MIT LGO CEE

Question

Hi Guys!

Context: I graduated in 2023 electrical engineering from LUMS (one of the best schools here in Pakistan) where I specialized on the ML/AI side and took a lot of overlapping CS courses i.e. ML, DL and DS etc. 
Post graduation I somehow landed up in a beverage CPG in Supply Chain, I lowkey wanted to work on the AI/ML side but trust me here in PK all the tech lies in outsourced software houses with little to no exposure.

Long story short, I have applied to the LGO (Civil Engineering + MBA) for fall 2026. I initially wanted to apply for EECS but looking at my supply chain + applied data science project stories at my CPG - I decided to opt for CEE as a better fit. Alongside, all EECS alumni were mainly from tech companies working as software developers or in hard-core technical roles which made me realize that I have more chances of getting admitted in CEE as compared to EECS.

Now, post the application deadline, I am having second thoughts on the department. Post graduation, I want to explore Data Scientist / ML Engineer type of roles AND also Consulting roles. I am really worried that CEE might not allow me to explore the former and I'll be spending so much in the wrong direction (IF I GET IN).

Although, I have talked to a few CEE alumni who are taking majority of CS courses while being in CEE but I really am confused.

Open to your opinions.

tpriya96 · Answer

good luck

dee1pug1 · Answer

Cross the bridge when you reach it. Secure an admit, and then explore if MIT is willing to change the department from CEE to some other of your choice.

dotsoftme · Answer

Hello! It's completely understandable to have second thoughts after submitting such a critical application. You've made a  smart, strategic move  to maximize your admission chances, and I believe the  LGO program structure  offers you enough flexibility to pursue both your Data Science/ML Engineering and Consulting goals, even from the Civil and Environmental Engineering (CEE) track.
 
🎯 CEE for Admission vs. EECS for Career
Your choice of  CEE  was a strong play for admission. Your background—Electrical Engineering with an ML focus  plus  real-world  Supply Chain/Operations  experience in a CPG—creates a unique profile that aligns well with the  systems, logistics, and optimization  focus found within CEE departments, especially for an Operations-focused program like LGO. This made you a stronger fit for CEE than for the traditionally software/hardware-heavy EECS alumni pool.
The LGO Advantage
The key here is that you're applying to  LGO , not just a standalone MS program. LGO is a highly integrated, cohort-based, dual-degree program (Engineering + MBA) that consulting firms and tech companies recruit from holistically.
 
  Consulting:  For consulting roles, especially in  Operations or Tech Strategy , the  MBA  coupled with your  Supply Chain/CEE optimization background  is extremely strong. Your engineering department matters less than the overall LGO brand and your operations experience. 
  Data Scientist/ML Engineer:  This is your primary concern. While EECS is the direct path, you can absolutely pivot by leveraging the  cross-departmental flexibility  of a top-tier institution like MIT.  
 
🛠️ Pivoting to Data Science/ML from CEE
You are right to be concerned, but the path is not blocked. You must be  intentional  about your course selection and project work.
1. Coursework: The Unofficial Major
As your CEE alumni contacts mentioned, you can and should enroll heavily in  graduate-level courses  offered by the  EECS (Course 6)  and  Sloan (Course 15)  departments.
 
  Focus Areas:  Prioritize courses in Machine Learning, Deep Learning, Algorithms, Data Mining, and Optimization (Operations Research). These courses will appear on your official transcript, effectively serving as your technical specialization regardless of your departmental affiliation. 
  Transcript Over Department:  When you apply for a Data Scientist role, the recruiter is looking for specific keywords on your resume and transcript, like  Neural Networks ,  Distributed Systems , and  Optimization . Your coursework will provide this evidence.  
2. Thesis and Capstone Project
Your required LGO thesis or capstone project is your single best tool for demonstrating ML/AI proficiency.
 
  Select an Applied ML Topic:  Choose a thesis topic that focuses on using advanced ML/AI techniques (e.g., Reinforcement Learning, advanced Time Series forecasting, Computer Vision for infrastructure monitoring) to solve a problem in supply chain, transportation, or civil systems. 
  Supervision:  Seek a thesis advisor who either has an ML/AI focus within CEE (e.g., in Transportation Systems, Infrastructure Systems) or who is  cross-listed  with EECS or Sloan. This lends crucial technical credibility.  
3. The LGO Internship
The LGO summer internship is a critical bridge. Aim for a  Data Scientist, ML Engineer, or Applied Science  role at a technology company or a highly quantitative firm. Landing this internship will officially validate your career pivot and make your department choice irrelevant for your post-graduation job search.
 
⚖️ Final Assessment
You do not need to worry that you are "spending so much in the wrong direction." You chose a path that  significantly improved your admission odds  to an elite, career-launching program (LGO).
The  LGO brand and the freedom of cross-registration  provide the necessary mechanism to build an ML/AI profile. Your background is already solid; you just need to be disciplined in how you execute your degree plan.

MIT LGO CEE

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