STEM grant awarded to Math/Stat Department
Last year, the APLU was awarded a 5-year, $3,000,000 collaborative NSF grant called SEMINAL (Student Engagement in Mathematics through an Institutional Network for Active Learning). Through SEMINAL, faculty will collaborate to better understand both how to sustain success in implementing active learning in undergraduate mathematics classes and how to influence similar success at other institutions. In January 2018, 9 more universities joined the grant, inlcuding Loyola University Chicago which was the first private university to be awarded this grant.
Active Learning of Mathematics is defined as teaching methods and classroom norms that promote:
- students’ deep engagement in mathematical reasoning;
- peer-to-peer interaction; and
- instructor interest in and use of student thinking.
Loyola's application was judged based on the following criteria:
- Curriculum should focus on key mathematical ideas (sense making & procedural fluency).
- Students propose questions, communicate reasoning, & share solutions in process.
- Instructors promote student engagement & build on student thinking."
Loyola has strong motives for pursuing the SEMINAL grant. For one, students who do poorly in a pipeline (precalculus and calculus) math course greatly reduce their options for majoring in STEM fields and pursuing STEM related jobs. By improving student success rates and confidence in pipeline math courses at Loyola, we hope pipeline math courses at Loyola serve as a gateway to STEM not a barrier. Secondly, enrollment in pipeline mathematics courses at Loyola is soaring: the result both of growing numbers of incoming freshmen and the recent introduction of new STEM initiatives, including the Engineering Science Program, Applied Mathematics Major, as well as some work behind the scenes. Our targeted introductory courses (Math 117/118 and Math 161/162) play a crucial role in increasing the number of students from underrepresented populations like Women in STEM. Finally, the largest study of undergraduate STEM education literature to date — a meta-analysis of 225 studies published by the National Academies in 2014 — stated 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.
The Department of Mathematics and Statistics is excited and honored to be awarded this grant, as we seek to prepare people to lead extraordinary lives.
openWAR on Baseball Analytics
One of Loyola's very own professors, Dr. Greg Matthews, created a competitive aggregate measure with baseball analytics called openWAR, that is based upon public data and methodology with greater rigor and transparency. WAR stands for Wins Above Replacement. This measure aggregates the contributions of a player in each facet of the game: hitting, pitching, base running, and fielding. Current versions of WAR depend upon proprietary data, ad hoc methodology, and opaque calculations to capture overall player performance.
As MLB award season arrives, no prize looms larger than Most Valuable Player. From a statistical perspective, the best guide to the MVP award is undoubtedly wins above replacement, and some voters develop their MVP ballots at least in part based on WAR. The problem is that we don’t know who truly has the most WAR in each league. WAR looks like a single easy-to-understand stat, but it’s the product of a complex model.
The true value of a player varies from what you find on the leaderboard — but we're not sure by how much. That’s because of sample size. Although a whole season of baseball seems like a lot, it still doesn’t provide enough data to allow us to be completely sure of each player’s value. With statistics, comes uncertainty. We measure that uncertainty with confidence intervals. The smaller intervals yield greater confidence and larger intervals yield a lesser confidence. A confidence interval is a range that, based on statistical analysis, is thought to contain the true value of a player a certain percentage of the time.
Dr. Matthews uses openWAR to generate fictional seasons with randomized plays. He found the confidence intervals are simply too large to surely determine who is the more valuable player. Much of the uncertainty in WAR comes from our imperfect measurements of defense. Because the vast majority of defensive plays are routine, a player’s true defensive skill can be seen only on the few plays that are between the impossible and the everyday. Without detailed data on how defenders are positioned before the play starts, most of our metrics are confounded by the front office’s ability to instruct its players on where to stand.
WAR itself is not complete. Although all versions of WAR available today cover the basics of player value (hitting, fielding, base running and pitching), no current version picks up on some of the more esoteric skills in baseball, such as a catcher’s pitch framing. Despite all of its flaws, WAR is still the best available tool for judging value, and certainly exceeds the older alternatives, such as RBIs and pitcher wins. At a minimum, WAR can tell us who the MVP isn’t.
