جست‌وجوی استاد و پژوهشگر

Carol Hullsمشاهده پروفایل

مدرس

• Engineering Education
• Educational Technology
• Student Learning Strategies
• +۴ مورد دیگر

Carol Hulls is a Continuing Lecturer and Associate Chair, Teaching at the University of Waterloo. She holds a Professional Engineer (PEng) designation. Her roles include faculty administration within the university's engineering programs. She contributes to both teaching and administrative responsibilities in the field of engineering education. Dr. Hulls' research focuses on improving engineering education through innovative teaching methodologies and curriculum design. Her work emphasizes student learning strategies, the impact of online instruction, and teamwork development. She investigates how students engage with coursework, particularly in first-year programming and design projects. Her studies also explore the effectiveness of project-based learning and the integration of interdisciplinary teamwork in undergraduate programs. In her research, Dr. Hulls has consistently addressed challenges in engineering education, particularly in transitioning to online environments, optimizing student study habits, and enhancing collaborative learning experiences. Her work often bridges theory with practical applications, such as using design theory to analyze engineering problem-solving and evaluating the outcomes of open-ended projects in programming courses. Dr. Hulls is affiliated with the University of Waterloo's engineering faculty, contributing to both teaching and administrative roles. No awards or grants are explicitly mentioned in the provided texts. Her work includes advising on curriculum design and experiential learning modules, though specific advisees are not listed. Her research and teaching activities are centered around enhancing first-year student experiences, including programming instruction, design projects, and teamwork training. She explores the intersection of educational technology and traditional pedagogy to improve student outcomes in STEM fields.

Matthias Schonlauمشاهده پروفایل

استاد

• Survey Methodology
• Natural Language Processing
• Data Visualization
• +۳ مورد دیگر

Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.

Chris Lehnertمشاهده پروفایل

مدرس ارشد

• Agricultural Robotics
• Robotic Perception
• Grasping Systems
• +۴ مورد دیگر

Dr Chris Lehnert is a Senior Lecturer at the Queensland University of Technology (QUT) within the Faculty of Engineering, School of Electrical Engineering & Robotics. With over a decade of experience, he specializes in robotic perception and grasping systems, particularly in agricultural robotics. His research focuses include Robotic manipulation in cluttered environments Vertical farming automation Agricultural robotics Dynamic grasping systems He leads projects like 'Moving to See' and 'Harvey – The Robotic Capsicum Harvester', aiming to revolutionize agricultural automation through advanced robotic solutions. Dr Lehnert's publications and supervised research emphasize cooperative learning algorithms and 3D shape completion for harvesting tasks. He collaborates with prominent researchers including Peter Corke and Niko Sünderhauf, advancing practical applications in indoor cropping systems.

• Speech Processing
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Murat Saraçlar is a full professor at the Electrical and Electronic Engineering Department of Boğaziçi University. He holds a B.S. from Bilkent University (1994), and M.S.E. and Ph.D. degrees from Johns Hopkins University (1997, 2001). Before joining Boğaziçi in 2005, he worked at AT&T Labs Research (2000–2005). He has held visiting positions at Google (2011–2012) and IBM T.J. Watson Research Center (2012–2013). Affiliations: Editorial roles include IEEE Transactions on Audio, Speech, and Language Processing (Senior Area Editor), and Computer Speech & Language (Editorial Board). Research Interests: Speech Processing, Natural Language Processing, Machine Learning, and Artificial Intelligence. His work focuses on speech recognition, keyword search, and medical signal analysis. Over 100 publications span journals like IEEE/ACM Transactions on Audio, Speech, and Language Processing. Awards include TÜBA-GEBİP Young Scientist Award (2009) and IBM Faculty Award (2010). Recent articles emphasize multimodal systems, unsupervised learning, and medical diagnostics. His contributions bridge theoretical advancements with practical applications in healthcare and communication systems.

