Harsha Teja Garimella is a PhD candidate in Mechanical Engineering at Penn State. His research focuses on developing finite element models of the human head with axonal tractography to study traumatic brain injury. He has created high-resolution meshes and simulations of the head to analyze stresses experienced by axonal fibers under blast loading. Garimella has also developed electromechanical models of single axons and axonal bundles using COMSOL. He holds a BS in Mechanical Engineering from IIT Guwahati and expects to earn a PhD and MS from Penn State in August 2017.
January 2021: Top Ten Cited Article in Computer Science, Engineering IJCSEA Journal
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International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
Shehan Jayasekera is seeking an entry-level position as a mechanical engineer with interests and experience in experimentation, testing, and simulation. He has a 3.95 GPA in mechanical engineering from Rochester Institute of Technology and will graduate in May 2016. His engineering experience includes modeling abdominal aortic aneurysms, characterizing fluid flow through a lung model, and improving a production delay recording system in Thailand. For his senior design project, he is designing an inflatable robotic hand for an RC vehicle. He also designed an autonomous quadcopter and has skills in MATLAB, LabVIEW, ANSYS, and particle imaging velocimetry setup and operation.
Zachary Santer is a recent graduate of West Virginia University with a B.S. in Computer Science and dual B.S. degrees in Mechanical and Aerospace Engineering. He has experience in research, development, and leadership roles related to algorithms, image processing, weather data analysis, and power grid optimization. His technical skills include programming languages like C, Java, and MATLAB as well as software like SQL, LabVIEW, and Microsoft Office.
Feasibility of Artificial Neural Network in Civil Engineeringijtsrd
油
An Artificial neural network ANN is an information processing hypothesis that is stimulated by the way natural nervous system, such as brain, process information. The using of artificial neural network in civil engineering is getting more and more credit all over the world in last decades. This soft computing method has been shown to be very effective in the analysis and solution of civil engineering problems. It is defined as a body which works out the more and more complex problem through sequential algorithms. It is designed on the basis of artificial intelligence which is proficient of storing more and more information's. In this work, we have investigated the various architectures of ANN and their learning process. The artificial neural network based method was widely applied to the civil engineering because of the strong non linear relationship between known and un known of the problems. They come with good modelling in areas where conventional approaches finite elements, finite differences etc. require large computing resources or time to solve problems. These includes to study the behaviour of building materials, structural identification and control problems, in geo technical engineering like earthquake induced liquefaction potential, in heat transfer problems in civil engineering to improve air quality, in transportation engineering like identification of traffic problems to improve its flexibility , in construction technology and management to estimate the cost of buildings and in building services issues like analyzing the water distribution network etc. Researches reveals that the method is realistic and it will be fascinated for more civil engineering applications. Vikash Singh | Samreen Bano | Anand Kumar Yadav | Dr. Sabih Ahmad ""Feasibility of Artificial Neural Network in Civil Engineering"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22985.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/22985/feasibility-of-artificial-neural-network-in-civil-engineering/vikash-singh
Introduction to the ELIXIR-UK Biomedical Atlas Centre presented by Richard Baldock at the ELIXIR-UK Workshop during Genome Science 2016 in Liverpool on 31st August 2016
Dr. Jean N. Koster is a Professor Emeritus of Aerospace Engineering Sciences at the University of Colorado Boulder. He has over 35 years of experience in research and teaching in fields related to fluid mechanics, materials science, and alternative energy systems. Some of his notable achievements include developing experimental technologies in fields like particle image velocimetry, leading experiments aboard the Space Shuttle Columbia, and receiving over $3.2 million in research funding. He has also founded two startup companies focused on hybrid propulsion systems and has received several awards for his research and teaching work.
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The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications
This document is a resume for Dapeng Hong that outlines his education, experience, projects, and skills. He is expected to graduate in April 2016 with a B.S.E. in Computer Science from the University of Michigan, Ann Arbor and in July 2016 with a B.S.E. in Electrical and Computer Engineering from Shanghai Jiaotong University, Shanghai, China. His experience includes internships at Allmobilize Inc. and as a research assistant at the University of Michigan. Some of his projects include developing a mobile application with React and Angular, building a web app to turn selfies into emojis, and researching human tracking in construction sites using computer vision. His skills include programming languages like C
Harsha Teja Garimella is a PhD candidate in Mechanical Engineering at Penn State. He received his BTech from IIT Guwahati in 2013 and is expected to receive his PhD from Penn State in June 2017. His research focuses on developing finite element models of the human head and brain using techniques like embedded elements to model axonal tractography and investigate brain injury from extreme loading. He has published journal articles and conference papers on this topic and received the second prize in the ASME IMECE 2016 PhD paper competition.
