Faez Ahmed

• University of Tikrit

Aims Advixa is a monoclonal antibody manufactured as a biosimilar to adalimumab (Humira, Abbvie Inc.). The key objectives of this study were to use a healthy population group to assess pharmacokinetics (PK) and safety similarity between Advixa and Humira in healthy participants and to quantify the effects on adalimumab PK and safety to assess the i...

Model merging, a method that combines the parameters and embeddings of multiple fine-tuned large language models (LLMs), offers a promising approach to enhance model performance across various tasks while maintaining computational efficiency. This paper introduces Activation-Informed Merging (AIM), a technique that integrates the information from t...

Aim: This study evaluated the efficacy, safety, and immunogenicity of biosimilar adalimumab drug Advixa® (Incepta Pharmaceuticals Ltd, Dhaka, Bangladesh) in patients with moderate to severe rheumatoid arthritis (RA) compared to the reference drug Humira® (AbbVie Inc., North Chicago, IL, USA). Methods: In this randomized, double-blind, prospective,...

The field of engineering is shaped by the tools and methods used to solve problems. Optimization is one such class of powerful, robust, and effective engineering tools proven over decades of use. Within just a few years, generative artificial intelligence (GenAI) has risen as another promising tool for general-purpose problem-solving. While optimiz...

This paper introduces a generative model designed for multimodal control over text-to-image foundation generative AI models such as Stable Diffusion, specifically tailored for engineering design synthesis. Our model proposes parametric, image, and text control modalities to enhance design precision and diversity. Firstly, it handles both partial an...

Topology optimization is a ubiquitous task in engineering design, involving the optimal distribution of material in a prescribed spatial domain. Recently, data-driven methods such as deep generative AI models have been proposed as an alternative to iterative optimization methods. However, existing data-driven approaches are often trained on dataset...

This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs. This model functions as an AI design co-pilot, providing multiple design options for incomplete designs, which we demonstrate using the bicycle design CAD dataset. Throug...

Designers may often ask themselves how to adjust their design concepts to achieve demanding functional goals. To answer such questions, designers must often consider counterfactuals, weighing design alternatives and their projected performance. We introduce Multi-objective Counterfactuals for Design (MCD), a computational tool that automates and st...

This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs. This model functions as an AI design co-pilot, providing multiple design options for incomplete designs, which we demonstrate using the bicycle design CAD dataset. Throug...

Sketch2Prototype is an AI-based framework that transforms a hand-drawn sketch into a diverse set of 2D images and 3D prototypes through sketch-to-text, text-to-image, and image-to-3D stages. This framework, shown across various sketches, rapidly generates text, image, and 3D modalities for enhanced early-stage design exploration. We show that using...

Sketch2Prototype is an AI-based framework that transforms a hand-drawn sketch into a diverse set of 2D images and 3D prototypes through sketch-to-text, text-to-image, and image-to-3D stages. This framework, shown across various sketches, rapidly generates text, image, and 3D modalities for enhanced early-stage design exploration. We show that using...

Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial. To make generative models performance-aware and enable them to create high-performing designs, surrogate modeling of these me...

Aims Advixa is a monoclonal antibody manufactured as a biosimilar to adalimumab (Humira, Abbvie Inc.). The key objectives of this study were to use a healthy population group to assess pharmacokinetics (PK) and safety similarity between Advixa and Humira in healthy participants and to quantify the effects on adalimumab PK and safety to assess the i...

In many design automation applications, accurate segmentation and classification of 3D surfaces and extraction of geometric insight from 3D models can be pivotal. This paper primarily introduces a machine learning-based scheme that leverages Graph Neural Networks (GNN) for handling 3D geometries, specifically for surface classification. Our model d...

When designing evidence-based policies and programs, decision-makers must distill key information from a vast and rapidly growing literature base. Identifying relevant literature from raw search results is time and resource intensive, and is often done by manual screening. In this study, we develop an artificial intelligence (AI) agent based on a b...