• New Product Development
• Engineering Design Thinking
• Human-Machine Interaction
• +۷ مورد دیگر

Martin Steinert is a Professor of Engineering Design and Innovation at the Norwegian University of Science and Technology (NTNU), affiliated with the Faculty of Engineering and the Department of Mechanical and Industrial Engineering. He leads the Design & Engineering research group and heads TrollLABS, a prototyping lab he founded in 2014. His research focuses on the fuzzy front end of product/service development, human-machine interactions in critical environments, and disruptive technology management. Education: Ph.D. in Engineering Design. Affiliations: Norwegian Academy of Technological Sciences (NTVA). Teaching: Courses include Innovation by Design Thinking, Fuzzy Front End Engineering, and Engineering Design Methodology for PhD students. Research Interests: 3D printing and additive manufacturing for medical and aerospace applications. Prototyping methodologies and open-source hardware paradigms. Human factors in remote operations and autonomous systems. Generative AI's role in design innovation and ethical challenges. Articles Trends: Recent work emphasizes blood glucose prediction algorithms, haptic surgical simulations, and material science in 3D printing. Themes include clinical relevance in AI-driven healthcare and low-cost space prototyping. Awards: Top 1% in Mechanical Engineering (Stanford, 2022). Grants: Over $15M from NSF, Swiss NSF, and Norwegian Research Council. Advising: Supervising 9 PhD students and leading international collaborations like the CERN Challenge-Based Innovation program. Labs/Teams: TrollLABS pioneers open-source prototyping tools and hosts hackathons (e.g., IDEA Challenge). Collaborations include Stanford University and CERN.

Daniel Barowyمشاهده پروفایل

دانشیار

• Programming Languages
• End-user Programming
• Spreadsheets
• +۴ مورد دیگر

Daniel Barowy is an Associate Professor in the Department of Computer Science at Williams College. His academic journey includes a Ph.D. in Computer Science from the University of Massachusetts Amherst (2017), an M.S. in Computer Science from UMass Amherst (2013), a B.S. in Computer Science from Boston University (2010), and a B.A. in Legal Studies and Philosophy from UMass Amherst (2002). Education: Ph.D. Computer Science, University of Massachusetts Amherst, 2017 M.S. Computer Science, University of Massachusetts Amherst, 2013 B.S. Computer Science, Boston University, 2010 B.A. Legal Studies and Philosophy, University of Massachusetts Amherst, 2002 Daniel Barowy's research focuses on programming languages, particularly in the domain of end-user programming and human-computer interaction. His work centers on improving spreadsheet programs' reliability and enhancing human-in-the-loop algorithms. He explores how programming language technology can be applied to emerging domains such as crowdsourcing and data analysis. His research often blends traditional programming language techniques like program analysis with statistical methods to create tools that make programming more accessible and robust for non-experts. His publication record shows a consistent focus on improving spreadsheet reliability (ExceLint, CheckCell, FlashRelate) and integrating human computation with programming languages (AutoMan, VoxPL). His work spans multiple prestigious venues including PLDI, OOPSLA, CHI, and USENIX ATC, demonstrating both theoretical rigor and practical impact. The most recent work (Riker) focuses on build systems, showing an expansion of his research interests while maintaining the core theme of improving software reliability. Scientific Awards: Best Paper Award at USENIX ATC 2022 for "Riker: Always-Correct and Fast Incremental Builds from Simple Specifications" PLDI 2015 Distinguished Artifact Award for "FlashRelate: Extracting Relational Data from Semi-Structured Spreadsheets Using Examples" Research Highlight in Communications of the ACM for AutoMan Verified artifact badges for CheckCell, ExceLint, and FlashRelate Barowy actively mentors students, as evidenced by his detailed process for providing letters of recommendation. His teaching portfolio includes core computer science courses like Principles of Programming Languages (CSCI 334) and Introduction to Computer Security (CSCI 331). He has developed educational tools like SWELL for introductory programming education. His research is supported by grants from the National Science Foundation (CCF-1144520, CCF-0953754) and DARPA (N10AP2026), with additional support from Microsoft Research. Barowy leads research projects that often result in open-source software tools including ExceLint (an Excel plugin for finding formula errors), AutoMan/VoxPL (a DSL for crowdsourcing), and several related tools for spreadsheet analysis and crowdsourcing integration. His work bridges theoretical programming language concepts with practical applications that directly benefit end-users.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +۴ مورد دیگر

