Department of Mathematics and Statistics, Binghamton University people:kargin

people:kargin:calculations

2026-01-24T18:54:51-04:00

Some calculations for the paper about Meander Systems:

Statistics of Meander Systems

Some random triangulations:

Triangulations

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people:kargin:genealogy

2022-12-23T20:30:39-04:00

I got my PhD in mathematics under supervision of Gerard Ben Arous at NYU in 2008.

So, my math genealogy is French-oriented.
According to Math Genealogy Project, it goes like this:

$ \uparrow $

Robert Azencott

Sarah Rothenberg and Robert Azencott (photo by Dave Rossman for The Chronicle)

$\uparrow$

Jacques Neveu

$\leftarrow$ Robert Fortet

$\leftarrow$ Maurice Fréchet

$\uparrow$

Jacques Hadamard

$\leftarrow$ Émile Picard

$\leftarrow$ Gaston Darboux

My PhD in Economics was written under supervision of Robert W. Rosenthal who was a mathematically trained economist and who worked in game theory. He taught me a lot about doing research.

On this side, my genealogy eventually goes to Germany.

Vladislav Kargin's Published Papers

2026-01-24T18:50:34-04:00

Vladislav Kargin's Published Papers

Papers related to random matrices are marked Papers on free probability are ♥

2026

The smallest singular value of large random rectangular Toeplitz and circulant matrices

(joint with A. Onatski)
Electronic Journal of Probability 31 (2026), #2 1–30.
DOI: 10.1214/25-EJP1462

2025

An upper bound on the per-tile entropy of ribbon tilings

(joint with S. R. Blackburn and Y. Chen)
Combinatorial Theory 5 (2025), #14 1-10
PDF (journal version)
DOI:10.5070/C65365562

2024

On the joint distribution of the area and the number of peaks for Bernoulli excursions

Bernoulli 30 (2024), 2700–2720.
PDF (journal version)
DOI: 10.3150/23-BEJ1691

2023

Scaling limits of slim and fat trees

Journal of Theoretical Probability 36 (2023), 2192–2228.
PDF (journal version)
DOI: 10.1007/s10959-023-01261-w

The number of ribbon tilings for strips

(joint with Y. Chen)
Discrete Applied Mathematics 340 (2023), 85–103.
arXiv: 2307.00767 · Journal link (paywall)

On enumeration and entropy of ribbon tilings

(joint with Y. Chen)
Electronic Journal of Combinatorics 30(2) (2023), P2.15.
PDF (journal version)

2020

Cycles in random meander systems

Journal of Statistical Physics 181 (2020), 2322–2345.
PDF (journal version)
arXiv: 2011.13449

2018

A 3D Ginibre point field

Journal of Statistical Physics 171 (2018), 1067–1095.
PDF (journal version)
arXiv: 1705.03118

2016

On variation of word frequencies in Russian literary texts

Physica A: Statistical Mechanics and its Applications 445 (2016), 328–334.
PDF (journal version)
arXiv: 1503.00339

2015

Limit theorems for linear eigenvalue statistics of overlapping matrices

Electronic Journal of Probability 20 (2015), Art. 121, 1–30.
PDF (journal version)
arXiv: 1407.4743

On estimation in the reduced-rank regression with a large number of responses and predictors

Journal of Multivariate Analysis 140 (2015), 377–394.
PDF (journal version)
arXiv: 1409.6779

Subordination of the resolvent for a sum of random matrices

Annals of Probability 43 (2015), 2119–2150.
PDF (journal version)
arXiv: 1109.5818

2014

On the largest Lyapunov exponent for products of Gaussian matrices

Journal of Statistical Physics 157 (2014), 70–83.
PDF (journal version)
arXiv: 1306.6576

Statistical properties of zeta functions' zeros

Probability Surveys 11 (2014), 121–160.
PDF (journal version)
arXiv: 1302.1452

On Pfaffian random point fields

Journal of Statistical Physics 154 (2014), 681–704.
PDF (journal version)
arXiv: 1210.6603

2013

An inequality for the distance between densities of free convolutions ♥

Annals of Probability 41 (2013), 3241–3260.
PDF (journal version)
arXiv: 1107.0477

On fluctuations of Riemann's zeta zeros

Probability Theory and Related Fields 157 (2013), 575–604.
PDF (journal version)
arXiv: 1203.5309

2012

A concentration inequality and a local law for the sum of two random matrices

Probability Theory and Related Fields 154 (2012), 677–702.
PDF (journal version)
arXiv: 1010.0353

On eigenvalues of the sum of two random projections

Journal of Statistical Physics 149 (2012), 246–258.
PDF (journal version)
arXiv: 1205.0993

2011

On free stochastic differential equations ♥

Journal of Theoretical Probability 24 (2011), 821–848.
PDF (journal version)
arXiv: 1101.2697

Relaxation time is monotone in temperature in the mean-field Ising model

Statistics and Probability Letters 81 (2011), 1094–1097.
PDF (journal version)
arXiv: 1103.0327

2010

Free point processes and free extreme values ♥

(joint with G. Ben Arous)
Probability Theory and Related Fields 147 (2010), 161–183.
PDF (journal version)
arXiv: 0903.2672

Bounds for mixing time of quantum walks on finite graphs

Journal of Physics A: Mathematical and Theoretical 43 (2010), 335302.
PDF (journal version)
arXiv: 1004.0188

Continuous-time quantum walk on integer lattices and homogeneous trees

Journal of Statistical Physics 140 (2010), 393–408.
PDF (journal version)
arXiv: 0912.0232

Products of random matrices: Dimension and growth in norm

Annals of Applied Probability 20 (2010), 890–906.
PDF (journal version)
arXiv: 0903.0632

2009

Spectrum of random Toeplitz matrices with band structure

Electronic Communications in Probability 14 (2009), 412–423.
PDF (journal version)

2008

Curve forecasting by functional autoregression

(joint with A. Onatski)
Journal of Multivariate Analysis 99 (2008), 2508–2526.
PDF (journal version)

Lyapunov exponents of free operators ♥

Journal of Functional Analysis 255 (2008), 1874–1888.
PDF (journal version)
arXiv: 0712.1378

A limit theorem for products of free unitary operators ♥

Probability Theory and Related Fields 141 (2008), 603–623.
PDF (journal version)
arXiv: 0805.0374

On the asymptotic growth of the support of free multiplicative convolutions ♥

Electronic Communications in Probability 13 (2008), 415–421.
PDF (journal version)

Coordination Games with Quantum Correlations

International Journal of Game Theory 37 (2008), 211–218.
PDF (journal version)
Preliminary version

2007

The norm of products of free random variables ♥

Probability Theory and Related Fields 139 (2007), 397–413.
PDF (journal version)
arXiv: math/0611593

A proof of a non-commutative central limit theorem by the Lindeberg method ♥

Electronic Communications in Probability 12 (2007), 36–50.
PDF (journal version)
arXiv: math/0703345

Berry-Esseen for free random variables ♥

Journal of Theoretical Probability 20 (2007), 381–395.
PDF (journal version)
arXiv: math.PR/0610072
Comment: this paper has a mistake; see Theorem 2.4 in Chistyakov–Götze (Ann. Probab. 36 (2008), 54–90).

