AI/ML Researcher Exploring Neural Network Optimization, Quantitative Alpha Research, and Intelligent Learning Systems
Research Intern @ Indian Statistical Institute (ISI), Kolkata
- WorldQuant Brain Rank 1104 — 80+ validated alpha contributions
- PUF Research Paper — Hardware security and authentication systems
- Top 800 Scalar RL Hackathon — Reinforcement learning agent design
- Neural Networks Research — Backpropagation derivation + 97.65% MLP accuracy
Research-first approach to AI engineering. Focused on neural network optimization, quantitative signal generation, and experimental rigor. Currently investigating hardware security (PUFs), deep learning generalization, and retrieval-augmented systems at ISI Kolkata.
Research Internship
ISI Kolkata
Alpha Contributions
80+
WorldQuant Rank
1104
RL Hackathon
Top 800
Professional Portrait
Shridipa Dhar
B.Tech CSE (AI & ML) student at KIIT University | Research Intern @ ISI Kolkata | ML Lead @ KIIT Nexus
Trusted Research Impact
High-visibility achievements and ongoing research work recruiters scan first.
Research Internship
ISI Kolkata
PUF Research
Ongoing
WorldQuant Brain
Rank 1104
Alpha Signals
80+
Scalar RL Hackathon
Top 800
CGPA
8.49
Research Snapshot
Quantified research signal and ongoing investigations.
Research Internship
ISI Kolkata
Neural Networks & Optimization
Research Paper
PUF Systems
Hardware Security (Ongoing)
WorldQuant Rank
1104
80+ Alpha Contributions
MLP Accuracy
97.65%
From-scratch Neural Network
RL Hackathon
Top 800
Scalar Reinforcement Learning
ML Leadership
KIIT Nexus
Research Culture & Mentoring
Research Experience
Problem-driven investigations with methodology, tools, and outcomes.
Started AI & ML Specialization
2024
Began focused research in deep learning fundamentals, neural network theory, and mathematical foundations.
- Problem: Build theoretical foundation for research-grade AI systems
- Methodology: Studied Haykin's Neural Networks textbook, derived backpropagation from first principles
- Tools: Python, PyTorch, Jupyter
- Status: Foundation complete — enabled experimental research
ML Lead — KIIT Nexus Society
2025
Led AI/ML research initiatives and organized technical workshops for peer learning.
- Problem: Establish research-first culture in student ML community
- Methodology: Organized seminars on neural networks, experimentation, and model design
- Impact: Mentored 15+ students on research pipelines and validation methods
- Status: Ongoing leadership
Scalar RL Hackathon — Top 800
2025
Developed and trained reinforcement learning agent with research-driven optimization.
- Problem: Design RL agent for competitive benchmark
- Methodology: Experimented with policy gradient methods, reward shaping, and hyperparameter tuning
- Result: Achieved Top 800 ranking via agent training and empirical validation
- Status: Completed
Research Intern — Indian Statistical Institute (ISI), Kolkata
2026
Active research internship on neural network optimization and generalization analysis.
- Problem: Investigate neural network training dynamics and generalization behavior
- Methodology: Mathematical analysis, backpropagation derivation, experimental benchmarking
- Tools: Python, PyTorch, statistical analysis, empirical evaluation
- Status: Ongoing — neural networks and optimization research
Physically Unclonable Functions (PUF) Research Paper
2026
Investigating hardware security architectures and authentication mechanisms.
- Problem: Evaluate reliability and uniqueness of PUF systems for secure authentication
- Methodology: Empirical analysis of PUF entropy, stability metrics, and security properties
- Research Area: Hardware security, cryptography, IoT authentication
- Status: Manuscript in preparation
WorldQuant Brain — Rank 1104
2026
Developing and validating quantitative alpha signals for financial markets.
- Problem: Generate statistically validated trading signals
- Methodology: Factor modeling, signal backtesting, statistical evaluation, risk analysis
- Achievement: 80+ alpha contributions ranked globally
- Status: Ongoing quantitative research
Publications & Research
Peer-reviewed work and ongoing research manuscripts.
Physically Unclonable Functions for Secure Authentication Systems
Authors: Shridipa Dhar et al.
