, x):
        x = self.features(x)
        x = x + self.residual(x)  # skip connection
        return self.head(x)

scheduler = torch.optim.lr_scheduler.CosineAnnealingLR  # convergence stability
# gradient clipping · early stopping (patience=20) · physics-preserving augmentation

# biometric_emotion_detection.py
# Undergraduate ML Research Co-Lead
# Assistive tech for nonspeaking individuals with profound disabilities

from sklearn.mixture import GaussianMixture
from scipy.stats import ks_2samp
from models import LSTM, CNN, Transformer, SVM, XGBoost, LightGBM

# 100 clinical samples → 5,000 via GMM synthesis
# Validated: 29/30 features passed Kolmogorov-Smirnov tests
gmm = GaussianMixture(n_components=5).fit(clinical_data)
synthetic = gmm.sample(5000)

# Benchmarked 9 architectures on EMG + EDA biosignals
# Best: LSTM — 70.8% accuracy, 0.68 F1
# Key markers: Corrugator EMG, Zygomaticus EMG, skin conductance
results = benchmark([SVM, XGBoost, LightGBM, CNN, LSTM, Transformer], 
                    signals=["EMG_corrugator", "EMG_zygomaticus", "EDA"])

# fytospot.py — Plant Identification System
# ResNet-50 backbone · real-time OpenCV tracking · CustomTkinter GUI

model = torchvision.models.resnet50(pretrained=True)
model.fc = nn.Linear(2048, num_species)
# Live camera feed → detection + classification in variable lighting

# drug_discovery.py — AI-Driven Molecular Interaction Analysis
# Protein-drug binding prediction · DNA sequence analysis · immune response modeling

pipeline = FeatureEngineering() >> MolecularModel() >> InteractionPredictor()
# Biological data → optimized representations → predicted interactions