AI Researcher and Engineer

taneem@erisinnovate.ai

CV

Taneem Ullah Jan

I am currently the AI Lead at ErisAI, where I drive research and engineering in multimodal and generative modeling to build foundation models for agentic AI applications. I oversee model design, system architecture, and deployment of next-generation interactive agents. Previously, I was an AI Researcher at VOLV AI and a Research AI Engineer at BHuman AI.

Research Work

My work spans multimodal learning, generative modeling, and intelligent systems, with a focus on building reliable AI that moves from research into real-world deployment.

DarwinPatch is a budgeted repair-search controller for coding agents that converts failed patch attempts into bounded evidence, routes follow-up repairs through hard verification gates, and records auditable candidate lineage.

A research-first look at how Synapse combines retrieval, routing, memory, verification, and runtime controls into a dependable applied intelligence system.

tldr: An embedding-driven approach combines audio encoders with multimodal projectors to enable direct speech-to-text processing, achieving significant performance while training minimal parameters through block optimization.

tldr: An interactive 3D body modeling system that allows real-time manipulation of human body shapes through intuitive measurement sliders with immediate visual feedback in a fully navigable 3D environment.

tldr: VMVLM enhances vision-language models by using dual visual pathways, combining Q-Former queries with direct ViT feature injection for improved multimodal instruction following.

tldr: A self-improving AI system that uses LLMs and evolutionary algorithms to autonomously enhance code across multiple quality dimensions.

tldr: A unified framework for 3D virtual try-on that transforms simple 2D images into realistic 3D representations, by efficiently integrating clothing with the human body in a pose-adaptive manner.

tldr: A two-stage unified audio-driven talking face generation framework, which can render high-fidelity, lip-synchronized videos with improved inference speed.

tldr: Current image inpainting techniques are too heavy; this paper introduces a Row-wise Flat Pixel LSTM, a small hybrid model for the efficient and high-quality restoration of small images.

tldr: A robust and efficient talking face generation model with highly accurate lip synchronization and full facial expressiveness with more extended audio and high-quality video resolutions.

tldr: An unsupervised one-shot talking head video generation model using neural rendering and motion transfer techniques with non-linear transformation to animate static images.

tldr: Innovative face-swapping model that preserves the source identity features accurately while seamlessly adapting target attributes applicable to images and videos.

tldr: An accurate deep learning model converting GUI mockups into HTML code, streamlining web development for non-programmers.

Thanks to Jon Barron for this template.