<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Parham Sharafoleslami</title><description>UC Berkeley engineer working on autonomy, controls, and machine learning.</description><link>https://parham-sharaf.github.io/</link><language>en-us</language><item><title>Personal Website</title><link>https://parham-sharaf.github.io/projects/personal-website/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/personal-website/</guid><description>Initial repository for a personal website. Currently a minimal public scaffold with the GitHub repo in place and ready for iterative development.</description><pubDate>Tue, 21 Apr 2026 00:00:00 GMT</pubDate><category>Web Development</category><category>portfolio</category><category>personal-website</category><category>web</category></item><item><title>Modern NLP — From Statistical MT to Multimodal Foundation Models</title><link>https://parham-sharaf.github.io/projects/modern-nlp/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/modern-nlp/</guid><description>Four paradigm shifts in one semester: IBM Model 1 → attention-based NMT → transformer parsing → LLM fine-tuning → CLIP multimodal retrieval with pragmatic reasoning. Each technique subsumes and extends the previous.</description><pubDate>Tue, 10 Dec 2024 00:00:00 GMT</pubDate><category>Natural Language Processing</category><category>nlp</category><category>machine-translation</category><category>attention</category><category>transformers</category><category>llms</category><category>multimodal</category><category>pragmatics</category></item><item><title>Neural Radiance Fields (NeRF)</title><link>https://parham-sharaf.github.io/projects/neural-radiance-fields/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/neural-radiance-fields/</guid><description>Training an MLP to represent a 3D scene as a continuous function from (x, y, z, θ, φ) to (RGB, density). Volume rendering turns the field back into images; the field itself is the 3D model.</description><pubDate>Tue, 10 Dec 2024 00:00:00 GMT</pubDate><category>Computer Vision</category><category>nerf</category><category>neural-rendering</category><category>volumetric-rendering</category><category>3d-reconstruction</category></item><item><title>Vision Transformer + Masked Autoencoder</title><link>https://parham-sharaf.github.io/projects/vit-masked-autoencoder/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/vit-masked-autoencoder/</guid><description>ViT classifier achieving 73.5% on CIFAR-10, then self-supervised MAE pretraining boosts finetuned accuracy to 76.8%. Full implementation of patchify, attention pooling, and mask reconstruction.</description><pubDate>Mon, 25 Nov 2024 00:00:00 GMT</pubDate><category>Deep Learning</category><category>vision-transformer</category><category>masked-autoencoder</category><category>self-supervised</category><category>transformers</category></item><item><title>Fun With Diffusion Models</title><link>https://parham-sharaf.github.io/projects/diffusion-models/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/diffusion-models/</guid><description>Sampling from DeepFloyd IF — CFG, SDEdit, inpainting, visual anagrams. Then training a time- and class-conditioned U-Net from scratch on MNIST to learn the diffusion process end-to-end.</description><pubDate>Wed, 20 Nov 2024 00:00:00 GMT</pubDate><category>Computer Vision</category><category>diffusion</category><category>generative-models</category><category>stable-diffusion</category><category>unet</category></item><item><title>Transformer for News Summarization</title><link>https://parham-sharaf.github.io/projects/transformer-summarization/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/transformer-summarization/</guid><description>Self-attention, multi-head attention, and encoder-decoder architecture implemented from scratch. Trained on CNN/DailyMail achieving 35.1 ROUGE-L, outperforming LSTM baseline by 60%.</description><pubDate>Fri, 15 Nov 2024 00:00:00 GMT</pubDate><category>Deep Learning</category><category>transformers</category><category>attention</category><category>summarization</category><category>nlp</category></item><item><title>RNN Sequence Modeling</title><link>https://parham-sharaf.github.io/projects/rnn-sequence-modeling/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/rnn-sequence-modeling/</guid><description>Recurrent networks from scratch — forward pass, backpropagation through time, and gradient flow analysis. Vectorized NumPy implementation validated to 5e-5 tolerance.</description><pubDate>Sat, 02 Nov 2024 00:00:00 GMT</pubDate><category>Deep Learning</category><category>rnn</category><category>lstm</category><category>sequence-modeling</category><category>backpropagation</category></item><item><title>Auto-Stitching Photo Mosaics</title><link>https://parham-sharaf.github.io/projects/auto-panorama/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/auto-panorama/</guid><description>Building a panorama pipeline from scratch — Harris corner detection, Adaptive Non-Maximal Suppression, feature matching, RANSAC for homography estimation, and Laplacian-pyramid blending.