ishtiaque ( ishti ) mahbub
Advanced Transportation Simulation at National Renewable Energy Laboratory
graduate research assistant | phd candidate university delaware ( optimal control | connected automated vehicle | machine learning )
highy motivated control/systems engineer expertise state-of-the-art optimal control connected automated vehicle ( cavs ) hybrid electric vehicle ( hevs ) automated highway system ( ahs ) v2x communication framework . primary interest lie interconnectivity control autonomous agent enables novel computational framework process massive amount data deliver real-time control action optimize energy consumption associated benefit . end , develop , simulate test optimal algorithm controlling vehicle dynamic powertrain hybrid electric connected automated vehicle .
National Renewable Energy Laboratory
https://www.linkedin.com/company/national-renewable-energy-laboratory
1. Analyzed the traffic and vehicle data from TomTom and transportation secure data center for traffic demand modeling
2. Characterized the energy impact of connected autonomous vehicles for Denver metro region using SUMO and Python
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Advanced Transportation Simulation
University of Delaware
https://www.linkedin.com/company/university-of-delaware
Newark, Delaware
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Graduate Research Assistant
University of Delaware
https://www.linkedin.com/company/university-of-delaware
My research interests concern 
1. Connected and Automated Vehicle (CAV) Technology, 
2. Decentralized Optimal Control, 
3. Mixed Traffic Simulation, 
4. V2V and V2I Communication
5. Smart Cities and Optimal Routing etc.
Newark, DE 19716
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PHD Candidate
University of Delaware
https://www.linkedin.com/company/university-of-delaware
Course Affiliation:

Controls and Vibration Lab (MEEG 312)
Newark, DE 19716, USA
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Graduate Teaching Assistant
National Renewable Energy Laboratory
https://www.linkedin.com/company/national-renewable-energy-laboratory
1. Development and improvement of NREL's RouteE energy prediction engine with random forest, XGBoost, etc.
2. Feature selection optimization, feature engineering and hyper-parameter tuning of RouteE
https://media-exp1.licdn.com/dms/image/C4E0BAQGCCaf28KbbbQ/company-logo_100_100/0/1536940984745?e=1655942400&v=beta&t=8laRuTCJRGI-wIJ1T61qvgGaLqdH29XTPBKAy8x4mjY
Advanced Data Analyst (Transportation)
Robert Bosch LLC
1. Vehicle dynamics (VD) controller development and testing for Audi A3-etron
2. Establishing V2X communication between Audi A3-etron and the UMTRI framework
3. Design, setup and conduct vehicle testing in Mcity with augmented reality
Farmington Hills, Michigan
PhD Intern
Military Institute of Science and Technology (MIST)
Courses Instructed:
1. Engineering Thermodynamics
2. Instrumentation and Measurement
3. Thermodynamics Lab
4. Heat Transfer Lab
5. Instrumentation and Measurement Lab
6. Workshop
Dhaka, Bangladesh
Assistant Professor, Dept. of Mechanical Engineering
Military Institute of Science and Technology (MIST)
Courses Instructed:
1. Numerical Analysis, 
2. Computer Programming Language C, 
3. Engineering Mechanics, 
4. Engineering Drawings II Lab, 
5. Numerical Analysis Lab,
6. Programming Language Lab
Dhaka, Bangladesh
University Lecturer
Bosch
https://www.linkedin.com/company/bosch
Topic: 3D Dynamic Simulation of Concrete Hammer Drilling For SDSmax Using FEM
 Creation of Abaqus Explicit CAE Model and Implementation of VUMAT
 Material Parameter Identification and Design Optimization

Tools: Abaqus, Creo-ProE , Famos, VideoMach, Image J, Python
Stuttgart Area, Germany
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Master Thesis Student
Institute of Polymer Technology, University of Stuttgart
Effect of Injection Molding parameters on Fiber characteristics (Length and Orientation) within the composite matrix. (MATLAB GUI)
 Modeling and Experimental Study of Hygroscopic Polymer (BASF-PA6) Drying Kinetics using Finite Difference Formulation.