We introduce Multi-Objective Counter/actuals for Design (MCD), a novel method for counterfactual optimization in design problems. Counterfactuals are hypothetical situations that can lead to a different decision or choice. In this paper, the authors frame the counterfactual search problem as a design recommendation tool that can help identify modif...

Accurate vehicle rating prediction can facilitate designing and configuring good vehicles. This prediction allows vehicle designers and manufacturers to optimize and improve their designs in a timely manner, enhance their product performance, and effectively attract consumers. However, most of the existing data-driven methods rely on data from a si...

Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly refined meshes may ensure precision, they come with high computational costs. Similarly, adaptive remeshing techniques require multiple simulations and come at a great c...

Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial. To make generative models performance-aware and enable them to create high-performing designs, surrogate modeling of these me...

When designing evidence-based policies and programs, decision-makers must distill key information from a vast and rapidly growing literature base. Identifying relevant literature from raw search results is time and resource intensive, and is often done by manual screening. In this study, we develop an AI agent based on a bidirectional encoder repre...

Topology Optimization is a widely used method to find optimal designs that meet constraints and maximize system performance. However, traditional iterative optimization methods such as SIMP are computationally expensive and prone to getting stuck in local minima, limiting their effectiveness for complex or large-scale problems. To overcome these ch...

Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly refined meshes may ensure precision, they come with high computational costs. Similarly, adaptive remeshing techniques require multiple simulations and come at a great c...

Generative models have demonstrated impressive results in vision, language, and speech. However, even with massive datasets, they struggle with precision, generating physically invalid or factually incorrect data. This is particularly problematic when the generated data must satisfy constraints, for example, to meet product specifications in engine...

Generative models have had a profound impact on vision and language, paving the way for a new era of multimodal generative applications. While these successes have inspired researchers to explore using generative models in science and engineering to accelerate the design process and reduce the reliance on iterative optimization, challenges remain....

We introduce Multi-Objective Counterfactuals for Design (MCD), a novel method for counterfactual optimization in design problems. Counterfactuals are hypothetical situations that can lead to a different decision or choice. In this paper, the authors frame the counterfactual search problem as a design recommendation tool that can help identify modif...

Deep generative models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a variety of applications, including image and speech synthesis, natural language processing, and drug discovery. However, when applied to engineering design problems, evaluating th...

Aims Advixa is a monoclonal antibody manufactured as a biosimilar to adalimumab (Humira, Abbvie Inc.). The key objectives of this study were to use a healthy population group to assess pharmacokinetics (PK) and safety similarity between Advixa and Humira in healthy participants and to quantify the effects of potential covariates on adalimumab PK an...

In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger than any existing database of planar mechanisms and is not limited to specific kinds of mechanisms such as four-bars, six-bars, etc. which are typically what most databases...

Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While early works are promising, further advancement will depend on addressing several critical considerations such as design quality, feasibility, novelty, and targeted inverse d...

This paper presents a data-driven analysis of the structural performance of 4500 community-designed bicycle frames. We introduce FRAMED – a parametric dataset of bicycle frames based on bicycles designed by bicycle practitioners from across the world. To support our data-driven approach, we also provide a dataset of structural performance values su...

As the volume and complexity of distributed online work increases, collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of online collaborations by grouping workers in a top-down fashion and according to a set of predefined decis...

In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger than any existing database of planar mechanisms and is not limited to specific kinds of mechanisms such as four-bars, six-bars, \etc which are typically what most databases...

Deep Generative Machine Learning Models (DGMs) have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. DGMs are conventionally trained to minimize statistical divergence between the distribution over generated data and distribution over the dataset on which they are trained....

Understanding relationships between different products in a market system and predicting how changes in design impact their market position can be instrumental for companies to create better products. We propose a graph neural network-based method for modeling relationships between products, where nodes in a network represent products and edges rep...

Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While early works are promising, further advancement will depend on addressing several critical considerations such as design quality, feasibility, novelty, and targeted inverse d...

Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative machine learning to design engineering may enable such automated design synthesis and is a research subject of great importan...

This paper presents a data-driven analysis of the structural performance of 4500 community-designed bicycle frames. We present FRAMED -- a parametric dataset of bicycle frames based on bicycles designed by bicycle practitioners from across the world. To support our data-driven approach, we also provide a dataset of structural performance values suc...

Multi-objective optimization is key to solving many Engineering Design problems, where design parameters are optimized for several performance indicators. However, optimization results are highly dependent on how the designs are parameterized. Researchers have shown that deep generative models can learn compact design representations, providing a n...

Bangladesh remains cholera endemic with biannual seasonal peaks causing epidemics. At least 300,000 severe cases and over 4,500 deaths occur each year. The available oral cholera vaccines have not yet been adopted for cholera control in Bangladesh due to insufficient number of doses available for endemic control. With a public private partnership,...

A picture is worth a thousand words, and in design metric estimation, a word may be worth a thousand features. Pictures are awarded this worth because they encode a plethora of information. When evaluating designs, we aim to capture a range of information, including usefulness, uniqueness, and novelty of a design. The subjective nature of these con...

Automated design synthesis has the potential to revolutionize the modern human design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may be the key to such automated design synthesis and is a research subject of great importa...

In this paper, we present “BIKED,” a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of design information including a...

Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among t...

Worldwide Hepatitis B is known as one of the imperative causes of mortality and morbidity as well as occupational health hazard among health workers. Bangladesh is intermediate endemic country for Hepatitis B infection for which the government has introduced hepatitis B vaccination into the Expanded Programme on Immunization (EPI) nationwide since...

Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which is crucial for creating products and enabling inn...

A picture is worth a thousand words, and in design metric estimation, a word may be worth a thousand features. Pictures are awarded this worth because of their ability to encode a plethora of information. When evaluating designs, we aim to capture a range of information as well, information including usefulness, uniqueness, and novelty of a design....

Graph representation learning has revolutionized many artificial intelligence and machine learning tasks in recent years, ranging from combinatorial optimization, drug discovery, recommendation systems, image classification, social network analysis to natural language understanding. This paper shows their efficacy in modeling relationships between...

Estimating the form and functional performance of a design in the early stages can be crucial for a designer for effective ideation Humans have an innate ability to guess the size, shape, and type of a design from a single view. The brain fills in the unknowns in a fraction of a second. However, humans may struggle with estimating the performance o...

Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among t...

In this paper, we present “BIKED,” a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of design information including a...

Engineering design tasks often require synthesizing new designs that meet desired performance requirements. The conventional design process, which requires iterative optimization and performance evaluation, is slow and dependent on initial designs. Past work has used conditional generative adversarial networks (cGANs) to enable direct design synthe...

Design variety metrics measure how much a design space is explored. This article proposes that a generalized class of entropy metrics based on Sharma–Mittal entropy offers advantages over existing methods to measure design variety. We show that an exemplar metric from Sharma–Mittal entropy, namely, the Herfindahl–Hirschman index for design (HHID) h...

Graph Neural Networks have revolutionized many machine learning tasks in recent years, ranging from drug discovery, recommendation systems, image classification, social network analysis to natural language understanding. This paper shows their efficacy in modeling relationships between products and making predictions for unseen product networks. By...

Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which is crucial for creating products and enabling inn...

Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among t...

In this paper, we present "BIKED," a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and generally support the development of data-driven design methods. The dataset is comprised of a variety of design information i...

As the volume and complexity of distributed online work increases, the collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of such teams by grouping workers according to a set of predefined decision criteria. This approach micro...

Metamaterials are emerging as a new paradigmatic material system to render unprecedented and tailorable properties for a wide variety of engineering applications. However, the inverse design of metamaterial and its multiscale system is challenging due to high-dimensional topological design space, multiple local optima, and high computational cost....

Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: (1) generated designs lack diversity and do not cover all areas of the design space, (2) it is difficult to explicitly improve the overall performanc...

