جست‌وجوی استاد و پژوهشگر

Jim McCannمشاهده پروفایل

دانشیار

• Creativity Support Tools
• Real-time Systems
• Textiles Fabrication
• +۷ مورد دیگر

Jim McCann is an Associate Professor at the Robotics Institute of Carnegie Mellon University. He holds a PhD from Carnegie Mellon University (2010), advised by Nancy Pollard, and has held positions at Adobe Research and Disney Research Pittsburgh. His academic journey includes postdoctoral work and industry experience in game development before joining CMU's faculty in 2017. His research focuses on creativity support tools spanning real-time systems, textiles fabrication, machine knitting, and interactive design. Key themes include: Developing compilers and interfaces for machine knitting (e.g., 3D shape knitting, knitout semantics) Building accessible fabrication tools for textiles and soft objects Creating parameterized design spaces enhanced with machine learning Advancing physics-based animation and simulation tuning His publications demonstrate strong interdisciplinarity, with recent work emphasizing textiles computing (knitting compilers, fabric 3D printing), human-AI collaboration (design adjectives), and novel interfaces (infinity mirrors, RFID systems). Earlier contributions established foundations in gradient-domain editing, fluid control, and motion synthesis. He leads the Carnegie Mellon Textiles Lab and has advised 9+ graduate students on topics ranging from knit microstructures to robot design. His teaching includes courses on Algorithmic Textiles Design, Real-Time Graphics, and Game Programming.

• Audio Engineering
• Computer Networking
• Mobile Computing
• +۳ مورد دیگر

Deshinder Singh Gill serves as a Principal Lecturer at the School of Architecture, Technology and Engineering, University of Brighton, with contact email D.S.Gill@brighton.ac.uk. His research career spans from 2000 to 2016, demonstrating sustained contributions across engineering and computing disciplines through 12 documented research outputs and supervision of 2 students. His research profile centers on Audio Engineering (specializing in loudspeaker design, directivity control, and transducer arrays), Computer Networking (focusing on mobile IPTV infrastructure and bandwidth-efficient routing algorithms), and Educational Technology (particularly open-source software applications in electronic engineering education). Additional expertise includes Product Design methodologies and practical case study implementations addressing real-world engineering challenges. Publication trends from 2014-2016 reveal concentrated innovation in mobile video streaming optimization, including adaptive CDN architectures and unicast routing protocols for resource-constrained devices. Concurrently, his educational research pioneered CAD teaching frameworks using open-source tools, establishing interdisciplinary connections between networking theory and pedagogical practice. Dr. Gill's academic supervision encompasses 2 graduate students, reflecting his commitment to mentoring within engineering education, though specific student names and grant details remain undocumented in the source material.

Darren Wattsمشاهده پروفایل

مدرس ارشد

• Additive Manufacturing
• CAD/CAE Software Development
• Design Optimization
• +۷ مورد دیگر

Dr Darren Watts is a Senior Lecturer and Senior Teaching Fellow in CAD & CAE at the Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University. He serves as Director of Professional Training and IMechE Academic Liaison Officer. His expertise spans Additive Manufacturing, CAD/CAE software development, and design optimization. Education: BEng (Hons) Mechanical Engineering and MSc(Eng) Product Design & Management from the University of Liverpool, followed by a PhD in Topology Optimization for Rapid Manufacturing at Loughborough University. Research interests include Bespoke CAE software, Genetic Algorithms, Virtual Assembly Methods, and Medical Applications of Additive Manufacturing. He has led projects such as the EPSRC-funded 'Topologically Optimised Street Furniture for Concrete Printing' and 'Exploitation of Sinus Surgery Simulation Phantoms'. Awards include the Loughborough University Enterprise Award for Cultural Impact (2013) and a Royal Academy of Engineering Industrial Secondement Award (2013-2014). He is a Chartered Engineer and Fellow of the Higher Education Academy. Key roles: Co-Investigator on multiple EPSRC grants, Principal Investigator for surgical simulation projects, and industrial collaboration through Majenta PLM Ltd secondment. Labs/Teams: Core academic member of the Additive Manufacturing Research Group (AMRG), contributing to customized assembly and medical device prototyping advancements.