On superconvergence of convolutions of free random variables ♥

Annals of Probability 35 (2007), 1931–1949.
PDF (journal version)
arXiv: math.PR/0610075

A large deviation inequality for vector functions on finite reversible Markov chains

Annals of Applied Probability 17 (2007), 1202–1221.
PDF (journal version)
arXiv: 0508538
Comment: published version contains a gap; the original correct proof is in arXiv v1 (PDF).

2005

On the Chernoff bound for efficiency of quantum hypothesis testing

Annals of Statistics 33 (2005), 959–976.
PDF (journal version)

Lattice Option Pricing by Multidimensional Interpolation

Mathematical Finance 15 (2005), 635–647.
PDF (journal proof version)

Uncertainty of the Shapley Value

International Game Theory Review 7(4) (2005), 517–529.
PDF (journal proof version)

2003

Prevention of Herding by Experts

Economics Letters 78(3) (2003), 401–407.
PDF (journal proof version)

Optimal Asset Allocation with Asymptotic Criteria

IJTAF 6(6) (2003), 593–604.
PDF (arXiv version)

2002

Value Investing in Emerging Markets: Risks and Benefits

Emerging Markets Review 3(3) (2002), 233–244.
PDF (journal proof version)

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people:kargin:letterinfo

2026-01-24T18:53:07-04:00

1) If you took one class from me and got a B or B+, the letter is unlikely to be helpful to you (unless it is a B in a graduate class). Most likely, I will decline to write the letter, although I did once write a letter for a student who got a C. There were some unusual circumstances in that case.

2) if you got an A or A-, you should still keep in mind that the essence of a good recommendation letter is a concrete comparison with other students. I am expected to disclose your percentile ranking in the class and describe the level of hardness of the class. Being in the upper 25% in Calc 3 certainly won't hurt you but all alone it is not going to get you into an Ivy League school. If you are unsure about your standing in a class, you are welcome to ask me for this information.

3) Unless I got to know you personally, I will not be able to write much besides this basic ranking information. If you want me to comment on something you achieved, tell me about it.

4) Remind me about deadlines.

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Math 404 Advanced Linear Algebra. Spring 2024.

2024-01-11T05:41:21-04:00

Syllabus

Math 404 Advanced Linear Algebra. Spring 2024.

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: MWF – 11:20-12:50 – LN 2403
Office hours: MWF – 14:00-15:00 (2PM - 3PM)

This course is a 4-credit course, which means that in addition to the scheduled lectures/discussions, students are expected to do at least 9.5 hours of course-related work each week during the semester. This includes things like: completing assigned readings, participating in lab sessions, studying for tests and examinations, preparing written assignments, completing internship or clinical placement requirements, and other tasks that must be completed to earn credit in the course.

The course will in general follow the syllabus of the Math404 course given in Spring 2023: see

“http://people.math.binghamton.edu/alex/Math404_Spr2023.html”

Tentative Exam Schedule:

Exam 1: Friday, February 23

Exam 2: Wednesday, March 27

Exam 3: Wednesday, May 1

Final Exam: TBA, During Final Week.

people:kargin:math447

2016-05-21T16:41:35-04:00

Syllabus; Homework at WebWork; Forum at Piazza.

Results for the final exam: Median = 61, Std = 19, top score = 94 (out of 100 possible).

Old final exams: S2013, S2013 solutions, F2014, S2015, S2015 solutions, F2015 with solutions.

Thusday's quizzes: Quiz 8, Quiz 9, Quiz 10, Quiz 11.

Lecture slides for Chapter 6: Slides-ch6 (updated on May 9).

Here are the solutions and the original test for the third midterm: Test 3 Solutions and Test 3.

Results of the third midterm: Median = 32, Standard Deviation = 14, Top score = 66 (out of 72 possible).

The third midterm covers the material in Chapter 5. Sample questions can be found in Test 3 - S2013, Test 3 - S2015 ( S2015 Solutions), and Test 2 - F2015 ( F2015 Solutions).

Lecture slides for Chapter 5: Slides-ch5 (updated April 18).

Here are the solutions and the original test for the second midterm: Test 2 Solutions and Test 2.

Results of the second midterm: Median = 34.5, Standard Deviation = 13, Top score = 68 (out of 73 possible).

For preparation, here is Test 3 from Spring 2013. and its solutions: Test 3 Solutions.

Examples of R-code: Example 1 - “Approximation of binomial distribution by normal”,
Example 2 - “Computing covariance by numerical integration”.

Lecture notes for Chapter 4: Slides-ch4 (updated on March 12, 2016).

Here are the solutions and the original test for the first midterm: Test 1 Solutions and Test 1.

Results of the first midterm: Median = 37, Standard Deviation = 11, Top score = 54 (out of 60 possible).

Here are the sample tests from Spring 2013: Test 1, Test 2, Test 1 Solutions, Test 2 Solutions.

A nice tutorial for R: Codeschool: Try R.

Slides for Chapter 3: Slides-ch3 (updated on March 12, 2016).

Lecture slides for Chapter 2 of the textbook: Slides-ch2 (updated on March 12, 2016).

people:kargin:math447_fall2020

2020-08-14T20:09:33-04:00

Course: Math 447 Introduction to Probability

Meeting times: MWF 11:20am - 12:50pm Place: Online (Zoom ID 918 2625 1611)

Office hours: MWF 3:30PM - 4:30PM; Zoom ID 949 5616 9870

email: vkargin@binghamton.edu

Prerequisites: A grade of C or better in Math 323. If you have no better than a C in Math 323 you will probably struggle in this course. This is not an easy course and mathematical sophistication is expected.

