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ARC staff

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ARC staff

Sokratis Koumas

Director

Academic Resource Center
Liberal Arts 010B

508-999-8859
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Corinne Cortes

Administrative Assistant I

Academic Resource Center
Liberal Arts 005B

508-999-8709
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Sharon Allen

Outreach Counselor

Academic Resource Center
Liberal Arts 010

508-910-6866
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STEM Learning Lab

John Fernandes

Director of STEM Learning Lab

Academic Resource Center
Science & Engineering 217B

508-999-8718
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Chris Peter

Assistant Director of STEM Learning Lab

Academic Resource Center
Science & Engineering 217

508-999-8716
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How to schedule an appointment

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Events

Feb

2:00PM

Pre-Career Fair Resume Workshop Drop-Ins

Polish up your resume before the Career Fair! Join one of our rotating drop-in sessions for a brief presentation on resume best practices followed by time to work independently on your professional documentation under the guidance of Career Ambassadors. Receive tips and personal feedback to make your resume shine!

Blue & Gold Room (MacLean Campus Center 007)

Feb

2:00PM

Pre-Career Fair Resume Workshop Drop-Ins

Blue & Gold Room (MacLean Campus Center 007)

Feb

2:00PM

Pre-Career Fair Resume Workshop Drop-Ins

Blue & Gold Room (MacLean Campus Center 007)

Feb

1:00PM

How to Network at the Career Fair

Did you know career Fairs aren't just about handing out your resume? Learn how to effectively connect with employers and make a strong impression. Help yourself put your best foot forward. Use this opportunity to showcase your professionalism and grow your network!

Grand Reading Room

Feb

9:30AM

EAS Doctoral Dissertation Defense by Esmaeil Rezaei

Topic: Towards better electricity supply management: reduced-basis enabled dimension reduction for demand forecasting using smart-meter big data Location: CSCDR TXT 105 Abstract: Real-world datasets often comprise a large number of features or variables, encompassing billions of samples. Manipulating such high-dimensional data is computationally expensive. Although some advanced strategies leverage high performance and parallel computing capabilities, this could entail complex and costly resources for users. In response, dimension reduction techniques have been developed that transform high-dimensional data into a low-dimensional space while preserving properties critical to the original data. Dimension reduction has found widespread use across various domains, including pattern recognition, clustering, classification, and accelerating numerical simulations of complex physical phenomena. Regardless of the strategy adopted, available techniques heavily rely on computationally intensive matrix factorizations, such as Singular Value Decomposition (SVD). These techniques are thus quickly becoming intractable as the world adapts to a new norm where massive datasets have become everyday commodities. In the first part of this research, we introduce a novel dimension reduction strategy, HYBRID: HYper-reduced Basis Reduction via Interactive Decomposition, which offers remarkable efficiency and includes an error indicator certifying the accuracy by which the properties of the original data are preserved. HYBRID draws upon reduced basis decomposition and recent advancements in Reduced Basis Methods (RBMs), which have garnered attention as efficient dimension reduction tools for solving parametrized partial differential equations. Specifically, we propose speeding up the construction of the reduced basis by adopting the concept of Reduced Residual, enabling efficient error measurement on a subset of dimensions proportional to the intrinsic dimension of the given dataset. We present numerical examples to demonstrate the performance of the proposed HYBRID technique and its competitive edges over exiting dimension reduction techniques. In the second part of this research, we focus on electricity demand forecasting. In a highly competitive electricity market, securing the largest share through high-quality services demands meticulous planning, particularly in accurately forecasting future loads to prevent shortages. While oversupplying can satisfy demand, the costs of energy storage technology are prohibitively high. Therefore, effective and purposeful planning is necessary for companies to ensure precise load forecasting, crucial in maintaining the reliability and resilience of electricity services. Here, we apply HYBRID to a large-scale utility dataset encompassing 3.7 million customers of Commonwealth Edison (ComEd) company in the state of Illinois to accurately forecast demand. Finally, to enhance model selection processes, we integrate HYBRID with Gradient Boosting algorithms significantly speeding up computations while introducing a minimal error. ADVISOR(S): Dr. Mazdak Tootkaboni, Dept of Civil and Environmental Engineering (Advisor) (mtootkaboni@umassd.edu) Dr. Arghavan Louhghalam, Dept of Civil and Environmental Engineering (Co-Advisor) (Arghavan_Louhghalam@uml.edu) COMMITTEE MEMBERS: Dr. Yanlai Chen, Department of Mathematics Dr. Alfa Heryudono, Department of Mathematics Dr. Negin Alemazkoor, Dept of Civil and Environmental Engineering, UVA NOTE: All EAS Students are ENCOURAGED to attend.

Textiles Building 105

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Spring 2025 Hours

ARC Central Office, LARTS 005A
Monday-Friday, 8am-4pm

Business Center, LARTS 010
Monday-Friday, 9am-5pm

STEM Learning Lab, SENG 217
Monday-Friday, 9am-5pm

Online tutoring available nights and weekends.

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