• Reinforcement Learning
• Military Simulation Technologies
• Medical Signal Processing
• +۳ مورد دیگر

Hüseyin Oktay Altun is an Assistant Professor at Bogazici University, serving as a full-time faculty member. His research focuses on advanced topics such as smart virtual entity design via reinforcement learning, multimedia security with convex optimization, medical/military signal processing, and time series prediction for environmental and financial data. His work bridges theoretical computer science with practical applications in healthcare, defense, environmental science, and energy systems. Notable research interests include the development of autonomous systems for military simulations, AI-driven medical diagnostics (e.g., hip dislocation detection in infants and epilepsy diagnosis), and environmental modeling for pollution prediction. His contributions also extend to cybersecurity, particularly in watermarking and steganography, as well as renewable energy grid management. His publication trends highlight a strong emphasis on reinforcement learning (RL) for multi-agent systems and tactical simulations, alongside deep learning applications in medical imaging, environmental monitoring, and maritime security. His work often integrates interdisciplinary approaches, combining machine learning with domain-specific challenges in defense, healthcare, and energy sectors. While no formal awards or grants are mentioned in the provided text, his extensive list of publications reflects significant contributions to both theoretical and applied computer science. No information on academic advising or lab affiliations is available here, but his research collaborations likely span multiple departments at Bogazici University and international partners.

Krishnan Sureshمشاهده پروفایل

استاد

• Topology Optimization
• Additive Manufacturing
• Computational Mechanics
• +۵ مورد دیگر

Prof. Krishnan Suresh is a Professor in Mechanical Engineering at the University of Wisconsin-Madison, affiliated with the College of Engineering. He directs the Engineering Representations and Simulation Laboratory (ERSL) and co-leads the Wisconsin Applied Computing Center. His research focuses on topology optimization, additive manufacturing, computational mechanics, and quantum computing applications. He holds prestigious awards including the Vilas Associate (2023), Mead Witter Professorship (2022), and Woodburn Teaching Award (2018). Education: PhD 1998, Cornell University MS 1995, Cornell University MS 1992, UCLA B.Tech 1990, IIT Madras Research Interests: Dr. Suresh pioneers methods for efficient finite element analysis over tangled meshes, integrates machine learning into topology optimization, and develops quantum computing solutions for engineering problems. His work bridges computational mechanics with advanced manufacturing, emphasizing eco-friendly design and multiscale material systems. Publications: Recent work explores surrogate neural networks for topology optimization, quantum annealing for linear systems, and AI-driven detector design for physics experiments. His methods address challenges in handling inverted elements and improving additive manufacturing support structures. Awards: 2023 Vilas Associate 2022 Mead Witter Professorship 2018 Woodburn Teaching Award 2017 ASME Fellow Advising & Grants: He advises on NSF/DOE-funded projects and collaborates with Sandia National Labs/Autodesk. Courses taught include design optimization, CAD engineering, and graduate research supervision. Labs & Teams: ERSL develops simulation tools for complex systems, integrating high-performance computing and AI for next-gen engineering solutions.

Ran Jinمشاهده پروفایل

استاد

• Manufacturing Systems Engineering
• System Informatics and Control
• Data Science and Visualization
• +۶ مورد دیگر

Ran Jin is a Professor in the Department of Industrial and Systems Engineering at Virginia Tech, leading the Data Science and Visualization Lab. He holds academic appointments in the College of Engineering. His research focuses on integrating data science with manufacturing systems engineering, emphasizing system informatics, quality control, and process optimization. He has contributed to advancements in profile modeling, defect detection, and statistical process monitoring in industries like additive manufacturing. Education : Ph.D., Georgia Institute of Technology, 2011 M.A. and M.S., University of Michigan, 2009 and 2007 B.Eng., Tsinghua University, 2005 Research Interests : Jin's work bridges statistical methodologies with engineering systems, particularly in high-definition profile data analysis, ensemble modeling for manufacturing scale-up, and cybermanufacturing challenges. He develops algorithms for process variation reduction and real-time monitoring. His recent focus includes AI-driven solutions for manufacturing resilience and distributed data systems. Recognition : Jin has received awards such as the Best Applied Paper Award (IIE Transactions) and NSF grants for projects like 'EAGER: AM: CoFedAI' and 'QQ Models' for mixed quality responses. His contributions span academic journals and industry partnerships, including collaborations with JSME Co. Ltd. and the Commonwealth Center for Advanced Manufacturing. Grants & Projects : Key initiatives include 'Distributed Data Filtering for Fog Manufacturing' and 'AI Incubation in Manufacturing.' He has served as PI or Co-PI on NSF-funded projects addressing experimental design, anomaly detection, and thermal spray process modeling. Labs & Teams : Directs the Data Science and Visualization Lab, fostering innovation in AI applications for smart manufacturing and system informatics. Engages in interdisciplinary research through roles like Co-Chair of the Commission on Research at Virginia Tech.

