The Dual Degree Journey: MS Computer Science & MS Quantitative Computational Finance at Georgia Tech

Two Years, Two Degrees, One Ambitious Quest

The Beginning: One Degree Wasn’t Enough

Walking into Georgia Tech in Fall 2021 with my MS Computer Science acceptance letter, I thought I had my path figured out. Machine learning specialization, research in AI systems, straightforward trajectory. That confidence lasted exactly until I attended the graduate student orientation where I learned about the interdisciplinary opportunities available.

During the first week, I found myself in a conversation with Professor Alex Gray from the Scheller College of Business who mentioned their MS QCF (Quantitative and Computational Finance) program. “You know,” he said, “the most impactful work in finance today is happening at the intersection of AI and quantitative methods. Students with both skill sets are writing their own tickets in the industry.”

That night, I spent hours researching the QCF program. Ranked #8 nationally by QuantNet among financial engineering programs, jointly offered by Business, Industrial & Systems Engineering, and Mathematics. The curriculum combined financial theory with computational methods—derivatives pricing, risk management, portfolio optimization using advanced mathematical models.

The seed was planted. By November 2021, I was seriously considering adding the QCF as a second degree.

The Decision: Going All In

The conversation that changed everything happened during office hours with Professor Tumanov in early December 2021. I mentioned my interest in finance applications for ML systems.

“You know,” he said, “some of the most challenging systems problems I’ve seen are in high-frequency trading and risk management. Latency requirements measured in microseconds, enormous datasets, real-time decision making with millions of dollars on the line. If you really want to understand systems at scale, finance is where the stakes are highest.”

That was it. I spent winter break 2021 preparing my QCF application for Spring 2022 admission. The application required:

• Essay on motivation: I wrote about the convergence of AI and finance, how algorithmic trading was essentially real-time machine learning deployment at massive scale
• Quantitative background verification: Calculus, differential equations, linear algebra, probability and statistics—thankfully my engineering undergrad covered these
• Programming experience: Python, R, C++—my CS coursework provided this
• Three recommendation letters: Two from CS professors, one from an undergrad math professor

The interview in January 2022 was intense. The Associate Director grilled me for 30 minutes on why I wanted to pursue both degrees simultaneously. “This isn’t a decision to take lightly,” she warned. “You’re looking at 18 courses instead of 12, much heavier courseload, different types of thinking required for each field.”

I was accepted for Spring 2022 start.

Spring 2022: The Reality Check Begins

Starting the QCF program in my second semester while continuing CS coursework was a shock. The QCF curriculum required 36 credit hours total—12 core courses, 4 targeted electives, and 2 free electives. Combined with my CS degree requirements, I was looking at nearly 18 courses total over four semesters.

My first QCF course was ISYE 6227: Foundations of Financial Engineering. Professor Sherry Suyu’s class was a different world from computer science. Instead of algorithms and data structures, we dove into:

• Time value of money and arbitrage theory: Understanding why pricing discrepancies create profit opportunities
• Fixed income mathematics: Bond pricing, duration, convexity—mathematical models for interest rate sensitivity
• Derivatives fundamentals: Options, forwards, swaps—financial instruments for risk transfer

The mathematical rigor was comparable to CS theory classes, but the applications were completely foreign. We derived the Black-Scholes partial differential equation from first principles:

∂V/∂t + (1/2)σ²S²(∂²V/∂S²) + rS(∂V/∂S) - rV = 0

Where V is option value, S is stock price, r is risk-free rate, σ is volatility. The elegant mathematics described how option prices evolve over time.

But understanding the equation was just the beginning. Professor Suyu’s assignments required implementing numerical methods to solve it—finite difference schemes, Monte Carlo simulation, binomial trees. This was where my CS background became invaluable.

The Convergence: CS Meets Finance

The real magic happened when I started seeing connections between my two programs. In CS 7643 (Deep Learning), we learned about stochastic gradient descent for optimizing neural networks. In ISYE 6227, we studied stochastic calculus for modeling stock price movements. Same mathematical foundations, completely different applications.

