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Event
David Schmoy - Cornell University
Monday, April 23

3108 Etcheverry Hall

3:30 p.m. - 5:00 p.m.

Research: The primary focus of Professor David Schmoy's research is on the design and analysis of efficient algorithms for discrete optimization problems, and in particular, on approximation algorithms for NP-hard and other computationally intractable problems. Linear programming relaxations have played a fundamental role in obtaining good solutions to hard optimization problems, and he continue to study their application to a range of problems in clustering, sequencing and scheduling, and inventory problems, in both deterministic and stochastic optimization settings. In addition to studying these problems with a theoretical lens, he has been involved in the practical application of these techniques in settings ranging from genomics to medical aircraft scheduling to the long-term planning for the preservation of the red-cockaded woodpecker to the operational logistics and design of bike-sharing systems.

Event
Barna Saha - Efficient Fine-Grained Algorithms

Abstract: One of the greatest successes of computational complexity theory is the classification of countless fundamental computational problems into polynomial-time and NP-hard ones, two classes that are often referred to as tractable and intractable, respectively. However, this crude distinction of algorithmic efficiency is clearly insufficient when handling today's large scale of data. We need a finer-grained design and analysis of algorithms that pinpoints the exact exponent of polynomial running time, and a better understanding of when a speed-up is not possible. Over the years, many polynomial-time approximation algorithms were devised as an approach to bypass the NP-hardness obstacle of many discrete optimization problems. This area has developed into a rich field producing many algorithmic ideas and has lead to major advances in computational complexity. So far, however, a similar theory for high polynomial time problems to understand the trade-off between quality and running time is vastly lacking. 

In this presentation, I will give you an overview of the newly growing field of fine-grained algorithms and complexity, and my contributions therein. This will include fundamental problems such as edit distance computation, all-pairs shortest paths, parsing and matrix multiplication. They have applications ranging from genomics to statistical natural language processing, machine learning and optimization. I will show how as a natural byproduct of improved time complexity, one may design algorithms that are highly parallel as well as streaming algorithms with sublinear space complexity. Finally, motivated by core machine learning applications, I will discuss alternative measures of efficiency that may be equally relevant as time complexity.

Bio: Barna Saha is currently an Assistant Professor in the College of Information & Computer Science at the University of Massachusetts Amherst. She is also a Permanent Member of Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers. Before joining UMass in 2014, she was a Research Scientist at AT&T Shannon Laboratories, New Jersey. She spent four wonderful years (2007-2011) at the University of Maryland College Park from where she received her Ph.D. in Computer Science. In Fall 2015, she was at the University of California Berkeley as a Visiting Scholar and as a fellow of the Simons Institute. Her research interests include Theoretical Computer Science, Probabilistic Method & Randomized Algorithms and Large Scale Data Analytics. She is the recipient of NSF CAREER award (2017), Google Faculty Award (2016), Yahoo Academic Career Enhancement Award (2015), Simons-Berkeley Research Fellowship (2015), NSF CRII Award (2015) and Dean's Dissertation Fellowship (2011). She received the best paper award at the Very Large Data Bases Conference (VLDB) 2009 for her work on Probabilistic Databases and was chosen as finalists for best papers at the IEEE Conference on Data Engineering (ICDE) 2012 for developing new techniques to handle low quality data.

 

    News
    Prof. Grigas Receives NSF CRII Award To Improve Machine Learning Methods
    February 7, 2018

    IEOR professor Paul Grigas was recently awarded the National Science Foundation's Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII) grant in the amount of $175,000 to investigate large-scale optimization methods and develop algorithms to improve scalability and accuracy with large datasets. Grigas will work with graduate students on the research, and the results will be integrated into the curriculum of the IEOR undergraduate machine learning and graduate-level optimization and statistical learning courses.

    Award info here and abstract below:

    Large-scale optimization methods have been paramount to the successes of recent applications of machine learning and data analysis in a wide variety of domains. At the same time, certain structural properties of statistical models, such as sparsity or low-rank structure, have proven to be crucial for obtaining meaningful and accurate results in high dimensions. In addition to being highly scalable to large datasets, some optimization algorithms have the desirable property that they directly promote the aforementioned valuable structural properties of models. This project involves developing, analyzing, and implementing novel optimization algorithms that have such beneficial structure-exploiting and also memory-efficiency properties. This project directly involves the mentoring of graduate students, as well as integration of research results into an undergraduate level machine learning course and a graduate level course in optimization and statistical learning.

    The foundation for this project is the Frank-Wolfe Method, a particular structure-exploiting first-order gradient optimization algorithm, and the related methodology of in-face directions. In-face directions automatically promote well-structured near-optimal solutions and have encouraging memory-efficiency properties. This research will investigate conditions whereby methods with in-face directions, as applied to convex relaxations of matrix completion and more general atomic norm regularization problems, are guaranteed to have a low memory footprint. Furthermore, this project will extend the reach of methods that incorporate in-face directions to new problem classes, including non-smooth objective functions, non-convex objective functions, and stochastic gradient estimates. The proposed optimization framework and in-face methodology applies very generally, and has potential for broader impact in several areas, including recommender systems, bioinformatics, customer segmentation, sentiment analysis, and medical imaging.

    The NSF CISE CRII program supports independent research for faculty starting in their first academic position. 

