جست‌وجوی استاد و پژوهشگر

• mRNA Vaccine and Therapeutics Manufacturing
• Continuous Bioprocessing
• Process Intensification
• +۳ مورد دیگر

Dr. Zoltán Kis serves as a Senior Lecturer (Associate Professor) in the School of Chemical, Materials and Biological Engineering at The University of Sheffield and holds an Honorary Lecturer position at Imperial College London's Department of Chemical Engineering. His research focuses on innovating disease-agnostic RNA vaccine and therapeutics manufacturing platforms through process digitalization and intensification. Dr. Kis earned his Ph.D. in Bioengineering from Imperial College London, complemented by an M.Sc. in Applied Biotechnology and a B.Eng. in Chemical with Biochemical Engineering. His interdisciplinary training bridges chemical engineering, biotechnology, and bioengineering disciplines. His research integrates experimental and computational methodologies to revolutionize mRNA production: Development of continuous enzymatic synthesis, purification, and LNP formulation processes Process intensification through novel unit operations and equipment design Digital twin deployment for real-time monitoring and control Techno-economic modeling to reduce production costs Quality by Digital Design framework implementation for regulatory compliance Analysis of recent publications reveals dominant trends in continuous bioprocessing and digital transformation of mRNA manufacturing. Key subfields include oligo-dT chromatography optimization, tangential flow filtration for mRNA purification, and digital twin applications for process control, with strong emphasis on pandemic-response capabilities and cost reduction strategies. Dr. Kis actively supervises PhD students in mRNA bioprocessing and teaches Biopharmaceutical Manufacturing (CPE336/CPE6043) and Introduction to Bioengineering (BIE103). His industry engagement includes advisory roles on Sanofi's mRNA CMC Board and Pfizer's mRNA Technology Advisory Board. He leads the RNA Manufacturing Innovation Team and has secured substantial research funding, including: £3.7 million CEPI grant for RNAbox platform (2024-2027) £7.6 million UK-SEA Vaccine Manufacturing Hub (2023-2028) £2 million Innovate UK project for automated RNA platform (2023-2025) Multi-million USD Wellcome Leap R3 grant for distributed RNA production His work demonstrates significant impact through industry partnerships, policy advisory roles including WHO mRNA Technology Transfer Hub consultancy, and leadership in advancing global vaccine manufacturing capabilities.

Joshua Glazerمشاهده پروفایل

دانشیار

• Real-time risk stratification of critically ill patients
• Electronic health record (EHR) data analysis
• Emergency Medicine
• +۴ مورد دیگر

Joshua Glazer, MD, is an Associate Professor of Emergency Medicine, Internal Medicine, and Anesthesiology at the University of Wisconsin–Madison, with a focus on clinical care in the Emergency Department and inpatient Intensive Care Units (ICUs). As Division Chief of Emergency Critical Care and chair of the UW Health Critical Care Committee and Organ & Tissue Donation Advisory Committee, he leads multidisciplinary initiatives to enhance critical care delivery and neurologically intact survival post-cardiac arrest. Undergraduate: University of Wisconsin–Madison Medical School: University of Wisconsin School of Medicine and Public Health Residency: University of Michigan (Chief Resident) Fellowship: University of Michigan (Critical Care, Health Care Administration) Dr. Glazer’s research centers on real-time risk stratification of critically ill patients and leveraging EHR data for early detection of life-threatening illnesses. His operational work includes founding the UW extracorporeal cardiopulmonary resuscitation (ECPR) program, integrating EMS, cardiothoracic surgery, emergency medicine, and critical care teams. His recent publications emphasize ECPR implementation, geospatial mapping for prehospital care, predictive modeling for resuscitation strategies, health equity in critical interventions, and EHR-driven disease identification. These span disciplines including Emergency Medicine, Critical Care, Public Health, and Medical Informatics, with subfields like resuscitation science, clinical workflow optimization, and multidisciplinary care protocols. BerbeeWalsh Faculty Award for Excellence in Scholarship (2021-22) UW Health Rising Star Leadership Award (2020) BerbeeWalsh Ripple Award for Leadership & Service (2019-20) BerbeeWalsh Faculty Award for Excellence in Teaching (2017-18) Dr. Glazer spearheads UW Health’s ECPR program, a multidisciplinary effort involving EMS, cardiothoracic surgery, and critical care teams. He also contributes to quality improvement in organ donation and resuscitation science.