Design researchers have long sought to understand the mechanisms that support creative idea development. However, one of the key challenges faced by the design community is how to effectively measure the nebulous construct of creativity. The social science and engineering communities have adopted two vastly different approaches to solving this prob...

Multi-objective optimization is key to solving many Engineering Design problems, where design parameters are optimized for several performance indicators. However, optimization results are highly dependent on how the designs are parameterized. Researchers have shown that deep generative models can learn compact design representations, providing a n...

Statistical network models allow us to study the co-evolution between the products and the social aspects of a market system, by modeling these components and their interactions as graphs. In this paper, we study competition between different car models using network theory, with a focus on how product attributes (like fuel economy and price) affec...

Design researchers have long sought to understand the mechanisms that support creative idea development. However, one of the key challenges faced by the design community is how to effectively measure the nebulous construct of creativity. The social science and engineering communities have adopted two vastly different approaches to solving this prob...

Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale structure can be quickly filled via combinatorial search algorithms, and machine learning models can be trained to a...

Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: 1) generated designs lack diversity and do not cover all areas of the design space, 2) it is difficult to explicitly improve the overall performance...

Deep generative models have proven useful for automatic design synthesis and design space exploration. However, they face three challenges when applied to engineering design: 1) generated designs lack diversity, 2) it is difficult to explicitly improve all the performance measures of generated designs, and 3) existing models generally do not genera...

Bipartite b-matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas such as healthcare, advertising, education, and general resource allocation. Traditionally, the primary goal of such models is to maximize a linear function of the constitu...

The inverse design of metamaterials is difficult due to a high-dimensional topological design space and presence of multiple local optima. Computational cost is even more demanding for design of multiscale metamaterial systems with aperiodic microstructures and spatially-varying or functionally gradient properties. Despite the growing interest in a...

Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale structure can be quickly filled via combinatorial search algorithms, and machine learning models can be trained to a...

Collaborative work often benefits from having teams or organizations with heterogeneous members. In this paper, we present a method to form such diverse teams from people arriving sequentially over time. We define diversity using a submodular function and adopt an online algorithm to solve monotone submodular maximization problem with multiple capa...

Collaborative work often benefits from having teams or organizations with heterogeneous members. In this paper, we present a method to form such diverse teams from people arriving sequentially over time. We define a monotone submodular objective function that combines the diversity and quality of a team and propose an algorithm to maximize the obje...

Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face two challenges: 1) generated designs lack diversity and do not cover all areas of the design space and 2) it is difficult to explicitly improve the overall performance...

In this paper, we propose a new design variety metric based on the Herfindahl index. We also propose a practical procedure for comparing variety metrics via the construction of ground truth datasets from pairwise comparisons by experts. Using two new datasets, we show that this new variety measure aligns with human ratings more than some existing a...

Exposing people to concepts created by others can inspire novel combinations of concepts, or conversely, lead people to simply emulate others. But how does the type of exposure affect creative outcomes in online collaboration where dyads interact for short tasks? In this paper, we study the creative outcomes of dyads working together online on a sl...

The present study designed to investigate the effect of three entomopathogenic fungi (EPF), Beauveria bassiana (Biovar), Trichoderma album (Biozed) and Metarhizium anisopliae (Bioranza) on greater wax moth Galleria mellonella larvae. The results revealed that the LC25 values of T. album, M. anisopliae and B. bassiana against G. mellonella larvae we...

The journal of Toxicology and pest control is one of the series issued twice by the Egyptian Academic Journal of Biological Sciences, and is devoted to publication of original papers related to the interaction between insects and their environment. The goal of the journal is to advance the scientific understanding of mechanisms of toxicity. Emphasi...

Following successful crowd ideation contests, organizations in search of the "next big thing" are left with hundreds of ideas. Expert-based idea filtering is lengthy and costly; therefore, crowd-based strategies are often employed. Unfortunately, these strategies typically (1) do not separate the mediocre from the excellent, and (2) direct all the...