Dr. Matthews earned a Ph.D. in statistics in 2011 from the University of Connecticut. Prior to this, he spent two years in the "real world" in a direct marketing department building predictive models after receiving his B.S. in actuarial science and M.S. in applied statistics from Worcester Polytechnic Institute (WPI) in 2004 and 2005, respectively. Most recently Dr. Matthews completed a 3 year appointment as a post-doctoral research fellow at the University of Massachusetts-Amherst. His research interests include statistical disclosure control, missing data methods, statistical genetics, and statistics in sports. He runs a blog about statistics and its applications. Check it out for more updates on the openWAR discussion and a direct download to his application.
What did you do this summer?
From Left to Right: Alicia Kantor, Nicholas Marey, Shayna Milstein, Madison McCallister
- How and where did you spend your summer? What was your position? And for some context, what level of college study are you currently pursing?
Alicia: I had an internship at Nokia in Naperville, IL this summer. I was an intern in the quality planning department doing data analytics. I am currently a junior pursuing my Bachelor’s in Mathematics and Statistics.
Nicholas: I spent my summer working for Allant Group as an Analytic Solutions Intern, imputing values for missing data based on the type of data that was missing, as well as creating customer waterfall data visualizations in Power BI. I am currently pursuing a Masters of Science in Applied Statistics.
Shayna: I spent my summer as an operations intern at Riptide Autonomous Solutions, a underwater robotics company located in Boston, MA. We manufacture micro-UUVs, or micro-unmanned, underwater vehicles. I am currently a senior pursuing my Bachelor’s in Mathematics.
Madison: I spent my summer researching Temperley-Lieb algebras. I was a research assistant under Dr. Emily Peters. About once a week, we would meet on campus to discuss the material and exercises I was studying. I am currently in my senior year of my undergraduate studies.
- How did you get the position? Where did you find it? Did someone offer it to you?
Alicia: I added some adults from my neighborhood at home on LinkedIn and one reached out to me asking if I had an internship for the summer. I did not have anything lined up yet so said I would be interested in working in his department at Nokia. After this initial introduction to the position I was given a phone interview with the man who would be my direct manager and I got the job!
Nicholas: Similar for me, I found the job through LinkedIn. After going through several interviews, I received an offer to work for Allant Group.
- Describe your average day on the job.
Nicholas: On an average day, we went through different SAS or SQL code and talked about how it applied to the problem I was working on. It was tricky because the data set was very large. Thus, it took a long time to find out whether what I coded was correct and if not, where the problem was.
- What was your favorite part? Were there any difficult or challenging tasks?
Madison: My favorite part was that I was researching a topic of mathematics that has a lot of unanswered questions. It was exciting to think that I was helping produce more knowledge in an area so unfamiliar to most. And even influencing what these findings could be used for in the future. The most difficult task was working through the exercises Dr. Peters assigned me. One problem took an entire month to solve. After writing up the solution neatly, it was nearly 17 pages long. Every week I would have more questions, locate and correct my errors until finally I found my desired solution.
- What did you get out of it? Did you learn any skills or something about yourself?
Shayna: The environment was just astounding!! The smartest, most ambitious people I have ever been around. I loved working so closely with such remarkable engineering and businessmen, getting to see and understand how our vehicles are created and further built. I asked so many questions! I took every single opportunity to learn more and grow. That was how I was able to be successful.
- Did you use any programming language(s)? If so, what language(s)? Did you already know them, or were you trained?
Alicia: I primarily used Excel. I had not taken any Excel classes yet but I did have some previous knowledge about it. I learned a ton about excel functions and capabilities by teaching myself what I needed to use for the job. I don't know where I'd be without Google! I also was vaguely introduced to R and I helped a few other people with Python on occasion but that was it. I was able to do some statistical modeling, which I had not done a ton of before. I applied basic statistical concepts and even used matrices on occasion.
- Who did you work with? Did you make any new connections? Did it reaffirm or lead you to re-evaluate your career goals?