Jairo Maldonado-Contreras is a mechanical engineering student at California State University Long Beach with extensive research experience in robotics. He has interned at NASA JPL, MIT, and Northwestern University researching topics like hybrid vehicles, prosthetics, and community mobility monitoring. Maldonado-Contreras maintains a 3.62 GPA and has presented his work at several conferences, including IEEE EMBC and SHPE National. Upon graduation, he intends to pursue a PhD in robotics.
Dr. Dattatreya Rachakonda has over 20 years of experience in academia and industry. He has a Ph.D in physics from IISc and has worked at IBM for 7 years managing modeling programs. He is currently a professor teaching undergraduate physics. He has extensive experience in areas such as semiconductor physics, fluid mechanics, and computational modeling.
Kasra Mokhtari is a PhD candidate in mechanical engineering at Penn State University. His research focuses on incorporating social information into autonomous vehicle decision making using machine learning techniques like deep reinforcement learning. He has developed algorithms for pedestrian collision avoidance and risk-aware path planning. Mokhtari has published several papers in conferences and journals and has relevant work experience as a graduate research assistant developing models for pollutant estimation and autonomous systems risk evaluation.
Rakshit Bhansali is a highly motivated mechanical engineering student seeking a summer internship. He has research experience building fish-like robots using Arduino and studying their energy harvesting abilities. He also has work experience in manufacturing, including reducing process inefficiencies at a CNC machining company. Additionally, he has project experience conducting vibration control studies using simulation tools like ANSYS and MATLAB. He is skilled in various CAD, CAE, and programming tools and maintains a high GPA in his masters program in mechanical engineering at UNC Charlotte.
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An expert in theoretical and mathematical modeling of cellular and molecular biomechanical characteristics of muscles.
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Adithya Rajan is seeking a career in machine learning and big data. He has a Ph.D. in Electrical Engineering from Arizona State University with extensive coursework in machine learning, optimization, and statistics. He has over 3 years of industry experience as a data scientist and research engineer developing machine learning algorithms. His research focuses on applying statistical techniques like stochastic ordering and information theory to wireless communications and signal processing.
Jason K. Johnson is a postdoctoral fellow at Los Alamos National Laboratory who received his Ph.D from MIT. His research interests include machine learning, graphical models, and signal processing. He has over 250 citations and has published works in various journals and conferences on topics related to inference in graphical models.
Avinash Kumar is a PhD candidate in electrical and computer engineering at the University of Illinois. His research focuses on camera calibration, computational imaging, and computer vision applications to railroad monitoring. He has developed new models and analytical solutions for small field of view camera calibration. His other research includes developing an omnifocus imaging technique, extracting shadows obscured by scattering media, and structure from motion for indoor scenes. He has also worked on a machine vision system to analyze gaps between freight loads on trains.
This document is Raj Parihar's PhD dissertation submitted to the University of Rochester in partial fulfillment of the requirements for a Doctor of Philosophy degree. It explores techniques to accelerate decoupled look-ahead to better exploit implicit parallelism in programs. The dissertation contains contributions from collaborations with other researchers to speculate parallelization, remove weak dependencies, tune look-ahead skeletons, and apply self-tuning to shared caches. The work was supervised by Professor Michael C. Huang and evaluated using simulation experiments.
The document provides a summary of Riadul Islam's resume. It outlines his education background including a Ph.D. in Computer Engineering from UCSC and an M.A.Sc. in Electrical and Computer Engineering from Concordia University. It also lists his research and work experience in the areas of digital circuit design, ASIC design, and low-power VLSI.
Thanh Nguyen has over 20 years of experience in engineering fields including structural analysis, mechanical design, finite element analysis, and research. He has worked as a senior specialist analyst at Hatch Consultant Engineering, focusing on analyzing and designing heavy machinery used in mining. Prior to that, he conducted PhD research modeling the structure of articular cartilage at Queensland University of Technology. He is proficient in various engineering software and analytical techniques. His objective is to utilize his expertise and experience to contribute to engineering projects, especially those related to sustainable energy and affordable housing.