Learning outcomes: This is a prerequisite for Math 448 (the statistics half of the sequence) and several other actuarial/statistics courses. The learning outcome is the ability to work with probability tools necessary for these courses. Topics include: basic combinatorial probability, common discrete and continuous distributions, probability conditioning, moments, multivariate distributions and some limit theorems.

ZOOM lectures: – All lectures will be delivered through ZOOM. – You are expected at least initially login with video. You can switch off the video later if needed – Questions are welcomed, especially questions that catch my typos, – you can unmute and ask a question. You can also ask a question in the chat, although I will not be able to monitor the chat closely while lecturing. – The attendance will not affect the course grade directly. While it is advisable to attend lectures to make sure that you are on track, I will also post lecture recordings so you can view them later asynchronously.

Communication: I will mostly use Piazza Forum (https://piazza.com/binghamton/fall2020/math447). In particular, I will post all announcements and lecture notes on Piazza. So make sure that you are enrolled in this course at Piazza. Questions and answers by students are encouraged. With respect to MyCourses/Blackboard, I will use it only minimally.

As a text, I will use “Mathematical Statistics with Applications” by Wackerly, Mendenhall, and Scheaffer. We will cover Chapter 2-7. Buy the book only if you need a paper copy. Electronic copy will be made available. I will also provide (on Piazza) my lecture notes or slides that are mostly based on this book.

Homework will be delivered through the WebAssign (https://www.webassign.com). The key for enrolling will be provided. You will need to pay for the WebAssign account.

Exams: There will be two midterms and a “final” exam. The final will be in November before the Thanksgiving break. All students living on campus or taking at least one class in person will be required to take all of these tests in person. If you are fully online, you should contact me as soon as possible. For these students, an online exam will be given at the same time.

The lectures will continue after Thanksgiving with an online test given in the period December 8 – December 10.

Grading: Online Homework 20% Midterms 40% (20 each) Final Exam 30% Online test in December: 10%

Math 447 Introduction to Probability Theory. Fall 2024.

2024-08-20T13:45:25-04:00

Syllabus

Math 447 Introduction to Probability Theory. Fall 2024.

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: MWF 9:40 - 11:10 am at CW 112.
Office hours: MWF 2 - 3pm (in person, office WH136).

Prerequisite

A grade of C or better in Math 323

Description

This is an introductory course that will cover basic combinatorial probability and essential tools of modern probability, including mathematical expectation, probability conditioning, moments and moment generating function. It will discuss common discrete and continuous distributions, multivariate distributions and some limit theorems. It is prerequisite course for Math 448 (the statistics half of the sequence) and several other actuarial/statistics courses. The learning outcome is the ability to work with probability tools necessary for these courses.

Recommended Texts

“Introduction to Probability”, 2nd edition, by Blitzstein and Hwang.

We will cover Chapters 1-10.

Online resources

Some resources for this book are available at stat110.net

Piazza

We will use Piazza (“http://piazza.com/”) for communication. All announcements will be sent to the class using Piazza. Signup is possible at this link: https://piazza.com/binghamton/fall2024/math447.

Quizzes

There will be an in-class quiz every week except for weeks of exams, which will likely to result in around 12 quizzes. The quizzes cannot be taken at a different time. The three lowest quiz scores will be dropped in the sum.

Homework

Homework usually will be assigned weekly, but it will not be graded. Problems similar to homework can be put on quizzes or exams. There will be occasional homework questions on Gradescope, which are considered take-home quizzes.

Exams

There will be three in-class midterms and a final exam. There will be no makeups for midterms. If you have to miss a midterm (for a valid reason), its weight will be replaced by an increase in the weight of the final exam.

Preliminary schedule (can change!):
Midterm 1 - Mon, September 23
Midterm 2 - Mon, October 21
Midterm 3 - Mon, November 18
Final - as determined by the university

Grading

Quizzes 25%
Midterms 45 = 15 + 15 + 15%
Final Exam 30%

people:kargin:math447_spring2021

2021-02-08T21:09:22-04:00

Course: Math 447 Introduction to Probability

Meeting times: 8:00am - 9:30am Place: Online (Zoom ID 918 2625 1611)

Office hours: MWF 10:00AM - 11:00AM (or by appointment); Zoom ID 949 5616 9870

email: vkargin@binghamton.edu

ZOOM lectures: – All lectures will be delivered through ZOOM. – You are expected at least initially login with video. There will be a small bonus for active class participation and “video on” counts as participation. Bonus is solely at the instructor's discretion. – Questions are welcomed, especially questions that catch my typos, – you can unmute at any time and ask a question. You can also ask a question/correct typo in the chat, although I am not able to monitor the chat closely while lecturing. – The attendance will not affect the course grade directly except for the small bonus for active participation. While it is advisable to attend lectures regularly to make sure that you are on track, the lecture recordings from the previous semester are available, so you can view them asynchronously.

Communication: I will mostly use Piazza Forum (https://piazza.com/signup/binghamton). It is crucial that you enroll for this course at Piazza since I will post all announcements and lecture notes there. Questions and answers by students are encouraged. With respect to MyCourses/Blackboard, I will use it only minimally.

As a text, I will use “Mathematical Statistics with Applications” by Wackerly, Mendenhall, and Scheaffer. We will cover Chapter 2-7. Buy this book only if you need a paper copy. Electronic copy will be made available at Piazza. I will also provide (at Piazza) my lecture notes that are mostly based on this book.

Homework will be delivered through WebAssign (https://www.webassign.com). The key for enrolling will be provided. You will need to pay for your WebAssign account (around $120 per term).

Exams: There will be two midterms and a final exam. All of them will be online. Approximate schedule:
Midterm 1 - March 15
Midterm 2 - April 19
Final - as determined by the university

Grading: The scores will be calculated in two ways

Method I: Online Homework 20% Midterms 50% (25 each) Final Exam 30%

Method II: Online Homework 10% Midterms 50% (25 each) Final Exam 40%

Then, the maximum score for these two methods will be calculated and used as the final total score (subject to small discretionary bonus from class participation not to exceed 3%). The letter grade will be curved according to the overall class performance due to the difficulty of the material.

Math 447 Introduction to Probability Theory. Spring 2022.

2022-03-02T11:47:39-04:00

Syllabus

Math 447 Introduction to Probability Theory. Spring 2022.

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: MWF 8:00 - 9:30 am at CW 212.
Office hours: MWF 10:00 - 11:00 (in person, office WH136) or Zoom by appointment.

