Open to opportunities

Hi there, I'm

MANU
MATHEW
JISS

Co-Founder

2 AI Startups

Research Assistant

x5 at University of the Pacific

Teaching Assistant

Security Analytics

IEEE Published

ISTAS25 Conference

Master's student in Computer Science at University of the Pacific. Building AI systems that work in the real world, from multi-model trading platforms to autonomous robots.

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Manu Mathew Jiss

Titles

Masters graduate · Research Assistant · Teaching Assistant · Co-founder · Machine Learning Engineer

Expertise

AI Research Engineer · Software Engineer · Robotics Engineer · Generative AI Engineer

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About Me

Building intelligent systems at the intersection of research and production.

I'm a Master's student in Computer Science at the University of the Pacific, specializing in Machine Learning, Generative AI, and AI systems engineering. I thrive at the boundary between research and real-world deployment.

From co-founding a GenAI trading startup (Stock Crusher) and launching Meal Muse Recipes on the App Store, to publishing research at IEEE ISTAS25 on fake account detection. I build things that work.

My research spans NLP, transformer models, autonomous robotics, and data engineering. I've worked as a Graduate Research Assistant across three labs simultaneously and as a Teaching Assistant for security analytics.

Based in the San Francisco Bay Area, I'm actively looking for roles in ML engineering, AI/software engineering, and research.

Get In TouchLinkedIn Profile

5x

Graduate research roles

Pacific labs covering NLP, robotics, student wellness, Reddit/VADER workloads

4

Documented builds

Weather multi-DB pipeline plus three agritech CV studies (crop, areca, pest)

1+

Peer-reviewed research

IEEE ISTAS25 (LIMFADD); SMC three-phase NLP work under review

4+

Data stack layers

MongoDB lake, ClickHouse warehouse, Redis cache, Express services (weather system)

Education

M.S. Computer Science

University of the Pacific

Stockton, CA

Machine Learning, Generative AI, AI Systems

Spring 2026

B.Tech Computer Science & Engineering

APJ Abdul Kalam Technological University

Kerala, India

Computer Vision, Deep Learning, Embedded Systems

Summer 2023

San Francisco Bay Area, USA

Available for on-site, hybrid, and remote

Experience

Résumé snapshot: co-founded two shipped GenAI products (LLM-guided trading insights and Meal Muse on iOS), held five concurrent graduate research roles at University of the Pacific spanning Reddit trajectory analytics, NVIDIA Jetson / ROS 2 racing stacks, SMC-review NLP authenticity pipelines, ISTAS-published LIMFADD, and RoBERTa-driven AIMoodDiary, backed by SOC analytics teaching support. Gold buttons surface live products, dashboards, conferences, or preprints; outlined rows jump to GitHub repositories.

Co-Founder & Software Engineer

Stock Crusher (GenAI Startup)

Startup

Summer 2025 – Present

stockcrusher.comGitHub repo
  • Built and shipped Stock Crusher, a production multi-agent trading intelligence platform integrating 8+ real-time APIs (Reddit, Finnhub, NewsAPI, X/Twitter).
  • Implemented weighted triple-AI consensus (Gemini, Perplexity, OpenAI), custom momentum scoring, and deterministic guardrails powering BUY / SELL / HOLD outputs.
  • Delivered Flask services and a React + Tailwind analytics dashboard deployed on Render for resilient day-to-day use.
PythonFlaskReactOpenAIGeminiPerplexityRender.com

Co-Founder & Software Engineer

MealMuse (AI-Powered iOS Startup, live on App Store)

Startup

Fall 2025 – Present

App StoreGitHub repo
  • Launched Meal Muse Recipes on the App Store: SwiftUI client with Fastify + PostgreSQL backend for secure, scalable recipe workflows.
  • Shipped multimodal prompting (text, optional voice/image) against GPT-style models plus TTL caching and quotas, targeting ~80–90% fewer redundant LLM calls.
  • Productized onboarding, recipe steps, calorie estimates, and privacy-forward analytics hooks suitable for iterative App Store releases.
SwiftSwiftUINode.jsTypeScriptPostgreSQLOpenAIFastify

Graduate Teaching Assistant

University of the Pacific (Advisor: Dr. Sethuraman Kuruvimalai)

Teaching

Spring 2026 – Present

  • Assisted in ANLT-293B: Introduction to Security Analytics.
  • Supported hands-on instruction in Linux, virtualization, Splunk SIEM, and SOC-based security investigations.
  • Created quizzes, graded assignments, guided lab sessions, and prepared course materials.
SplunkLinuxSIEMCybersecurityEDR

Graduate Research Assistant

University of the Pacific (Advisor: Dr. Pramod Gupta)