     

     

    Event
    Max Shen - University of California, Berkeley

    Research: Max Shen is a professor at UC Berkeley in the Industrial Engineering and Operations Research Department. He researches supply chain design, design of optimization algorithms, energy systems optimation, and transportation system planning among other strategies.

    He currently works on the following projects:

    1. Projects funded by National Science Foundation
    2. Energy Management Systems for Smart Grid Users with Flexible Demand (Siemens)

    3. Towards a Green Supply Chain. Life Cycle Implications of Shipping Goods to California from Mexico vs. China (Blum Center)

    4. California Department of Transportation

     

    News
    Prof. Oren video interview with INFORMS History and Traditions
    February 2, 2018

    IEOR Professor Shmuel Oren was recently interviewed by Dr. Ramteen Sioshanshi, professor of integrated systems engineering at The Ohio State University, presented by INFORMS as part of the History and Traditions series.

    Prof. Oren was recently inducted into the National Academy of Engineering.

     

    News
    Fall 2018 Grassi & Marshall-Oliver Rosenberger (MOR) Fellowship
    February 8, 2018

    IEOR Endowed PhD Fellowships 

    - Application opens March 1st. Please check back here then for more details on how to apply.
     
    Purpose:  
    The Berkeley IEOR Department has two endowed PhD fellowships and anticipates growth in the number of endowed fellowships. This document describes the process and accompanying rationale to award our PhD fellowships. The two Fellowships currently available for awarding are: 
    Grassi Fellowship - This fellowship is for domestic PhD students working in the areas of Manufacturing, Industrial Systems, and Service Systems. 
    Marshall-Oliver-Rosenberger (MOR) Fellowship - This fellowship is for PhD students with a demonstrated interest in decision science and analysis for important societal and industrial problems. 
     
    Rationale:  
    In choosing an awarding process, several goals were considered. Given the increasing competition in PhD student and faculty recruitment - it is important that the fellowships gain an external reputation of being a prestigious award, which will attract top students and help the most promising students from the department secure faculty positions. As a result, additional eligibility and evaluation criteria are included beyond the endowment-established criteria. It is also desirable to have an equitable allocation (over multiple years) of the fellowships across the departmental faculty and across eligible students.  
     
    Number of Awards: 
    Each award shall be for one semester: 
    To ensure equitable allocation of the fellowships, two PhD students will be awarded the Grassi fellowship each year, and two PhD students will be awarded the MOR fellowship each year.  Winners of the fellowship will have the option to accept the support for exactly one of either the Fall or the Spring semester. Note that both winners of a particular fellowship could choose to receive their support during the same semester.  
     
    Eligibility Criteria and Nomination Process: 
    Grassi Fellowship Criteria: ​Nominees must have not previously received the Grassi 
    Fellowship, hold an ABET-accredited undergraduate degree in engineering, be a US citizen, and be pursuing an exciting PhD research in the areas of Manufacturing, Industrial Systems, and Service Systems.  If no sufficiently qualified candidates are available to meet these criteria, the first criterion may be waived.  
     
    MOR Fellowship Criteria:​  
    Nominees must have not previously received the MOR 
    Fellowship, pursuing an exciting research topic, and should have have passed the PhD Qualifying Examination (if no post-Qual students are eligible the award may go to pre-Qual PhD students).  
     
    Applications:  
    IEOR PhD Students in good standing may apply for either or both Fellowships by sending an email to Anayancy by the date specified with Endowed PhD Fellowship Application on the Subject Line and including as one .pdf file: 
     
    1. Full Name, SID, start date, expected graduation date, and future career plans. 
    2. Advisor, Title of Research Project, and Prior/Existing sources of funding. 
    3. Have you previously received the Grassi or MOR Fellowship and if so when? 
    4. If applying for the Grassi Fellowship, confirmation you are a US Citizen and have completed an ABET-accredited undergraduate degree in engineering. 
    5. If applying for the MOR Fellowship and already passed the Quals, date when you passed the Qual Exam. 
    6. Half-page summary of your PhD Research Project, potential impact on research and applications, and initial results. 
    7. Copy of your CV with list of publications and URL of your website with links to key publications. 
    8. Being in the last year or having passed the qualifying exam is not a requirement for either award.
     
    Selection: 
    This fellowship will be awarded by  the EPFS Committee appointed by the Department Chair.   Students will be selected by each August, and a record of past advisors of student winners will be considered in an effort to allocate the student awards somewhat fairly among full-time faculty (eg, as many faculty as possible should benefit, with a slight preference for junior faculty). 
     
    Evaluation Criteria for Grassi Fellowship: 
    The committee will select the fellowship recipients among the eligible nominated students by careful consideration of the achievements of the students. Particular emphasis will be given on the student’s excellence in research, and priority will be given to students that have successfully passed the qualifying examination, are in their last year of PhD study, and are planning to apply to faculty positions in the coming academic year. Being in the last year or having passed the qualifying exam is not a requirement for either award.
     
    Evaluation Criteria for MOR Fellowship: 
    This fellowship will be awarded by the EPFS committee appointed by the Department Chair. The committee will select the fellowship recipients among the eligible nominated students by careful consideration of the achievements of the students. Particular emphasis will be given on the student’s excellence in research and for societally or industrially impactful research, and priority will be given to students that are in their last year of PhD study and are planning to apply to faculty positions in the coming academic year. 
     
     

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