open

Open Positions
Consensus Based Machine Learning \| Healthcare Analytics \| Digital Humanities Research
Design and Analysis of a Large Scale First Order Optimizer A research project is available in the Department of Management Science and Systems which involves the development and testing of a large-scale optimization algorithm for distributed compute environments. This position is paid for 10 hrs per week between Dec 21st, 2025 to Jan 20th, 2025 and UNPAID thereafter, although research credits (such as independent study) are available for graduate students over the spring, summer and fall semesters as appropriate. It requires the incumbent to commit to at least two semesters of work (possibly more, if the project so demands). S/he is expected to gain experience in doing research. The position is available immediately. The student is expected to be: Very well-versed in coding with Java/Python with demonstrated experience in several projects Perform empirical analysis with Python code for Federated Learning environments Must have worked on / be currently engaged in research projects at the graduate level and be comfortable in reading research papers and implementing algorithms described in them. Must have prior knowledge of optimization algorithms and should have taken courses in them. Alternatively, they may have to learn unconstrained optimization techniques as part of the project. Understand unconstrained optimization -- should have taken prior courses in optimization. Will be required to implement and test large scale optimization algorithms and should be able to look into pre-existing code from other researchers if they exist. Be able to interpret, visualize, analyze and plot results from empirical studies. Enthusiastically participate in reading groups and brown-bag sessions and Present work at conferences or journals as appropriate. If interested, please email haimonti@buffalo.edu highlighting how the above requirements make you a good match for the project. Project outcome Implementation and Testing of Large Scale Optimization Algorithms Presentation of results in a conference and journal
Healthcare Analytics A research project is currently underway in the Department of Management Science and Systems at the State University of New York at Buffalo -- the goal of which is clinical outcome prediction (estimation of length of stay, mortality, readmissions, diagnosis, and procedure prediction). Data was collected in two different settings - during a pandemic (COVID-19) and under normal circumstances. The data contains structured (physiological characteristics, vitals, etc.) and unstructured (EHRs) information -- and we study patient flows, staff, physician, and nurse schedules and their impact on clinical outcomes (focusing primarily on length of stay) and overall healthcare scheduling. One of the datasets is proprietary, while the other is available from online sources. The student(s) who are expected to join the project will help process unstructured EHR data by careful extraction of features, performing statistical analysis, and finding how they affect the performance of the overall models for clinical outcome prediction. In particular, they will be involved with the following activities: Become familiar with the overall architecture of the project, including the multimodal data collected. Be familiar with Natural Language Processing techniques (including Large Language Models LLMs for Clinical Research) and use tools that are developed for processing clinical notes, EHRs, etc. Will be involved in reading research papers, interpreting them, and using implementations as available from Github repositories. Enthusiastically participate in brown bag sessions and group presentations. Expected to work with graduate students (Master's and PhD) and undergraduate students who are involved in the project. Teamwork is highly encouraged, although some tasks will have to be accomplished individually. There will be group meetings every week to discuss project-related and unrelated issues. The ideal student would work with us for at least TWO semesters (preferably more!), including winter or summer break as appropriate. What we offer: This is an UNFUNDED project. However, we do have the opportunity to do any of the following: For UB students - Offer independent study/thesis credits as appropriate. For industry partners, this could be a way to participate in more rigorous, long-term, academic projects to keep abreast of state-of-the-art technology Recommendation letters, ONLY after the student has made substantial contributions to the project, worked for at least two semesters, and ideally has co-authored a paper with any of the faculty members involved in the project. For external collaborators, there exist multiple opportunities for discussing how you can be involved with and contribute to our research (such as new projects and data sharing, problems that are of interest to industry, funding opportunities for new work and more!). The projects are available immediately and are very hands-on, fast-paced, with a lot of opportunity to develop skills pertaining to real-world data processing, programming (Java, Python, or R), visualization, use of machine learning knowledge acquired in the curriculum. It is anticipated that the project will continue for several years. If the above is of interest, please send your resumes and a few paragraphs on why this is of interest to you and what you hope to accomplish through your participation to me (haimonti@buffalo.edu)
Design of a recommendation system A research project is currently underway in the Department of Management Science and Systems at the State University of New York at Buffalo -- the goal of which is to design a graph neural network based recommendation system for scroll paintings (folk art) that can run on decentralized compute environments (such as peer-to-peer systems). The data is proprietary, collected by field research and funded by internal grants from the State University of New York at Buffalo and a fellowship from the National Endowment of Humanities. The undergraduate/graduate student(s) who are expected to join the project will help compare our existing recommendation system to state-of-the-art baselines. In particular, they will be involved with possibly the following activities: Become familiar with the overall architecture of the project, the multimodal data collected, design of the vision-language (LLM) models required for the design of recommendation engine Read papers about state-of-the-art graph based recommendation systems to be used as baselines. Obtain code in Python for state-of-the-art recommendation system(s) and learn the basic algorithms involved. Compare our recommendation engine with baselines and present results. Be involved in reading research papers and putting together bibliographies on certain relevant topics Enthusiastically participate in brown bag sessions and group presentations. Help with write research results as appropriate. Students are expected to work with current students (Masters and PhD) and undergraduate students who are involved in the project and industry collaborators. Team work is highly encouraged although some tasks will have to be accomplished individually. There will be individual meetings with the faculty mentor and students every week to discuss project related and unrelated issues. Students must be Very well-versed with Machine Learning algorithms having taken course(s) before Have prior experience with experimenting with novel machine learning algorithms and Be fluent in Python programming. The project is available immediately and will be unpaid, but with a potential of earning course credits if relevant. We expect the incumbent to be available to work on the project at least until summer 26, possibly even during summer! If interested, please contact Dr. Haimonti Dutta (haimonti@buffalo.edu) with a copy of your resume, explaining why your background is a good fit for the above project.