 A complete MATLAB GUI program of “Polymer Characteristic variation” in an Extrusion Process

Tools: Matlab GUI, Image J
Pfaffenwaldring 32, 70569 Stuttgart
Research Assistant
Fraunhofer IPA
https://www.linkedin.com/company/fraunhofer-ipa
Multi Scale simulation of Electrical Properties of CNT-based Composites
 Percolation Threshold and Conductivity Analysis
 Microstructure modelling and numerical simulation
Tools Used: DIGIMAT, ANSYS Workbench, Design Modeler
Nobelstraße 12, 70569 Stuttgart
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Intern
Engineering Resource International (ERI) LTD
Task:Transformation of old FCK brick manufacturing kilns into modern Zig-Zag kilns by developing and designing an efficient and environment friendly exhaust system and combustion chamber.

Tools Used: SolidWorks2012, AutoCAD 2010, COMSOL 4.2
House:60, Road:18 Block-J, Banani, Dhaka 1213, Bangladesh
Mechanical Engineering Intern
Novartis (Bangladesh) Limited, Tongi Plant, Gazipur, Bangladesh
Tongi plant, Gazipur, Dhaka
Industrial Training
Doctor of Philosophy - PhD
1. Synthesized optimal vehicle dynamics controller for coordination of connected automated vehicles (CAVs) in traffic corridors including highway on-ramp merging, roundabout, urban intersection and speed reduction zone
2. Analyzed and simulated emerging mobility systems in high-fidelity simulation (VISSIM, PreScan, SUMO)
3. Derived optimal powertrain controller for plugin hybrid electric vehicles, and validated with Simulink and HIL test
4. Developed the augmented reality-based vehicle test procedure at UMTRI using VISSIM for CAV coordination
5. Developed novel optimal coordination and platoon formation framework under mixed traffic environment
Mechanical Engineering (Optimal Control in Autonomous Hybrid Vehicle)
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University of Delaware
https://www.linkedin.com/school/university-of-delaware/
Master of Science - MS
Network Optimization,
Optimal Control,
Convex Optimization,
Probability and random processes,
Decentralized Control,
Nonlinear Control,
Nonlinear Programming,
Advanced Engineering Mathematics
Linear Systems
Mechanical Engineering (Control/Robotics)
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University of Delaware
https://www.linkedin.com/school/university-of-delaware/
Master of Science (MSc)
The topics covered in this M.Sc. Study are as follows:
Continuum Mechanics
Computational Mechanics of Materials
Computational Mechanics of Structures
Discretization Methods
Software Development/Numerical Programming
Optimisation of Mechanical Systems
Engineering Materials
Micromechanics of Materials and Homogenization Methods
Advanced Computational Mechanics of Structures
Implementation and Algorithms for Finite Elements
Numerical Methods for Differential Equations
Computational Materials Science
Composite Modelling
Computational Mechanics of Materials and Structures (COMMAS)
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University of Stuttgart
https://www.linkedin.com/school/universit%C3%A4t-stuttgart/
Bachelor of Science (BSc)
Some of the topics covered in this B.Sc. program are as follows:
Aerodynamics
Machine Design
Fluid Mechanics
Thermodynamics
Heat and Mass Transfer
Automobile Engineering
Production Process
Mechanics of Machinery
Numerical Analysis
Machine Tools
Fluid Machinery
Industrial Management
Mechanical Engineering
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Bangladesh University of Engineering and Technology
https://www.linkedin.com/school/bangladesh-university-of-engineering-&-technology/
Higher Secondary School Certificate
Science
Rangpur Cadet College
Secondary school certificate (SSC)
Science
Rangpur Cadet College
Mechanical Engineering Graduate Association, UD
Secretary
SAE (Society of Automotive Engineers) International
Student Member
Society for Industrial and Applied Mathematics (SIAM)
Student Member
IEEE
Student Member
Vehicle platooning using connected and automated vehicles (CAVs) has attracted considerable attention. In this paper, we address the problem of optimal coordination of CAV platoons at a highway on-ramp merging. We present a single-level constrained optimal control framework that optimizes fuel economy and travel time of the platoons while satisfying the state, control, and safety constraints. We also explore the effect of delayed communication among the CAV platoons and propose a robust coordination framework to enforce lateral and rear-end collision avoidance constraints in the presence of bounded delays. We provide a closed-form analytical solution of the optimal control problem with safety guarantees that can be implemented in real time. Finally, we validate the effectiveness of the proposed control framework using a high-fidelity commercial simulation environment.
