MFE for 2026 Fall evaluation

[ZizheZhou]

Undergrad school: University of Colorado Denver, China Agricultual University
Undergrad GPA: 3.94
Undergrad major: Economics, Mathematics
Minor: Data Science
(It's a cooperate program so I have 2 undergraduate school)
Math Courses:

• Calculus II: A-
• Multivariable Calculus: A-
• Linear Algebra: A
• Ordinary Differential Equation: A
• Statistic Theory: A
• Abstract Math: A
• Real Analysis: A
• Numerical Analysis: A
• Data Wrangling and Visualization: A
• Machine Learning: A
• Math Clinic: A ( It's a project oriented seminar)

Economic Courses:

• Microeconomic: A
• Macroeconomic: A-
• Statistics and Economics: B+
• Economic of Race and Gender: A
• Managerial Economics: A
• Econometrics: A
• Data Analysis with SAS: A
• Internmed Microeconomic Theory: A
• Financial Economics: In progress
• International Finance: In progress

Programming Courses

• SQL foundations: A
• Programming for Data Science: A

Business Courses

• Accounting: A
• Operation Management: A

Scientific Research
Stability and Efficiency Analysis of Non-SPD Spectral Super-Time-Stepping (STS) Methods in Convection–Diffusion Problems
(This is the project I worked in Math Clinic Course. My professor was willing to cooperate with me after the course and we are writing an essay together)
Jan 2025 – May 2025

• Reviewed and analyzed the stability and acceleration mechanisms of classical STS methods under SPD operators, clarifying their limitations in non-SPD convection–diffusion operators;
• Proposed a stability polynomial construction method based on complex-domain Chebyshev polynomials and a spectral shift parameter μ to enlarge the stability region;
• Derived conformal mapping formulas for spectral ellipses and designed a monotonically increasing substep sequence τj to ensure numerical stability;
• Conducted numerical experiments to verify stability, convergence, and accuracy under different Péclet numbers, achieving significant acceleration compared with classical STS methods.

Lightweight Multimodal Transformer for Apple Disease Segmentation and Experimental Validation
Feb 2025 – Jun 2025

• Took primary responsibility for the multimodal Transformer testing pipeline, including data preprocessing, model construction, and debugging, ensuring reliable experimental results;
• Handled memory optimization, training log management, and parameter comparison tasks to maintain stable model performance;
• Completed multiple rounds of testing on an apple disease dataset, achieving 92.6% mIoU and 94.1% F1-score, with inference speed approximately 25% faster than traditional CNN methods;
• Contributed to manuscript writing and multiple rounds of revision, ensuring logical consistency, completeness, and academic precision; results published in “A lightweight method for apple disease segmentation using multimodal transformer and sensor fusion.”

Transformer-Based Sequential Strategy Optimization in Sparse Environments
Dec 2024 – Apr 2025

• Proposed a feature modeling framework using Transformer’s global modeling capability for reinforcement learning in sequential decision-making, enhancing the modeling of state–action dependencies in sparse environments;
• Improved multi-head attention and positional encoding design to capture long-term dependencies, mitigating instability issues in reinforcement learning with sparse rewards;
• Designed a Transformer-driven policy update module to balance exploration efficiency with policy convergence stability;
• Experiments demonstrated 12%–15% improvement in average return and over 20% faster convergence compared with baselines such as LSTM-RL; results published in “A Transformer-Based Reinforcement Learning Framework for Sequential Strategy Optimization in Sparse Data.”

Privacy-Preserving Large Language Models with User Attention Mechanism
Dec 2023 – Apr 2024

• Introduced a user attention mechanism to dynamically allocate privacy weights during training, enabling precise protection of sensitive information;
• Built a user-centered large language model framework integrating differential privacy with attention mechanisms to balance privacy security and computational efficiency;
• Designed a unified loss function (Uni-Loss) to simultaneously optimize classification accuracy, privacy protection, and attention effectiveness in multi-task learning;
• Achieved significant improvements across CV and NLP tasks (e.g., YOLO precision 0.87, BLIP2 accuracy 0.88); results published in “A User-Centered Framework for Data Privacy Protection Using Large Language Models and Attention Mechanisms.”