Shayna: Riptide was housed in a sustainable tech incubator, so I was surrounded by dozens of young millennials who were mostly recent graduates of top-tier schools like Harvard and MIT. I had never been surrounded by people like that, more specifically motivated, intelligent, engineer type people. It completely changed my life and set me on a new path to want to return to that atmosphere for graduate school in Mechanical Engineering.
Alicia: I had two direct managers - the R&D engineer and engineer manager - who I did most of my work for but I worked frequently with the data management director. There was also another intern who I worked with to help implement automated metric targets. I was able to meet many other people in the department as well who were working on finance or robotics. This made me want to pursue actuarial science instead of data analytics. I found myself very intrigued with the financial projects of the company as opposed to my projects so I think actuarial science is the best route for me.
- Which Loyola courses prepared you the most for summer experience?
Alicia: The courses that best prepared me for this internship were STAT 305 (Probability and Statistics 2) and MATH 212 (Linear Algebra). INFS 247 (Business Information Systems) would have been really beneficial to have taken before the internship.
Nicholas: I utilized SAS and SQL programming languages. I was taught SAS by Dr. Perry in STAT 403 , but SQL I learned through free, massive open online courses.
- How soon did you start looking for summer opportunities? Do you have any advice for undergraduate students when looking for a similar summer experience?
Nicholas: I started looking around September/October for a summer position, but I put the most effort into it around the start of the new year. I would advise other students that they should be persistent if they would like to find a summer internship.
Alicia: I started looking for internships in February. I realized that for any actuarial internship you really need to have one test under your belt so this year I am going to apply hopefully with one under my belt. I think you need to apply by 2018 to find any good internship without connections.
Welcome New Faculty!
The Department of Mathematics & Statistics welcomes three new faculty members: Marius Radulescu, Sheila Gudehithlu, & Antonio Mastroberardino. Read more about their educational background and research interests.
Ms. Gudehithlu has undergraduate and M.S. degrees in Statistics from UIUC. After completing these degrees, Ms. Gudehithlu worked in industry as well as serving as an intern at NIH. More recently Ms. Gudehithlu has returned to teaching statistics and she joins us after serving as an adjunct instructor at several Chicago area institutions.
Mr. Radulescu holds a B.S. in Mathematics from the University of Bucharest and pursued graduate studies in Mathematics at the same institution and at the Institute of Mathematics of the Romanian Academy. He also has a M.A. degree in Education and a M.S. degree in Mathematics from Chicago State University.
Mr. Radulescu has an extensive background in teaching in the Chicago area at high school, college and university levels. He holds an Illinois Professional Educator License with endorsements for Mathematics and Secondary Education. Before joining the Department of Mathematics & Statistics at Loyola University Chicago, he taught at Chicago State University, Truman College, Daley College and Morgan Park Academy. In 2015, Mr. Radulescu was nominated as a recipient of The University of Chicago Outstanding Educator Award.
Mr. Radulescu's research interests are in the area of algebraic geometry and commutative algebra. Primarily, he is interested in the study of algebraic vector bundles over elliptic fibrations.
Dr. Mastroberardino has an undergraduate degree in Mechanical and Aerospace Engineering from Cornell University and a Ph.D. in Mathematics from SUNY Buffalo. He joins us from Penn State Erie where he spent nine years on the faculty teaching a wide variety of courses.
His research interests include tear film dynamics, fluid mechanics, mathematical biology and differential equations.
Congratulations to 2017 Graduates!
At the close of the academic year, the Department of Mathematics and Statistics wishes to recognize all of our students' outstanding achievements, especially our graduates. Our 2017 graduates have excelled in difficult classes, done amazing research and secured amazing futures for themselves. Our graduates are pursuing a wide range of activities, some of which are highlighted here:
- PhD in Physics, Harvard University
- PhD in Computational Biology, Yale University
- PhD in Biomedical Informatics, Stanford University
- MS in Applied Mathematics, Tulane University
- Research Analyst, IPSOS
- Actuary, PwC
- With many more!
In addition, each year the Mathematics Department gives out awards to outstanding graduates for their service, leadership and academic achievement! Congratulations to all of the recipients below!
