Statistics Track Overview

Our faculty (in alphabetical order): (*Email is the best way to reach us*) 

Core faculty 

Professor Hongshik Ahn (Hongshik.Ahn@stonybrook.edu), math tower 1-112 

Professor Pei-fen Kuan (Peifen.Kuan@stonybrook.edu), math tower 1-113 

Professor Jian Li (jian.li.3@stonybrook.edu), math tower 1-106  

Professor Yi Liu (yi.liu.4@stonybrook.edu), math tower 1-103   

Professor Silvia Sharna (silvia.sharna@stonybrook.edu), Physics A-137 

Professor Weihao (Ian) Wang (weihao.wang@stonybrook.edu), Physics A-137 

Professor Song Wu (song.Wu@stonybrook.edu), math tower 1-114 

Professor Haipeng Xing (Haipeng.Xing@stonybrook.edu), math tower 1-102 

Professor Chenyu You (chenyu.you@stonybrook.edu), Physics A-148 

Professor Jiawei (Joe) Zhou (jiawei.zhou.1@stonybrook.edu), math tower 1-101 

Professor Wei Zhu (Wei.Zhu@stonybrook.edu), math tower P-138 

Affiliated faculty from the Department of Preventive Medicine 

Professor Jie Yang (Jie.Yang@stonybrook.edu) 

Other affiliated faculty: http://www.stonybrook.edu/commcms/ams/people/affiliatedfaculty.php 

Masters course requirement (10 courses <30-credit>, no thesis): 

Required Courses for M.S. Degree in Statistics Track 
AMS 507 Introduction to Probability (Fall) 
AMS 510 Analytical Methods for Applied Mathematics and Statistics (Fall)  
AMS 550 Stochastic Models (Spring *** only required for PhD students ***) 
AMS 570 Mathematical Statistics I (Spring) 
AMS 572 Data Analysis (Fall) 
AMS 573 Design and Analysis of Categorical Data (Spring) 
AMS 578 Regression Theory (Spring) 
AMS 580 Statistical Learning (Spring) or AMS 586 Time Series Analysis (Fall) 
AMS 597 Statistical Computing (Spring) 

Plus, two electives chosen from other graduate courses in the department or (with approval) graduate statistics courses in other departments. Some popular choices: 

AMS 595 Fundamentals of Computing  (Fall) 

AMS 511 Foundation of Quantitative Finance (Fall)  

AMS 516 Statistical Methods in Finance  (Fall) 

AMS 520 Machine Learning in Quantitative Finance (Fall) 

AMS 521 Data Management (Spring)  

AMS 530 Principles in Parallel Computing (Fall)  

AMS 562 Introduction to Scientific Programming in C++ (Fall)  

AMS 563 Medical Image Analysis (Spring)  

AMS 598 Big Data Analysis (Fall) 

AMS 560 Big Data Systems (Fall) 

AMS 580 Statistical Learning  (Spring, if not chosen as a core course) 

AMS 586 Time Series Analysis  (Fall, if not chosen as a core course)

AMS 550 Stochastic Models (Spring) 

 
Please also note four new machine learning courses (AMS 691.01, 02, 03, 05) available for Fall at the end of this document, and also at the following website. 

• You will notice we have more electives in Fall than Spring because most of you will graduate in 3 semesters (Fall, Spring, Fall). Once you have taken all core courses and fulfilled the 30-credit required for the MS degree, you must graduate.  

• Typically, each student should take 3~4 courses (9~12 -credits) per semester. 

• Please refer to the University academic calendar for key dates and schedules. 

• Please refer to the AMS Department Graduate Course Schedule for all courses offered each semester. 

Recommended course schedule 

For our master’s students in statistics, we recommend the following schedule (*our doctoral students can follow the same schedule for the first 3 semesters):  

*Please refer to the department course schedule website for courses offered. 

*Please do NOT choose sections of courses marked “for Data Science Students Only”. 

Year 1, Fall semester: AMS 507, AMS 510, AMS 572, AMS 595 (*Those who have already learned Python which is taught in AMS 595, can register for AMS 562 instead to learn C++.) (*Some of our doctoral students who serve as TA must register for an English course [OAE] – you can take AMS 595 the next Fall semester, or alternatively, take AMS 561, nearly identical to 595, in Spring.) 

