LLM Systems + Agents
Production language systems that route, reason, adapt, and stay observable under real-world constraints.
Intent classification · Model operations · Agent orchestration Applied Scientist · Researcher · Engineer
Building reliable
LLM, agentic, and
multimodal AI systems.
Ph.D. researcher and engineer working across large language models, conversational AI, vision-language evaluation, computer vision, and scalable GPU infrastructure.
CurrentApplied ScientistCapital One
EducationPh.D., Computer ScienceGeorgia State University · 2025
I build AI systems that must work beyond the demo—systems that can be measured, understood, and trusted when the inputs get messy.
ABOUT / ABBAAS
Research depth.
Research depth.
Production instincts.
I’m Abbaas Alif Mohamed Nishar, an Applied Scientist at Capital One and a Computer Science Ph.D. from Georgia State University. My path spans enterprise language systems, video-language evaluation, event-based vision, optical communication, and GPU-accelerated ML platforms.
I’m most interested in the space between a promising model and a dependable system: evaluation, failure analysis, adaptation, infrastructure, and the evidence needed to know what actually works.
Ph.D.Computer Science · 2025300+Enterprise intents100×GPU encoder speedup
01 / RESEARCHQuestions worth
Questions worth
staying with.
My work connects model capability with evidence: how systems reason, where they fail, and what it takes to make them useful at scale.
Multimodal AI + Vision
Vision-language evaluation and computer vision for systems that must understand more than text.
Video-language models · Event cameras · VLM stress testingEvaluation + Safety
Adversarial evaluation and interpretability methods that turn model behavior into evidence.
Red-teaming · Failure taxonomies · ReliabilityScalable ML Infrastructure
GPU-aware platforms and research tooling that move models from experiments into dependable workflows.
Distributed training · Model hubs · MLOps02 / SELECTED WORKFrom research
From research
to working systems.
Selected AI systems and research, presented through the problem, technical approach, and measurable outcome.
Flagship case studiesSelected for impact, evidence, and range
More selected workProduction, research, and open source
01
ProductionLLM systems
Enterprise LLM Intent Classification
ProblemRoute complex, ambiguous customer requests reliably across 300+ intents.
BuiltQLoRA fine-tuning with retrieval-constrained decoding, multi-turn context, and calibrated evaluation.
ResultImproved long-tail intent accuracy by approximately 12 percentage points without increasing model size.
02
ProductionLLM systems
LLM Model Hub + Operations Copilot
ProblemMake model discovery, deployment, and incident response easier for ML teams.
BuiltA unified model catalog and agentic operations layer for repeatable, governed workflows.
ResultFaster model onboarding and a clearer path from experiment to supported service.
03
ProductionEvaluation
Conversational AI Failure Analyzer
ProblemFind recurring failure modes hidden across large volumes of conversations.
BuiltAn evidence-first analyzer that clusters breakdowns, traces causes, and prioritizes fixes.
ResultTurns qualitative transcripts into measurable reliability work.
04
ResearchAdaptation
Self-Adapting Language Models
ProblemImprove from feedback without unstable, costly retraining loops.
BuiltA framework for selective adaptation under shifting tasks and constraints.
ResultExplores safer, more data-efficient continual language-model improvement.
05
ResearchSafety
GFlowNet LLM Red-Teaming
ProblemDiscover diverse failures beyond repetitive adversarial prompts.
BuiltA generative flow network search that rewards severity and diversity.
ResultBroader failure coverage for more informative safety evaluation.
06
ResearchScreen-camera
Revelio
ProblemEmbed machine-readable data in television content without disrupting viewing.
BuiltA real-world screen-camera system with visually imperceptible embedding and neural mobile decoding.
ResultAchieved a 100× encoder speedup and improved mobile decodability by 60%.
07
ResearchMultimodal
Video-Language Model Stress Testing
ProblemExpose brittle temporal reasoning before models reach high-stakes use.
BuiltPerturbation and counterfactual tests for temporal and semantic consistency.
ResultA practical map of where video-language systems fail and why.
08
ResearchVision
Neuromorphic-Camera Optical Communication
ProblemRecover high-speed optical signals under challenging conditions.
