Congratulations to our 2018 graduates in the Department of Mathematics & Statistics. Awards have been presented to our Outstanding Graduates and Faculty. The following graduating candidates have been nominated by department faculty to recognize their outstanding academic performance and achievements. We extend our sincere congratulations to the awardees and to all candidates graduating with their Baccalaureate, Master, and Doctoral degrees. Good luck in your future endeavors ahead. Commencement Ceremonies were held on Saturday, May 12 in Viejas Arena on the campus of SDSU. Click here to view the College of Sciences Commencement.
Academic Excellence in Mathematics
JUSTIN GIOVANNI GALIOTO
B.S. Mathematics, Applied Arts & Sciences, emphasis Applied Mathematics
andKYLE DOUGLAS LEMAIRE
B.S. Mathematics, Applied Arts & Sciences, emphasis Applied Mathematics
Academic Excellence in Mathematics Education
JACOB MICHAEL HAMPSON
B.A. Mathematics, Single Subject Teaching Credential
Academic Excellence in Statistics
NATALIE VICTORIA SISTO
B.S. Statistics, Applied Arts & Sciences, emphasis Actuarial Science
andGUY YIFRAH
B.S. Statistics, Applied Arts & Sciences, emphasis Actuarial Science
Nominee for College of Sciences Outstanding Student
BRENDA LEE MELENDREZ
B.A. Mathematics, Single Subject Teaching Credential
andNATALIE VICTORIA SISTO
B.S. Statistics, Applied Arts & Sciences, emphasis Actuarial Science
Academic Excellence in Mathematics
ANTONIO JOSE SILVETI-FALLS
M.S. Applied Mathematics, concentration Dynamical Systems
andGEORGE ZOUHEIR ISTAMBOULI
M.S. Applied Mathematics, concentration Math Theory
Academic Excellence in Mathematics Education
BERTHA ELIZABETH ALVAREZ
M.A. Teaching Service, specialization Math Community College Teaching
Academic Excellence in Statistics
MARC SCHNEBLE
M.S. Statistics
andKEVIN PELAEZ
M.S. Statistics
Academic Excellence in Biostatistics
RORY MICHAEL BLOCH
M.S. Statistics, concentration Biostatistics
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Dr. Shen’s speech focused on the curriculum modernization to meet the educational demand of tomorrow’s carbon-neutral lifestyle which will be supported by technical languages, big data, and artificial intelligence. The technical languages will be computer codes, the languages’ vocabularies will be big data, and the languages’ grammar will be artificial intelligence.
Dr. Shen said, “The most essential skill a college student will have to learn is the future technical languages based on computers. People will use computer codes to make daily communications, to speak, to write, and to present. Each university student will have to learn one or multiple computer languages, such as Python, but easier than Python. The languages’ vocabularies will be big data. Students will need to learn thousands of big datasets for their effective and accurate communications. The languages’ grammar will be artificial intelligence. Students will need to take machine learning and other artificial intelligence classes so that they can use their technical languages with correct grammar.”
Mathematics curricula will have to be modernized more urgently than other areas, because the future technologies will use more mathematics than ever before. Future mathematics education may need to be more customized to specific majors than before. The one-size-fits-all curricula of mathematics education will not work anymore. This requires a mathematics department to design new curricula for many different disciplines, such as engineering mathematics, biomathematics, climate mathematics, chemical mathematics, psychology mathematics, musical mathematics, fine arts mathematics, computer mathematics, pure mathematics, and so on. The customized mathematics education requires the instructors to be specialized in not only mathematics, but also a customized area as well as statistics and computing. Teaching requires the frequent use of the examples and big datasets from the customized area. Therefore, universities will have to produce more cross-disciplinary PhD’s and make more joint faculty appointments between mathematics and other departments.