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The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. To enhance the accuracy and convergence rate of this algorithm, an improved cuckoo search algorithm is proposed in this paper. Normally, the parameters of the cuckoo search are kept constant. This may lead to decreasing the efficiency of the algorithm. To cope with this issue, a proper strategy for tuning the cuckoo search parameters is presented. Then, it is employed for training feedforward neural networks for two benchmark classification problems. Finally, the performance of the proposed algorithm is compared with that of the standard cuckoo search. Simulation results demonstrate the effectiveness of the proposed algorithm.
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Clayson C. Spackman has extensive experience in 3D printing soft composites and fiber networks. He received his PhD in Mechanical Engineering from Rensselaer Polytechnic Institute where he developed a 3D printing technology for soft composites using inkjet printing and electrospun fibers. His research focused on investigating material failure mechanisms and developing innovative testing protocols. He has published several peer-reviewed articles and led multiple research projects funded by the NSF and DoD involving 3D printing composites, modeling fiber interactions, and developing educational lab modules.
I am Ph.D. student at Wright State University. My research area is computational mechanics and design optimization. I mainly focused on design optimization for coupled fluid-solid interaction problems with applications to aerospace vehicles.
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Harsha Teja Garimella is a PhD candidate in Mechanical Engineering at Penn State. He received his BTech from IIT Guwahati in 2013 and is expected to receive his PhD from Penn State in June 2017. His research focuses on developing finite element models of the human head and brain using techniques like embedded elements to model axonal tractography and investigate brain injury from extreme loading. He has published journal articles and conference papers on this topic and received the second prize in the ASME IMECE 2016 PhD paper competition.
Jairo Maldonado-Contreras is a mechanical engineering student at California State University Long Beach with extensive research experience in robotics. He has interned at NASA JPL, MIT, and Northwestern University researching topics like hybrid vehicles, prosthetics, and community mobility monitoring. Maldonado-Contreras maintains a 3.62 GPA and has presented his work at several conferences, including IEEE EMBC and SHPE National. Upon graduation, he intends to pursue a PhD in robotics.
Dr. Dattatreya Rachakonda has over 20 years of experience in academia and industry. He has a Ph.D in physics from IISc and has worked at IBM for 7 years managing modeling programs. He is currently a professor teaching undergraduate physics. He has extensive experience in areas such as semiconductor physics, fluid mechanics, and computational modeling.
Kasra Mokhtari is a PhD candidate in mechanical engineering at Penn State University. His research focuses on incorporating social information into autonomous vehicle decision making using machine learning techniques like deep reinforcement learning. He has developed algorithms for pedestrian collision avoidance and risk-aware path planning. Mokhtari has published several papers in conferences and journals and has relevant work experience as a graduate research assistant developing models for pollutant estimation and autonomous systems risk evaluation.
Rakshit Bhansali is a highly motivated mechanical engineering student seeking a summer internship. He has research experience building fish-like robots using Arduino and studying their energy harvesting abilities. He also has work experience in manufacturing, including reducing process inefficiencies at a CNC machining company. Additionally, he has project experience conducting vibration control studies using simulation tools like ANSYS and MATLAB. He is skilled in various CAD, CAE, and programming tools and maintains a high GPA in his masters program in mechanical engineering at UNC Charlotte.
Omid Komari, PhD
6015 Spirit Street, Pittsburgh, PA, 15206
(619) 888-9211
omid.komari@gmail.com
EXECUTIVE SUMMARY
Accomplished, progressive professional and PhD with over seven years of experience conducting expert scientific research in computational design and four years of experience in experimental developments and experimental data analysis. Dedicated scholar with extensive teaching experience and strategic approach to problem-solving.
CORE COMPETENCIES AND TECHNICAL SKILLS
An expert in theoretical and mathematical modeling of cellular and molecular biomechanical characteristics of muscles.