AMS 507 Introduction to Probability  AMS 507 Webpage
Note: Cross-listed with HPH 696.  
89809 LEC 01 TR 3:30-4:50PM Loc: Harriman Hall 116 Mode: In Person Inst: Eugene Feinberg 
96141 REC R03 RECR 6:20-7:25PM Loc: Harriman Hall 116 Mode: In Person Inst: Eugene Feinberg 

AMS 510 Analytical Methods for Applied Mathematics and Statistics  AMS 510 Webpage
89810 LEC 01 TR 12:30-1:50PM Loc: Javits Lectr. 101 Mode: In Person Instr: Xiaolin Li 
89811 REC R01 RECF 8:25-9:20AM Loc: Javits Lectr. 101 Mode: In Person Instr: Xiaolin Li 

AMS 572 Data Analysis I  AMS 572 Webpage 
Note:  Department Consent Required 
89708 LEC 02 Friday 11:00AM-1:50PM Loc: Frey Hall 305 Mode: In Person Inst: Pei-Fen Kuan 

AMS 595 Fundamentals of Computing  AMS 595 Webpage 
Antirequisite:  AMS 561 
89802 LEC 01 F 2:00-4:50PM Loc: Harriman Hall 104 Mode: In Person Inst: Chenyu You 

** Those who consider themselves already have a solid background in statistics (for example, one can consider taking AMS 586 Time Series Analysis (Prof Zhu) or AMS 691.01, 02, 03, 05. Please be sure to consult with the instructors for the courses you wish to take first, sending them your CV/transcripts, so that they can decide whether you are ready.  

AMS 586 Time Series AMS 586 Webpage 
Prerequisites: AMS 570 or AMS 572 
90201 LEC 01 TR 9:30-10:50AM Loc: Earth & Space 177 Mode: In Person Inst: Wei Zhu 
90228 LEC 30 TR 9:30-10:50AM Loc: ONLINE Mode: SYNCHRONOUS Inst: Wei Zhu 

** Those of our master’s and doctoral students who are interested in our sister-track of Quantitative Finance (QF), can also consider taking AMS 511 Foundation of Quantitative Finance. 

AMS 511 Foundations of Quantitative Finance AMS 511 Webpage 
89217 LEC 01 W 3:30-6:20PM Loc: ONLINE Mode: SYNCHRONOUS Inst: Robert Frey 

* All graduate students are expected to maintain a B or better grade average; Otherwise, one cannot graduate. ** The general advice is to take 3~4 courses/semester.  

Year 1, Spring semester: AMS 570, AMS 573, AMS 578, AMS 597 (*Note 1: If you are a doctoral student, we recommend you to take AMS 550 instead of AMS 597 this semester in order to get ready for the Statistics area exam. Note 2: If you are an international master’s student who wishes to study for 4 semesters instead of 3 semesters – you will need to hold a core course till the last Spring semester – so instead of that core course, you must choose a different 3-credit course, for example, AMS 562, to reach a 4-course full-time schedule). 

Year 2, Fall semester: AMS 586, AMS 560, AMS 598, AMS 595 (*for those who did not take it in the first fall), or other related courses such as a machine learning/AI course (AMS 691.01, 02, 03, 05) (***Note you can graduate with your MS degree at the end of this semester for you have already taken at least 10 courses including all the core courses) 

** Year 2, Spring semester: the AMS core course (for example AMS 597 that you have not taken yet), AMS 580, AMS 550 (or other elective courses of interest).  

Advanced Graduate Certificate in Data Science: 

Data science has been gaining increasing job market in the recent years – especially with the advent of advanced computers and the internet. “Data Scientist” has been ranked as #8 in 100 Best Jobs by the US News & World Report.  Computer programming and data analysis are the two main pillars of data science. Aside from our rigorous data analysis training, we have also strengthened your programming training with many programming and algorithm classes. The following courses are the subset that we hope you can all master: 

AMS 595 Fundamentals of Computing (matlab, Python, C++) – everyone should take this if one is not a master of the materials yet. 

AMS 580 Statistical Learning – introduction to common statistical learning and machine learning procedures, and how to run them in R (mainly R, some Python). 
 

AMS 597 Statistical Computing (R, and a bit Perl) – this is also a core course in statistics. 
 

AMS 598 Big Data Analysis -- application of the supercomputing for statistical data analyses, particularly on big data (R & Python) 

AMS 530 Principles in Parallel Computing – this course is also closely related to big data analysis, AMS 598. (C/C++/Python; JAVA also allowed; teach C++ for 2-3 weeks)  

AMS 520, Machine Learning in Quantitative Finance 

AMS 560 Big Data System -- Recent progress on big data systems, algorithms and networks including the web graph, search engines, online algorithms, etc. (JAVA) 

Advanced Graduate Certificate in Data & Computational Science: 

Through ICAS (https://iacs.stonybrook.edu/opportunities/graduate-certificates/cdcs), we have a 17-credit Graduate Certificate in Data and Computational Science available to both AMS MS and PhD students. Here are some key points for AMS graduate students: 

(1). For the core course AMS 561 -- we can replace it with AMS 595 

(2). For the two Journalism (JRN) courses (1 credit each), they can be taken within one semester, please see the following site for details: 

https://iacs.stonybrook.edu/opportunities/graduate-certificates/cdcs 

(3). We can use 6 credits you have already earned before being registered to this certificate.  

