Achieved at age 17 on Iran's National University Entrance Exam (Konkur), one full year ahead of the standard cohort.
University of Central Florida · 4.0 GPA · Completed in 3 years · President's Honor Roll every semester.
UCF CECS Accelerated B.S.-to-M.S. track for exceptional students · 4.0 GPA · Completed in 1 year.
UCF · Research in Computer Vision, Robotics, Deep Learning & LLMs · Advisor: Dr. Ladislau Bölöni · CSRI Recipient.
I am a graduate researcher in Computer Science at the University of Central Florida, working in Dr. Ladislau Bölöni's lab. My research sits at the intersection of robotics, computer vision, and deep learning.
My focus is on robotic perception and manipulation — giving robots the ability to see, understand, and interact with the world using computer vision and large language models. I'm particularly interested in how visual sensing can replace or augment traditional robot sensors, enabling more capable and adaptable systems that work in unstructured, real-world environments.
My current research explores how deep learning architectures — from CNNs and Vision Transformers to variational autoencoders and multi-view fusion strategies — can extract meaningful representations from camera images for robot state estimation, sim-to-real transfer, and autonomous decision-making.
Outside the lab, I'm an avid steep-incline runner. My best PR was a 26-month stretch where I logged 6,044 miles and climbed about 10,428,000 ft of elevation. I love learning new languages — currently working on Spanish as my fourth. Outside of work, academia, and running shoes, you'll find me hiking, traveling, experimenting with photography, and occasionally putting paint on some leftover blank paper.
I have volunteered for three consecutive years (2023–2026) at UCF's Camp Connect and STEM Day events, organizing hands-on workshops and interactive demonstrations to introduce K–12 students to computer science, robotics, and AI. I actively mentor and encourage young learners to explore STEM fields.
Achieved at age 17, one full year ahead of the standard cohort.
CECS Accelerated program for high-achieving students, allowing simultaneous completion of B.S. and M.S. degrees.
Perfect 4.0 grade point average every semester attended.
Recovering robot joint configurations from single RGB camera images using compact latent encodings.
Bridging the reality gap with domain randomization, camera-position-aware training, and encoder transfer.
Comparing Conv-VAEs, VGG-19, ResNet-50, ViTs, and fiducial markers for proprioception-dedicated representations.
Systematically studying how 112 camera positions affect accuracy across all encoder types and config dimensions.
Evaluating how visual diversity (backgrounds, distractors, lighting, occlusions) affects robustness.
Making manipulation accessible with practical hardware, uncalibrated setups, and minimal training data.
Surgical outcome prediction, medical image segmentation, and clinical data modeling with interpretable ML.
GANs, diffusion models, VAEs, and LLM fine-tuning (LoRA/QLoRA/PEFT) for vision and language tasks.
Designing ML architectures (XGBoost, DNNs, Temporal Transformers, LSTMs/GRUs, Mamba) to predict operative duration and 30-day hospital readmissions across ~20,000 neurosurgical cases.
Applying SHAP/LIME-based interpretability to isolate surgeon-specific vs. patient frailty factors; integrating ICD-9/10, CPT codes, FHIR-structured data, and social determinants of health.
Validating SAM alongside U-Net/3D U-Net and DeepLab baselines for automated volumetric segmentation of the foramen magnum and spinal canal on MRI/CT imaging data.
Benchmarking AI-generated measurements against expert neurosurgeon annotations; evaluating vision-based feature extraction for few-shot medical image classification.
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Interested in computer vision, robotics, or LLMs
University of Central Florida · Orlando, FL 32816