</description><pubDate>Mon, 28 Oct 2024 00:00:00 GMT</pubDate><category>Computer Vision</category><category>panorama</category><category>feature-detection</category><category>ransac</category><category>homography</category></item><item><title>Deep Learning from Scratch</title><link>https://parham-sharaf.github.io/projects/deep-learning-scratch/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/deep-learning-scratch/</guid><description>Backpropagation, BatchNorm, Dropout, and CNNs implemented from first principles in NumPy — then PyTorch deployment achieving 74.8% on CIFAR-10.</description><pubDate>Tue, 15 Oct 2024 00:00:00 GMT</pubDate><category>Deep Learning</category><category>neural-networks</category><category>backpropagation</category><category>convolutional-networks</category><category>batch-normalization</category></item><item><title>Face Morphing with Delaunay Triangulation</title><link>https://parham-sharaf.github.io/projects/face-morphing/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/face-morphing/</guid><description>Smooth warping between two faces via point correspondences, Delaunay triangulation, and affine warps per triangle. Plus: population mean faces and caricature generation by extrapolation.</description><pubDate>Sat, 12 Oct 2024 00:00:00 GMT</pubDate><category>Computer Vision</category><category>face-morphing</category><category>delaunay-triangulation</category><category>affine-warping</category><category>image-interpolation</category></item><item><title>Filters &amp; Frequencies — Edges, Hybrid Images, and Blending</title><link>https://parham-sharaf.github.io/projects/filters-frequencies/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/filters-frequencies/</guid><description>Working in the frequency domain to extract edges, create hybrid images that change meaning with viewing distance, and blend images seamlessly via Laplacian pyramids. Ends with the famous &apos;oraple.&apos;</description><pubDate>Wed, 25 Sep 2024 00:00:00 GMT</pubDate><category>Computer Vision</category><category>image-filters</category><category>frequency-domain</category><category>hybrid-images</category><category>pyramid-blending</category></item><item><title>Colorizing 1907 Russian Empire Photographs</title><link>https://parham-sharaf.github.io/projects/prokudin-gorskii/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/prokudin-gorskii/</guid><description>Reconstructing color from Sergei Prokudin-Gorskii&apos;s glass plate negatives (captured 1907–1915) using image pyramids and normalized cross-correlation alignment.</description><pubDate>Tue, 10 Sep 2024 00:00:00 GMT</pubDate><category>Computer Vision</category><category>image-alignment</category><category>computational-photography</category><category>historical-photography</category></item><item><title>Pacman AI — A Tour of Classical AI</title><link>https://parham-sharaf.github.io/projects/pacman-ai/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/pacman-ai/</guid><description>End-to-end AI agent built across four paradigms: A* pathfinding, Minimax/Expectimax game trees, HMM + particle filter tracking, and Q-learning. Each piece solves one Pacman problem; together they cover the full classical AI curriculum.</description><pubDate>Mon, 05 Aug 2024 00:00:00 GMT</pubDate><category>Artificial Intelligence</category><category>search</category><category>minimax</category><category>hmm</category><category>particle-filter</category><category>q-learning</category><category>mdp</category></item><item><title>Image Geolocation with k-NN &amp; Linear Regression</title><link>https://parham-sharaf.github.io/projects/image-geolocation/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/image-geolocation/</guid><description>Computer vision system predicting photo locations from visual features. Combines k-nearest neighbors with regression models, achieving 127km median error on global street-view dataset.</description><pubDate>Wed, 01 May 2024 00:00:00 GMT</pubDate><category>Machine Learning</category><category>computer-vision</category><category>geolocation</category><category>k-nearest-neighbors</category><category>image-processing</category></item><item><title>17× Faster 2D Convolution: AVX2 + OpenMP</title><link>https://parham-sharaf.github.io/projects/parallel-convolve/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/parallel-convolve/</guid><description>Hand-optimized SIMD kernels with parallel tiling achieve 17× speedup over naive implementation. Deep dive into vectorization, memory patterns, and performance engineering.</description><pubDate>Wed, 01 May 2024 00:00:00 GMT</pubDate><category>Parallel Computing</category><category>systems</category><category>performance</category><category>simd</category></item><item><title>32-bit RISC-V CPU from Logic Gates</title><link>https://parham-sharaf.github.io/projects/cs61cpu/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/cs61cpu/</guid><description>A complete 2-stage pipelined RV32I processor built from first principles in Logisim — ALU, register file, control unit, and memory system, all hand-wired from basic gates.