u/MightyZinogre

Title. I would like to get more on the multiobjective side of things with mathematical optimization, what are the techniques that work the best for this area? And using which tools? Do we always have to move to single objective by the means of penalties, weighted sums, and what not?

Hello,

I have a PhD in Operations Research and Machine Learning, and I recently accepted a new job as an R&T scientist at a private research lab. I'll be working on a project for a government agency involved in logistics, and it seems the main part of the job will be process mining. My future manager sent me van der Aalst's Process Mining book to study before I officially start.

I'm currently working through the core topics (Petri nets, WF-nets, C-nets, etc.), though to be fair, my background and stronger interest lie in optimization and classical ML/DL. I'm quite proficient in Python, but I've never used Disco, SAP, Celonis, or other commercial process mining tools.

1. What should I realistically expect to be doing day-to-day? Which topics deserve the most focus as I study process mining from scratch — discovery, conformance checking, something else?

2. Will I have room to bring in ML or OR methods eventually? If so, how and when does that usually happen in practice — and are teams generally more receptive to interpretable models (decision trees, simple heuristics) over more complex ones (deep learning, black-box optimization) in this kind of setting?

I hold a Ph.D. in Operations Research, along with a BSc/MSc in Engineering and OR. I previously worked in Big Tech, but I’m currently looking to transition.

My primary goal is to upgrade my technical skillset to maximize my industry-related profitability and marketability. I want to get away from generic data science and move into high-value, math-heavy engineering and modeling roles.

• My Core Interests: Forecasting, predictive analytics, and machine learning applied to industrial settings.
• Target Industries: Robotics/Autonomous Systems, Defense/Aerospace, and Quantitative Finance.
• What I want to skip: I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I am right now: I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm looking to learn:

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML).
• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch).
• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense.

My questions for the community:

1. Skill Prioritization: From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?
2. Portfolio/Proof: How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?
3. Positioning: How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!

I hold a Ph.D. in Operations Research, along with a BSc/MSc in Engineering and OR. I previously worked in Big Tech, but I’m currently looking to transition.

My primary goal is to upgrade my technical skillset to maximize my industry-related profitability and marketability. I want to get away from generic data science and move into high-value, math-heavy engineering and modeling roles.

• My Core Interests: Forecasting, predictive analytics, and machine learning applied to industrial settings.
• Target Industries: Robotics/Autonomous Systems, Defense/Aerospace, and Quantitative Finance.
• What I want to skip: I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I am right now: I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm looking to learn:

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML).
• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch).
• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense.

My questions for the community:

1. Skill Prioritization: From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?
2. Portfolio/Proof: How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?
3. Positioning: How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!

I hold a Ph.D. in Operations Research, along with a BSc/MSc in Engineering and OR. I previously worked in Big Tech, but I’m currently looking to transition.

My primary goal is to upgrade my technical skillset to maximize my industry-related profitability and marketability. I want to get away from generic data science and move into high-value, math-heavy engineering and modeling roles.

• My Core Interests: Forecasting, predictive analytics, and machine learning applied to industrial settings.
• Target Industries: Robotics/Autonomous Systems, Defense/Aerospace, and Quantitative Finance.
• What I want to skip: I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I am right now: I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm looking to learn:

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML).
• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch).
• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense.

My questions for the community:

1. Skill Prioritization: From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?
2. Portfolio/Proof: How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?
3. Positioning: How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!

My background is in Operations Research, stochastic optimization, simulation-based decision systems, and machine learning. I completed a PhD in OR and currently work on large-scale logistics planning systems involving forecasting, simulation, and optimization.

I try to stay current with the literature, but over the last few years I've seen a growing number of new themes and buzzwords: learning-augmented optimization, graph neural networks, reinforcement learning, digital twins, decision intelligence platforms, foundation models, and various hybrid ML/OR approaches.

At the same time, most successful production systems I encounter still seem to rely heavily on a combination of forecasting, simulation, mathematical optimization, heuristics, and strong software engineering.

I'm therefore interested in the perspective of researchers and practitioners working on real-world decision systems.

Which ideas that emerged roughly after 2020 have actually demonstrated sustained practical value?

More specifically:

Which techniques are now routinely deployed and are likely to become part of the standard OR toolkit? Which directions received significant attention but have not delivered the expected impact? Where do you see the next major shift occurring in industrial optimization and decision-making systems?

Examples from logistics, defense, robotics, cyber security, energy, or finance would be particularly interesting.