A Constrained Optimal Control Framework for Vehicle Platoons with Delayed Communication
Network and Heterogeneous Media: Traffic and Autonomy ( in review, arXiv:2111.08080)
Emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. However, before connectivity and automation are deployed en masse, a thorough evaluation of CAVs is required -- ranging from numerical simulation to real-world public roads. In particular, assessment of CAVs performance in scaled testbeds has recently gained popularity as a robust approach that ensures absolute safety, complete control of the test-environment variables, and quick, repeatable experiments. This article introduces the Information and Decision Science Lab's Scaled Smart City (IDS3C), a 1:25 scaled testbed that is capable of replicating different real-world urban traffic scenarios. IDS3C was designed with the capabilities to investigate the effect of emerging mobility systems, such as CAVs, electric vehicles, and shared mobility, on energy consumption and transportation efficiency. In our overview of the IDS3C, we provide our framework that allows to optimally coordinate CAVs at traffic scenarios such as merging at roadways and roundabouts, cruising in congested traffic, passing through speed reduction zones, and lane-merging or passing maneuvers. As a tutorial, we present the application of our control framework to a multi-lane roundabout scenario in the IDSC, and demonstrate the scalability of our testbed in a fleet of  CAVs cruising in a transportation corridor.
A Scaled Smart City for Emerging Mobility Systems
Control Systems Magazine (in review: arXiv:2109.05370)
Connected and automated vehicles (CAVs) provide the most intriguing opportunity to reduce pollution, energy consumption, and travel delays. In this letter, we address the problem of vehicle platoon formation in a traffic network with partial CAV penetration rates. We investigate the interaction between CAV and human-driven vehicle (HDV) dynamics, and provide a rigorous control framework that enables platoon formation with the HDVs by only controlling the CAVs within the network. We present a complete analytical solution of the CAV control input and the conditions under which a platoon formation is feasible. We evaluate the solution and demonstrate the efficacy of the proposed framework using simulation.
A Platoon Formation Framework in a Mixed Traffic Environment
IEEE Control System Letters (LCSS)
Coordination of connected and automated vehicles (CAVs) in mixed traffic environment poses significant challenges due to the presence of human-driven vehicles (HDVs) with stochastic dynamics and driving behavior. In earlier work, we addressed the problem of platoon formation of HDVs led by a CAV using a model-dependent, open-loop controller. In this paper, we develop a comprehensive model-agnostic, multi-objective optimal controller which ensures platoon formation of the trailing HDVs by directly controlling the leading CAV without having explicit knowledge of the HDV dynamics. We provide a detailed exposition of the control framework that uses instantaneous motion information from multiple successive HDVs to enforce safety while achieving the optimization objectives. To demonstrate the efficacy of the proposed control framework, we evaluate its performance using numerical simulation and provide associated sensitivity and robustness analysis.
Platoon Formation in a Mixed Traffic Environment: A Model-Agnostic Optimal Control Approach
IEEE American Control Conferece (in review: arXiv: 2110.04896)
Connected and automated vehicles (CAVs) provide the most intriguing opportunity to improve energy efficiency, traffic flow, and safety. In earlier work, we addressed the constrained optimal coordination problem of CAVs at different traffic scenarios using Hamiltonian analysis. In this paper, we investigate the properties of the unconstrained problem and provide conditions under which different combination of the state and control constraints become active. We present a condition-based computational framework that improves on the standard iterative solution procedure of the constrained Hamiltonian analysis. Finally, we derive a closed-form analytical solution of the constrained optimal control problem and validate the proposed framework using numerical simulation. The solution can be derived without any recursive steps, and thus it is appropriate for real-time implementation on-board the CAVs.
Conditions to provable system-wide optimal coordination of connected and automated vehicles
Automatica
https://doi.org/10.1016/j.automatica.2021.109751
The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework for real-time control aimed at optimizing energy consumption with associated benefits. In this paper, we implement an optimal control framework, developed previously, in an Audi A3 etron plug-in hybrid electric vehicle, and demonstrate that we can improve the vehicle's efficiency and travel time in a corridor including an on-ramp merging, a speed reduction zone, and a roundabout. Our exposition includes the development, integration, implementation and validation of the proposed framework in (1) simulation, (2) hardware-in-the-loop (HIL) testing, (3) connectivity enabled virtual reality based bench-test, and (4) field test in Mcity. We show that by adopting such inexpensive, yet effective process, we can efficiently integrate and test the control framework, establish proper connectivity and data transmission between different modules of the system, and reduce uncertainty. We evaluate the performance and effectiveness of the control framework and observe significant improvement in terms of energy and travel time compared to the baseline scenario.