Prerequisite

A grade of C or better in Math 323

Description

Recommended Texts

“Mathematical Statistics with Applications” by Wackerly, Mendenhall, and Scheaffer

We will cover Chapters 2-7. Electronic copy will be made available. I will also provide a set of lecture notes mostly based on this book.

Online resources

Two previous versions of this course were recorded for online semesters and available on YouTube.

Piazza

We will use Piazza (“http://piazza.com/”) for communication. All announcements will be sent to the class using Piazza.

Homework

Majority of homework will be assigned through WebAssign (https://www.webassign.net). The key for enrolling will be provided. You will need to pay for your WebAssign account (around $120 per term).

Some of homework will be assigned through Gradescope (“https://www.gradescope.com/”).

Brightspace will only be used minimally.

Attendance, Polls and Quizzes

We will use iClicker Cloud. Download application at (https://www.iclicker.com/students). The subscription is around $16 for 6 months.

Exams

There will be two midterms and a final exam. Approximate schedule:
Midterm 1 - March 7
Midterm 2 - April 11
Final - as determined by the university

Grading

Homework 15%
Midterms 50% (25 each)
Final Exam 35%
Lecture attendance and participation – possible bonus of up to 5% according to the instructor judgement.

Math 448 Introduction to Statistics. Fall 2022.

2022-08-23T15:08:40-04:00

Syllabus

Math 448 Introduction to Statistics. Fall 2022.

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: MWF 8:00 - 9:30 am at CW 212.
Office hours: MWF 9:45 - 10:30, Monday 1 - 2 PM (in person, office WH136, or Zoom by appointment).

Prerequisite

A grade of C or better in Math 447

Description

Understand the fundamental idea of statistical inference; conduct standard inferences including point estimation, confidence interval and hypothesis testing. Derive, evaluate and compare point estimators and confidence intervals. If time permits: Apply statistical inference to simple linear regression models. Provides understanding of basic concepts needed for more advanced courses in statistics. Gives initial exposure to statistical software.

Recommended Texts

Course Lecture Notes – they will be made available at the Piazza webpage
“Mathematical Statistics with Applications” by Wackerly, Mendenhall, and Scheaffer

We will cover Chapters 8 - 11 of the book. Electronic copy will be made available at Piazza. Lecture notes are mostly based on the book, however, sections are rearranged and some material is added.

Piazza

We will use Piazza (“http://piazza.com/”) for communication. All announcements will be sent to the class using Piazza. You can sign up at “http://piazza.com/binghamton/fall2022/math448” or you can send me an email and I sign up you.

Homework

Majority of homework will be assigned through WebAssign (https://www.webassign.net). The key for enrolling is: binghamton 6504 3711.

After a short grace period, you will be required to pay for your WebAssign account (around $120 per term).

Additional homework will be assigned through Gradescope (“https://www.gradescope.com/”).

Brightspace will only be used minimally if at all.

Software

Some homework will involve R, which is a statistical software package popular among statisticians. Installation instructions and downloads can be found at https://www.rstudio.com/products/rstudio/download/.

Exams

There will be two midterms and a final exam. Approximate schedule:
Midterm 1 - Sep 28
Midterm 2 - Nov 4
Final - as determined by the university

Grading

Homework 15%
Midterms 50% (25 each)
Final Exam 35%

Math 448 Mathematical Statistics. Spring 2025.

2025-03-17T17:29:17-04:00

Syllabus

Math 448 Mathematical Statistics. Spring 2025.

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: MWF 2:20 - 3:50 pm at CW 113.
Office hours: MF 4 - 5pm, Tuesday 11:30AM - 12:30pm.

Prerequisite

A grade of C or better in Math 447

Description

Recommended Texts

“Mathematical Statistics with Applications” by Wackerly, Mendenhall, and Scheaffer. (We will cover Chapters 8 - 10 of the book and possibly some additional topics.)
Course Lecture Notes – they will be made available at the Piazza webpage.

Lecture notes are mostly based on the book, however, sections are rearranged and some material is added.

Piazza

We will use Piazza (“http://piazza.com/”) for communication. All announcements will be sent to the class using Piazza. Brightspace will only be used minimally if at all.

Homework

Majority of homework will be assigned through WebAssign (https://www.webassign.net).

After a short grace period, you will be required to pay for your WebAssign account (around $120 per term).

Additional homework will be assigned through Gradescope (“https://www.gradescope.com/”).

Quizzes

There will be from 6 to 10 quizzes. The lowest scores for a third of the quizzes will be dropped.

Software

Some classroom demonstrations or homework may involve R or Python. Installation instructions and downloads for R can be found at https://www.rstudio.com/products/rstudio/download/. Python's notebooks can be written at https://colab.research.google.com/

Exams

There will be two midterms and a final exam. Approximate schedule:
Midterm 1 - February 28, Friday
Midterm 2 - ~~March 28, Friday~~ March 31, Monday
Final - as determined by the university

Grading

Homework 15%
Quizzes 15%
Midterms 40% (20 each)
Final Exam 30%

Math 457 Introduction to Statistical Learning. Fall 2021.

2021-08-24T16:20:02-04:00

Syllabus

Math 457 Introduction to Statistical Learning. Fall 2021.

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: MWF 8:00 - 9:30 am at OH-G102.
Office hours: MWF 9:45 - 10:30 (in person, office WH136), Tue 4:00PM - 5:00PM (via Zoom, ID 949 5616 9870), or by appointment

Prerequisite

Scientific programming in a language such as R, Matlab, or Python.
Linear regression and its inference
Matrix algebra, preferably including orthogonality, eigenvalues and eigenvectors, and singular value decomposition.

Description

This course is a survey of statistical learning methods. It will cover major statistical learning methods and concepts for both supervised and unsupervised learning. Topics covered include regression methods with sparsity or other regularizations, model selection, introduction to classification, including discriminant analysis, logistic regression, support vector machines, and kernel methods, nonlinear methods, clustering, decision trees, random forest, boosting and ensemble learning, deep learning

Learning Outcomes

Students will learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms. Students completing this course should be able to

process and visualize different data types,
apply basic statistical learning methods to build predictive models or perform exploratory analysis
have basic understanding of the underlying mechanism of predictive models and evaluate and interpret such models,
properly tune, select and validate statistical learning models,
use analytical tools and software widely used in practice,
work both independently and in a team to solve problems, and
learn to present and communicate the findings effectively.

Recommended Texts

James, Witten, Hastie and Tibshirani, 2021. “An Introduction to Statistical Learning with Applications in R.2nd edition” The Book Home Page is at “http://www-bcf.usc.edu/~gareth/ISL/index.html”. A pdf file can be downloaded from this page.

