class BJnyanadeep:
    def __init__(self):
        self.name = "B Jnyanadeep"
        self.language_spoken = ["Python", "C++", "Java", "SQL"]
        self.current_focus = [
            "ML Security & Adversarial Robustness",
            "Quantitative Research & Algo Trading",
            "Systems Engineering & Low-Latency Design",
            "Secure ML Pipelines & Red-Teaming LLMs"
        ]
        self.coding_platforms = {
            "leetcode": {"problems_solved": "425+", "contest_rating": "1500+", "streak": "100+ days"},
            "hackerrank": {"stars": "5⭐", "domain": "Problem Solving"},
            "github": {"contributions": "500+", "repos": "50+"}
        }
        self.quant_stack = {
            "math": ["Stochastic Calculus", "Linear Algebra", "Probability Theory", "Time Series"],
            "tools": ["NumPy", "Pandas", "SciPy", "QuantLib", "Backtrader"],
            "strategies": ["Statistical Arbitrage", "Factor Modeling", "Options Pricing", "Backtesting"]
        }
        self.ml_security = {
            "adversarial_ml": ["FGSM", "PGD Attacks", "Model Inversion", "Membership Inference"],
            "defenses": ["Adversarial Training", "Certified Robustness", "Differential Privacy"],
            "llm_security": ["Prompt Injection", "Jailbreaking", "Red-Teaming", "Model Fingerprinting"]
        }
        self.swe_principles = [
            "Design for scale, optimize for latency",
            "Security-first architecture",
            "Test-driven, benchmark everything",
            "Clean abstractions over clever hacks"
        ]
        self.achievements = [
            "Hacktoberfest Contributor",
            "Adversarial ML Researcher",
            "Open Source Advocate",
            "Quant Systems Builder"
        ]

    def current_research(self):
        return {
            "ml_sec": "Robustness of financial ML models under distribution shift & adversarial perturbation 🛡️",
            "quant":  "Building alpha signals using NLP + order book microstructure 📈",
            "swe":    "Low-latency data pipelines and execution engine design ⚡"
        }

    def say_hi(self):
        print("Thanks for dropping by! Let's connect and build something amazing together.")
        print("🚀 Always ready for new coding challenges and collaborations!")
        print("📐 Currently bridging quant finance, ML security, and systems engineering — one algorithm at a time!")

me = BJnyanadeep()
me.say_hi()
print(f"🔬 Research: {me.current_research()}")