A Decentralized Time- and Energy-Optimal Control Framework for Connected Automated Vehicles: From Simulation to Field Test
SAE World Congress 2020
https://www.researchgate.net/publication/337060382_A_Decentralized_Time-and_Energy-Optimal_Control_Framework_for_Connected_Automated_Vehicles_From_Simulation_to_Field_Test
Connected and automated vehicles (CAVs) provide the most intriguing opportunity to reduce energy consumption and travel delays. In this paper, we propose a two-level control architecture for CAVs to optimize (1) the vehicle’s speed profile, aimed at minimizing stop-and-go driving, and (2) the powertrain efficiency of the vehicle for the optimal speed profile derived in (1). The proposed hierarchical control framework can be implemented onboard the vehicle in real time with minimal computational effort. We evaluate the effectiveness of the efficiency of the proposed architecture through simulation in Mcity using a 100% penetration rate of CAVs. The results show that the proposed approach yields significant benefits in terms of energy efficiency.
Concurrent Optimization of Vehicle Dynamics and Powertrain Operation Using Connectivity and Automation
SAE World Congress 2020
https://www.researchgate.net/publication/337184104_Concurrent_Optimization_of_Vehicle_Dynamics_and_Powertrain_Operation_Using_Connectivity_and_Automation
Connected and automated vehicles (CAVs) provide the most intriguing opportunity to reduce pollution, energy consumption, and travel delays. In earlier work, we addressed the optimal coordination of CAVs using Hamiltonian. In this paper, we investigate the nature of the unconstrained problem and provide conditions under which the state and control constraints become active. We derive a closed-form analytical solution of the constrained optimization problem and evaluate the solution using numerical simulation.
Conditions for State and Control Constraint Activation in Coordination of Connected and Automated Vehicles
2020 American Control Conference (Accepted)
https://www.researchgate.net/publication/331999073_Conditions_for_State_and_Control_Constraint_Activation_in_Coordination_of_Connected_and_Automated_Vehicles/stats
The implementation of connected and automated vehicles (CAVs) can enable a novel control framework for real-time control to optimize energy consumption. Several approaches have been proposed in the literature that allow CAVs to communicate with each other and coordinate in conflict zones, where there is a potential conflict, for example, in traffic lights, stop signs, merging roadways, roundabouts. In this paper, we consider the problem of coordinating CAVs in a corridor, consisting of several conflict zones where collision may happen. We derive a solution that yields the optimal control input, in terms of fuel consumption, for each CAV to cross the corridor. We validate the effectiveness of the solution through simulation, and we show that both fuel consumption and travel time can significantly be improved.
Impact of Connected and Automated Vehicles in a Corridor
Proceedings of 2020 American Control Conference (Accepted)
The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework to deliver real-time control actions that optimize travel time, energy, and safety. Hardware is an integral part of any practical implementation of CAVs, and as such, it should be incorporated in any validation method. However, high costs associated with full scale, field testing of CAVs have proven to be a significant barrier. In this paper, we present the implementation of a decentralized control framework, which was developed previously, in a scaled-city using robotic CAVs, and discuss the implications of CAVs on travel time. Supplemental information and videos can be found at https://sites.google.com/view/ud-ids-lab/tfms.
Demonstration of a Time-Efficient Mobility System Using a Scaled Smart City
Vehicle Systems Dynamics (Special Issue) [In press]
https://www.researchgate.net/publication/331543769_Demonstration_of_a_Time-Efficient_Mobility_System_Using_a_Scaled_Smart_City
Urban intersections, merging roadways, roundabouts, and speed reduction zones along with the driver responses to various disturbances are the primary sources of bottlenecks in corridors that contribute to traffic congestion. The implementation of connected and automated technologies can enable a novel computational framework for real-time control aimed at optimizing energy consumption and travel time. In this paper, we propose a decentralized energy-efficient optimal control framework for two adjacent intersections. We derive a closed-form analytical solution that includes interior
boundary conditions and evaluate the effectiveness of the solution through simulation. Fuel consumption and travel time are significantly reduced compared to the baseline scenario designed with conventional fixed time signalized intersections.