Paper

• A lightweight method for apple disease segmentation using multimodal transformer and sensor fusion, Yi-hong Song, Manzhou Li, Zizhe Zhou, Jiahe Zhang, Xiangge Du, Min Dong, Qinhong Jiang, Che Li, Yuantao Hu, Qiulin Yu, Dongmei Wang, Hegan Dong, Shuo Yan. Computers and Electronics in Agriculture, Volume 237, Part C, October 2025, 110737. Jul 2025.
• A Transformer-Based Reinforcement Learning Framework for Sequential Strategy Optimization in Sparse Data, Zizhe Zhou, Liman Zhang, Xuran Liu, Siyang He, Jingxuan Zhang, Jinzhi Zhu, Yuanping Pang. Appl. Sci. 2025, 15, 6215. May 2025.
• Enhancing Data Privacy Protection and Feature Extraction in Secure Computing Using Hash Tree and Skip Attention Mechanism, Zizhe Zhou, Yaqi Wang, Lin Cong. Appl. Sci. 2024, 14, 10687. Nov 2024.
• Implementing Real-Time Image Processing for Radish Disease Detection Using Hybrid Attention Mechanisms, Mengxue Ji, Zizhe Zhou, Xinyue Wang. Plants 2024, 13, 3001. Oct 2024.
• A User-Centered Framework for Data Privacy Protection Using Large Language Models and Attention Mechanisms, Shutian Zhou, Zizhe Zhou, Chenxi Wang. Appl. Sci. 2024, 14, 6824. Sep 2024

Internship

• Algorithm Engineer Intern, Ant Group — Aug 2025 – Oct 2025 (Data Science Internship)
• Catastrophe Modeling Engineer Assistant, China Re Catastrophe Risk Management Co., Ltd. — Jul 2025 – Aug 2025 (Data Science Internship)
• Research Assistant, Beijing Mining Goat Data Technology Co., Ltd. — Nov 2024 – May 2025 (Financial Engineering Internship)
• Intern, Jinghua Secure Computing Technology Co., Ltd. — Aug 2024 – Nov 2024 (Computer Science Internship)
• Java Application Development Engineer Intern, Ant Financial Group — Jun 2024 – Jul 2024 (Financial Engineering Internship)

Prize

• 2020 International Genetically Engineered Machine Competition (iGEM) — Silver Medal, Team Leader of Web Design Group
• 2024 China Agricultural University Programming Contest — First Prize (University Level)
• 2025 Mathematical Contest in Modeling / Interdisciplinary Contest in Modeling (MCM/ICM) — Honorable Mention
• Scholarships & Honors: Academic Second-Class Scholarship, Academic Progress Scholarship; Academic First-Class Scholarship at the University of Colorado Denver; “Outstanding Scholar” Honor

Programming Language
C++，Python，SQL，Sublime，SAS，R，Stata，Tableau，LaTeX（Overleaf），SPSS，Matlab，Maple
Certification： C++ Programming for Financial Engineering (In progress), SAS Programming Fundamentals

English Test Scores
IELTS： R:8 L: 8 S:7 W:6 Overall: 7.5

Main Questions

• At this stage, is it necessary for me to achieve a very high GRE score? I am still preparing for the test, but I’m not sure how essential it is for my applications.
• With my current background and experiences, what kind of graduate programs or universities do you think I would be competitive for?
• My dream program is the MFE at Baruch College. I’ve recently started studying Dan’s 150 Questions for Quant Interviews and Math Primer for FE. Since I plan to apply for the January intake, do you think I still have enough time to prepare?
• What else could I do to further strengthen my profile and improve my chances of admission?

 

[ZizheZhou]

I noticed that in my transcript it does not explicitly show a standalone Probability course. During my first semester of junior year, I took Statistics, which included a full chapter on probability. In the following semester, I initially enrolled in Probability course, but I found that much of the content overlapped with my Statistics class (covering topics such as continuous probability, independence, and the Central Limit Theorem). Since I felt it would not add much value, I decided to withdraw from the course. Given that the Baruch MFE program explicitly requires one semester of Probability, do you think this could affect my application? Would you recommend that I take Baruch’s Pre-MFE Probability for FE course to strengthen my preparation? Really need a guidance !!