</description><pubDate>Mon, 15 Apr 2024 00:00:00 GMT</pubDate><category>Computer Architecture</category><category>architecture</category><category>hardware</category><category>riscv</category></item><item><title>Collaborative Filtering Movie Recommender</title><link>https://parham-sharaf.github.io/projects/movie-recommender/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/movie-recommender/</guid><description>SVD-based matrix factorization system with 0.89 RMSE on MovieLens dataset. Handles sparse ratings, cold start problems, and scales to 100k+ users through optimized gradient descent.</description><pubDate>Fri, 05 Apr 2024 00:00:00 GMT</pubDate><category>Machine Learning</category><category>recommender-systems</category><category>matrix-factorization</category><category>svd</category><category>optimization</category></item><item><title>Decision Trees &amp; Ensemble Methods</title><link>https://parham-sharaf.github.io/projects/decision-trees/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/decision-trees/</guid><description>From-scratch implementation of decision trees with pruning, random forests, and AdaBoost. Comprehensive analysis of overfitting, feature selection, and ensemble performance on real datasets.</description><pubDate>Sun, 10 Mar 2024 00:00:00 GMT</pubDate><category>Machine Learning</category><category>decision-trees</category><category>ensemble-methods</category><category>random-forest</category><category>adaboost</category></item><item><title>MNIST Neural Network in Pure RISC-V Assembly</title><link>https://parham-sharaf.github.io/projects/riscv-classifier/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/riscv-classifier/</guid><description>A complete 2-layer MLP for digit classification — matrix multiply, ReLU, argmax, file I/O, and all infrastructure — written entirely in hand-coded RISC-V assembly without any library calls.</description><pubDate>Fri, 01 Mar 2024 00:00:00 GMT</pubDate><category>Systems Programming</category><category>systems</category><category>ml</category><category>assembly</category><category>low-level</category></item><item><title>Neural Network from Scratch</title><link>https://parham-sharaf.github.io/projects/neural-network-scratch/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/neural-network-scratch/</guid><description>Pure NumPy implementation achieving 99.6% MNIST accuracy through optimized gradient descent, backpropagation, and regularization techniques.</description><pubDate>Thu, 15 Feb 2024 00:00:00 GMT</pubDate><category>Machine Learning</category><category>neural-networks</category><category>gradient-descent</category><category>backpropagation</category></item><item><title>Entropy Wordle Solver</title><link>https://parham-sharaf.github.io/projects/entropy-wordle-solver/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/entropy-wordle-solver/</guid><description>Information-theoretic greedy solver that picks each guess to maximize expected entropy over the remaining word set — averaging 3.92 guesses across 300+ games.</description><pubDate>Wed, 15 Nov 2023 00:00:00 GMT</pubDate><category>Machine Learning</category><category>entropy</category><category>optimization</category><category>information-theory</category></item><item><title>MCMC Cipher Decoder</title><link>https://parham-sharaf.github.io/projects/mcmc-cipher-decoder/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/mcmc-cipher-decoder/</guid><description>Metropolis-Hastings breaking substitution ciphers against bigram frequencies — watch garbled text slowly resolve into English over MCMC iterations.</description><pubDate>Sat, 28 Oct 2023 00:00:00 GMT</pubDate><category>Machine Learning</category><category>mcmc</category><category>cryptography</category><category>probability</category></item><item><title>Build Your Own World</title><link>https://parham-sharaf.github.io/projects/byow/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/byow/</guid><description>A sophisticated 2D world generator with dynamic lighting, line-of-sight exploration, custom tilesets, and interactive minimaps — infinite deterministic worlds from a single seed.</description><pubDate>Mon, 01 May 2023 00:00:00 GMT</pubDate><category>Game Development</category><category>games</category><category>procedural</category><category>graphics</category><category>ui-design</category></item><item><title>ngordnet — Semantic Evolution Explorer</title><link>https://parham-sharaf.github.io/projects/ngordnet/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/ngordnet/</guid><description>Interactive tool fusing WordNet&apos;s semantic graph with Google Books NGram data to explore how language evolves — query &apos;transportation&apos; and watch centuries of linguistic change unfold.</description><pubDate>Sat, 15 Apr 2023 00:00:00 GMT</pubDate><category>NLP &amp; Data Engineering</category><category>nlp</category><category>data-viz</category><category>graphs</category><category>linguistics</category></item><item><title>Pocket Planet</title><link>https://parham-sharaf.github.io/projects/pocket-planet/</link><guid isPermaLink="true">https://parham-sharaf.github.io/projects/pocket-planet/</guid><description>A 100×100 world carved from Perlin noise, then colonized by simulated plants that mutate, compete, and converge on the terrain they&apos;re fittest for.</description><pubDate>Sat, 01 Apr 2023 00:00:00 GMT</pubDate><category>Procedural Generation</category><category>procedural</category><category>simulation</category><category>evolution</category></item></channel></rss>