Open Positions

Consensus Based Machine Learning | Healthcare Analytics | Digital Humanities Research

Design and Analysis of a Large Scale First Order Optimizer
A research project is available in the Department of Management Science and Systems which involves the development and testing of a large-scale optimization algorithm for distributed compute environments. This position is paid for 10 hrs per week between Dec 21st, 2025 to Jan 20th, 2025 and UNPAID thereafter, although research credits (such as independent study) are available for graduate students over the spring, summer and fall semesters as appropriate. It requires the incumbent to commit to at least two semesters of work (possibly more, if the project so demands). S/he is expected to gain experience in doing research. The position is available immediately.
The student is expected to be:

Very well-versed in coding with Java/Python with demonstrated experience in several projects
Perform empirical analysis with Python code for Federated Learning environments
Must have worked on / be currently engaged in research projects at the graduate level and be comfortable in reading research papers and implementing algorithms described in them.
Must have prior knowledge of optimization algorithms and should have taken courses in them. Alternatively, they may have to learn unconstrained optimization techniques as part of the project.
Understand unconstrained optimization -- should have taken prior courses in optimization.
Will be required to implement and test large scale optimization algorithms and should be able to look into pre-existing code from other researchers if they exist.
Be able to interpret, visualize, analyze and plot results from empirical studies.
Enthusiastically participate in reading groups and brown-bag sessions and
Present work at conferences or journals as appropriate.

If interested, please email haimonti@buffalo.edu highlighting how the above requirements make you a good match for the project.

Project outcome

Implementation and Testing of Large Scale Optimization Algorithms
Presentation of results in a conference and journal

Healthcare Analytics
A research project is currently underway in the Department of Management Science and Systems at the State University of New York at Buffalo -- the goal of which is clinical outcome prediction (estimation of length of stay, mortality, readmissions, diagnosis, and procedure prediction). Data was collected in two different settings - during a pandemic (COVID-19) and under normal circumstances. The data contains structured (physiological characteristics, vitals, etc.) and unstructured (EHRs) information -- and we study patient flows, staff, physician, and nurse schedules and their impact on clinical outcomes (focusing primarily on length of stay) and overall healthcare scheduling. One of the datasets is proprietary, while the other is available from online sources.

