Ph.D. Opportunities in Mechanical & Industrial Engineering – Fall 2026 | UMass Amherst

University of Massachusetts Amherst

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Vacancy

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Description

Dr. Mengfan Xu earned his Ph.D. in Operations Research from Northwestern University, where his research concentrated on online learning, distributed systems, and stochastic optimization. His scholarly work has been published in premier conferences, including NeurIPS, ICML, AISTATS, ACM SIGMETRICS, and WSC. Notably, one of his papers received a Spotlight recognition at NeurIPS 2023 (Top 3%), along with an Honorable Mention at the RLC Workshop. Applications are being invited for fully funded Ph.D. positions in the Department of Mechanical & Industrial Engineering at the University of Massachusetts Amherst, starting in Fall 2026. The positions are available within the research group led by Dr. Mengfan Xu, Assistant Professor in Mechanical & Industrial Engineering and Adjunct Assistant Professor in the Manning College of Information & Computer Sciences. The selected candidates will receive full financial support, which includes tuition coverage, competitive stipends, health insurance, and travel assistance.

Responsibilities

Research Focus

The research group is engaged in advancing the fields of online learning, sequential decision-making, and multi-agent systems. The group’s work emphasizes developing principled, data-driven algorithms designed for dynamic systems operating under uncertainty and interdependence. 

1. The research group’s primary focus lies in advancing the understanding and development of intelligent learning systems that can adapt and make decisions in uncertain and dynamic environments. A major area of exploration involves multi-armed bandits and reinforcement learning, where the goal is to design algorithms capable of learning optimal strategies through experience and feedback over time. These methods are foundational to many modern AI systems and are essential for applications requiring sequential decision-making under uncertainty.
2. Another significant research direction involves multi-agent learning and coordination, where multiple intelligent agents interact, collaborate, or compete within shared environments. This area examines how agents can learn collectively and make cooperative or strategic decisions that enhance system-wide performance. The team also investigates robust learning under heavy-tailed risks, focusing on how learning algorithms can remain stable and reliable when faced with irregular, volatile, or extreme data distributions.
3. In addition, the research extends into simulation and data-driven optimization, leveraging large-scale computational models and real-world data to support efficient, informed decision-making. These simulations serve as testbeds for validating theoretical models and for developing algorithms that can function effectively in complex, uncertain systems.
4. Practical applications of this research span a wide range of domains, including supply chain management, where dynamic optimization helps improve efficiency and resilience. The methods are also applied to cyber-physical systems, integrating data, computation, and physical processes to ensure reliable system performance. Furthermore, the research contributes to security systems, enhancing the ability of intelligent technologies to detect risks, adapt to threats, and safeguard critical infrastructures. Collectively, these efforts aim to develop principled, data-driven approaches that advance the reliability, scalability, and trustworthiness of modern intelligent systems. 

Funding Package

The following benefits are included in the assistantship:

• Full tuition coverage
• Competitive graduate stipend
• Comprehensive health insurance
• Conference and research travel support 

Qualification

Desired Candidate Profile

Applicants for this Ph.D. opportunity are expected to:

1. Hold a strong academic foundation in one or more relevant disciplines that align with the research themes of the program. Ideal candidates typically come from fields such as Operations Research, Applied Mathematics, Computer Science, or Statistics, where analytical thinking and quantitative modeling play a central role. A solid understanding of optimization, probability, and data-driven decision-making will serve as a significant advantage in pursuing this line of research.
2. Preference will be given to applicants who exhibit a genuine enthusiasm for theory-driven yet practically applicable research - those who not only appreciate mathematical rigor but also seek to translate theoretical insights into real-world systems. Candidates with prior exposure to topics such as machine learning, stochastic modeling, or computational algorithms are particularly encouraged to apply.
3. Previous research experience, whether through academic projects, publications, or industrial collaborations, will be considered a valuable asset. Evidence of curiosity, creativity, and the ability to work independently are qualities highly regarded in prospective students. Furthermore, a strong sense of collaboration and communication is essential, as much of the research will involve interdisciplinary teamwork and multi-agent problem-solving.
4. Successful applicants are expected to be self-motivated, disciplined, and eager to learn, with a mindset oriented toward continuous improvement. They should demonstrate both intellectual independence and a willingness to engage actively with peers and faculty in a shared pursuit of academic excellence. Above all, candidates should possess a passion for advancing the frontiers of learning, optimization, and intelligent systems, contributing meaningfully to a research environment that values curiosity, innovation, and collaboration. 

Application Process

Interested candidates are invited to:

• Visit the faculty webpage.

Send an email with a CV and a brief statement of research interests to: 

• Mengfan Xu; Assistant Professor at UMass Amherst - mengfanxu@umass.edu 
• (Subject line: Ph.D. Application – Fall 2026)

Submit the formal application through the UMass Graduate School Portal before January 15, 2026. Applications will be reviewed on a rolling basis, and interview invitations will be extended until all available positions are filled.

Further details can be found here.