My CS research with Professor Tumanov on distributed systems suddenly had new relevance. Financial markets generate massive data streams: NYSE processes 5 billion quotes daily, algorithmic traders need sub-millisecond response times, risk calculations require real-time aggregation across thousands of positions.

During a late-night study session with other QCF students, Maria Santos (a former Goldman Sachs trader) made an observation that stuck with me: “You CS people think about scalability in terms of users and data. In finance, scalability is about money. A 1ms delay in execution can cost millions in a volatile market.”

Summer 2022: The ISYE Deep Dive

Summer semester brought ISYE 6228: Stochastic Processes in Finance, taught by Professor Liang Peng. This course pushed the mathematical boundaries harder than anything in my CS program.

We studied martingales, Brownian motion, and Itô calculus—the mathematical machinery underlying modern quantitative finance. The Itô lemma, in particular, was beautiful and mind-bending:

For a function f(t,Xt) where Xt follows a stochastic differential equation, the differential df follows specific rules that account for the “rough” nature of stochastic processes. This mathematics enabled pricing of complex derivatives.

But again, the computational aspect fascinated me. Professor Peng’s assignments required implementing sophisticated simulation algorithms:

• Monte Carlo methods for European options: Simulating thousands of price paths to estimate option values
• Variance reduction techniques: Control variates, antithetic variables, importance sampling to improve simulation accuracy
• Jump-diffusion models: Extending basic models to handle sudden price movements

My CS background in algorithms and statistical computing gave me an edge. While classmates struggled with implementation details, I was optimizing code, parallelizing simulations, and exploring advanced techniques.

The breakthrough moment came during a particularly challenging assignment on American option pricing using least-squares Monte Carlo (Longstaff-Schwartz method). This required:

1. Simulating thousands of stock price paths
2. At each exercise date, determining optimal exercise strategy using regression
3. Backward induction to find option value

The computational complexity was substantial. My implementation ran in minutes while other students’ took hours. Professor Peng asked me to present my optimization techniques to the class—my first taste of how CS skills could differentiate me in finance.

Fall 2022: Advanced Derivatives and Market Microstructure

Fall semester brought two challenging QCF courses alongside my CS research commitments.

ISYE 6229: Advanced Derivatives with Professor Alex Hristov was intense. The course covered exotic options, credit derivatives, and structured products. Each week brought new complexity:

• Barrier options: Knock-in, knock-out options with path-dependent payoffs
• Asian options: Options whose payoff depends on average price over time
• Credit default swaps: Instruments for trading credit risk

The mathematical models required sophisticated numerical methods. For my final project, I implemented a finite difference solver for barrier options with time-dependent volatility. The project combined:

• Partial differential equations: Extending Black-Scholes to handle barriers
• Numerical analysis: Implicit finite difference schemes for stability
• Financial engineering: Understanding how barriers affect option Greeks

Professor Hristov’s feedback was encouraging: “Your implementation handles edge cases that commercial software often misses. Have you considered applying to trading firms’ quantitative development roles?”

Simultaneously, I took MGT 6351: Market Microstructure, taught by Professor Craig Holden from the Business school. This course examined how financial markets actually operate—the mechanics behind trading, price discovery, and market efficiency.

We studied:

• Bid-ask spreads and transaction costs: Why trading isn’t free and how spreads compensate market makers
• High-frequency trading strategies: Arbitrage, market making, momentum trading at microsecond timescales
• Market impact models: How large trades move prices and optimal execution strategies

The assignments involved analyzing real trading data from major exchanges. Using R and Python, we examined patterns in order flow, price impact, and trading volumes. This was computational finance at its most applied level.

One project particularly stood out: analyzing the impact of high-frequency trading on market quality. Using millisecond-level data from NYSE, I documented how algorithmic trading reduced bid-ask spreads but increased volatility during stress periods. The analysis required processing gigabytes of tick data—exactly the kind of big data problem that CS training prepared me for.