BuiltAn event-camera receiver and learning pipeline for low-latency communication.
ResultConnects neuromorphic sensing with robust optical links.
09
Open SourceNetworks
Text2Net
ProblemTranslate natural-language network requirements into usable configurations.
BuiltA language-driven system for synthesizing and validating network intent.
ResultAccessible network automation with verification.
10
Open SourceImaging
OpenRadon
ProblemMake computational imaging research easier to reproduce.
BuiltAn open toolkit for Radon-transform experiments and reconstruction.
ResultReusable foundations for transparent imaging research.
03 / PUBLICATIONSEvidence,
Google Scholar ↗Evidence,
published.
Showing all 9 publications
Preprint + workshop · 2026
Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models
Sethuraman T. et al. · Abbaas Alif Mohamed Nishar · Simon Jenni · Derek Hoiem
Conference · 2026
ART: Adaptive Reasoning Trees for Explainable Claim Verification
Sahil Wadhwa · Himanshu Kumar · Guanqun Yang · Abbaas Alif Mohamed Nishar · et al.
Conference · 2025
Revelio: A Real-World Screen-Camera Communication System with Visually Imperceptible Data Embedding
Abbaas Alif Mohamed Nishar · Shrinivas Kudekar · Bernard Kintzing · Ashwin Ashok
Conference · 2025
Text2Net: Transforming Plain-text to a Dynamic Interactive Network Simulation Environment
Alireza Marefat · Abbaas Alif Mohamed Nishar · Ashwin Ashok
Conference · 2025
Non Line-of-Sight Optical Wireless Communication Using Neuromorphic Cameras
Abbaas Alif Mohamed Nishar · Alireza Marefat · Ashwin Ashok
Conference · 2024
Towards Modeling Underground Soil Radon Gas Emanation
Abbaas Alif Mohamed Nishar · Alireza Marefat · Razat Sutradhar · et al.
Conference · 2024
A Framework for Classifying Applications from Raw Network Traffic Traces
Alireza Marefat · Abbaas Alif Mohamed Nishar · Ashwin Ashok
Poster · 2024
Joint Optical Wireless Communication and Sensing Using Neuromorphic Cameras
Abbaas Alif Mohamed Nishar · Sonipriya Paul · Ashwin Ashok
Patent application
Machine-Learning-Based System and Method for Controlling Residual Artifacts in Media Content
Abbaas Alif Mohamed Nishar · co-inventors
This page highlights selected publications. View Google Scholar for the complete, current list and citation profile.
04 / EXPERIENCEA research path,
A research path,
built in practice.
Capital One
01Applied Scientist
Building reliable LLM, agentic, and conversational AI systems for enterprise use.
Revelio Communications
02Founding Engineer
Developed multimodal evaluation methods and research infrastructure for video-language systems.
Georgia State University
03Graduate Research Assistant
Ph.D. research across deep learning, event-based vision, and optical communication.
American Family Insurance
04Data Science Intern
Developed home-inspection image segmentation, reduced the training codebase by 80%, and implemented automated multi-GPU training.
Tata Consultancy Services
05DevOps Engineer
Improved retail forecasting accuracy by 25% and built CI/CD pipelines that reduced deployment time by 50%.
Tata Consultancy Services
06Research Intern
Built AR/VR and computer-vision prototypes for clothing-pattern recognition, PoseNet surveillance, and text-to-avatar interfaces.
05 / RECOGNITIONResearch,
Research,
recognized.
Best Student Paper
IEEE SoutheastCon 2024
Also Third Place Best Poster at ACM/IEEE IPSN 2024 and Third Place Oral Presentation at the GSU Graduate Research Conference.
President
IEEE at GSU
Led the student chapter from June 2024 through 2025 and organized IdEEEathon 2024 with IEEE Atlanta and Young Professionals.
Guest lecturer
and mentor
Guest lectures in Advanced Computer Vision and Introduction to Robotics, plus a Girls Who Code Raspberry Pi workshop.
06 / RESUME + CV
Choose the lens.
Three focused versions for applied AI, multimodal computer vision, and the complete research record.
Have a difficult AI system
worth making reliable?