Academic Research
Powered by AI.
From algorithm design to journal-ready publications — we help researchers and PhD scholars produce high-impact, publication-ready work.
18+
Papers / Month
450+
researches so far
95%
Acceptance Rate
10+
Journals & Conferences
Research Services
Algorithm Design & Implementation
Custom algorithm development and optimization for complex computational problems. From concept to production-ready code with performance analysis and validation.
- Custom Algorithm Development
- Performance Optimization
- Complexity Analysis
- Production-Ready Implementation
- Validation & Testing
Conference Paper
Complete algorithm implementation + paper writing. IEEE/ACM/LNCS compliant, 4-8 pages with novel contributions, baseline comparisons, and plagiarism-safe content.
- IEEE/ACM/LNCS Compliant Format
- 4-8 Pages with Novel Contributions
- Baseline Comparisons & Analysis
- Plagiarism-Safe Content
- Camera-Ready Formatting
Journal Paper
Full research paper with extensive experiments, statistical validation, and ablation studies. 8-20+ pages, IEEE/ACM/LNCS compliant, essential for PhD thesis submission.
- Extensive Experiments & Validation
- Statistical Analysis & Ablation Studies
- 8-20+ Pages, IEEE/ACM/LNCS Compliant
- PhD Thesis Submission Ready
- Long-Term Academic Impact
Research Assistantship
Comprehensive research support — from literature survey and gap analysis to experiment design, implementation, and manuscript preparation for students and scholars.
- Literature Survey & Gap Analysis
- Experiment Design & Execution
- Manuscript Drafting & Review
- Mentor-Guided Research Workflow
- Publication Strategy Planning
Our Novel Research Works
Opt-InNet
Deep Reinforcement Learning for Online Grooming Detection
Opt-InNet redefines grooming detection as a Markov Decision Process (MDP), enabling proactive, real-time intervention instead of post-facto classification. Unlike supervised methods, it uses reward shaping based on real trafficking profiles to minimize false alarms and detect grooming early.
Novelty
First system to introduce context-aware reinforcement learning with trafficking profiles, achieving proactive safety interventions.
Key Algorithms
Policy Gradient (REINFORCE), Actor-Critic, PPO, Sentence-BERT embeddings, Kalman filtering
MM-FuseNet
Dual-Stream Attention Model for Early Child Exploitation Risk Prediction
MM-FuseNet fuses chat logs (sequential text) and tabular trafficking case data using a dual-stream neural network. A novel attention-based fusion mechanism dynamically allocates importance across modalities for robust predictions.
Novelty
Pioneers multi-modal attention fusion of text + structured data for exploitation risk detection, with state-of-the-art performance (F1 = 0.86, AUC = 0.88).
Key Algorithms
LSTM for sequential chat modeling, MLP for tabular data, Attention-based fusion, Mixed Sampling, PyTorch pipeline
ACS-CEG
Cryptographically-Secured GNN for Robotic Misuse Detection
ACS-CEG secures robotic systems against insider misuse by encoding commands as cryptographic hashes and modeling them in a Command Evolving Graph (CEG). An inductive Graph Attention Network (GAT) detects subtle, malicious command sequences in real time.
Novelty
First to integrate cryptographic hashing (SHA-256) + GAT-based inductive learning for detecting insider robotic misuse.
Key Algorithms
SHA-256 hashing, Command Evolving Graphs, Graph Attention Networks (multi-head GAT), Global Mean Pooling, Inductive Learning
CHIM-FAST
Scalable Cellular Coverage Hole Mapping with Fuzzy Graphs
CHIM-FAST detects and maps cellular coverage holes across wide areas using crowdsourced data instead of expensive drive-tests. It transforms noisy, unreliable data into reliable insights by combining fuzzy logic, graph theory, and contraction hierarchies.
Novelty
First to apply fuzzy inference + contracted bidirectional graph search for nation-scale cellular coverage hole detection.
Key Algorithms
Fuzzy Inference System, Sigmoid Membership Functions, BFS for hole detection, Contraction Hierarchies, Bidirectional Dijkstra
Global Client Research
6G Beamforming Optimization
Federated Learning | Vision Transformers | Privacy-Preserving AI
InterDigital, CanadaWe developed a 6G Beamforming algorithm powered by Federated Nova, enabling distributed model training and secure weight aggregation for online training with strong privacy guarantees. Raw user data never leaves the edge devices while still contributing to the global optimization model.
Novelty
First to combine federated optimization with spatio-temporal Vision Transformers to analyze beam spatial data, predict interference patterns, and compute the optimal trajectory for adaptive beam steering.
Key Algorithms
Federated Nova, Secure Weight Aggregation, Vision Transformers for spatio-temporal beam analysis, RL for dynamic beam trajectory control, Graph-based modeling of beam-user associations.
Who We Help
We collaborate with students, researchers, startups, and enterprises who want to leverage AI to solve real-world problems. Explore our full range of services or reach out to start a conversation.
Students & Scholars
Guidance on thesis, projects, and publications.
Startups & SMEs
End-to-end AI solutions to launch faster and smarter.
Enterprises
Scalable systems that integrate AI seamlessly into existing workflows.
Have a Research Idea?
Whether it's an algorithm, a paper, or full research support — let's turn your idea into a published contribution.