Assessing similarity between design ideas is an inherent part of many design evaluations to measure novelty. In such evaluation tasks, humans excel at making mental connections among diverse knowledge sets to score ideas on their uniqueness. However, their decisions about novelty are often subjective and difficult to explain. In this paper, we demo...

Assessing similarity between design ideas is an inherent part of many design evaluations to measure novelty. In such evaluation tasks, humans excel at making mental connections among diverse knowledge sets and scoring ideas on their uniqueness. However, their decisions on novelty are often subjective and difficult to explain. In this paper, we demo...

Bisociative knowledge discovery is an approach that combines elements from two or more "incompatible" domains to generate creative solutions and insight. Inspired by Koestler's notion of bisociation, in this paper we propose a computational framework for the discovery of new connections between domains to promote creative discovery and inspiration...

Bisociative knowledge discovery is an approach that combines elements from two or more "incompatible" domains to generate creative solutions and insight. Inspired by Koestler's notion of bisociation, in this paper we propose a computational framework for the discovery of new connections between domains to promote creative discovery and inspiration...

When selecting ideas or trying to find inspiration, designers often must sift through hundreds or thousands of ideas. This paper provides an algorithm to rank design ideas such that the ranked list simultaneously maximizes the quality and diversity of recommended designs. To do so, we first define and compare two diversity measures using Determinan...

When selecting ideas or trying to find inspiration, designers often must sift through hundreds or thousands of ideas. This paper provides an algorithm to rank design ideas such that the ranked list simultaneously maximizes the quality and diversity of recommended designs. To do so, we first define and compare two diversity measures using Determinan...

Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation. A practitioner's goal is typically to maximize a matching market's economic efficiency, po...

This paper describes how to find or filter high-quality ideas submitted by members collaborating together in online communities. Typical means of organizing community submissions, such as aggregating community or crowd votes, suffer from the cold-start problem, the rich-get-richer problem, and the sparsity problem. To circumvent those, our approach...

Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation. A practitioner's goal is typically to maximize a matching market's economic efficiency, po...

This paper describes how to select diverse, high quality, representative ideas when the number of ideas grow beyond what a person can easily organize. When designers have a large number of ideas, it becomes prohibitively difficult for them to explore the scope of those ideas and find inspiration. We propose a computational method to recommend a div...

Suspension systems with terrain preview have wide applicability for off-road and military vehicles moving on rough surfaces. Preview control of active suspension systems gives it great flexibility in controlling system behavior. The focus of the study is evolutionary multi-objective optimization of preview controlled active suspension system with s...

Off-line point to point navigation to calculate feasible paths and optimize them for different objectives is computationally difficult. Path planning problem is truly a multi-objective problem, as reaching the goal point in short time is desirable for an autonomous vehicle while ability to generate safe paths in crucial for vehicle viability. Path...

Five districts and four cities of Bangladesh. To study the role of informal health practitioners in delays in initiating tuberculosis (TB) treatment in new smear-positive TB patients. A cross-sectional study of all patients registered within specific projects in Bangladesh using routine records from projects. Definitions were as follows: 1) total d...

Opinions on reproductive health education at the onset of puberty at present were studied by using a structured questionnaire consisting of 13 questions with a view to know their conception about it. A total of 800 male students were randomly selected of which 400 were from two public and the rest 400 from private universities situated in Dhaka, Ba...

A registry data-based study of 3,600 patients systematically drawn from out-patient clinic, laboratory and tuberculosis (TB) treatment registers (1200 from each) examined female-male differences at various clinical steps of TB management and compared selective indicators with published results. Female-to-male ratios (FMR) declined at the following...

Background Rheumatoid Heart Disease (RHD) (pericarditis, myocarditis, endocarditis) has been reported in 30–40% of RA patients in several echocardiographic studies conducted before the era of aggressive, early therapy with disease-modifying drugs (DMARDs). Objectives We assessed whether the recent therapeutic trend of early DMARD therapy has had a...