• Machining Simulation
• Thermal Management in Manufacturing
• Advanced Materials Processing
• +۳ مورد دیگر

Łukasz Ślusarczyk is an Assistant Professor at the Faculty of Mechanical Engineering, Jagiellonian University, affiliated with the Chair of Production Engineering. His research focuses on machining processes, thermal management in manufacturing, and advanced materials processing. Key areas include cutting tool dynamics, titanium alloy machining, and experimental methods in manufacturing. He has published 49 papers and supervised 1 promoted thesis. With an h-index of 6 (WoS), his work emphasizes thermal analysis, cutting force modeling, and material deformation during machining. Research interests include optimizing cutting parameters, analyzing chip formation using high-speed cameras, and developing predictive models for temperature and tool wear. He has contributed to advancements in grinding wheel design, thin-walled object milling, and burr minimization in drilling. His experiments often combine thermovision and analytical methods to improve machining efficiency and material integrity. Notable projects include the development of a measuring stand for recording physical phenomena during turning and the application of infrared cameras for deep vein thrombosis diagnostics. He co-edited a textbook on CAD/CAM software and contributed to studies on WEDM (Wire Electrical Discharge Machining) accuracy and efficiency. Labs/teams: Active in the Chair of Production Engineering's machining simulation and experimental mechanics groups. Collaborations include work on FDM multi-material 3D printing verification and optimization of sintered carbides turning processes using CBN tools.

• Engineering Design
• Additive Manufacturing
• Computer-Aided Design (CAD)
• +۷ مورد دیگر

Dr. Ian Graham is a Lecturer in Engineering Design at Loughborough University, part of the Wolfson School. He holds a BEng (Product Design) and PhD (Evolutionary Design) from Loughborough University. Prior to academia, he worked in automotive engineering (Toyota), portable electronics (GE), and architecture. He joined Loughborough in 2006 as a Research Associate in the School of Civil and Building Engineering, moved to the Design School in 2007 as Senior Research Associate and Enterprise Fellow, and transitioned to the Wolfson School in 2012. Education: BEng in Product Design, Loughborough University PhD in Evolutionary Design, Loughborough University Research Interests: Graham’s work focuses on Computer-Aided Design (CAD) , Evolutionary Algorithms , Additive Manufacturing , Remanufacturing , and Haptic Technologies . He explores applications in industrial efficiency, cultural heritage restoration, and product design innovation. His research bridges theoretical design optimization with practical industry challenges, including modular construction, artifact preservation, and sustainable manufacturing. Articles Trends: His publications emphasize CAD optimization (e.g., parametric model inefficiency analysis), evolutionary design systems, additive manufacturing for art/archaeology, and modular construction case studies. Recent work highlights cost estimation in remanufacturing and haptic sketching interfaces. Advising & Grants: No specific advisees or grant details are listed in the text, but his roles suggest involvement in research funding and collaborative projects. Labs/Teams: Founder of EvoShape , developing evolutionary design tools. Active in cross-disciplinary teams within Loughborough’s Wolfson School, focusing on CAD innovation and sustainable manufacturing.

• Sports Engineering
• Biomechanics
• Computer Aided Design
• +۴ مورد دیگر

Dr. Sean Mitchell is a Senior Lecturer in Sports Technology at Loughborough University, affiliated with the Wolfson School of Mechanical & Manufacturing Engineering. He holds the Sports Engineering Research Fellowship and contributes to the Editorial Board of Sports Engineering . His background includes a BTech and PhD in Mechanical Engineering from Loughborough University, followed by industry roles at CMB Technology and Marconi Command & Control before returning to academia. His research focuses on sports equipment design, human performance implications, and novel materials. Key interests include computer-aided design (CAD), biomechanical testing, and injury prevention through advanced surrogate models. Recent work explores sports equipment safety, pandemic impacts on athletes, and sports-related injury dynamics. Publications span topics like racket dynamics, surrogate headform design, and cricket safety during the pandemic. Awards include the Sports Engineering Research Fellowship. Mitchell's lab develops synthetic human surrogates for impact testing, aiming to improve sports equipment standards and athlete protection.