Research

Spring 2026 – Present

AIMoodDiaryGitHub repo
  • Shipped AIMoodDiary: a student emotional wellness journaling suite that blends GPT-assisted reflections with interactive mood analytics dashboards.
  • Fine-tuned a RoBERTa classifier on supervised affect corpora to surface weekly emotion trends alongside estimated confidence bands.
  • Next.js frontend, FastAPI microservices, and PostgreSQL back-end deliver rapid iteration cycles for stakeholder demos and longitudinal studies.
Next.jsFastAPIPostgreSQLRoBERTaGPT-4o-mini

Graduate Research Assistant

University of the Pacific (Advisor: Dr. Tapadhir Das)

Research

September 2025 – April 2026

TechRxiv publicationGitHub repo
  • Developed LIMFADD, an LLM-generated dataset for Instagram fake account detection, enabling classification into real, spam, scam, and bot categories with 97% accuracy.
  • Designed data collection and XAI-based validation pipeline; paper published at IEEE ISTAS 2025 (preprint on TechRxiv).
PythonTensorFlowInstagram APIsLIMEXAILLM-assisted labeling

Graduate Research Assistant

University of the Pacific (Advisor: Dr. Tapadhir Das)

Research

January 2025 – May 2025

IEEE SMC 2026 conferenceGitHub repo
  • Developed a three-phase AI pipeline combining sentiment analysis, toxicity detection, and account authenticity classification using XGBoost and LIME, achieving 96.9% accuracy.
  • Designed an end-to-end system linking comment sentiment with account-level risk profiling for bot, scam, spam, and real account detection; paper under review at IEEE SMC 2026.
PythonXGBoostLIMENLPSocial media analytics

Graduate Research Assistant

University of the Pacific (Advisor: Dr. Solomon Berhe)

Research

Summer 2025 – Present

Live dashboardGitHub repo
  • Modeled longitudinal sentiment on 324+ ReleaseTrain-fed Reddit cohorts spanning minScore/maxScore-balanced slices pulled via official APIs.
  • Engineered reproducible ingestion + QA scripts (duplicate filtering, lexical checks) before VADER-derived trajectories are visualized in Plotly.
  • Deployed a public comparative VADER storytelling dashboard highlighting author vs community drift, backed by adjudicated confusion-matrix baselines.
PythonVADERREST APIsNLPData Visualization

Graduate Research Assistant

University of the Pacific (Advisor: Dr. Dongbin Lee)

Research

August 2025 – Present

OpenReview submissionF1TENTH platform docs
  • Integrated LiDAR, ZED stereo, CUDA-aware Docker stacks, ROS 2 Humble controls, waypoint tracking, and regression ROS bags atop the NVIDIA Jetson Orin Nano F1TENTH stack used in Dr. Lee’s autonomy lab sequences.
  • Extended the program with SignSight, a vision-language fusion template matcher that grounds CLIP-style semantics with handcrafted sign primitives for credible onboard detections during high-speed racing loops.
  • CVPR 2026 manuscript under peer review via OpenReview while iterating hardware bring-up notebooks, Pure Pursuit baselines, and obstacle recovery behaviors for classroom demonstrations.
ROS 2OpenCVPyTorchDockerNVIDIA JetsonCLIPLiDARZED SDK

Selected Projects

Deep dives from coursework documentation: UAV crop analytics, classical ML scouting, and a multi-database weather stack. Where a demo URL exists it appears as the gold button.

Data Engineering

Weather Data Engineering Pipeline

Multi-database weather telemetry stack (Project Documentations) pairing a MongoDB raw lake, ClickHouse warehousing, Redis hot cache, Node/Express ingestion, and Chart.js KPI views for sub-second exploratory loads.

Node.jsMongoDBClickHouseRedisChart.js+3
Computer Vision

Crop Monitoring & Maturity Detection

Phase 1 report: custom convolutional backbone over drone-derived plantation imagery for harvest readiness scoring above 86% hold-out accuracy, with UAV capture discipline and preprocessing called out explicitly.

PythonCNNTensorFlowOpenCVDrone Imagery
Computer Vision

On-Tree Areca Nut Fruit Maturity Detection

Mini project report Spring 2022: DJI Mini SE mediated capture coupled with YOLOv5 for dense canopy occlusion, emphasizing deployment constraints and labeling taxonomy for staged maturity checkpoints.

PythonYOLOv5DJI Mini SEOpenCVDeep Learning
Computer Vision

Pest Detection Using SVM

mini_project_report (Fall 2022): handcrafted colour/texture cues feeding an RBF SVM separating healthy vs infected tea leaves for early scouting loops without heavy GPUs.

PythonSVMScikit-learnOpenCVImage Processing

Research

Ongoing research

Featured publication: LIMFADD: LLM-Enabled Instagram Multi-Class Fake Account Detection Spring 2024. Concurrent tracks include the SMC three-phase sentiment and authenticity pipeline on social comments, Reddit software-update sentiment trajectories with a live dashboard, and Jetson ROS 2 perception plus SignSight for F1TENTH. Cards link to papers, dashboards, conferences, and GitHub.