Andreas Fischer is an Ordentlicher Professor at the Fribourg School of Engineering and Architecture (HES-SO), specializing in Pattern Recognition, Machine Learning, and Document Analysis. His research focuses on handwriting recognition, graph-based methods, and applications in cultural heritage preservation and medical imaging. Education: BSc in Computer Science from Fribourg School of Engineering and Architecture Research Interests: Graph Neural Networks for automata universality analysis Hybrid systems for Vietnamese stele keyword spotting Medical image analysis (colorectal cancer, tumor budding) Large language models for post-OCR correction Key Projects: TAINA Technology (handwriting validation for tax forms) Swisscom (Swiss German to High German translation) Hasler Foundation (Vietnamese stele graph-based analysis) Publications: Over 30 peer-reviewed articles in top journals/conferences (IEEE Access, Medical Image Analysis, Pattern Recognition, etc.), with focus on graph-based methods, handwriting recognition, and medical applications. Grants & Roles: Principal Applicant for multiple industry-funded projects (TAINA, Swisscom) Co-developer of DIVA-DAF deep learning framework

• Artificial Intelligence
• Causal Inference
• Machine Learning
• +۴ مورد دیگر

Emre Kiciman is a leading researcher in artificial intelligence, causal inference, and social computing. His work spans machine learning, ethics in AI, and applications in healthcare and cybersecurity. He has authored over 135 publications in top venues such as ICWSM, KDD, and NeurIPS, focusing on causal discovery, LLM security, and human-AI collaboration. His research bridges theoretical advancements with real-world impacts, including improving mental health monitoring via social media and developing causal tools for robust decision-making. He collaborates widely with institutions and tech companies, contributing to open-source causal AI frameworks and ethical guidelines for AI systems.

Joshua Hertzمشاهده پروفایل

استاد آموزشی

• Materials Science and Engineering
• Engineering Pedagogy
• STEM Education
• +۳ مورد دیگر

Dr. Joshua Hertz is a Teaching Professor in the First Year Engineering Program at Northeastern University, where he focuses on curriculum development and innovative pedagogy. Previously, he held an Assistant Professorship in Mechanical Engineering at the University of Delaware, leading research on ceramic materials' nanostructure effects on ion conduction. He earned a B.S. in Ceramic Engineering from Alfred University (1999) and a Ph.D. in Materials Science and Engineering from MIT (2006). His research interests span materials science and engineering education, with recent work emphasizing team-based learning tools and experiential pedagogy. He has been recognized with Northeastern’s Excellence in Teaching Award and the Martin W. Essigmann Outstanding Teaching Award. Publications highlight his dual focus: early materials science studies on electrolyte thin films and later educational innovations like GRUEPR, a team-formation software. His work bridges traditional engineering research with modern teaching methodologies. Dr. Hertz mentors undergraduate researchers through PEAK Experiences Awards, fostering global health innovations and collaborative problem-solving. His labs previously explored ceramic material properties at Delaware, while current efforts prioritize curriculum design for first-year engineering students. Education: MIT (Ph.D., 2006), Alfred University (B.S., 1999) Key Awards: Northeastern Excellence in Teaching Award (2024), Martin W. Essigmann Award Labs/Teams: Former materials lab at University of Delaware; current focus on Northeastern’s First-Year Engineering initiatives

• Microwave Engineering
• Dielectric Properties
• Biomedical Engineering
• +۳ مورد دیگر