(4). It is important to register for the certificate program early (*definitely before the second Fall semester) because up to 12 credits can be counted towards both your AMS degree program and this certificate. 

(5). The key is that you need to take one 3-credit CS course (*that is not cross-listed with AMS), plus another 3-credit course that is from ANY non-AMS department (CS, ECE, College of Business etc., not cross-listed with AMS)  

For CS courses https://www.cs.stonybrook.edu/students/Graduate-Studies/courses, we think the following might be viable: 

CSE505 Computing with Logic 

CSE512 Machine Learning 

CSE519 Data Science Fundamentals 

CSE525 Introduction to Robotics 

CSE532 Theory of Database Systems 

CSE544 Prob/Stat for Data Scientists 

CSE545 Big Data Analytics 

CSE549 Computational Biology 

CSE564 Visualization 

For our international master students in statistics who wish to get the advanced graduate certificate in Data Science, we recommend the following schedule (*our domestic master students and doctoral students can follow the same schedule except you can take AMS 597 <core course> first, and AMS 562 <elective> last): 

Year 1, Fall semester: AMS 507, AMS 510, AMS 572, AMS 595 

Year 1, Spring semester: AMS 570, AMS 573, AMS 578, AMS 580 (or AMS 530) 

Year 2, Fall semester: AMS 586, CS graduate course, AMS 598, another machine leaning/AI course such as AMS 691.02, 03, 04 

Year 2, Spring semester: JRN 501 (1 credit), JRN 503 (1 credit), AMS 597, CS graduate course (or another non-AMS graduate course), AMS 550 

For our master’s students who are determined to graduate in 3 semesters, you can follow the schedule (35 credits in total) below: 

Year 1, Fall semester: AMS 507, AMS 510, AMS 572, AMS 595 (12 credits) 

Year 1, Spring semester: AMS 570, AMS 573, AMS 578, AMS 597 (12 credits) 

Year 2, Fall semester: AMS 586, JRN 501 (1 credit), JRN 503 (1 credit), CS graduate course, CS graduate course (or another non-AMS graduate course) (11 credits) 

Advanced Graduate Certificate in Quantitative Finance (QF): 

 ** Given that the track of Statistics is highly correlated with the track of Quantitative Finance, interested students can choose to take selected courses in QF and obtain the 15-credit Advanced Graduate Certificate in Quantitative Finance introduced below. 

Any strong student (3.5+ GPA in first-semester core courses) in another track (such as statistics) may enroll in AMS 511, Foundations in Quantitative Finance.  With the permission of the Quantitative Finance Program Director (Prof. Stan Uryasev), one may take additional quantitative finance courses to earn an Advanced Graduate Certificate in Quantitative Finance. You must formally apply for the secondary certificate program prior to taking the required courses. Only a maximum of six credits taken prior to enrolling in the certificate program may be used towards the requirements. The QF certificate requires AMS 511, 512, 513, one additional QF elective, and one additional AMS course.  

AMS 511 Foundations of Quantitative Finance 

AMS 512 Portfolio Theory 

AMS 513 Financial Derivatives and Stochastic Calculus 

Permission to enroll in the certificate program will require the permission of Prof. Stan Uryasev and Prof. David Green. 

The form to apply for the secondary certificate program: 

https://www.stonybrook.edu/commcms/grad/academics/student_resources.php 

For our international master’s students in statistics who wish to get the advanced graduate certificate in QF, we recommend the following schedule (*our domestic master’s students and doctoral students can follow the same schedule except you can take AMS 597 <core course> first, and AMS 586 <elective> last): 

Year 1, Fall semester: AMS 507, AMS 510, AMS 572, AMS 595 

Year 1, Spring semester: AMS 570, AMS 573, AMS 578, AMS 580 (or AMS 550, AMS 562, etc.) 

Year 2, Fall semester: AMS 586, AMS 511, another machine leaning/AI course such as AMS 691.02, 03, 04, AMS 598 (*One must take AMS 586 & 511 – however, to maintain full time status requiring 9 credits, you only need one more elective, so choose one from AMS 598 and other graduate courses) 

Year 2, Spring semester: AMS 512, AMS 513, AMS 597, (AMS 580 etc. – optional)  

Advanced Graduate Certificate in Operations Research (OR): 

 The department also has an 18-credit advanced graduate certificate in Operations Research. This certificate has 5 REQUIRED COURSES (15 credits):  

(https://www.stonybrook.edu/commcms/spd/bulletin/programs/operations_research.php).   