Spring 2023: Synthesis and Capstone Work

My final semester brought together everything I’d learned across both programs.

ISYE 6230: Computational Finance was the perfect integration course. Professor Xiaoli Chen focused on numerical methods for complex financial problems:

• Monte Carlo methods for multi-asset derivatives: Pricing rainbow options, basket options with correlation structures
• Finite difference methods for American options: Handling early exercise optimality conditions
• Fourier transform methods: Using characteristic functions for option pricing under Lévy processes

Each assignment was a substantial programming project. For the final project, I implemented a multi-threaded Monte Carlo engine for pricing mortgage-backed securities using GPU acceleration. The project required:

• Financial modeling: Understanding prepayment models, default correlations, waterfall structures
• Computational optimization: CUDA programming for massively parallel simulation
• Systems design: Handling memory constraints, load balancing across GPU cores

Professor Chen’s reaction: “This is graduate-level research in computational finance. You should consider submitting this to a journal.”

Simultaneously, I was completing my CS research with Professors Tumanov and Kira on efficient continual learning systems. The combination proved synergistic—my finance background helped me understand real-world deployment constraints that pure CS researchers often missed.

QCF 6785: Practice of QCF was the capstone course, a project-based seminar where students presented substantial research projects. My project: “Real-Time Portfolio Risk Management Using Distributed Machine Learning.”

The project addressed a real problem: large institutional investors need to monitor portfolio risk across thousands of positions in real-time. Traditional Value-at-Risk calculations are computationally expensive and often stale by the time they complete.

My solution combined:

• Distributed computing: Spark-based architecture for parallel risk calculations
• Machine learning: Neural networks for fast approximation of complex risk models
• Financial engineering: Integration with standard risk management frameworks

The presentation to QCF faculty and industry visitors was a success. Several attendees asked for copies of my implementation. One portfolio manager from BlackRock mentioned they’d been exploring similar approaches.

The Challenges: Balancing Two Demanding Programs

Pursuing both degrees simultaneously wasn’t just academically challenging—it was exhausting.

Schedule Management: Some semesters I carried 15+ credit hours across completely different domains. CS courses required late-night coding sessions. Finance courses demanded intensive mathematical derivations and market analysis. Finding time for both was constant juggling.

Context Switching: Transitioning between CS and finance mindsets within the same day was mentally taxing. Morning might involve debugging distributed systems code, afternoon could be deriving partial differential equations for exotic options pricing.

Social Dynamics: CS students often viewed finance as too applied, lacking theoretical depth. QCF students sometimes saw CS as too abstract, disconnected from real-world problems. Finding peer groups in either program was challenging.

Research Integration: Convincing professors that my interdisciplinary approach was valuable required constant advocacy. Many research opportunities assumed single-program focus.

The lowest point came during Fall 2022 when I was simultaneously debugging my continual learning implementation for Kira’s lab, studying for Hristov’s derivatives midterm, and preparing a market microstructure presentation. I seriously considered dropping one program.

The Conversations That Kept Me Going

Coffee with Professor Tumanov (October 2022): “You know why financial systems are so interesting from a CS perspective? They’re the only systems where correctness and performance requirements are measured in dollars. Every optimization, every design decision has immediate financial consequences. That’s the purest form of systems engineering.”

Late-night study session with QCF classmate David Kim (December 2022): “You CS people think differently about problems. When we model market risk, we think about mathematical elegance. You think about computational scalability. Both perspectives are necessary for the next generation of financial technology.”

Office hours with Professor Chen (February 2023): “The financial industry is being revolutionized by people exactly like you—those who understand both the domain and the technology. The question isn’t whether you can handle both programs, it’s whether the industry can keep up with what you’ll build.”

Thesis defense practice with Professor Kira (April 2023): “Your work bridges theory and application in a way that’s rare in CS research. The financial applications provide concrete evaluation criteria that pure ML research often lacks.”