Cemanur Aydınalp is an Assistant Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University, Turkey, actively advancing research at the intersection of microwave engineering and biomedical applications. Her research expertise spans microwave dielectric properties , biological tissue characterization , and deep learning integration , with core focus areas: Development of microwave sensing systems for medical diagnostics (tumor boundary detection) and environmental monitoring (water salinity prediction) Design and optimization of antennas for microwave hyperthermia cancer treatment Analysis of dielectric property variations in biological tissues under temperature changes Application of U-Net and neural networks for electromagnetic field prediction Recent publications (2024-2025) reveal a concentrated research trajectory toward AI-enhanced microwave systems, particularly for breast cancer therapy and water quality assessment. Her work demonstrates strong interdisciplinary collaboration across electrical engineering, biomedical physics, and environmental science, with significant emphasis on practical implementations through IEEE conference publications. Dr. Aydınalp currently leads two major research projects as Principal Investigator: "Detection of Temperature-Dependent Changes in Dielectric Properties of Biological Tissue-Like Materials" (2024-2026) "Development of a Neural Network Based Microwave Sensing System for Accurate Salinity Prediction in Water" (2024-2025) These projects, funded by Istanbul Technical University's Scientific Research Projects unit, drive innovation in medical diagnostics and environmental engineering while mentoring next-generation researchers through hands-on project involvement.

Luke Landherrمشاهده پروفایل

استاد آموزشی

• Engineering Education
• STEM Education
• Comics in Education
• +۳ مورد دیگر

Luke Landherr serves as a COE Distinguished Teaching Professor and Associate Chair for Undergraduate Studies in the Department of Chemical Engineering at Northeastern University's College of Engineering. He is also a Center Member at The Plastics Center, where he bridges materials science with educational outreach. His academic credentials include a B.S. in Chemical Engineering from Lafayette College (2005) and a Ph.D. in Chemical Engineering from Cornell University (2010), followed by an NRC Postdoctoral Research Associateship at NIST. Dr. Landherr's research pioneers comics and visual tools for STEM education across all age groups. His Drawn to Engineering series, published in Chemical Engineering Education, transforms complex concepts like thermodynamics and unit operations into accessible narratives. Collaborating with artists, he creates comics adopted internationally—from U.S. colleges to institutions in the U.K., Belgium, and Denmark—and serves as engineering consultant for PBS's Crash Course Engineering YouTube series. His work targets student engagement, confidence, and conceptual understanding through innovative visual pedagogy. Analysis of his publications reveals consistent focus on comics as assessment tools, diversity in engineering, and real-world problem-solving. The Drawn to Engineering comics systematically address classroom challenges—from exam anxiety to inclusive teaching—while his NSF-funded research quantifies comics' impact on learning conservation principles. His scientific awards include: Joseph J. Martin Award (2023) for Chemical Engineering Division paper excellence Diversity Recognition Award (2025) from ASEE Multiple Omega Chi Epsilon Professor of the Year Awards (2015-2023) Ray W. Fahien Award (2019) and AIChE Innovation Award (2018) Faculty Leadership Award (2019) from student engineering chapters Dr. Landherr secures substantial grants including a $300,000 NSF award (2023) for comics-based conservation principles research and AIChE Foundation funding for high school/college learning tools. He mentors students who win national AIChE poster competitions and present at ASEE conferences. His leadership spans AIChE's Societal Impact Operating Council (chair 2019), K-12 Committee (chair 2020), and co-founding the Disabilities Outreach and Inclusion Committee. Departmentally, he chairs the Undergraduate Education Committee and Student Awards Committee. He actively promotes inclusive engineering through comics addressing diversity statements, LGBTQA advocacy (Gratitude Award 2019), and accessible education frameworks developed with student artists. His work with The Plastics Center integrates polymer science outreach with K-12 curriculum development.

Rick Franichمشاهده پروفایل

استاد

• Medical Physics
• Radiotherapy
• Radiation Dosimetry
• +۵ مورد دیگر

Professor Rick Franich is a faculty member in the School of Science at RMIT University, specializing in Medical Physics and Radiotherapy. His research focuses on radiation dosimetry, radiation transport modeling, and applications in healthcare. He has collaborated with institutions like Peter MacCallum Cancer Centre and the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) on projects addressing radiation delivery accuracy, beam characterization, and in vivo dose assessment. Education: Details not explicitly provided in the text. Affiliations: RMIT University, RMIT Institute for Sustainable Health and Well-Being, IAEA advisory group. His research interests include dosimetric modeling in radiotherapy, Monte Carlo simulations for radiation transport, and ion beam analysis. He has secured grants such as the 2007 RMIT Emerging Researcher Grant and received teaching awards including the 1999 RMIT Physics Students Association Lecturer of the Year Award. Teaching roles include coordinating the Master of Applied Science (Medical and Health Physics) and courses in Medical Imaging Physics and Radiation Protection. He supervises PhD and Master’s students on projects related to radiation shielding, brachytherapy verification, and adaptive radiotherapy. Notable consultancy includes advising the International Atomic Energy Agency (IAEA) on improving heavy ion beam analytical techniques. His work spans clinical applications like proton therapy verification and space radiation modeling for astronaut safety. Scientific awards include recognition for teaching excellence and research contributions. His lab and team focus on advancing medical physics through interdisciplinary collaborations in oncology, radiation safety, and technology integration.

