The FinEval financial domain evaluation benchmark, based on quantitative fundamental methods and developed through long-term objective research, summarization, and rigorous manual screening, utilizes over 26,000 diverse question types that are highly consistent with real-world application scenarios.

A research platform to develop automated security policies using quantitative methods, e.g., optimal control, computational game theory, reinforcement learning, optimization, evolutionary methods, and causal inference.

In recent years, community detection has received increased attention thanks to its wide range of applications in many fields. While at first most techniques were focused on discovering communities in static networks, lately the research community’s focus has shifted toward methods that can detect meaningful substructures in evolving networks because of their high relevance in real-life problems. This thesis explores the current availability of empirical comparative studies of dynamic methods and also provides its own qualitative and quantitative comparison with the aim of gaining more insight in the performance of available algorithms that are expected to perform well in the context of social community detection. The qualitative comparison includes 13 algorithms, namely D-GT, Extended BGL, TILES, AFOCS, HOCTracker, OLCPM, DOCET, LabelRankT, FacetNet, DYNMOGA, DEMON and iLCD. The empirical analysis compares TILES, HOCTracker, OLCPM, DEMON and iLCD on synthetic RDyn graphs and the real graph, DBLP. In addition to the results of the empirical and qualitative results of the analysis, the thesis’s value lies in its wide coverage of the dynamic community detection problem.

Course Website Repo for JOURN 8006: Quantitative Research Methods in Journalism

Course website for JOURN 8016: Advanced Quantitative Research Methods

This repository includes materials I prepared for courses in quantitative research methods. These courses were targeted mainly to graduate students with wide ranging backgrounds in statistics and quantitative methods.

SPIE-AAPM-NCI PROSTATEx Challenges-The PROSTATEx Challenge (" SPIE-AAPM-NCI Prostate MR Classification Challenge”) focused on quantitative image analysis methods for the diagnostic classification of clinically significant prostate cancers and was held in conjunction with the 2017 SPIE Medical Imaging Symposium. PROSTATEx ran from November 21, 2016 to January 15, 2017, though a "live" version has also been established at https://prostatex.grand-challenge.org which serves as an ongoing way for researchers to benchmark their performance for this task. The PROSTATEx-2 Challenge (" SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge" ) was focused on the development of quantitative multi-parametric MRI biomarkers for the determination of Gleason Grade Group in prostate cancer.

Automatic Analysis of Music Performance Style One fundamental problem in computational music is analysis and modeling of performance style. Last year’s successful CUROP project revealed, through perceptual experiments, that players' control over rhythm is the strongest factor in the perceived quality of performance (already a publishable result). This year's project will hence investigate the computer analysis of the rhythmic component of performances in more detail, with the following aims: Implement and improve upon state-of-the-art beat detection methods. Carry out statistical analysis of rhythmic variation on a corpus of performances: Train a classifier into professional/amateur performance. Investigate to what extent rhythmic variations are controlled as opposed to random. Devise rhythmic style signatures of various performers for style recognition and retrieval. Investigate operations on rhythmic styles, e.g. apply Rachmaninoff's style to one's amateur recording. Solving the above problems is paramount to our understanding of what makes a good performance and what, quantitatively, are the differences between professional musician's styles. Applications include: musicology, teaching, automatic performance of music, high-level editing of music. This project requires integration of data mining, machine learning, and digital signal processing techniques, which are closely aligned with the expertise of the two supervisors: Dr Kirill Sidorov and Dr Andrew Jones. who are also experienced musicians. Via this project, the student will learn a variety of digital signal processing and machine learning techniques and will develop enhanced MATLAB programming skills, that are increasingly in demand for graduates. The student will work in our lab, with state-of-the-art facilities (powerful audio workstation, digital piano, audio gear). We will work collaboratively to ensure successful completion, including daily 30 minute meetings and longer weekly review meetings. The student will participate in the recently established Computational Music research sub-group. This project will contribute to longer-term development of this sub-group and foster new research avenues. Project Start/End Dates: Any 8 week period from 13th June 2016 to September 19th 2016. Contact/Supervisors: Kirill Sidorov Andrew Jones

A research toolkit for extracting quantitative features from human movement data, offering computational methods to analyze movement qualities like smoothness, bilateral symmetry, contraction/expansion patterns, and synchronization. Built for researchers in motor control, biomechanics, and movement disorders.