Online resources

There is a course taught by Hastie and Tibshirani using the first edition of their book. This Course is available at edx. The course is not free, however the videos and some other resources are available to auditors. The videos can also be obtained at this website through playlist links.

Software

We will use R and R Markdown for this class. The IDE for R, RStudio can be downloaded from here.

Piazza

We will use Piazza (“http://piazza.com/”) for communication. All announcements will be sent to the class using Piazza.

Gradescope

We will use Gradescope (“https://www.gradescope.com/”) to submit and grade homework. This will allow the instructor to efficient grade all the work and give feedback in a timely manner.

Mycourses

Mycourses (“http://mycourses.binghamton.edu”) will only be used occasionally for recording grades on assignments and exams and for distributing solutions.

Homework Policy

Homework will be assigned approximately bi-weekly. It is expected that homework is prepared using R Markdown or LaTeX. Handwritten homework is not accepted. There will be a deduction of 15% of the grade for each day homework assignment is late (the final grade for a late homework that is N days late will be 0.85^N times the real grade). Homeworks may be discussed with classmates but must be written and submitted individually.

Midterm Exam

A midterm exam focusing on the theoretical part of the course will be administered in November.

Project

A group project will be assigned to each student (2 - 4 students in a group). Successful completion of the project includes an initial report, a presentation and a final report.

Grading

Homework (40%): homework is assigned biweekly.
Midterm exam (30%):
Project (30%)
Lecture attendance and participation – possible bonus of up to 3% according to the instructor judgement.

Tentative schedule

Midterm	Nov 22
Project Proposal	due Nov 24
Preliminary report	due Dec 3
Project presentations	December 6, 8, 10
Final Report	due December 13

Math 457 Introduction to Statistical Learning. Fall 2023.

2023-12-05T10:50:22-04:00

Syllabus

Math 457 Introduction to Statistical Learning. Fall 2023.

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: MWF – 11:20-12:50 – CW 202
Office hours: MWF – 9:50-10:50

Prerequisite

Scientific programming in a language such as R, Matlab, or Python.
Linear regression and statistical inference
Matrix algebra, preferably including orthogonality, eigenvalues and eigenvectors, and singular value decomposition.

Description

Learning Outcomes

process and visualize different data types,
apply basic statistical learning methods to build predictive models or perform exploratory analysis
have basic understanding of the underlying mechanism of predictive models and evaluate and interpret such models,
properly tune, select and validate statistical learning models,
use analytical tools and software widely used in practice,
work both independently and in a team to solve problems, and
learn to present and communicate the findings effectively.

Text

James, Witten, Hastie and Tibshirani, 2021. “An Introduction to Statistical Learning with Applications in Python.” The Book Home Page is at “https://www.statlearning.com”. A pdf file can be downloaded from this page.

Online resources

There is an online course taught by the book's author available at YouTube course.

Software

We will use Python for this class. We will use Google Colab. Optionally, you can install Anaconda and Jupiter on your computer to run Jupiter notebooks with Python code locally. See some instructions here: Installation instructions.

Piazza

We will use Piazza (“http://piazza.com/”) for communication. All announcements will be sent to the class using Piazza.

Mycourses

Mycourses (“http://mycourses.binghamton.edu”) will only be used occasionally.

Homework Policies

We will use Google Classrooms and Gradescope (“https://www.gradescope.com/”) to submit and grade homework. Homework may be discussed with classmates but must be written and submitted individually. ChatGPT and similar AI tools can be used for homework. They are not allowed during exams.

Exam

There will be a midterm and a final exam focusing on the theoretical part of the course. Final is cumulative.

Project

A group project will be assigned to each student (2 - 3 students in a group, 4 students are not allowed without a strong justification). Successful completion of the project includes an preliminary report, a presentation and a final report.

Grading

Homework (30%)
Midterm exam (20%)
Project (30%)
Final exam (20%)

Tentative schedule

Midterm	Oct 2
Project Proposal	due Oct 30
Preliminary report	due Nov 13
Project presentations	Dec 1 - Dec 8
Final Report	due Dec 8
Final Exam	As scheduled by the University, Dec 11

Math 457 Introduction to Statistical Learning. Fall 2024.

2024-07-18T09:44:11-04:00

Syllabus

Math 457 Introduction to Statistical Learning. Fall 2024.

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: MWF – 11:20-12:50 – CW 213
Office hours: MWF – 2 - 3pm

Prerequisite

Scientific programming in a language such as R, Matlab, or Python.
Linear regression and statistical inference
Matrix algebra, preferably including orthogonality, eigenvalues and eigenvectors, and singular value decomposition.

Description

Learning Outcomes

process and visualize different data types,
apply basic statistical learning methods to build predictive models or perform exploratory analysis
have basic understanding of the underlying mechanism of predictive models and evaluate and interpret such models,
properly tune, select and validate statistical learning models,
use analytical tools and software widely used in practice,
work both independently and in a team to solve problems, and
learn to present and communicate the findings effectively.

Text

James, Witten, Hastie and Tibshirani, 2021. “An Introduction to Statistical Learning with Applications in Python.” The Book Home Page is at “https://www.statlearning.com”. A pdf file can be downloaded from this page.

Online resources

There is an online course taught by the book's author available at YouTube course.

Software

Piazza

We will use Piazza (“http://piazza.com/”) for communication. All announcements will be sent to the class using Piazza. The signup is at this link: https://piazza.com/binghamton/fall2024/math457.

Mycourses

Mycourses (“http://mycourses.binghamton.edu”) will only be used occasionally.

Homework / Quizzes Policies

The Homework will have several components:

Several Assignments at Datacamp for learning python (worth 12.5% of HW)
Computational Assignments as Google Colab Notebooks (worth 37.5% of HW)
Assignments at Gradescope /(or possibly in-class Quizzes) (worth 50% of HW)

We will use Datacamp, Google Classrooms (Class code: rm3nfmh) and Gradescope (“https://www.gradescope.com/”) to submit and grade homework. Homework may be discussed with classmates but must be written and submitted individually. ChatGPT and similar AI tools can be used for homework. They are not allowed during exams.

If in-class quizzes are used, the policy is that two lowest or missing grades are dropped from total score calculation.

Exam

There will be a midterm and a final exam focusing on the theoretical part of the course. Final is cumulative.