Ullrich Kötheمشاهده پروفایل

دانشیار

• Machine Learning
• Neural Networks
• Explainable AI
• +۶ مورد دیگر

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Marco Zimmerlingمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Wireless Embedded Systems
• Battery-Free IoT
• +۱ مورد دیگر

Marco Zimmerling is a Full Professor in the Department of Computer Science at TU Darmstadt, specializing in cyber-physical systems with focus on wireless embedded systems. His research aims to design dependable, adaptive, and sustainable systems for the battery-free Internet of Things. Education: Ph.D. in Computer Engineering from ETH Zurich (2015), Diploma in Computer Science from TU Dresden. Research interests include battery-free IoT, wireless networked systems, concurrent transmissions, and dependable cyber-physical systems. His work bridges theoretical foundations with practical implementations in sustainable computing. Recent publications focus on energy-harvesting testbeds (Shepherd Nova), open-source platforms for battery-free devices (Riotee), and fault-tolerant networking. Key themes include energy efficiency, wireless protocols, and system reliability under constrained conditions. Honors include ACM SIGBED Early Career Researcher Award (2022), Test-of-Time Award (SenSys 2022), Best Paper Awards (EWSN 2022, ICCPS 2019), and DFG Emmy Noether Grant (2018). Leads research on sustainable IoT systems and wireless control networks, with projects spanning testbed development, protocol design, and real-world deployments.

• Psychology of Older Age
• Ageism and Elder Abuse
• Sexuality in Older Age
• +۱۲ مورد دیگر

Mike Murphy is a Lecturer in the Department of Applied Psychology at University College Cork (UCC), Ireland. He completed a PhD in Cognitive Function in Older Age at UCC (2008) after a BSc in Medical Science (1998) and Applied Psychology (2001). Currently, his research focuses on psychology of older age , ageism and elder abuse , sexuality in older age , right to die , wellbeing across the lifespan , and coaching psychology . Education: PhD, University College Cork (2008) H Dip in Coaching Psychology, UCC (2022) PG Certificate in Teaching & Learning in Higher Education, UCC (2013) BSc (Hons) in Applied Psychology, UCC (2001) BSc (Hons) in Medical Science, UCC (1998) Research Interests: His work explores ageism , elder abuse , end-of-life ethics , LGBTQ+ healthcare , and positive psychology interventions . Recent projects include studies on healthcare avoidance in transgender populations , coaching psychology methodologies , and social support across the lifespan . Article Trends: His publications (2023-2022) span mental health , gender studies , positive psychology , and clinical therapy evaluation . Key topics include posttraumatic growth in psychosis families , asexual identity development , and DBT outcomes for borderline personality disorder . Collaborations with European and Brazilian institutions highlight his international reach. Scientific Awards: UCC Research Award for Responsible Conduct of Research (2021) UCC Research Award for Responsible Conduct of Research (2018) Advising & Grants: He has supervised 19 postgraduate students (2012-2022) in clinical psychology and applied psychology. Secured €454,945.58 in research grants (2016-2024), including projects on transgender healthcare avoidance (€96k), men's cancer prevention (€29,670), and LIAM Mc Trial for metastatic cancer (€299,988). Labs & Teams: Active in UCC's research ecosystem, he collaborates with the School of Applied Psychology , Irish Cancer Society , and international teams in Spain, Brazil, and Italy. His work intersects with HRB Open Research , Frontiers in Psychology , and Transgender Health initiatives.