The Shared Credit Agreement Discovery

Midway through my dual degree journey, I discovered the Shared Credit Agreement between MS QCF and MS Computational Science & Engineering programs. While I was pursuing MS CS, not MS CSE, the advisor meetings revealed interesting possibilities.

The QCF-CSE agreement allowed double-counting 4 courses between programs, reducing total requirements from 22 to 18 courses. While not directly applicable to my CS-QCF combination, it suggested precedent for interdisciplinary flexibility.

Working with both program advisors, we crafted an informal arrangement where my CS electives could be carefully chosen to complement QCF coursework, and vice versa. This reduced redundancy and maximized learning efficiency.

For example:

• CS 6476 (Computer Vision) counted as CS elective while providing image processing skills relevant to financial chart analysis
• ISYE 6669 (Deterministic Optimization) fulfilled QCF requirements while supporting my CS research on efficient scheduling algorithms
• CS 7280 (Network Science) satisfied CS depth requirements while offering insights into financial network analysis

Industry Connections and Practical Applications

The QCF program’s industry connections proved invaluable. Guest speakers from major financial firms regularly visited classes, providing insights into real-world applications of our coursework.

Notable Guest Lectures:

• Jane Morrison from JPMorgan: Spoke about machine learning applications in credit risk modeling. Her description of ensemble methods for default prediction directly connected to my CS coursework on ensemble learning.

• Dr. Sarah Chen from Two Sigma: Discussed high-frequency trading strategies and the role of technology in modern markets. Her mention of latency-sensitive systems echoed themes from my distributed systems research.

• Mark Rodriguez from Goldman Sachs: Presented on algorithmic trading and the computational challenges of real-time decision making. His problems matched exactly what we were studying in systems research.

These connections led to interview opportunities that wouldn’t have been available through CS networks alone. The combination of CS technical skills and QCF domain knowledge proved attractive to firms seeking “unicorn” candidates.

Research Integration: The Best of Both Worlds

My interdisciplinary background enabled unique research opportunities that neither program alone could provide.

Collaboration with Professor Peng (ISYE) and Professor Tumanov (CS): We explored distributed algorithms for real-time risk aggregation in large portfolios. The project required:

• Financial modeling: Understanding correlation structures, risk factor models, regulatory requirements
• Systems design: Distributed computing architectures for low-latency calculation
• Algorithm development: Novel approaches to approximate complex calculations with bounded error

Industry Collaboration with Northern Trust: Through QCF industry connections, I worked on a project optimizing their overnight risk reporting system. The challenge: process risk calculations for millions of positions within strict overnight windows.

My solution leveraged both backgrounds:

• CS systems knowledge: Identified bottlenecks in their distributed processing pipeline
• QCF domain expertise: Understood which approximations were acceptable for regulatory reporting
• Implementation: Delivered 40% performance improvement while maintaining calculation accuracy

The Final Stretch: Thesis and Capstone Integration

My final semester required careful coordination between CS thesis work and QCF capstone requirements.

CS Thesis: “Dynamic Prompt Pruning for Real-Time Continual Learning” focused on efficiency optimizations for ML systems.

QCF Capstone: “Machine Learning Applications in Real-Time Risk Management” explored practical deployment of ML in financial systems.

While distinct projects, both addressed the fundamental challenge of making complex algorithms work in real-time, resource-constrained environments. The interdisciplinary perspective strengthened both works.

Key insight from integration: Financial systems provided the perfect testbed for CS research on real-time ML systems. Unlike academic benchmarks, financial applications have clear success criteria (profit/loss), regulatory constraints, and industry-standard performance requirements.

December 2023: Double Defense Day

My final hurdle: defending both degrees’ capstone work on the same day.

Morning (9 AM): CS thesis defense with committee of Tumanov, Kira, and Dellaert. Focus on technical contributions to continual learning systems.

Afternoon (2 PM): QCF capstone presentation to industry panel including faculty and visiting professionals from major financial firms.

The CS defense went smoothly. Committee was impressed with both technical depth and practical applications. Dellaert’s comment: “Your work demonstrates unusual appreciation for real-world deployment constraints. That perspective is rare in CS research.”