 

[Andy Nguyen]

Your profile looks strong.
You are addressing the programming aspect by taking the C++ here. Great decision.
Taking the Probability pre-MFE would definitely strengthen your case and it will help a lot with brainteaser interview questions during your admission process.
Other than that I think you should spend time to really learn about different programs, how they are different and pros/cons of each.
Reach out to current students and alum of the programs, many of them are members right here. Looks them up through the Tracker.
Admission people love applicants who did their homework.
You need to explore what you want to do and which career path you may enjoy. Some programs prepare you better for that.
Apply to all the competitive programs and see how you do.

 

[ZizheZhou]

Thank you very much for your advice! I’ve actually spent quite a lot of time researching different programs — I carefully reviewed their curriculum, student feedback, and placement outcomes, so I already have a fairly detailed understanding of each. That said, I’m still most motivated by the goal of becoming a quant trader, which is why the Baruch MFE program remains my top choice.
I also wanted to ask about the Pre-MFE program. If I take the Probability for FE course, would I be able to present its certificate separately when applying to the MFE program? I’m curious how much weight this course carries in the admissions process.
Finally, regarding the GRE — does it still play a major role in applications? At this stage, do you think I should continue focusing on getting a high GRE score, or would it be more beneficial to invest my effort in completing the Pre-MFE course?

 

[ZizheZhou]

student

Your profile looks strong.
You are addressing the programming aspect by taking the C++ here. Great decision.
Taking the Probability pre-MFE would definitely strengthen your case and it will help a lot with brainteaser interview questions during your admission process.
Other than that I think you should spend time to really learn about different programs, how they are different and pros/cons of each.
Reach out to current students and alum of the programs, many of them are members right here. Looks them up through the Tracker.
Admission people love applicants who did their homework.
You need to explore what you want to do and which career path you may enjoy. Some programs prepare you better for that.
Apply to all the competitive programs and see how you do.

Thank you very much for your advice! I’ve actually spent quite a lot of time researching different programs — I carefully reviewed their curriculum, student feedback, and placement outcomes, so I already have a fairly detailed understanding of each. That said, I’m still most motivated by the goal of becoming a quant trader, which is why the Baruch MFE program remains my top choice.
I also wanted to ask about the Pre-MFE program. If I take the Probability for FE course, would I be able to present its certificate separately when applying to the MFE program? I’m curious how much weight this course carries in the admissions process.
Finally, regarding the GRE — does it still play a major role in applications? At this stage, do you think I should continue focusing on getting a high GRE score, or would it be more beneficial to invest my effort in completing the Pre-MFE course?

 

[Andy Nguyen]

Yes, completing the pre-MFE in Probability will satisfy their requirement of a semester of probability. Same as other pre-MFE courses. Those courses are intensive, rigorous and money well-spent. It is time-consuming as well. Just ask those who completed it before.
GRE is optional for many programs. Some programs will waive GRE if your GPA is over a certain threshold (say 3.5, etc).
Unfortunately some programs may still require GRE.
In any case, GRE is not a deciding factor given your strong grades. If you have to take GRE to apply to some programs, try to get a high GRE Q. If not, it's not the end of the world. Spend more time on other aspect of the process.

 

[ZizheZhou]

Yes, completing the pre-MFE in Probability will satisfy their requirement of a semester of probability. Same as other pre-MFE courses. Those courses are intensive, rigorous and money well-spent. It is time-consuming as well. Just ask those who completed it before.
GRE is optional for many programs. Some programs will waive GRE if your GPA is over a certain threshold (say 3.5, etc).
Unfortunately some programs may still require GRE.
In any case, GRE is not a deciding factor given your strong grades. If you have to take GRE to apply to some programs, try to get a high GRE Q. If not, it's not the end of the world. Spend more time on other aspect of the process.

Really appreciate your response! I think it’s really a perfect guidance for me which is clear! I also wonder if it’s possible for me to explain the course syllabus when applying for MFE programs. Statistic theory indeed covers all the stuff in Probability in my school( I don’t know if it is common). If possible, I wish to show the syllabus of my statistic course to confirm that I have a semester of probability learning.
I will contact Baruch College as soon as possible to find if it’s possible to enroll into the Probability Pre-MFE program.

 

[ZizheZhou]

Also I am trying to complete a Probability course on Coursera to solidify my probability knowledge.