AMS 507 Introduction to Probability  

AMS 540 Linear Programming 

AMS 550 Stochastic Models  

AMS 553 Simulation and Modeling  

AMS 572 Data Analysis I 

Plus, one ELECTIVE (3 credits) which can be any graduate course in AMS, management and policy, or computer science, which has been approved by the student's advisor. For students in statistics, one only needs to be sure to take AMS 540, 550, and 553.  Permission to enroll in the certificate program will go through the School of Professional Development as shown in the above link.  

For our international master’s students in statistics who wish to get the advanced graduate certificate in OR, we recommend the following schedule (*our domestic master’s students and doctoral students can follow the same schedule except you may wish to take AMS 597 <core course> first, and AMS 586 <elective> last): 

Year 1, Fall semester: AMS 507, AMS 510, AMS 572, AMS 595 

Year 1, Spring semester: AMS 570, AMS 573, AMS 578, AMS 580 

Year 2, Fall semester: AMS 586, AMS 540, another machine leaning/AI course such as AMS 691.02, 03, 04, AMS 598 (*One must take 586 & 540 – however, to maintain full time status requiring 9 credits, you only need one more elective, so choose one from 598 and other graduate courses)  

Year 2, Spring semester: AMS 550, AMS 553, AMS 597, (AMS 580, AMS 562 etc. – optional)  

Doctoral Qualifying Exam Requirements: 

Our doctoral students are expected to take and pass the following doctoral qualifying exams in 1-2 years. Each exam is offered twice per year in January and June. (*During this special time, online exams are offered if deemed necessary*)  

Foundation Exam:  4-hour close-book exam covering AMS 507 and AMS 510. 

STAT Area Exam:  This is a 4-hour in-class exam with two parts: 

Math STAT Exam:  2-hour close-book exam covering AMS 570 and AMS 550. 

Applied STAT Exam:  2-hour open-book exam covering AMS 572, AMS 573, AMS 578, and AMS 586. One problem from each course will be given. One must choose to do exactly 3 out of these 4 problems given. Four books, 4 notes, & a calculator are allowed but no computers. 

*** Students are expected to take and pass the Foundation Exam first before taking the STAT Area Exam. However, they are allowed to take both exams together. Also, our master’s students in good standing (grades of B+ or better in all related courses) can take these doctoral qualifying exams. 

We urge those of you who wish to take the qualifying exams to study for the exams early. Please check out the outlines of these exams in the following website – and please note that at the end of the page, you have a link to past qualifying exam questions. Prepare early for success. 

https://www.stonybrook.edu/commcms/ams/academics/graduate/_resources/quals-website.php 

https://www.stonybrook.edu/commcms/ams/academics/graduate/_resources/past-qualifying-exams.php 

Be safe on campus and off campus: 

 We are a beautiful campus located in a very safe town. However, one must always be cautious and does not put oneself in any potentially dangerous position. For example, do not get into any stranger’s car; and always wait for the pedestrian walking sign before you cross the street – and look around before stepping into the cross section. When taking the train or subway, stay away from the edge of the platform. It is also very important that you do not drive without a proper driver’s license. For emergencies, contact University Police at 333 from campus phones or (631) 632-3333 from non-campus phones. The general emergency phone number is 911 for the entire USA. Our safety advice goes on and on, following the same lines as those from your parents. 

Be safe & diligent, we wish you all the successes! 

Four new machine learning courses for Fall 2025:  

AMS 691 Topics in Applied Mathematics  AMS 691 Webpage 

Lecture 01:  Recent Progress in AI/ML: Applications, Architectures, and Systems 
92437 LEC 01 R 9:30-10:50AM Loc: ONLINE Mode: ONLINE/IN PERSON-HYBRID Inst: Zhenhua Liu 
92437 LEC 01 T 9:30-10:50AM Loc: Javits Lectr. 101 Mode: ONLINE/IN PERSON-HYBRID Inst: Zhenhua Liu 

Lecture 02:  Natural Language Processing 
92616 LEC 02 MW 11:00AM-12:20PM Loc: Social Behav. Sci. S328 Mode: IN PERSON Inst. Jiawei Zhou 

Lecture 03:  Fundamentals of Reinforcement Learning 
92617 LEC 03 MW  9:30-10:50AM Loc: Frey Hall 301 Mode: IN PERSON Inst. Jian Li 

Lecture 05:  Deep Learning 
94854 LEC 05 M 9:30AM-12:20PM Loc: Frey Hall 201 Mode In Person Inst: Yi Liu