Project

Grading

Homework (30%)
Midterm exam (20%)
Project (30%)
Final exam (20%)

Tentative schedule

Midterm	Sep 27
Project Proposal	due Nov 1
Preliminary report	due Nov 13
Project presentations	Dec 2 - Dec 4
Final Report	due Dec 6
Final Exam	As scheduled by the University, —

Math 457 Introduction to Statistical Learning. Fall 2025.

2025-11-19T00:06:48-04:00

Syllabus

Math 457 Introduction to Statistical Learning. Fall 2025.

Binghamton University

Instructor: Vladislav Kargin (vkargin@binghamton.edu)
Class Meeting time and location: MWF – 11:45-1:15 in AP G014
Office hours: Thursday – 11:45-1:15 in office WH-136 or by arrangement by Zoom.
TA: McKenzie Skrastins: (mskrast1@binghamton.edu)
Office hours: Monday – 1:30-2:30, Tuesday – 12 - 1pm in Whitney Hall Undergraduate Student Lounge (2nd floor)

Prerequisite

Scientific programming in a language such as R, Matlab, or Python.
Linear regression and statistical inference
Matrix algebra, preferably including orthogonality, eigenvalues and eigenvectors, and singular value decomposition.

Description

Learning Outcomes

process and visualize different data types,
apply basic statistical learning methods to build predictive models or perform exploratory analysis
have basic understanding of the underlying mechanism of predictive models and evaluate and interpret such models,
properly tune, select and validate statistical learning models,
use analytical tools and software widely used in practice,
work both independently and in a team to solve problems, and
learn to present and communicate the findings effectively.

Text

James, Witten, Hastie and Tibshirani, 2021. “An Introduction to Statistical Learning with Applications in Python.” (ISLP) The book home page is at “https://www.statlearning.com”. A pdf file can be downloaded from this page.

Supplementary Text

Bishop, Bishop, 2024. “Deep Learning. Foundations and Concepts.” The book homepage is at “https://www.bishopbook.com”. At this page, a digital version of the book is available.

Online resources

There is an online course taught by the ISLP book's authors. It is available at edx and YouTube. This course has some intersection with our course but it is not identical. It has different order of topics and some topics are not covered.

Software

We will use Python and Google Colab.

Piazza

We will use Piazza (“http://piazza.com/”) for communication. All announcements will be sent to the class using Piazza. The signup is at this link: https://piazza.com/binghamton/fall2025/math457.

Brightspace

Brightspace will be used minimally.

Homework / Quizzes Policies

The Homework will have several components:

Several Assignments at Datacamp for learning python (worth 10% of HW) [This component can be waived if you have a proof of a Python class. Even in this case you might find it necessary to complete learning modules on basics of object oriented programming in Python.]
Computational Assignments as Google Colab Notebooks (worth 40% of HW)
Assignments at Gradescope (worth 50% of HW)

We will use Datacamp, Google Classrooms (Class code: 7osx6f2j) and Gradescope (“https://www.gradescope.com/”) to submit and grade homework. Homework may be discussed with classmates but must be written and submitted individually. ChatGPT and similar AI tools can be used for homework. They are not allowed during in-class quizzes and exams.

If in-class quizzes are used, the policy is that two lowest or missing grades are dropped from total score calculation.

Exams

There will be a midterm and a final exam focusing on the theoretical part of the course. Final is cumulative.

Project

Grading

Homework (20%)
In-class Quizzes (10%)
Midterm exam (20%)
Project (30%)
Final exam (20%)

Letter grades will be assigned according to a scale determined after the course ends, but you are guaranteed at least: A for ≥ 90, A− for ≥ 85, B+ for ≥ 80, B for ≥ 75, B− for ≥ 70, C+ for ≥ 65, C for ≥ 60, C− for ≥ 55, D for ≥ 50.

Tentative schedule

Midterm	Wed, October 8
Project Proposal	due Nov 1
Preliminary report	due Nov 13
Project presentations	Dec 3 - Dec 5
Final Report	due Dec 6
Final Exam	Friday, December 12, 8:00 - 10:00 AM, CW 331

Math 571 Advanced Probability. Fall 2023.

2025-10-18T20:43:31-04:00

Syllabus

Math 571 Advanced Probability. Fall 2023.

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: MWF – 8:00-9:30 – WH 329
Office hours: MWF – 9:50-10:50

Prerequisite

Probability Theory (MATH 501)

Description

This course is an introduction to the advanced concepts of probability theory. It covers topics such as: Measure theory, Probability spaces, Random variables, Conditional Expectations, Stochastic processes, Martingales, Limit Theorems Large deviations

The course is intended for students who have a strong foundation in probability theory.

Recommended Texts

Durrett “Probability: Theory and Examples” 5th edition, pdf available at PTE.

Piazza

We will use Piazza (“http://piazza.com/”) for communication. All announcements will be sent to the class using Piazza.

Homework Policies

The homework will not be graded with the exception of some marked problems. The solution for these problems must be typed in LaTeX, typeset to pdf and submitted by the due date. The late or hand-written or non-LaTeX solutions will not be accepted. All homework problems can be on exams.

Exam

There will be a midterm and a final exam focusing on the theoretical part of the course. Final is cumulative.

Project

You are supposed to prepare a project for this course and make a presentation on the project. The project should cover some topic in probability theory. You can choose your own topic. It might be a topic, which is not covered by the lecturer, or it might be a recent paper in a mathematical journal. You are supposed to give a 30-minute presentation on the topic, which should be a lecture to your fellow students. You may choose to do a blackboard lecture or a slide presentation, as you prefer. You presentation will be graded on the following criteria:

  • Clarity: Your presentation should be clear and easy to understand.
  • Engagement: Your presentation should be engaging and interesting.
  • Answering questions: You should be able to answer questions from the audience about your topic.

Grading

Homework (25%)
Midterm exam (25%)
Project (25%)
Final exam (25%)

Tentative schedule

Midterm	Oct 9
Project Proposal	due Oct 30
Preliminary report	due Nov 13
Project presentations	Nov 27 - Dec 1
Final Report	due Dec 4
Final Exam	As scheduled by the University, Dec 11 - Dec 15

Math 571: Advanced Probability — Spring 2026

2026-01-19T13:48:06-04:00

Math 571: Advanced Probability — Spring 2026

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: TR 8:00–9:30 AM, WH 329
Office hours: TR 10:00–11:00 AM

This course is a 4-credit course, which means that in addition to the scheduled lectures/discussions, students are expected to do at least 9.5 hours of course-related work each week during the semester. This includes things like: completing assigned readings, participating in lab sessions, studying for tests and examinations, preparing written assignments, and other tasks that must be completed to earn credit in the course.