The QCF presentation was more intense. Industry professionals asked probing questions about regulatory compliance, risk management frameworks, and production deployment considerations. My CS background proved crucial—I could discuss system architecture, scalability constraints, and implementation details at a level that surprised the finance practitioners.

Final verdict from both programs: successful completion with distinction.

Reflection: The Interdisciplinary Advantage

Completing both MS CS and MS QCF simultaneously was the most challenging academic experience of my life. But it provided unique advantages:

Technical Depth: CS provided algorithmic thinking, systems design, and implementation skills. QCF provided mathematical rigor, domain expertise, and practical applications.

Problem-Solving Approach: CS taught me to think about scalability, efficiency, and robustness. Finance taught me to think about risk, uncertainty, and real-world constraints.

Career Opportunities: The combination opened doors in quantitative finance, fintech, and technology companies serving financial markets—roles that required both skill sets.

Research Perspective: Interdisciplinary background enabled novel research questions at the intersection of both fields.

Industry Impact and Career Trajectory

The dual degree combination proved immediately valuable in the job market.

Interview Experiences:

• Tech companies: Impressed by financial domain knowledge for roles in payments, trading platforms, and fintech
• Financial firms: Attracted to deep technical skills for quantitative research and technology development
• Consulting firms: Valued combination for client work spanning technology and finance

Final Outcome: Accepted offer with major technology company’s financial services division, working on machine learning infrastructure for real-time trading systems. The role required exactly the combination of skills from both programs.

Lessons Learned

Interdisciplinary Education is Hard but Valuable: The cognitive overhead of managing two different academic cultures was substantial, but the perspective gained was unique.

Industry Connections Matter: QCF’s industry relationships provided opportunities and insights unavailable through CS alone.

Practical Applications Enhance Theory: Finance applications gave concrete meaning to abstract CS concepts and vice versa.

Time Management is Critical: Success required exceptional organization and priority management.

Collaboration Across Fields: The most interesting problems exist at boundaries between disciplines.

Looking Forward

The dual MS CS/QCF experience established a foundation for career-long learning at the intersection of technology and finance. As financial markets become increasingly algorithmic and technology companies expand into financial services, professionals who understand both domains become increasingly valuable.

The combination prepared me not just for a specific role, but for a career navigating the convergence of technology and finance—two fields that will continue growing closer together.

Future Opportunities:

• Cryptocurrency and DeFi: Blockchain-based financial systems requiring both technical and financial expertise
• Algorithmic Trading: Increasing sophistication requiring both CS and finance knowledge
• RegTech: Technology solutions for regulatory compliance combining domain knowledge with technical implementation
• AI in Finance: Machine learning applications requiring both algorithmic expertise and financial understanding

Final Thoughts

Two years, two degrees, countless late nights, and endless coffee. The dual MS CS/QCF journey was exhausting, challenging, and occasionally overwhelming. But it was also transformative.

I entered Georgia Tech thinking I wanted to be a machine learning researcher. I graduated understanding that the most impactful work happens at the intersection of fields, where different perspectives combine to solve problems that neither discipline could address alone.

The technical skills from CS taught me how to build systems that scale. The domain knowledge from QCF taught me how to build systems that matter. Together, they prepared me to work on problems where getting the answer wrong costs millions of dollars and getting it right changes how markets operate.

Georgia Tech’s strength is enabling exactly this kind of interdisciplinary education. The faculty, resources, and culture all support students who want to push boundaries and combine different fields of knowledge.

The dual degree wasn’t just an academic achievement—it was a foundation for thinking differently about complex problems, whether they involve machine learning, financial markets, or the increasingly blurry line between the two.

This story represents my personal journey through one of the most challenging and rewarding educational experiences possible—pursuing two rigorous master’s degrees simultaneously at one of the world’s leading technical universities. It taught me that the most interesting problems live at the boundaries between fields, and the most rewarding careers come from building bridges between them.