Energy-Optimal Coordination of Connectedand Automated Vehicles at Multiple Intersections
Proceedings of 2019 American Control Conference, 2019, pp. 2664–2669, 2019
The implementation of connected and automated vehicle technologies enables opportunities for a novel computational framework for real-time control actions aimed at optimizing energy consumption and associated benefits. In this paper, we present a two-level control architecture for a connected and automated plug-in hybrid electric vehicle to optimize simultaneously its speed profile and powertrain efficiency. We evaluate the proposed architecture through simulation in a
network of vehicles.
Optimal Vehicle Dynamics and Powertrain Control for Connected and Automated Vehicles
Proceedings of 2019 IEEE Conference on Control Technology and Applications, pp. 33-38, 2019
Some polymers tend to possess affinity with water and eventually, absorb significant moisture content from the surrounding air, causing difficulties during their industrial processing. Drying of these hygroscopic polymers, therefore, plays a vital role in their usability in industrial applications. In this work, the drying kinetics of the polymeric granulates is numerically formulated and the influence of different parameters pertaining to the drying procedure has been investigated. Backward Euler or implicit algorithm has been considered for solving the second order partial differential heat and mass transfer equations for simulating the drying kinetics of Polyamide 6 (PA-6). At first, the conduction of heat from the granulate surface towards the core was formulated using one dimensional transient heat conduction law and corresponding diffusion coefficients were determined using Arrhenius diffusion model. Afterwards, the migration of moisture from the granulate core towards the surface has been calculated using Fick’s second law of diffusion. The data obtained from the single polymer granulate was then used to calculate the amount of moisture removed and the drying rate. The numerical results showed similitude with the experimental data obtained from the literature, although deviated quantitatively. To investigate the influence of different parameters on the drying process, different cases with varying drying air temperature, granulate radius and initial moisture content were compared. The numerical analysis qualitatively predicted all the dependencies to be expected. With higher drying air temperature, drying rate was observed to be faster and with higher granulate radius, drying rate was slower. With better approximations of the applied parameters and algorithms, the accuracy of the developed numerical model could be improved and used as a prediction tool for the drying process of polymer samples with reasonable tolerance.
A numerical approach to drying process of hygroscopic polymeric granulates with different drying configurations and parameter comparison
AIP Conference Proceedings
https://aip.scitation.org/doi/abs/10.1063/1.4984689
The aim of this paper comprises of investigating the viability of using an available indigenous blade as horizontal axis micro wind turbine blade and further improvement of this particular blade performance through design modification and CFD simulation. The focus was to use this blade in the urban areas of Bangladesh where the air speed is not usually high enough to harness wind energy with a micro wind turbine efficaciously. Though it requires a minimum wind speed of around 4.2 m/s to start self-rotation of this available blade without the help of external aid, the optimized design would demand a less air speed as the torque would be increased and this might be successfully used in roadside or housetop applications. Experimental and numerical analyses were conducted with the blade and the design modification and optimization were done with the help of CFD analysis. The experimental and numerical plots showed excellent similitude in qualitative manner. We went through with the optimization of blade design
Design Optimization of a Horizontal Axis Micro Wind Turbine through Development of CFD Model and Experimentation
ELSEVIER
http://www.sciencedirect.com/science/article/pii/S1877705814029956
Global Position: 973/5561
US Position: 31/86
East Coast Regional Position: 4
IEEEXtreme
IEEEXtreme Programming Competition (15.0)
IEEE Conference on Decision and Control
Student Travel Grant, 60th Conference on Decision and Control
Transportation Networks and Smart Mobility:  Methods and Solutions
MIT Professional Education
Full-tuition Scholarship on Transportation Networks and Smart Mobility:  Methods and Solutions
10th Annual Graduate Students' Forum, UD
Hackathon on Misinformation and Cybersecurity (2nd Place)
10th Annual Graduate Students' Forum, UD
Outstanding Presentation Award (2nd Place)
The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework to deliver real-time control actions that optimize travel time, energy consumption and passenger safety. In the ongoing ARPA-e NEXTCAR project (Award Number: DE-AR0000796) led by Prof. Andreas A. Malikopoulos as the principal investigator from UD, we exploit the “vehicle-to-everything” (V2X) communication framework in a plug-in hybrid electric vehicle (PHEV) to develop and implement an energy efficient controller. We proposed a two-level supervisory control architecture that concurrently optimizes 1) speed profile and 2) the powertrain of the PHEV.