Prerequisite

Probability Theory (MATH 501)

Description

The course is intended for students who have a strong foundation in probability theory.

Recommended Text

Durrett, Probability: Theory and Examples, 5th edition. PDF available at PTE

Lecture Notes

Instructor's lecture notes will be provided and posted on Piazza.

Communication

We will use Piazza (piazza.com) for communication. All announcements will be sent to the class using Piazza.

Class Structure and Participation

Each class session is divided into two parts:

Student-led segment (30–45 minutes): Students take on rotating roles to present and critically examine the day's material.

Lecture segment (45–60 minutes): Instructor extends the material, addresses misconceptions, and covers additional applications.

Roles

Each session involves:

Presenters (2 students): One states definitions, notation, and theorem statements; the other outlines the proof and provides an example.
Skeptics (2 students): One checks correctness and catches errors; the other proposes counterexamples when assumptions are weakened.
Scribe (1 student): Records theorem statements, key proof steps, questions raised, and instructor additions. Notes should NOT include names—they are learning material, not meeting minutes. Submit within 24–48 hours; instructor reviews and shares with everyone.
Observers (3 students): Participate in discussion and ask questions; may be called on for examples or perspectives.

Role Assignments

Sunday evening: Instructor announces which pairs are presenters and skeptics for Tuesday and Thursday, and which results will be covered.
Within-pair role assignment: Students decide among themselves or flip a coin at the start of class.

Students are expected to pre-read the assigned material before each class.

Homework Policies

Weekly problem sets. I fully grade two or three problems (announced after submission); the others count for completion. Solutions must be concise (≤1 page per problem) and list the named results used (e.g., “DCT + UI”).

Format: Starting HW 3, solutions must be typeset in LaTeX and submitted as PDF. Non-LaTeX submissions will be returned without grading.

LaTeX resources: Homework templates will be posted on Overleaf. Students should create a free account at Overleaf.

Submission: Submit via Gradescope as PDF by the due date.

Late policy: 3 late-day tokens total for the term; beyond that, late work is not accepted.

Rubric:

4 = correct & clear
3 = essentially correct (minor gap)
2 = right idea with major gap
1 = meaningful progress
0 = off-track
+0.5 exposition bonus possible (capped at 4)

I may invite you to brief board checks on your own solutions; these verify understanding and may adjust the HW score slightly.

You may discuss ideas, but write your own solutions.

Exams

Midterm: One in-class exam (open-book, no internet). Thursday, March 5, 2026.

Final: Take-home exam with a brief (10–12 minutes) oral follow-up. I will choose one of your solutions and ask “why does this step hold?” / “where does the hypothesis matter?” questions. The final is cumulative.

Grading

Component	Weight
Homework	40%
Participation (presenter/skeptic/scribe)	10%
Midterm exam	15%
Final write-up	25%
Final oral follow-up	10%

Schedule

Event	Date
Classes begin	Tuesday, January 20
Midterm	Thursday, March 5
Spring break	March 28 – April 6
Last day of classes	Wednesday, May 6
Final exam	As scheduled by the University

people:kargin:math530

2022-12-30T18:48:06-04:00

Some materials for Math 530

Colab Notebooks:

Notebook I (Numpy library: tensors, matrices, matrix multiplication; sympy library: rank via rref; scipy.linalg library: LU decomposition)
Notebook II (Vector and matrix norms, orthogonality of matrices and vector subspaces, QR decomposition, Linear regression)

Homework:

Additional Python HW:

Math 530: Linear Algebra for Statisticians

2022-09-15T20:00:11-04:00

Math 530: Linear Algebra for Statisticians

Basic Info

Meeting times/Place: MWF 10:50am -11:50am at LN2403

Office hours: MWF 9:45 - 10:30 AM, Monday 1 - 2 PM (in person, office WH136, or by Zoom), or by appointment

Prerequisites: Math 304 (Linear Algebra), 329, 330 or equivalent.

Texts

We will use Strang “Linear Algebra with Applications”, and “Numerical Linear Algebra” by Lloyd Trefethen and David Bau.

The electronic versions of these books are available at the Piazza course webpage.

Learning outcomes

We will cover several types of matrices, various matrix decompositions including SVD, QR and Cholesky, their application to linear regression, and Multivariate Gaussian distribution.

Computing

I will ask students to subscribe to Datacamp.com for 1 or 2 months (do not pay annual subscription!) and take 3 Datacamp courses in “Python”. Specifically, “Introduction to Python”, “Intermediate Python”, “Python Data Science Toolbox 1”. Every datacamp course will be expected to be finished in 1 or 2 weeks and at the end of each one, the student will send me a proof that he or she has passed the course. There will be additional Python based exercises.

Communication

I will mostly use Piazza Forum (https://piazza.com). In particular, I will post all announcements and lecture notes on this website. So make sure that you are enrolled at Piazza. You can either sign up at “https://piazza.com/binghamton/fall2022/math488math530” or send me an email. Since this is a forum, questions and answers by students are encouraged. I will use MyCourses/Brightspace only minimally if at all.

Homework Policy

The homework on linear algebra will be assigned using Gradescope.

There will be a deduction of 25% of the grade for homework assignments that are not typeset using LaTeX. (For users with no experience with LaTex, I suggest trying “https://www.overleaf.com/”.)

There will be a deduction of 15% of the grade for each day homeworks are late (the final grade for a late homework that is N days late will be 0.85^N times the real grade). Homeworks may be discussed with classmates but must be written and submitted individually.

There will also be some Python homework which will have to be submitted both on Gradescope (pdf file) and through a Google form (source file).

Exams

The will be two midterms and one final exam.

Grading

The grading scale will be different for undergraduate and graduate students.

Linear Algebra Homework 25%
Python Courses + Python Homework 25%
Midterms 20% (10% each)
Final Exam 30%

Tentative Schedule

Midterm Exam I	September 30
Midterm Exam II	October 28

Math 571: Advanced Probability — Spring 2026

2026-02-22T20:36:05-04:00

Math 571: Advanced Probability — Spring 2026

Binghamton University

Instructor: Vladislav Kargin
Office: WH-136
Meeting time and location: TR 8:00–9:30 AM, WH 329
Office hours: TR 10:00–11:00 AM

This course is a 4-credit course, which means that in addition to the scheduled lectures/discussions, students are expected to do at least 9.5 hours of course-related work each week during the semester. This includes things like: completing assigned readings, participating in lab sessions, studying for tests and examinations, preparing written assignments, and other tasks that must be completed to earn credit in the course.