Technical Skills: Writing proposals, developing programs, collecting, analyzing and reporting data, familiar with FDA standards, excellent oral and writing skills and strong leadership and teaming skills
Adithya Rajan is seeking a career in machine learning and big data. He has a Ph.D. in Electrical Engineering from Arizona State University with extensive coursework in machine learning, optimization, and statistics. He has over 3 years of industry experience as a data scientist and research engineer developing machine learning algorithms. His research focuses on applying statistical techniques like stochastic ordering and information theory to wireless communications and signal processing.
Jason K. Johnson is a postdoctoral fellow at Los Alamos National Laboratory who received his Ph.D from MIT. His research interests include machine learning, graphical models, and signal processing. He has over 250 citations and has published works in various journals and conferences on topics related to inference in graphical models.
Avinash Kumar is a PhD candidate in electrical and computer engineering at the University of Illinois. His research focuses on camera calibration, computational imaging, and computer vision applications to railroad monitoring. He has developed new models and analytical solutions for small field of view camera calibration. His other research includes developing an omnifocus imaging technique, extracting shadows obscured by scattering media, and structure from motion for indoor scenes. He has also worked on a machine vision system to analyze gaps between freight loads on trains.
This document is Raj Parihar's PhD dissertation submitted to the University of Rochester in partial fulfillment of the requirements for a Doctor of Philosophy degree. It explores techniques to accelerate decoupled look-ahead to better exploit implicit parallelism in programs. The dissertation contains contributions from collaborations with other researchers to speculate parallelization, remove weak dependencies, tune look-ahead skeletons, and apply self-tuning to shared caches. The work was supervised by Professor Michael C. Huang and evaluated using simulation experiments.
The document provides a summary of Riadul Islam's resume. It outlines his education background including a Ph.D. in Computer Engineering from UCSC and an M.A.Sc. in Electrical and Computer Engineering from Concordia University. It also lists his research and work experience in the areas of digital circuit design, ASIC design, and low-power VLSI.
Thanh Nguyen has over 20 years of experience in engineering fields including structural analysis, mechanical design, finite element analysis, and research. He has worked as a senior specialist analyst at Hatch Consultant Engineering, focusing on analyzing and designing heavy machinery used in mining. Prior to that, he conducted PhD research modeling the structure of articular cartilage at Queensland University of Technology. He is proficient in various engineering software and analytical techniques. His objective is to utilize his expertise and experience to contribute to engineering projects, especially those related to sustainable energy and affordable housing.
TOP 1 CITED PAPER - International Journal of Artificial Intelligence & Appli...gerogepatton
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The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. To enhance the accuracy and convergence rate of this algorithm, an improved cuckoo search algorithm is proposed in this paper. Normally, the parameters of the cuckoo search are kept constant. This may lead to decreasing the efficiency of the algorithm. To cope with this issue, a proper strategy for tuning the cuckoo search parameters is presented. Then, it is employed for training feedforward neural networks for two benchmark classification problems. Finally, the performance of the proposed algorithm is compared with that of the standard cuckoo search. Simulation results demonstrate the effectiveness of the proposed algorithm.
Nathaniel Brewster Thompson is a Mechatronics Engineer II at NASA's Jet Propulsion Laboratory with over 10 years of experience managing mechanical design projects. He has extensive experience leading teams to design, analyze, fabricate, test and deliver mechanical subsystems for Mars rovers and other NASA projects. His background includes expertise in CAD, FEA, controls systems, metrology and cleanroom operations.
Recent articles published in VLSI design & Communication SystemsVLSICS Design
油
International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.
This resume summarizes the qualifications and experience of Damu C Madupoluru. He has over 10 years of experience in structural analysis, finite element analysis, and design engineering. He is currently working as an Engineer-FEA at MANNAND HUMMEL FILTER PVT.LTD. Previously, he worked as a Research Associate at the Indian Institute of Science, where he conducted research and design related to cryogenic engineering applications. He has extensive experience performing FEA simulations using various software like Abaqus, Ansys, and CATIA. He also has expertise in structural optimization, vibration analysis, and mesh generation for CFD.
This document provides a summary of an experienced educator seeking a challenging position utilizing over 32 years of experience in education administration and management. The educator holds a bachelor's degree in electronics and telecommunication engineering and has worked in various roles including principal, vice principal, and head of departments. Areas of expertise include leadership, management, teaching, and coordinating accreditation. The educator has international journal publications, guided PhD scholars, and is currently supervising research scholars. The objective is to find a dynamic position to apply skills and experience.