Fr. Rust, S.J. Memorial Award. The late Rev. Charles Rust, S.J. was a member of the Department of Mathematical Sciences from 1958 to 1984. This award is presented to a senior who has achieved an outstanding academic record in mathematics or statistics.
2017 awardee: Barbara Skrzypek
Dr. Robert Reisel Memorial Award. This award, in honor of the late faculty member Dr. Robert Reisel, is given to a student who has demonstrated outstanding achievement in advanced mathematics courses.
2017 awardee: Colin Williams
Fr. VandeVelde, S.J. Memorial Award. This award honors the late Fr. R.J. VandeVelde, S.J. who served as Chair and faculty member of the Department of Mathematics and Statistics and is awarded to a student who has demonstrated both academic excellence in mathematics and a commitment to service to others.
2017 awardee: Corey Kuhn
Fr. Gerst, S.J. Memorial Award. The late Francis Gerst, S.J. was a member of the Department of Mathematical and Computer Sciences from 1931 to 1962. This award is presented to a graduate who has achieved an outstanding academic record in mathematics or statistics.
2017 awardee: Colton Burns
Richard J. Driscoll Award. Dr. Richard J. Driscoll was a faculty member in the Department of Mathematics and Statistics for 35 years, retiring in 1993. This award is presented for outstanding academic achievement in mathematics.
2017 awardee: Neha Siddiqui
Dr. Laura Mayer Memorial Award. The late Dr. Laura Mayer was a faculty member in the Department of Mathematics and Computer Sciences from 1988 until 1997. The award is presented to a graduating senior who wishes to pursue a career in mathematics or mathematics education and has achieved a record of academic excellence and service.
2017 awardee: Alina Jenkins
Joseph Zajdel Memorial Award. This award, in memory of the late Professor Joseph Zajdel, is presented to a graduating senior who has exhibited remarkable academic performance in the mathematical sciences.
2017 awardee: Phillip Kulas
Departmental Honors. Conferred upon students who maintain an outstanding academic record within their mathematics and statistics courses.
2017 awardees: Saadia Ansari, Thomas Atchley, Krishna Doshi, Kajal Chokski, Patrick Fagan, Nathaniel Gross, Alexander Pizzuto, Justin Stuck Kara VanderMale
Mathematics and Statistics at URES 2017
The Department of Mathematics and Statistics is proud to have had a number of students participate in the Undergraduate Research Symposium this past weekend. Students' research had a variety of focuses. While some undergraduates focused on their second majors such as physics, psychology, and chemistry, others had projects on pure math such as combinatorics as well as statistics. Take a look at the photos of our students participating in the Weekend of Excellence.
Some of the Mathematics/Statistics Students and their Presentations
Congratulations to all of the student presenters from the weekend (below)! Their project titles and descriptions can be found in the URES Program here!
Poster Session 1: 11:00AM – 12:30PM
Krishma Doshi (Mathematics) (1)
Erik Mainellis (Mathematics) (26)
Oral Presentation (Mundelein 406) (12:50 – 1:50)
Should You Vote? Mathematical Analyses of Voting and Abstention – Colin Williams (Mathematics)
Parallel and Distributed Simulation Methods for Granular Fluid Dynamics – Justin Stuck (Mathematics)
Poster Session 2: 2:00 – 3:30 PM
Sadia Ansari (Mathematics) (57)
Evelyn Cody (Statistics) (64)
Evan Cudone (Mathematics) (94)
Xianghong Luo (Applied Mathematics) (97)
Joseph Macaluso (Mathematics) (43)
Jon Matthew(Bioinformatics) (67)
Alexander Pizzuto & Barbara Skrzypek (Applied Mathematics) (37)
Shyam Shah (Statistics) (51)
Barbara Skrzypek & Justin Stuck (Applied Mathematics) (58)
Neha Siddiqui (Mathematics) (68)
Marian Bocea appointed NSF Program Director
Congratulations to Marian Bocea, who will become a Program Director in the Applied Mathematics section of the NSF's Division of Mathematical Sciences beginning in September, 2017.