Diego Klabjan - US grants
Affiliations: | Industrial Engineering and Management Sciences | Northwestern University, Evanston, IL |
Area:
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The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Diego Klabjan is the likely recipient of the following grants.Years | Recipients | Code | Title / Keywords | Matching score |
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2000 — 2004 | Klabjan, Diego | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Robust Airline Crew Scheduling: Move-Up Crews @ University of Illinois At Urbana-Champaign This project develops a decision support tool for robust airline crew scheduling. A model that solves the airline crew-scheduling problem and captures two objectives the crew cost and the number of crews that can be swapped in operations will be devleoped. The former cost forms the traditional objective function and the latter cost is a measure of robustness since schedules with many swappable crews are likely to be robust. Two methodologies to solve the model are proposed. A Lagrangian decomposition approach relaxes the 'robustness' constraints and iteratively solves the crew-scheduling problem with different objective coefficients. A parallel branch-and-cut algorithm for solving these crew-scheduling problems will be developed. The second approach uses subgradient optimization and the new concept of computing a 'dual' of an integer program. An algorithm for computing such a dual vector will be developed and implemented. |
0.942 |
2003 — 2006 | Klabjan, Diego | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Duality in Integer Programming and Its Application to Integrated Airline Planning @ University of Illinois At Urbana-Champaign The strength of linear programming duality is well known and it is one of the most acclaimed results in theory and practice. On the other hand, it is usually taken for granted that duality is not doable for integer programs. The objective of this proposal is to break the perception barrier by showing that indeed it is possible to compute an analog to the linear programming dual vector for an integer program. A new family of dual functions for integer programs is proposed. Several properties and many results with linear programming counterparts are given. More importantly, an algorithm is proposed that computes such a function for an integer program and it is shown that in a reasonable amount of time an optimal dual function can be computed. The proposed dual functions apply only to pure integer programs and their extension to mixed integer programs is required. In addition, the framework for an algorithm that computes a dual function from the branch-and-cut tree is given. One of the applications of dual functions is in decomposition algorithms. We design a novel decomposition approach to integrated airline planning. |
0.942 |
2007 — 2012 | Klabjan, Diego | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Approximate Dynamic Programming in Complex Multi-Echelon Inventory and Production Systems @ Northwestern University In this proposal we plan to study solution methodologies for general multi-echelon systems with possible stochastic lead-times, economies of scale, production and transportation capacities, and demand occurring at each stage or node of the system. The main objective is in developing novel state of the art solution methodologies for these complex systems. |
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2010 — 2012 | Klabjan, Diego | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Eager: the Greenland Physics Problem @ Northwestern University 1010147- Klabjan |
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2012 — 2016 | Klabjan, Diego Arinez, Jorge |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Goali: Portfolio of Renewable Energy Generation @ Northwestern University The main objective of this project is to develop a stochastic portfolio optimization model to address the problem of renewable |
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2015 — 2019 | Klabjan, Diego Starren, Justin B. [⬀] |
T32Activity Code Description: To enable institutions to make National Research Service Awards to individuals selected by them for predoctoral and postdoctoral research training in specified shortage areas. |
Predoctoral Training Program in Biomedical Data Driven Discovery (Bd3) @ Northwestern University At Chicago ? DESCRIPTION (provided by applicant): The Biomedical Data Driven Discovery (BD3) Training Program at Northwestern University (NU) is a collaborative proposal that brings together Big Data educators and researchers from the Feinberg School of Medicine (FSM), the McCormick School of Engineering and Applied Science (MEAS), the Weinberg College of Arts and Sciences (WCAS) and the School of Communication. The goal of BD3 is to train Big Data scientists for both academic and industry research positions, who will develop the next generation of methodologies and tools. BD3 will to create a truly multidisciplinary data science training environment. In doing so, BD3 will encompass multiple departments and degree-programs, leveraging three existing data-intensive doctoral programs-- the well-established and nationally recognized program in Data Analytics in MEAS, led by Diego Klabjan, PhD, and two innovative and growing programs led by Justin Starren, MD, PhD: the Informatics track of the Driskill Graduate Program, focusing on Bioinformatics, and the Informatics track of the Health Sciences Integrated Program, focusing on clinical and population informatics. Together, Drs. Klabjan and Starren have expertise that spans three critical areas: computer science/informatics, statistics/mathematics, and biomedical domain knowledge. BD3 brings together the biomedical Big Data and domain expertise across multiple departments of FSM with methodological expertise in computation, informatics, statistics, and mathematics. The program will recruit three candidates per year and support each trainee for two years. Success in data science requires mastery of three distinct skill sets: 1) an understanding of the target domain, 2) an understanding of the nature and structure of the data within that domain, and 3) a mastery of the computational and statistical techniques for manipulating and analyzing the data. This translates into a number of more specific competencies, including: deep Domain Knowledge in the target domain, Statistical Methods, Computer Programming, Ontologies, Databases, Text Analytics, Predictive Analytics, Data Mining, Analytics for Big Data, and Responsible Conduct of Research. BD3 will utilize a co-mentoring model, with each student having a domain mentor and a methodological mentor. Each student's program will be based on an Individual Development Plan (IDP). Students will have many opportunities for both laboratory and industrial rotations leveraging well-established programs at MEAS. Additional educational activities include: an annual retreat, monthly trainee meetings, departmental seminars and speakers, journal clubs, teaching training and experience, and writing and presentation training. Trainees benefit from extensive institutional support for this program, such as: tuition supplements, stipend supplements, administrative support, the Writing Workshop for Graduate Students, the Searle Center for Advancing Learning and Teaching, the Management for Scientists and Engineers, nationally recognized mentor and mentee training programs, and formal training in Team Science. |
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2015 — 2020 | Kalogera, Vassiliki Schmitt, Michael (co-PI) [⬀] Schmitt, Michael (co-PI) [⬀] Van Der Lee, Suzan Katsaggelos, Aggelos (co-PI) [⬀] Trautvetter, Lois Klabjan, Diego |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ Northwestern University This National Science Foundation Research Traineeship (NRT) award prepares master's and doctoral students at Northwestern University with the data-analysis skills to advance the research frontiers in astronomy, physics, and Earth science. While providing students with training in data-enabled science and engineering, the program promotes collaborations with research and education that will lead to the development of new data tools with broad applicability. Through internships, evidence-based curricular approaches, and capstone citizen science projects, trainees will develop the core competencies in demand by a wide range of employers. Trainees will learn how to effectively engage the public in discoveries on the solar system, stellar explosions, star clusters and galaxies, gravitational waves, and seismic waves. The citizen science projects will be used for innovative recruiting, strengthening the participation of the public and students from underrepresented groups. By diversifying the graduate student population and improving instruction and mentoring, the research will be contributing to a diverse, inclusive scientific workforce in academia and industry. |
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