In previous milestones set by the ARPAE committee, we have showed the effectiveness of the vehicle dynamics controller developed at UD through simulation and hardware-in-the-loop test in a chassis-dyno setup. However, to validate and evaluate the performance of the proposed supervisory controller in real-world scenario, we needed to run field test on a connected automated PHEV over a traffic corridor including real-world driving scenarios representative of the characteristics of typical commute. To this end, we have integrated partial aspects of the supervisory controller developed at University of Delaware into a 2016 Audi A3 e-tron stationed at the Farmington Hills facility of Robert Bosch LLC, USA. The underlying idea is to test the proposed supervisory controller in the Audi in a selected traffic corridor including the highway on- ramp, speed reduction zone and roundabout of the Mcity testing facility located at the North campus of University of Michigan. We adopt a vehicle testing procedure in an augmented reality platform in the Mcity, a 32 acre autonomous vehicle testing facility located at the north campus of University of Michigan controlled by the University of Michigan Transportation Research Institute (UMTRI).
University of Delaware
UD Collection-Based Research Award
2019 Learning for Dynamic Control Conference
Graduate Travel Grant,2019 Learning for Dynamic Control Conference, MIT, USA
8th Annual Graduate Students’ Forum
Outstanding Presentation Award, 8th Annual Graduate Students’ Forum,University of Delaware
This competitive grant was awarded for the acceptance of a paper in the 2019 IEEE American Control Conference proceedings. This award facilitates the associated costs pertinent to the attendance of the conference.
University of Delaware
University of Delaware Professional Development Award, 2019
University of Delaware
University of Delaware Departmental Teaching Fellowship
This award is presented to a student with GCPA greater than 3.75 in consecutive terms in an academic year.
Bangladesh University of Engineering and Technology
Dean's List
Rajshahi Board, Bangladesh
Government Merit Scholarship
Rajshahi Board, Bangladesh
Government Merit Scholarship
• Analyzed the transportation data from TomTom and transportation secure data center for traffic demand modeling
• Characterized the impact of autonomous vehicles for Denver metro region using SUMO
Transportation Data Analysis and Simulation (NREL: Smart 2.0)
• Development and improvement of NREL’s RouteE energy prediction engine with random forest, XGBoost, etc.
• Feature selection optimization, feature engineering and hyper-parameter tuning of RouteE
Machine Learning and Optimization (NREL: SMART 2.0)
• Synthesized optimal vehicle dynamics controller for coordination of connected automated vehicles (CAVs) in traffic corridors including highway on-ramp merging, roundabout, urban intersection and speed reduction zone
• Analyzed and simulated emerging mobility systems in high-fidelity simulation (VISSIM, PreScan, Simulink)
• Derived optimal powertrain controller for plugin hybrid electric vehicles, and validated with Simulink and HIL test
• Developed the augmented reality-based vehicle test procedure at UMTRI using VISSIM for CAV coordination
Optimal Control of Connected Automated Vehicles (DOE ARPA-E NEXTCAR)
• Developed novel optimal platoon formation framework under mixed traffic environment
• Developed, simulated and validated optimal control algorithm for CAV platoon coordination at an on-ramp merging scenario
Platoon Formation and Coordination: A mixed traffic setup
https://sites.google.com/view/ud-ids-lab/CAVPLT
• Modified an Audi A3 etron architecture with connectivity and automation with associated HIL/VIL testing
• Established V2X communication between Audi A3-etron and the UMTRI’s augmented reality framework
• Integrated the test vehicle with optimal dynamics and powertrain controller, and conducted field tests at MCity
Connected Automated Vehicle Testing (HIL, Field Test with Augmented Reality)
Client - Engineering Resources Int. Ltd.