The student(s) who are expected to join the project will help process unstructured EHR data by careful extraction of features, performing statistical analysis, and finding how they affect the performance of the overall models for clinical outcome prediction. In particular, they will be involved with the following activities:

Become familiar with the overall architecture of the project, including the multimodal data collected.
Be familiar with Natural Language Processing techniques (including Large Language Models LLMs for Clinical Research) and use tools that are developed for processing clinical notes, EHRs, etc.
Will be involved in reading research papers, interpreting them, and using implementations as available from Github repositories.
Enthusiastically participate in brown bag sessions and group presentations.
Expected to work with graduate students (Master's and PhD) and undergraduate students who are involved in the project. Teamwork is highly encouraged, although some tasks will have to be accomplished individually.
There will be group meetings every week to discuss project-related and unrelated issues.
The ideal student would work with us for at least TWO semesters (preferably more!), including winter or summer break as appropriate.

What we offer: This is an UNFUNDED project. However, we do have the opportunity to do any of the following:

For UB students - Offer independent study/thesis credits as appropriate.
For industry partners, this could be a way to participate in more rigorous, long-term, academic projects to keep abreast of state-of-the-art technology
Recommendation letters, ONLY after the student has made substantial contributions to the project, worked for at least two semesters, and ideally has co-authored a paper with any of the faculty members involved in the project.
For external collaborators, there exist multiple opportunities for discussing how you can be involved with and contribute to our research (such as new projects and data sharing, problems that are of interest to industry, funding opportunities for new work and more!).

The projects are available immediately and are very hands-on, fast-paced, with a lot of opportunity to develop skills pertaining to real-world data processing, programming (Java, Python, or R), visualization, use of machine learning knowledge acquired in the curriculum. It is anticipated that the project will continue for several years.

If the above is of interest, please send your resumes and a few paragraphs on why this is of interest to you and what you hope to accomplish through your participation to me (haimonti@buffalo.edu)

Design of a recommendation system
A research project is currently underway in the Department of Management Science and Systems at the State University of New York at Buffalo -- the goal of which is to design a graph neural network based recommendation system for scroll paintings (folk art) that can run on decentralized compute environments (such as peer-to-peer systems). The data is proprietary, collected by field research and funded by internal grants from the State University of New York at Buffalo and a fellowship from the National Endowment of Humanities.
The undergraduate/graduate student(s) who are expected to join the project will help compare our existing recommendation system to state-of-the-art baselines. In particular, they will be involved with possibly the following activities:

Become familiar with the overall architecture of the project, the multimodal data collected, design of the vision-language (LLM) models required for the design of recommendation engine
Read papers about state-of-the-art graph based recommendation systems to be used as baselines.
Obtain code in Python for state-of-the-art recommendation system(s) and learn the basic algorithms involved.
Compare our recommendation engine with baselines and present results.
Be involved in reading research papers and putting together bibliographies on certain relevant topics
Enthusiastically participate in brown bag sessions and group presentations.
Help with write research results as appropriate.

Students are expected to work with current students (Masters and PhD) and undergraduate students who are involved in the project and industry collaborators. Team work is highly encouraged although some tasks will have to be accomplished individually. There will be individual meetings with the faculty mentor and students every week to discuss project related and unrelated issues. Students must be

Very well-versed with Machine Learning algorithms having taken course(s) before
Have prior experience with experimenting with novel machine learning algorithms and
Be fluent in Python programming.

The project is available immediately and will be unpaid, but with a potential of earning course credits if relevant. We expect the incumbent to be available to work on the project at least until summer 26, possibly even during summer! If interested, please contact Dr. Haimonti Dutta (haimonti@buffalo.edu) with a copy of your resume, explaining why your background is a good fit for the above project.