Clayson C. Spackman has extensive experience in 3D printing soft composites and fiber networks. He received his PhD in Mechanical Engineering from Rensselaer Polytechnic Institute where he developed a 3D printing technology for soft composites using inkjet printing and electrospun fibers. His research focused on investigating material failure mechanisms and developing innovative testing protocols. He has published several peer-reviewed articles and led multiple research projects funded by the NSF and DoD involving 3D printing composites, modeling fiber interactions, and developing educational lab modules.
I am Ph.D. student at Wright State University. My research area is computational mechanics and design optimization. I mainly focused on design optimization for coupled fluid-solid interaction problems with applications to aerospace vehicles.
This curriculum vitae summarizes Sanjay Goswami's academic and professional qualifications. It includes his educational background with a PhD in engineering submitted at Jadavpur University. It also outlines over 11 years of teaching experience focused on operating systems, networking, and programming. Additionally, it provides details of research experience including projects in molecular computing, bridge design, and damage detection in aerospace structures. The CV highlights publications, student projects supervised, administrative contributions, and awards.
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My Resume
1. Harsha Teja Garimella www.linkedin.com/in/harshatejagarimella
Contact
Information
10 Vairo Boulevard, Apt 40A, +1- 814-777-5285
State College, PA 16803. harshatejagarimella@gmail.com
Education The Pennsylvania State University, University Park, PA.
Ph.D. and M.S., Mechanical Engineering, Expected: August 2017
Ph.D. Minor, Computational Science, Expected: August 2017
Advisor - Dr. Reuben H. Kraft, CGPA- 3.87/4.00
Indian Institute of Technology Guwahati, Guwahati, Assam, India
B.Tech., Mechanical Engineering May 2013. CGPI - 8.71/10.00
Research
Experience
Ph.D. Candidate and Graduate Research Assistant August 2013 - present
Department of Mechanical Engineering, The Pennsylvania State University.
Ph.D. Thesis Project: Development of high-resolution embedded element based 鍖nite element
model of the human head with axonal tractography.
Advisor: Reuben H. Kraft, Ph.D.
Created high-quality hexahedral meshes of the human head using MATLAB and ANSYS
ICEMCFD.
Developed explicit simulations of the high-resolution 鍖nite element model of the human head
with axonal tractography, developed using the embedded element constraint in ABAQUS, to
study brain injury.
Developed the injury descriptions using the stress-strain response experienced by the axonal 鍖ber
tracts.
Developed blast (shock) loading simulations, in ABAQUS, of the human head model showing
evidence of axonal injury due to skull 鍖exures (bending vibrations).
Developing simulations to understand the e鍖ects of skulls natural frequencies on blast injury
analysis.
Other Projects:
Electromagnetic modeling of the axonal 鍖ber bundles in human brain using COMSOL.
Developed electromechanical model of single axon to study the changes in the electrical behaviour
with mechanical strains using COMSOL and MATLAB.
Currently developing electromagnetic models of single axon and axonal bundle.
Modeling blast and ballistic penetration injury in lower extremity using Material Point Method.
Developed innovative meshing tools, using MATLAB and C++, to create a high-resolution
bio鍖delic model of the human leg containing complex anatomical components such as musculature
Developed penetration simulations in Uintah to study the damage in di鍖erent anatomical regions.
Design of robotic gripper by optimization of motor load torque variation.
Collaborated with two team members and designed an optimal con鍖guration of gripper arm linkages
by minimizing the load torque variation on the motor. We have developed a robust MATLAB
code for this purpose and a computer aided design (CAD) model using SolidWorks.
Implementation of an in-house MATLAB and C++ code to address the issue of volume redundancy
- a limitation associated with embedded element technique in ABAQUS.
Simulation of dual-battery systems in micro-hybrid vehicles using MATLAB/Simulink.
Undergraduate Student August 2009 - May 2013
Department of Mechanical Engineering, Indian Institute of Technology Guwahati.
Bachelor Thesis Project: Design and development of micro-hydraulic power generator.
Advisors: P.S.Robi, Ph.D. and S.K.Dwivedy, Ph.D.
Developed a CAD model of a new hydraulic power generator prototype, in SolidWorks, that
utilizes the 鍖ow of energy of rivers to generate electricity with a novel concept of 鍖apper blades
arranged over the rotating belt to generate electricity.