Michael Spignerمشاهده پروفایل

استادیار

• Emergency Medicine
• Prehospital Medicine
• EMS Systems
• +۵ مورد دیگر

Michael Spigner, MD, serves as Assistant Professor (CHS) in the Department of Emergency Medicine and Internal Medicine at the University of Wisconsin-Madison School of Medicine and Public Health. He directs Prehospital Medicine Informatics and Research while chairing the EMS Quality & Patient Safety Committee. As Medical Director for Belleville EMS, Brooklyn Fire & EMS, and Associate Medical Director for Madison Fire Department, he bridges clinical practice and system improvement across multiple agencies. Education: BA: CUNY Queens College MD: Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Residency: University of Cincinnati Medical Center Fellowship: University of New Mexico Health System – EMS Dr. Spigner's research strengthens emergency care continuity through prehospital medicine, clinical informatics, and substance misuse interventions. His work spans airway management optimization, cardiac arrest resuscitation science, pediatric EMS readiness, and data-driven overdose response systems. He develops evidence-based protocols for EMS personnel, transport clinicians, and emergency departments while leveraging informatics to transform care delivery. His 2019-2025 publications reveal growing integration of predictive modeling and informatics in EMS research, with recurring themes in protocol standardization, substance misuse outcomes, and ultrasound applications. Key trends include variable post-ROSC care practices, first-pass success metrics in airway management, and dashboard development for overdose fatality review. Scientific Awards: Board certification, Clinical Informatics (2025) EMS Medicine Early Career Medical Director Award, NAEMSP & Stryker (2024) Faculty Award for Excellence in Scholarship, BerbeeWalsh Department (2024) Best Speaker, NAEMSP Annual Meeting (2023) Board certification, Emergency Medical Services (2021) Board certification, Emergency Medicine (2021) National Outstanding Medical Student Award, ACEP/EMRA (2016) Dr. Spigner secures significant funding including NIH U19 support for ED-LEAD (Alzheimer's/dementia care transformation), NAEMSP/Stryker's $60,000 Early Career Award, and Wisconsin Partnership Program grants for firefighter/EMT development and rural pediatric simulation training. His leadership in EMS quality initiatives and medical direction provides mentorship to prehospital providers despite no formal academic advisees listed in source materials. He leads UW's Prehospital Medicine Informatics program while collaborating with over 30 EMS agencies through medical directorships. His integrated approach combines clinical practice, research, and quality improvement to advance evidence-based prehospital systems across Wisconsin's urban and rural settings.

Cameron Brownمشاهده پروفایل

دانشیار

• Pharmaceutical Manufacturing
• Crystallization Processes
• Digital Design Tools
• +۴ مورد دیگر