Jack Wiledenمشاهده پروفایل

استاد

• Software Systems
• Computer Science
• Ontology Engineering
• +۵ مورد دیگر

Jack Wileden is a Professor of Computer Science at the University of Massachusetts Amherst and serves as Associate Dean of Student Affairs. He is also the Director of the Convergent Computing Systems Laboratory. His research focuses on software systems architecture, semantic frameworks, ontology engineering, and sustainable design. Wileden holds PhD, MS, and AB degrees in mathematics and computer science from the University of Michigan. Member of ACM, Senior Member of IEEE, Golden Core Member of IEEE Computer Society Recipient of IEEE CS Meritorious Service (1995) and Golden Core Member Awards (1997) Editor of IEEE Transactions on Parallel and Distributed Systems Research interests emphasize semantic integration across domains like healthcare, engineering, and sustainability. His work includes developing tools for CAD interoperability, knowledge management systems, and decision support ontologies. Recent efforts focus on applying semantic technologies to product lifecycle management and sustainable design methodologies. Articles from 2011–2015 highlight advancements in semantic frameworks, material selection algorithms, and ontology-based decision systems. His contributions span theoretical foundations (e.g., Arcadia environment architecture) to applied innovations like AIERO algorithms for ontology relationships. Lab leadership includes the Convergent Computing Systems Lab, advancing interdisciplinary computing solutions. Advising and grants information is not explicitly documented in provided texts.

• Radiomics
• Medical Imaging
• Artificial Intelligence in Radiology
• +۳ مورد دیگر

Bino Varghese, PhD, is an Associate Professor of Research in the Department of Radiology at the University of Southern California (USC), where he leads the USC Radiomics Laboratory. He holds a B.E. in Medical Electronics from Visveswariah Technological University (2002), a Master's in Biomedical Engineering from Wright State University (2005), and a PhD in Biomedical Sciences from Wright State University (2010). His doctoral research focused on CT-based bone-strength indicators for clinical applications. Dr. Varghese's research centers on multi-modal imaging biomarkers, radiomics, and AI-driven medical image analysis. His work spans: Quantitative imaging for oncology (renal, breast, soft tissue tumors) Neuroimaging harmonization and super-resolution techniques Machine learning for COVID-19 severity prediction and dementia risk assessment Technical validation of radiomic features across imaging platforms His recent publications demonstrate strong focus on AI applications in cancer imaging (renal cell carcinoma, sarcomas, breast lesions) and neurodegenerative diseases, with methodological emphasis on deep learning, feature standardization, and multi-scanner validation. Research consistently addresses clinical translation through phantom studies and biomarker development. Awards: USC's Mark and Mary Stevens Neuroimaging and Informatics Institute: Big Data to Knowledge Science Rotations for Advancing Discovery (RoAD-Trip) Award (2017) At the USC Radiomics Laboratory, Dr. Varghese develops computational methods to maximize diagnostic utilization of imaging data, with ongoing work in neuroimaging harmonization, radiomic feature robustness, and AI-based diagnostic tools across multiple disease domains.

Joshua Wongمشاهده پروفایل

استادیار

• Deep Brain Stimulation
• Artificial Intelligence in Medicine
• Movement Disorders
• +۳ مورد دیگر

Dr. Joshua Wong is an Assistant Professor in the Department of Neurology at the University of Florida, specializing in deep brain stimulation (DBS) and artificial intelligence applications in neurology. He completed his bioengineering degree at UC San Diego, medical degree at New York Medical College, and neurology residency/fellowship in movement disorders at UF. His laboratory is part of the University of Florida’s AI Initiative, focusing on AI-driven improvements in clinical care and medical education for movement disorder patients, particularly those with Parkinson’s disease, essential tremor, and dystonia. His research integrates advanced neurotechnology and AI to address challenges in DBS therapy, such as optimizing stimulation parameters, predicting treatment outcomes, and analyzing motor function through video-based algorithms. Key areas include circadian rhythm influences on DBS efficacy, structural MRI biomarkers for neurocognitive outcomes, and the relationship between basal ganglia anatomy and therapeutic side effects. Dr. Wong’s work also explores cardiovascular risks in cancer survivors, including heart failure and coronary artery disease, highlighting interdisciplinary collaborations between neurology and oncology. His studies emphasize both clinical and technological advancements, aiming to personalize treatments and enhance patient engagement in care planning. He has contributed to numerous peer-reviewed articles on DBS neurophysiology, AI in movement disorder analysis, and cancer survivorship health risks. His lab’s open-source tools, like VisionMD for motor function analysis and BRAVO for DBS data visualization, exemplify his commitment to advancing neurotechnology accessibility.