Institution: Indian Statistical Institute, Kolkata
Research Areas: Hardware Security • PUF Systems • Cryptography • IoT
Contribution: Empirical analysis of PUF reliability, uniqueness metrics, and authentication mechanisms
Expected: Q3 2026
Deep Learning Research Track
- Neural Network Optimization: Training dynamics, generalization analysis, backpropagation theory
- Retrieval Systems: Evidence-grounded AI, multi-document reasoning, semantic search
- Quantitative Modeling: Signal validation, statistical arbitrage, factor analysis
GitHub Metrics
Research repositories, language stack, and public contribution signal.
Repositories
31
Stars
1
Followers
12
Languages
Python • JavaScript • HTML • TypeScript
Quantitative Research
WorldQuant Brain and alpha generation expertise.
WorldQuant Brain
Rank 1104
Building alpha signals and quantitative models with an emphasis on statistical research, signal validation, and performance-driven outcomes.
• 80+ alpha contributions
• Statistical arbitrage research
• Quantitative signal generation
Quantitative Modeling
Research-grade frameworks
- • Alpha signal validation
- • Risk and performance analysis
- • Model-driven research experiments
Research Output
Data-backed results
• Ongoing research on quant strategies
• Experimental results with reproducible logic
• Research-first engineering mindset
Research Interests
Areas I am actively exploring through research and experimentation.
Featured Projects
Research-focused systems that show methodology, architecture, and impact.
Research Mind
LLM-Powered Research Copilot for Literature Review and Evidence-Grounded QA
• Research Areas: NLP, LLM Systems, Retrieval-Augmented Generation
• Problem: Accelerate literature review and enable multi-document reasoning
• Methodology: Hybrid retrieval (BM25 + semantic), embedding-based search, LLM-grounded QA
• Tech: FastAPI, React, FAISS, HuggingFace, LangChain
• Results: Evidence-grounded QA, multi-paper comparison, semantic retrieval validation
• Future: Multi-agent orchestration, hallucination evaluation, citation tracking
Medic AI
Multimodal Medical Intelligence System with Explainable Diagnosis
• Research Areas: Medical AI, Multimodal Learning, Explainable AI
• Problem: Integrate image and text for trustworthy medical reasoning
• Methodology: Vision encoder + text encoder, cross-modal fusion, confidence scoring
• Tech: PyTorch, CNNs, Transformers, FastAPI, SHAP interpretability
• Results: Multimodal symptom analysis, explainable predictions, confidence metrics
• Future: Clinical validation, uncertainty quantification, multi-modality expansion
Elo Learn
AI-Powered Adaptive Learning Platform with Knowledge Tracing
• Research Areas: Educational AI, Knowledge Tracing, Recommendation Systems
• Problem: Personalize learning at scale with adaptive difficulty and pacing
• Methodology: Bayesian knowledge tracing, spaced repetition, reinforcement learning for sequencing
• Tech: FastAPI, Streamlit, PyTorch, PostgreSQL, knowledge graph models
• Results: Adaptive learning paths, personalized recommendations, engagement metrics
• Future: Transfer learning across domains, temporal modeling, peer learning networks
Technical Skills
Mature capability categories for research, systems, and quantitative work.
Programming
Machine Learning
Deep Learning
NLP & LLM Systems
Quantitative Research
Backend Systems
Research Tools
Achievements
Strong signals for research performance and engineering excellence.
Scalar RL Hackathon
Top 800
WorldQuant Brain
World Rank 1104
Alpha Contributions
80+
Google Coding Competition
Top 15,000
Participant
Google Solution Challenge
Academic Performance
8.49 CGPA
Leadership
Machine learning leadership, mentoring, and collaborative research.
Machine Learning Lead — KIIT Nexus
- Led AI/ML initiatives and organized technical workshops.
- Mentored students on research pipelines, experimentation, and model design.
- Managed collaborative learning sessions for advanced AI topics.
- Built a research-first culture with practical engineering outcomes.
Research Focus
Neural network optimization, retrieval-augmented generation, quantitative alpha research, and hardware security are the current research focus areas.
Resume
Interested in research internships, systems, and quantitative modeling.
Ready to discuss research internships, machine learning systems, and quantitative engineering with a strong emphasis on clarity, rigor, and measurable impact.
Contact
Direct connections for research conversations and internship opportunities.