Dr. Cameron Brown is a Reader (equivalent to Associate Professor) at the Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow. He specializes in developing digital design tools and strategies for pharmaceutical manufacturing. Brown joined Strathclyde in 2014 and has progressed through research associate, research fellow, and Chancellor's fellow positions. He currently coordinates the Drug Substance Manufacturing module for the Advanced Pharmaceuticals Manufacturing MSc program. Education: Brown holds a PhD in crystallization process characterization and a Chemical Engineering degree, both from Heriot-Watt University. Research Focus: His work centers on three primary areas: Hybrid modeling approaches : Integrating physics-based and data-driven models to enhance drug substance manufacturing efficiency Self-driving labs : Developing automated systems for drug substance process development with model-based experimental design Digital decision-making : Implementing coupled models through GenAI and LLMs for rapid pharmaceutical process development His research contributes to UN Sustainable Development Goals through improved medicine manufacturing sustainability. Publication Trends: Brown's recent articles focus on pharmaceutical crystallization, digital design methodologies, AI applications in manufacturing, and process optimization. His work demonstrates strong emphasis on translating computational models into industrial practice, particularly in continuous manufacturing and quality-by-design frameworks. Honors: Elected staff officer of British Association of Crystal Growth (2024) Research Leadership: Brown serves as Principal Investigator for PharmaCrystNet and co-investigator on multiple major initiatives including Digital Design and Manufacturing of Amorphous Pharmaceuticals, Future CMAC Manufacturing Hub, Accelerated Discovery and Development of New Medicines Prosperity Partnership, and ARTICULAR. He leads knowledge exchange projects with pharmaceutical companies and manages knowledge transfer partnerships. Professional Engagement: Brown is active in the Acceleration Consortium and serves on the committee of the British Association of Crystal Growth.

Lei Chenمشاهده پروفایل

استاد

• Data-Driven Machine Learning
• Crowdsourcing-Based Data Processing
• Uncertain and Probabilistic Databases
• +۳ مورد دیگر

Lei Chen is a Chair Professor and Director of HKUST Big Data Institute at the Hong Kong University of Science and Technology , where he has served since 2005. His research spans data-driven machine learning , crowdsourcing systems , and uncertain database processing , with notable contributions in privacy-preserving spatial queries and graph neural networks . Ph.D. in Computer Science, University of Waterloo (2004) MS in Computer Science, Asian Institute of Technology (1997) BS in Computer Science, Tianjin University (1994) His work focuses on: Spatial Crowdsourcing - Efficient task assignment and privacy frameworks Graph Processing - Novel indexing for heterogeneous networks Explainable AI - Human-centric model interpretation techniques Uncertain Data - Probabilistic query processing with crowdsourcing Recent publications cluster around secure data federation , distributed graph training , and privacy-preserving mobility systems , reflecting his leadership in ACM and IEEE communities. Students include 15 active Ph.D. candidates and 20+ graduated researchers now at institutions like BeiHang University and Huawei Noah's Ark Lab . Awards: ACM Fellow (2024), VLDB Best Paper (2022), SIGMOD Test-of-Time Award (2015).

• Transportation Planning
• Mobility Behaviors
• Multimodal Transport Systems
• +۳ مورد دیگر

Marco Diana is a Full Professor in the Department of Environmental, Land and Infrastructure Engineering (DIATI) at the Polytechnic University of Turin, specializing in transportation planning and sustainable mobility. His academic career spans over two decades with significant contributions to multimodal transport systems, travel behavior analysis, and urban mobility solutions. Professor Diana's research interests encompass Transportation Planning, Mobility Behaviors, Multimodal Transport Systems, Sustainable Urban Transport, Travel Demand Analysis, and Shared Mobility Services. His work focuses on understanding how people interact with transportation systems, particularly examining the adoption and impact of shared mobility services like car-sharing, bike-sharing, and scooter-sharing systems in urban environments. His recent publications reveal a strong focus on shared mobility services, with particular attention to stakeholder perspectives, socio-demographic factors influencing adoption, and the spatial impacts of these services on urban infrastructure. The research shows a clear progression from traditional transportation planning toward data-driven analysis of emerging mobility services and their integration into sustainable urban transport systems. Accessit prize for the best paper of the joint ECRI-FERSI seminar conferred by ECTRI, France (2003) Fellow of the Transportation Research Board (2017-present), Member of the standing committee 'Travel Survey Methods' Fellow of the Transportation Research Board (2011-2020), Member of the standing committee 'Urban Transportation Data and Information Systems' Editorial board member of Transportation Research Record (2019-present) Editorial board member of European Transport Research Review (2008-2020) Professor Diana actively supervises PhD students including Amirehsan Charlang Bakhtyari and Erika Olivari, while previously guiding Ehsan Amirnazmiafshar to completion. His research is supported by numerous competitive grants including EU-funded projects such as GILL (2023-2025), STARS (2017-2020), SUITS (2016-2020), and DEMONSTRATE (2015-2017), with a focus on sustainable urban mobility solutions. His work bridges academic research with practical applications through commercial research contracts addressing specific urban transport challenges in Italian cities.