Conference PaperIEEE ISTAS25
September 2025 – April 2026

LIMFADD: LLM-Enabled Instagram Multi-Class Fake Account Detection Spring 2024

Paper on TechRxivGitHub

Developed LIMFADD, an LLM-generated dataset for Instagram fake account detection enabling classification into real, spam, scam, and bot categories with 97% accuracy. Designed the data-collection and XAI-based validation pipeline; paper published at IEEE ISTAS 2025 with TechRxiv preprint.

International Symposium on Technology and Society, Santa Clara University, CA

Dr. Tapadhir Das, University of the Pacific

Fake Account DetectionLLM AugmentationXAI / LIMEDeep LearningInstagram
Ongoing ResearchIEEE SMC 2026 (under review)
January 2025 – May 2025

A Three Phase Pipeline for Sentiment, Toxicity, and Account Authenticity on Social Media Comments

IEEE SMC 2026 (conference site)GitHub

Developed a three-phase AI pipeline combining sentiment analysis, toxicity detection, and account authenticity classification using XGBoost and LIME, achieving 96.9% accuracy. Designed an end-to-end system linking comment sentiment with account-level risk profiling for bot, scam, spam, and real account detection. Paper under review at IEEE SMC 2026.

Dr. Tapadhir Das, University of the Pacific

XGBoostLIMESentimentToxicityAuthenticitySocial Media
Ongoing Research
Summer 2025 – Present

Sentiment Trajectory Analysis in Software Update Reddit Discussions

Open live dashboardGitHub

Large-scale trajectory research on 324+ Reddit discussions in the ReleaseTrain.io ecosystem. Built a Python pipeline using VADER, REST cohort APIs, and custom reliability metrics comparing author versus community sentiment, validated via human labeling and confusion matrices.

Dr. Solomon Berhe, University of the Pacific

NLPVADERSentiment AnalysisReddit APIData Visualization
Ongoing ResearchCVPR 2026 (under review)
August 2025 – Present

AI-Driven Perception Stack for F1TENTH Autonomous Racing

OpenReview submissionF1TENTH platform docs

Hands-on autonomy track with Dr. Dongbin Lee on NVIDIA Jetson Orin Nano and ROS 2 Humble: fused LiDAR and ZED stereo streams, waypoint following, reactive obstacle resets, CUDA-aware Docker deployments, and regression-grade ROS bags. Update: layered in SignSight, a template-guided vision-language framework blending CLIP-style semantic cues with geometric sign templates so traffic-sign hypotheses stay stable at racing speeds. The integrated Jetson-centric study is undergoing peer review for a CVPR 2026 submission on OpenReview while F1TENTH experimentation continues for classroom demos.

Dr. Dongbin Lee, University of the Pacific

ROS 2Jetson OrinCLIPVLMSensor FusionAutonomous Racing

Skills & Technologies

A broad and deep technical toolkit across AI, full-stack development, robotics, and data engineering.

Machine Learning & AI

TensorFlowPyTorchScikit-learnHuggingFaceLLMsRAGNLPRoBERTaBERTweetBARTYOLOv5ResNet-50SHAPLIMEOpenAI APIGemini APIZero-Shot LearningSentiment Analysis

Programming Languages

PythonTypeScriptJavaScriptC++SwiftSQLMATLABBash/ShellHTMLCSS

Backend & Frameworks

FastAPIFlaskNode.jsExpress.jsNext.jsFastifyREST APIsStreamlitAWS Lambda

Databases & Data Engineering

MongoDBPostgreSQLRedisClickHouseETL PipelinesData WarehousingPrisma ORMIncremental ETLMetadata Lineage

DevOps & Tools

DockerGit/GitHubLinux (Ubuntu)VercelRender.comCUDAVS CodeJupyter Notebook

Robotics & Embedded

ROS 2 HumbleNVIDIA Jetson Orin NanoLiDARZED Stereo CameraSensor FusionRaspberry PiArduinoEmbedded LinuxJetPack SDK

Data & Visualization

PandasNumPyMatplotlibSeabornPlotlyChart.jsRechartsPower BITableau

Cybersecurity

Network SecuritySplunk SIEMEthical HackingPenetration TestingCryptographySOC Operations

APIs & Data Sources

Reddit API (PRAW)Twitter/X APIYouTube Data APIYahoo FinanceNewsAPIFinnhubAlpha VantageGNewsOpenWeather APIGoogle Cloud APIs

Résumé

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Get In Touch

Open to full-time roles, research collaborations, and interesting conversations.

Let's Connect

Whether it's a job opportunity, a research project, or just a chat about AI. I'm always happy to connect. Based in the SF Bay Area and open to remote work.

Email

manumathewjiss18@gmail.com

Phone

(209) 792-4475

LinkedIn

linkedin.com/in/manu-mathew-jiss

GitHub

github.com/manumathewjiss

Google Scholar

Google Scholar Profile

Instagram

@manu.mathew.jiss

Instagram (American Snaps)

@american.snaps

YouTube

@americansnaps

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