Main Project Features - Making Eco-friendly bricks without breaking traditional chimney
Activities Performed - Designing, modeling and Calculation
Green Brick Technology
Vehicle Velocity Measurement and Reporting
University of Michigan
coursera.org
Applied Machine Learning in Python
https://coursera.org/share/d460a82aa7f5fac5a8c427ad1bbc0219
University of California, Santa Cruz
coursera.org
Bayesian Statistics: From Concept to Data Analysis
https://coursera.org/share/72cd0581b2abc99ab7c784c48d2475be
IBM
coursera.org
Fundamentals of Scalable Data Science
https://coursera.org/share/1409a020d34311117faa739d3b5b1d50
The Hong Kong University of Science and Technology
coursera.org
Python and Statistics for Financial Analysis
https://coursera.org/share/bfe1b2f5f5ad89618b2219309a6f5ca9
MIT Professional Education
www.credential.net
Transportation Networks and Smart Mobility: Methods and Solutions
https://www.credential.net/cef37516-253c-4bb0-9494-021f8c67c84a
https://bd.linkedin.com/in/mohammad-tomal-hossain-73a8b393
Bangladesh
Mohammad Tomal Hossain
Graduate Student at University of Delaware
https://www.linkedin.com/in/atiarahman-ud
Newark, DE
Atia Rahman
Ph.D. Candidate | Mechanical Engineering | Material Modeling | Material Characterizations | Design | Instrumentation | Control
https://www.linkedin.com/in/joy-deep-chakrabartty
Newark, DE
Joy Deep Chakrabartty
Ph.D Candidate at University of Delaware
https://www.linkedin.com/in/tousif54
Poughkeepsie, NY
Tousif Ahmed
Packaging Development Thermomechanical Engineer | Ph.D. Mechanical Engineering
https://www.linkedin.com/in/arnab-bhattacharjee
Newark, DE
Arnab Bhattacharjee
Graduate Research Assistant at University of Delaware
https://www.linkedin.com/in/sabitkhan
State College, PA
Sabit Mahmood Khan
PhD Student at Penn State
https://nz.linkedin.com/in/taimur-rahman
Auckland
Taimur Rahman, CPEng, CMEngNZ
Engineering Manager at Fonterra
https://www.linkedin.com/in/md-nazmul-alam-88b404178
Newark, DE
Md Nazmul Alam
Graduate Research Assistant at University of Delaware
https://www.linkedin.com/in/arham-amin-khan-1b5466b3
Newark, DE
Arham Amin Khan
Computational Fluid Dynamics | Thermal Engineering | Aerodynamics | Hypersonics
https://www.linkedin.com/in/waatwah
El Paso, TX
Wissam Atwah
Environmental Sicence & Engineering, Research Associate, Ph.D Student
https://www.linkedin.com/in/chuchu-chen-0816
Greater Philadelphia
Chuchu Chen
Research Assistant at University of Delaware
https://www.linkedin.com/in/viet-anh-le-22831718b
Newark, DE
Viet-Anh Le
PhD student at the University of Delaware
https://www.linkedin.com/in/zachary-cao-2b9899131
Newark, DE
Zachary Cao
Doctor of Philosophy - PhD at University of Delaware
https://www.linkedin.com/in/brigid-m
Greater Philadelphia
Brigid Morrissey
Undergraduate Research Assistant
https://bd.linkedin.com/in/rahat-mahmud-khan-506
Dhaka, Bangladesh
Rahat Mahmud Khan
Lecturer at Dhaka University of Engineering and Technology
https://www.linkedin.com/in/akshay-bhosale-a0b5b1103
Newark, DE
Akshay Bhosale
PhD Intern
https://www.linkedin.com/in/farida-ahmed-koly-b72368102
Newark, DE
Farida Ahmed koly
Phd Student at University of Delaware ,Research Interest: Tribology
https://www.linkedin.com/in/zhaocai-liu-908595121
Denver, CO
Zhaocai Liu
Ph.D.
Liked by A M Ishtiaque (Ishti) Mahbub
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I’m pleased to announce that I successfully defended my doctoral thesis in adaptive control to make car-trailering safer. I’ll receive my Ph.D…
Liked by A M Ishtiaque (Ishti) Mahbub
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This is so true. Everyone should try to get the actual business needs of the machine learning projects. It will reduce the time and stress both.…
Liked by A M Ishtiaque (Ishti) Mahbub
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As a software engineer, some days I feel like I know a lot and others like I know nothing.#coding #programming #softwareengineering
Autonomous Vehicle Community of Practice
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PTV Vissim Forum
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Autonomous Vehicle Technology
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Autonomous Vehicles and Platforms
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E Mobility /  Autonomous driving / Fast Charging
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Connected Car - driverless, telematics, infotainment, autonomous vehicles
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Autonomous Driving
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Connected & Autonomous Vehicles
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