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Statistics 410 is a required course in the Statistics Major with an Emphasis in Data Science. For the general Statistics Major, Statistics 410 will be included in the list of twelve units selected from Statistics 325, 496, 510, 520, 560, 575, 580, and 596. For the Statistics Major with an Emphasis in Actuarial Science, Statistics 410 will be included in the list of three units selected from Statistics 325, 410, 496, 560, and 596.
Mathematics
Mathematics Course Renumbering
Course Title |
Old Number |
New Number |
Abstract Algebra |
Math 521A |
Math 320 |
Advanced Calculus I |
Math 534A |
Math 330 |
Programming in Mathematics |
Math 541/242 |
Math 340 |
Algebraic Structures |
Math 521B |
Math 520 |
Advanced Calculus II |
Math 534B |
Math 530 |
Example 534A is equivalent to 330, take only one of these.
In order to make the path to a Mathematics Bachelor’s degree clearer, several core courses were renumbered from 500 to 300-level. Mathematics majors should complete the 300-level courses right after completion of their required lower division classes. Thus, following Math 245, 252, and 254, Mathematics majors should next take the core courses in Abstract Algebra (MATH 320), Advanced Calculus (MATH 330), Differential Equations (MATH 337), and Numerical Analysis (MATH 340).
The core courses give a broad understanding of mathematics, and more formal training than lower division courses provide. They build a strong foundation for student success at the 500-level.
rev: 03/14/2018
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A National Science Foundation grant
supports a new approach for STEM degree hopefuls.
“There is clear and growing evidence that we can improve math learning and retention for all students through active learning that promotes cognitive engagement.”
Challenging introductory mathematics courses are the most common roadblock to earning undergraduate degrees in the STEM fields. In an effort to help students get past this roadblock, San Diego State University and 11 other universities across the nation announced they will scale up the adoption of “active learning” skills for undergraduate pre-calculus and calculus instruction.
Active learning, explained SDSU mathematician Chris Rasmussen, refers to a broad range of instructional approaches that provide students with opportunities to engage in the learning process with meaningful mathematical activities. Active learning also improves skills such as communication and teamwork, which are highly valued by employers.
“There is clear and growing evidence that we can improve math learning and retention for all students through active learning that promotes cognitive engagement,” he said.
Three SDSU faculty involved
Over the past year, SDSU has worked with the Association of Public and Land-Grant Universities (APLU), the University of Colorado-Boulder and the University of Nebraska-Lincoln to better understand how math departments can increase and sustain the use of active learning in introductory mathematics courses. Co-principal investigators Rasmussen, Mike O’Sullivan and Janet Bowers in the Department of Mathematics and Statistics at SDSU are leading this initiative.
Eight other institutions will join the effort to further study and develop practical models applicable to virtually any institution. Those additional partners include: California State University, East Bay; California State University, Fullerton; Kennesaw State University; Loyola University; Morgan State University; Ohio State University; the University of Maryland; the University of Oklahoma; and the University of Texas Rio Grande Valley.
The National Science Foundation (NSF) is supporting the project, known as SEMINAL: Student Engagement in Mathematics through an Institutional Network for Active Learning, with a $3 million, five-year grant. The initiative will place particular emphasis on helping underrepresented minority students succeed in introductory math courses that are the foundation of STEM fields.
Research-based effort
Far too many students hoping to pursue careers in STEM fields get tripped up by introductory math courses right from the start, explained Howard Gobstein, executive vice president of the APLU and one of the principal investigators of the NSF-funded initiative.
“With a persistent shortage of skilled workers in STEM fields and unequal access to all students, we have a tremendous opportunity to broaden participation and address the biggest hurdle for students’ success,” he said. “We are thrilled to scale an approach that we know works to help more students realize their dreams in STEM fields.”
Research has shown that introductory math courses provide the cornerstone for success in STEM majors and fields, and active learning has proven highly effective in helping more students succeed in such core courses. For example, the largest study of undergraduate STEM education literature to date—a meta-analysis of 225 studies published by the National Academies in 2014—found that undergraduate students in classes using active learning methods had higher course grades by half a letter grade, and students in classes with traditional lectures were 1.5 times more likely to fail.