A comprehensive collection of quantitative finance research spanning classical trading strategies, deep learning models for price prediction, ensemble ML methods, and modern LLM-powered financial analysis.

📙 Course repository for JOURN 8006: Quantitative Research Methods in Journalism

In-class material for INTL 601 Quantitative Research Methods (Koç University)

Course materials for class at TAMU COMM 611 Advanced Quantitative Research Method

R Scripts for Health in Numbers: Quantitative Methods in Clinical & Public Health Research

Materials for PUBH614: Quantitative and Qualitative Data Analysis Methods in Public Health Research

This repo contains teaching materials for the Modern Middle Eastern Studies core course in Quantitative Research Methods, University of Oxford.

2020 VHB-ProDok Course Quantitative Empirical Accounting Research and Open Science Methods

Website for Quantitative Methods Research Group

Experimental Designs and Statistical Methods for Quantitative Research in Management

Some code for the Quantitative Research Methods course

Repo associated with graduate course on quantitative methods for neuroscience research at Pitt

Research articles and documentation on financial astrology methods, planetary cycles, and market timing for quantitative validation.

My first venture into the field of quantitative research, started as my final project for the TSA course, using purely TSA methods.

MCP Setup Assessment is a hands-on project demonstrating the configuration and optimization of a Tenx MCP server, creation of AI agent rules, and thorough documentation of processes and testing. The project showcases technical proficiency, AI workflow understanding, and iterative improvement through quantitative metrics and research-backed methods.

In the late 1960s, computer vision began at universities which were pioneering artificial intelligence. It was meant to mimic the human visual system, as a stepping stone to endowing robots with intelligent behavior.[11] In 1966, it was believed that this could be achieved through a summer project, by attaching a camera to a computer and having it "describe what it saw".[12][13] What distinguished computer vision from the prevalent field of digital image processing at that time was a desire to extract three-dimensional structure from images with the goal of achieving full scene understanding. Studies in the 1970s formed the early foundations for many of the computer vision algorithms that exist today, including extraction of edges from images, labeling of lines, non-polyhedral and polyhedral modeling, representation of objects as interconnections of smaller structures, optical flow, and motion estimation.[11] The next decade saw studies based on more rigorous mathematical analysis and quantitative aspects of computer vision. These include the concept of scale-space, the inference of shape from various cues such as shading, texture and focus, and contour models known as snakes. Researchers also realized that many of these mathematical concepts could be treated within the same optimization framework as regularization and Markov random fields.[14] By the 1990s, some of the previous research topics became more active than the others. Research in projective 3-D reconstructions led to better understanding of camera calibration. With the advent of optimization methods for camera calibration, it was realized that a lot of the ideas were already explored in bundle adjustment theory from the field of photogrammetry. This led to methods for sparse 3-D reconstructions of scenes from multiple images. Progress was made on the dense stereo correspondence problem and further multi-view stereo techniques. At the same time, variations of graph cut were used to solve image segmentation. This decade also marked the first time statistical learning techniques were used in practice to recognize faces in images (see Eigenface). Toward the end of the 1990s, a significant change came about with the increased interaction between the fields of computer graphics and computer vision. This included image-based rendering, image morphing, view interpolation, panoramic image stitching and early light-field rendering.[11] Recent work has seen the resurgence of feature-based methods, used in conjunction with machine learning techniques and complex optimization frameworks.[15][16] The advancement of Deep Learning techniques has brought further life to the field of computer vision. The accuracy of deep learning algorithms on several benchmark computer vision data sets for tasks ranging from classification, segmentation and optical flow has surpassed prior methods.[citation needed]

Quantitative Methods in International Business Research

SLAT7855 Quantitative Research Methods

Quantitative Research Methods

Research project for Econ 712, Quantitative Methods for Heterogeneous Agent Models

UG Quantitative research methods workbook