Prerequisite

Probability Theory (MATH 501)

Description

The course is intended for students who have a strong foundation in probability theory.

Recommended Text

Durrett, Probability: Theory and Examples, 5th edition. PDF available at PTE

Lecture Notes

Instructor's lecture notes will be provided and posted on Piazza.

Communication

We will use Piazza (piazza.com) for communication. All announcements will be sent to the class using Piazza.

Class Structure and Participation

Each class session is divided into two parts:

Student-led segment (30–45 minutes): Students take on rotating roles to present and critically examine the day's material.

Lecture segment (45–60 minutes): Instructor extends the material, addresses misconceptions, and covers additional applications.

Roles

Each session involves:

Presenters (2 students): One states definitions, notation, and theorem statements; the other outlines the proof and provides an example.
Skeptics (2 students): One checks correctness and catches errors; the other proposes counterexamples when assumptions are weakened.
Scribe (1 student): Records theorem statements, key proof steps, questions raised, and instructor additions. Notes should NOT include names—they are learning material, not meeting minutes. Submit within 24–48 hours; instructor reviews and shares with everyone.
Observers (3-5 students): Participate in discussion and ask questions; may be called on for examples or perspectives.

Role Assignments

Sunday evening: Instructor announces which pairs are presenters and skeptics for Tuesday and Thursday, and which results will be covered.
Within-pair role assignment: Students decide among themselves or flip a coin at the start of class.

Students are expected to pre-read the assigned material before each class.

Problem Sets

HW 1–3 were graded and count toward the final grade at a reduced weight (see Grading below).

HW 4 onward are ungraded practice problems. Weekly problem sets will continue to be assigned, and detailed solutions will be posted after each set. The problems are not collected or graded, but the material is essential preparation for the midterm, final, and presentations. If you have questions about the problems or want feedback on your proofs, I am happy to discuss them in office hours.

Exams

Midterm: One in-class exam (open-book, no internet). Thursday, March 5, 2026.

Grading

Revised February 22, 2026. See Piazza announcement for details.

Component	Weight
Homework (HW 1–3 only)	10%
Participation (presenter/skeptic/scribe)	25%
Midterm exam	20%
Final write-up	25%
Final oral follow-up	20%

~~Original grading policy: Homework 40%, Participation 10%, Midterm 15%, Final write-up 25%, Final oral 10%.~~

Schedule

Event	Date
Classes begin	Tuesday, January 20
Midterm	Thursday, March 5
Spring break	March 28 – April 6
Last day of classes	Wednesday, May 6
Final exam	Tuesday, May 12, 12:50 – 2:50PM, CW320

people:kargin:pictures

2018-02-17T22:08:04-04:00

Vladislav Kargin

2026-04-14T05:10:36-04:00

Vladislav Kargin
Associate Professor; Director of Undergraduate Studies
Department of Mathematics and Statistics, Binghamton University

Research: Random matrix theory, free probability, and probabilistic combinatorics.

E-mail:	vkargin@binghamton.edu
Phone:	(347) 468-0342
Office:	WH-136
Office hours:	Tuesday, Thursday 10:00–11:00 or by appointment (Zoom is OK)

CV · Google Scholar · ORCID · Scopus

Current Teaching (Spring 2026)

Math 571: Advanced Probability (Syllabus)

Complete teaching history · Information for letter requests

Research

Publications (42 peer-reviewed papers)

Some unpublished materials (lecture notes, technical reports, etc.)

Calculations

Miscellaneous

My Erdős number is 4: → Gerard Ben Arous → Daniel Stroock → Persi Diaconis → Paul Erdős.

My Einstein number is 5: → Gerard Ben Arous → Stefano Olla → Joel Lebowitz → Peter Bergmann → Albert Einstein.

Mathematical genealogy

Teaching Archive

2026-01-24T17:39:20-04:00

Teaching Archive

Complete teaching history for Vladislav Kargin.

“Please Do Not Shoot the Pianist. He Is Doing His Best.”

Binghamton University (2015–present)

2024–2025

Math 457: Statistical Learning — F2024 (Syllabus, Project), F2025 (Syllabus, Project)
Math 447: Probability Theory — F2024 (Syllabus)
Math 448: Mathematical Statistics — S2025 (Syllabus)

2023–2024

Math 404: Advanced Linear Algebra — S2024 (Syllabus, Lecture Notes)
Math 457: Statistical Learning — F2023 (Syllabus, Project, Sample projects)
Math 571: Advanced Probability — F2023 (Syllabus, Project)

2022–2023

Spring 2023: Sabbatical
Math 448: Mathematical Statistics — F2022 (Syllabus, Lecture Notes, More)
Math 530: Linear Algebra for Statisticians — F2022 (Syllabus, Lecture Notes, More)

2021–2022

Math 447: Intro to Probability — S2022 (Syllabus, Lecture Notes)
Math 457: Statistical Learning — F2021 (Syllabus, Project, Sample projects)

2020–2021

Math 447: Intro to Probability — F2020, S2021
Math 553: Linear Algebra for Statistics — F2020, F2021

2019–2020

Math 448: Mathematical Statistics — F2019, S2019, S2020
Math 571: Advanced Probability — F2019

2018–2019

Math 447: Intro to Probability — F2018, S2018
Math 529: Applied Probability and Stochastic Processes — F2018

2017–2018

Math 478: Actuarial Mathematics I — F2017
Math 323: Multivariate Calculus — F2017

2016–2017

Math 571: Advanced Probability — F2016
Math 478: Actuarial Mathematics I — F2016
Math 479: Actuarial Mathematics II — S2017

2015–2016

Math 447: Intro to Probability — F2015, S2016
Math 323: Multivariate Calculus — F2015

University of Cambridge (2011–2012)

Random Matrices (Part III / graduate) — F2011
Stochastic Finance Models — F2011

Stanford University (2008–2011)

Math 136: Discrete Probabilistic Methods (graduate) — W2011
Math 393: Free Probability (graduate seminar) — S2009
Math 171: Elementary Functional Analysis — S2010
Math 151: Introduction to Probability Theory — W2010, W2011
Math 51: Linear Algebra and Multivariate Calculus — F2008, W2009, S2009, F2009
Math 53: Ordinary Differential Equations — S2011