“In response, the presidents of the professional societies in the mathematical sciences have called for the incorporation of these practices into all mathematics courses,” said David M. Bressoud, director of the Conference Board of the Mathematical Sciences. “But most faculty are not conversant with how to do this effectively, and most departments do not know how to foster the changes that need to be made. SDSU, APLU and their partnering universities through SEMINAL are demonstrating how departments can enable and support these innovations.”
By Jill Esterbrooks
http://newscenter.sdsu.edu/sdsu_newscenter/news_story.aspx?sid=77079
January 25, 2018
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My interests in mathematics have a wide range, including numerics, optimization and graph theory to insurance. I try to acquire insights in many different areas of mathematics to broaden my knowledge. At SDSU, I can attend courses in various areas that enrich my mathematical interests. I am grateful to have received the Presidential Graduate Fellowship and a scholarship from the Talanx Foundation. These resources have made it possible for me to attend SDSU.
MARC SCHNEBLE
For the last five years, I was studying mathematics and management at Ulm University in the south of Germany – with three years in the undergraduate program and two years in the graduate program. I had an early interest in actuarial sciences and probability theory. I acquired a profound knowledge of insurance mathematics/economics and stochastic processes. My master’s thesis covered a stochastic capital market model and applications in life insurance.
I selected SDSU for graduate studies in applied statistics for my desired profession as an actuary. This semester, I am attending statistics courses with the main objective being how to apply the theoretical knowledge learned with the statistics software R.
I received a Fulbright Grant and a Presidential Research Graduate Fellowship from SDSU to support my studies and be able to live in San Diego. I am writing a thesis in the area of climate research. After graduation, I shall return to Germany and pursue a PhD at Ulm University.
MAXIMILLIAN AUTENRIETH
I was always fascinated by detecting logical conclusions under given conditions and assumptions. I did my undergraduate study in mathematics and management at the University of Ulm in Germany. I am close to completing my master’s degree in mathematics and management. Participating in the exchange program allows me to achieve an American and German master’s degree. This affords me the opportunity to gain highly valuable experiences and deep insights into the field of statistics and the practical analysis of data. In the context of data science, the versatile applicability of statistics and stochastic models caught my interest. In particular, the development of machine learning algorithms and the detection of existing patterns and prediction of further patterns allows for intriguing opportunities for me.
I received a Fulbright Grant, the Presidential Graduate Research Fellowship from the SDSU and a scholarship from the Foundation of German Business. After completing my year in San Diego, I plan to earn my master’s degree in mathematics and management. My goal is to pursue a PhD or to work in an international operating company.
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LUDWIG SIEGERT: I finished my undergraduate study in mathematics and management at the University of Ulm in Germany and continued with a master’s degree in the same area of studies. I appreciate the opportunity to continue my studies at the SDSU in the next year. The SDSU is accepting students with a mathematical background from Ulm for years and I am glad to participate in this exchange program that led me to the SDSU. After completing my graduate studies at the SDSU I plan to go back to the University Ulm to finish my master’s degree there.
My interests in mathematical topics have a wide range, from topics in numerics, over optimization and graph theory to insurance related topics. I try to get insights in many different areas of mathematics, to broaden my knowledge and gain insights in many different topics. Here at the SDSU, I can hear courses in various areas that enrich my mathematical knowledge.
I’m very grateful to receive the Presidential Graduate Fellowship and a scholarship from Talanx Foundation. With this help, it became possible for me to attend the SDSU.
MARC SCHNEBLE: The last five years I was studying mathematics and management at Ulm University in the south of Germany – thereof, three years in the undergraduate program and the last two years in the graduate program. I began early to deepen in actuarial sciences and probability theory obtaining profound knowledge in insurance mathematics/economics and stochastic processes. My master’s thesis covered a stochastic capital market model and one of its applications in life insurance.