• Smart Cities
• Data-Driven Design
• AI in Architecture Education
• +۳ مورد دیگر

Victor Calixto is a Research Fellow at the Department of Architecture, Monash University, specializing in smart cities, data-driven design, and the integration of emerging technologies in architectural education and practice. His work contributes to UN Sustainable Development Goals related to sustainable cities and communities. He actively collaborates in international research networks, particularly focusing on computational methods and their applications in architecture and urban planning. Research interests include AI impacts on design education, smart city frameworks, sustainable building systems, and algorithmic design processes. His publications highlight critical approaches to technology adoption, responsive building systems, and computational sustainability. Recent work emphasizes ethical considerations of AI tools in architectural education and data-driven urban development strategies. Calixto has presented at leading conferences such as CAADRIA, addressing topics ranging from smart city frameworks to thermoresponsive building systems. His contributions bridge theoretical research with practical applications in digital fabrication, evolutionary algorithms, and BIM integration. Collaborations span institutions globally, reflecting interdisciplinary engagement with architecture, engineering, and urban planning.

• Infrastructure Management
• Transportation Engineering
• Optimization Algorithms
• +۲ مورد دیگر

Josia Meier serves as a Lecturer at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, specializing in optimization methodologies for transportation infrastructure systems. His work bridges theoretical algorithm development with practical applications in road and railway network management. Education: Master's degree in Spatial Development and Infrastructure Systems (ETH Zurich) Bachelor's degree in Civil Engineering (ETH Zurich) Research Focus: Meier develops computational frameworks for optimizing infrastructure intervention programs, particularly examining how algorithmic solutions can enhance maintenance scheduling and resource allocation in transportation networks. His thesis demonstrated practical implementation for Swiss regional railways using custom optimization tools from ETH's Chair of Infrastructure Management. Professional Background: Prior to academic work, he completed vocational training as a Civil CAD designer. Recent activity includes Assistant System Engineering roles in 2023, indicating ongoing industry engagement alongside academic duties. Affiliations: Based at ETH Zurich's Institute for Construction Management and Infrastructure (IBI) in building HIL F 22.3, Stefano-Franscini-Platz 5, Zurich. Scientific Recognition: No awards or fellowships were documented in the provided materials. Academic Contributions: The available information does not reference student supervision, research grants, or laboratory affiliations. His current research appears focused on translating optimization theory into infrastructure management workflows.

Jiang Huمشاهده پروفایل

استاد

• Electronic design automation
• VLSI physical design
• Optimization of large computing systems
• +۵ مورد دیگر

**Jiang Hu** is a Full Professor of Electrical and Computer Engineering at Texas A&M University, holding a Hans Fischer Senior Fellowship at the Technical University of Munich (TUM-IAS). He earned his B.S. in Optical Engineering from Zhejiang University (1990), M.S. in Physics from the University of Minnesota (1997), and Ph.D. in Electrical Engineering (2001). Before joining Texas A&M in 2002, he worked at IBM Microelectronics. His research focuses on Electronic Design Automation (EDA), VLSI physical design, and hardware security, with notable contributions to machine learning-driven CAD tools and approximate computing architectures. **Education**: B.S., Optical Engineering, Zhejiang University (1990) M.S., Physics, University of Minnesota (1997) Ph.D., Electrical Engineering, University of Minnesota (2001) **Research Interests**: Jiang Hu's work bridges EDA and computer architecture, emphasizing optimization of large-scale computing systems. His projects include automated power modeling frameworks (e.g., APOLLO), neural network-driven placement algorithms, and security-enhanced dataflow architectures. He also explores plasmonic materials and perovskite solar cells through interdisciplinary collaborations. **Awards**: IEEE Fellow (2012) Alexander von Humboldt Research Fellowship (2011) Multiple best-paper awards at IEEE/ACM conferences (2003–2021) **Grants & Leadership**: He served as Technical Program Chair for ACM International Symposium on Physical Design (2011–2012) and co-chaired the ACM/IEEE Workshop on Machine Learning for CAD (2023). His research has led to 10 patents and supervision of 24 Ph.D. students. **Labs & Teams**: His group collaborates with TUM-IAS on projects like *A New and Scalable Methodology for Fast Machine Learning Accelerator Design*, advancing EDA tools for next-generation hardware systems.