Developed a 鍖nite element model of the 鍖apper blade to analyze the structural response of the
blade under di鍖erent loading scenarios. Built the prototype using various model-shop instruments.
Relevant
coursework
Finite Elements, Non-linear Finite Elements, Mathematical Theory of Elasticity, Continuum Mechanics,
Optimal Design of Mechanical Structures, Simulation of Mechanical Systems, Engineering Materials,
Measurement System Design, Advanced Solid Mechanics, Machine Design, Dynamics of Machinery,
Engineering Design Methodology.
1 of 2
2. Journal
Publications
Garimella, H.T. and Kraft, R.H. Modeling the mechanics of axonal 鍖ber tracts using embedded
鍖nite element method. International Journal for Numerical Methods in Biomedical Engineering, DOI:
10.1002/cnm.2823 (in press).
Garimella, H.T. and Kraft, R.H. Investigation of axonal response due to blast-induced skull 鍖exures
using male and female 鍖nite element head models. (in progress)
Garimella, H.T. and Kraft, R.H. Insights into axonal injury using embedded element head model.
Neural Regeneration Research (invited).
Technical
Strengths
FEA Software: ABAQUS, LS-DYNA, COMSOL, Uintah, ANSYS ICEMCFD,
Design Software: AutoCAD, SolidWorks, SolidEdge, MSC ADAMS.
Programming Languages: C, C++, MATLAB
Operating Systems: Mac OSX, Linux, Windows.
Positions of
Responsiblity
Graduate Research Assistant August 2013 - Present
Advisor: Dr. Reuben H. Kraft, The Pennsylvania State University.
Working with Computational Fluid Dynamics Research Corporation (CFDRC).
President, Engineering Graduate Student Council April 2015 - April 2016
The Pennsylvania State University.
Served as Graduate Student Representative in the Engineering Faculty Council.
Collaborated with other student associations to organise the most prestigious College of Engineering
Research Symposium 2016.
Teaching Assistant, The Pennsylvania State University. August 2013 - May 2014
Internships
Vizag Steel Plant and Hindustan Aeronautics Limited, India. Summer 2011/2012
Core Team Member, Brand Communications, Techniche October 2010 - October 2011
Indian Institute of Technology Guwahati.
Brand communications in-charge for South India
Techniche, The annual techno-management festival.
Conferences Proceedings
H.T.Garimella and R. H. Kraft. Validation of Embedded Element Method in the Prediction of
White Matter Disruption in Concussions. Proceedings of ASME IMECE 2016, November 11-17,
2016, Phoenix, Arizona, USA.
H.T.Garimella, H. Yuan, B. D. Johnson, S. L. Slobounov, R. H. Kraft. A Two-Fiber Anisotropic
Constitutive Model of Human Brain With Intravoxel Heterogeneity of Fiber Orientation Using Di鍖usion
Spectrum Imaging (DSI). Proceedings of ASME IMECE 2014, November 14-20, 2014, Montreal,
Canada.
Presentations
Validation of embedded element method in the prediction of white matter disruption in concussions,
ASME IMECE 2016, November 11-17, 2016, Phoenix, Arizona, USA. (Accepted)
Modeling the electromechanical behavior of axonal 鍖ber bundles, WCCM XII and APCOM VI, July
24-29, 2016, Seoul, South Korea.
Modeling the mechanics of axonal 鍖ber tracts using the embedded element method, WCCM XII
and APCOM VI, July 24-29, 2016, Seoul, South Korea.
Modeling of electromechanical de鍖cits in the human brain, ASME IMECE 2015, November 13-19,
2015, Houston, Texas.
Computational modeling of axonal injury using the embedded element method, PANACM 2015,
April 27-29, 2015, Buenos Aires, Argentina.
Professional
Membership and
Achievements
Student member of American Society of Mechanical Engineers (ASME).
Student Ambassador, Stand for State, The Pennsylvania State University.
Member of the Academic Integrity Committee, The Pennsylvania State University.
Received the prestigious State Bank of India Scholarship during undergraduate studies.
Recipient of CMR Pratibha Award for excellence in the Class 10 board examinations.
Received Teaching Assistantship O鍖er (included with Graduate Admission) from the Department
of Mechanical Engineering, The Pennsylvania State University.
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