To attend more applied classes I chose the graduate statistics program at San Diego State University. In particular having knowledge in applied statistics is essential for an actuary which is my desired profession. This semester, I’m attending three courses (STAT 580,673,700) plus a seminar. In all of them the main objective is how to apply the theoretical knowledge learned in the lecture with the statistics software R.
I’m funding my studies at SDSU being a teaching assistant for STAT 250. Therefore I’m the instructor for two lab sessions and am offering office hours in the statistics learning center. Additionally I’m grateful to have received a Fulbright Grant and a Presidential Research Graduate Fellowship from SDSU. In order to receive my master’s degree in statistics at SDSU, I’m writing a thesis in the area of climate research. After having graduated next year, I will go back to Germany and pursue the aim to make a PhD at Ulm University.
MAXIMILLIAN AUTENRIETH: In general, I was always fascinated of detecting logical conclusions under given conditions and assumptions. Therefore, I did my undergraduate study in mathematics and management at the University of Ulm in Germany and I have almost finished my master degree in mathematics and management. Participating in the exchange program of Ulm University, I chose to attend SDSU, since my one-year program in San Diego not only allows me to achieve both, the American and German master’s degrees, but it rather gives me the opportunity to gain highly valuable experience and deep insights into the field of statistics and the practical analysis of data. In the context of data science, the versatile applicability of statistics and stochastic models caught my interest. In particular, the development of machine learning algorithms and hereby the detection of existing and prediction of further patterns allow intriguing opportunities.
I’m very grateful to receive a Fulbright Grant, the Presidential Graduate Research Fellowship from the SDSU and a scholarship from the Foundation of German Business (sdw). Thereby, in conjunction with my Teaching Assistantship at the Department of Mathematics and Statistics, my stay at SDSU was made possible. In addition to my academical education I consider the year in California related with the immersion in the local culture as a significantly enrichment of my personal development.
After my year in San Diego, I am planning to earn my master degree in mathematics and management and my goal is to pursue a PhD or to apply my study in an international operating company.
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As Program Chair, Rich will play a leadership role on the American Statistical Association Committee on Meetings. He will organize the JSM Round Table Luncheons at the 2018 JSM in Vancouver, and then Chair a 44 person Program Committee of Society and Section representatives to organize the scientific program for the 2019 JSM in Denver, Colorado.
National statistics organizations from many countries participate in the JSM including
• American Statistical Association
• International Biometrics Society
• Institute of Mathematical Statistics
• Statistical Society of Canada
• International Chinese Statistical Association
• International Indian Statistical Association
• Korean International Statistical Society
• International Society for Bayesian Analysis
• Royal Statistical Society
• International Statistical Institute
For more information see
http://ww2.amstat.org/
Antonio Palacios and Visarath In of SPAWAR have just published a book in Springer’s Complexity Series, Symmetry in Complex Network Systems: Connecting Equivariant Bifurcation Theory with Engineering Applications. Dr. Palacios has been collaborating with SPAWAR for many years, and the work has resulted in numerous patents and products.
This book bridges the current gap between the theory of symmetry-based dynamics and its application to model and analyze complex systems. As an alternative approach, the authors use the symmetry of the system directly to formulate the appropriate models, and also to analyze the dynamics. Complex systems with symmetry arise in a wide variety of fields, including communication networks, molecular dynamics, manufacturing businesses, ecosystems, underwater vehicle dynamics, celestial and spacecraft dynamics and continuum mechanics.
The book is intended for a broad audience. For engineers who might be interested in applying ideas and methods from dynamical systems with symmetry and equivariant bifurcation theory to design and fabricate novel devices. For mathematicians and physicists who might be interested in translation research work to extrapolate fundamental research theorems into practical applications. And for scientists from STEM disciplines who might be interested in the interplay between theory and real-life applications from the general field of nonlinear science.