• Catalysis
• CO₂ Valorization
• Kinetic Modeling
• +۴ مورد دیگر

Dr. Sofia Angeli is a Group Leader at the Institute of Catalysis Research and Technology (IKFT), Karlsruhe Institute of Technology (KIT), Germany, since March 2024. Previously, she held roles as Senior Scientist/Group Leader (2021–2024) and Postdoctoral Researcher (2017–2019) at KIT. Her research focuses on catalysis for energy transition, CO₂ valorization, kinetic modeling, and digitalization of catalytic processes. She leads interdisciplinary projects integrating experimental and computational methods to advance sustainable chemical processes. PhD in Chemical Engineering (2016): Aristotle University of Thessaloniki, Greece. Thesis: Hydrogen production via intensified methane steam reforming processes. M.Sc. in Advanced Materials (2012): Aristotle University of Thessaloniki. Diploma in Chemical Engineering (2009): Aristotle University of Thessaloniki. Research Interests : Sofia’s work spans kinetic modeling, CO₂ conversion, catalytic pollutant removal, and digital tools for catalysis research. She develops novel catalysts for methane reforming and designs data management systems like Adacta and CaRMeN to streamline reaction mechanism analysis. Her projects often address industrial challenges in emissions reduction and renewable energy. Her recent publications emphasize catalytic processes for energy sustainability (e.g., methane oxidative coupling) and automated modeling frameworks. She collaborates widely, contributing to journals like Chemical Engineering Journal and ACS Catalysis . Advising & Teams : As a Group Leader, she oversees researchers in catalysis and data-driven processes. Her team works on cutting-edge projects supported by KIT’s infrastructure and interdisciplinary networks. Labs/Teams : Active in the IKFT and the Deutschmann Group, focusing on catalyst design and process intensification.

Yili Hongمشاهده پروفایل

استاد

• Machine Learning
• Reliability Analysis
• Survival Analysis
• +۴ مورد دیگر

Yili Hong is a Professor in the Department of Statistics at Virginia Tech's College of Science. Her research focuses on statistical methodologies in reliability analysis, machine learning, survival analysis, and spatial data analysis. She holds a Ph.D. from Iowa State University (2009), with prior degrees from the same institution and the University of Science and Technology of China. Hong has been recognized with awards such as the ASA Physical and Engineering Sciences Award (2017) and the DuPont Young Professor Award (2011). She serves on editorial boards for journals like Technometrics and Journal of Quality Technology, emphasizing contributions to statistical reliability and big data applications. Her work bridges theoretical statistics with practical challenges in engineering and public health, including studies on Lyme disease emergence in Virginia through spatial modeling. Education: Ph.D. in Statistics, Iowa State University, 2009 M.S. in Statistics, Iowa State University, 2005 B.S. in Statistics, University of Science and Technology of China, 2004 Research interests span machine learning robustness, reliability of AI systems, degradation analysis, and statistical computing. Notable achievements include pioneering work on mixture experiments for AI algorithm evaluation and statistical methods for high-performance computing variability. Her recent projects address the reliability of autonomous systems and AI safety through recurrent event data analysis. Awards and recognitions highlight her contributions to statistical methodology and industrial applications. Editorial activities reflect her leadership in advancing statistical practice through peer-reviewed journals.

• Meat Science
• Food Packaging
• Animal Production
• +۴ مورد دیگر

Gretchen Mafi, a Professor and Ralph & Leila Boulware Endowed Chair in the Department of Animal and Food Sciences at Oklahoma State University, specializes in meat science with a focus on meat quality, color stability, and postmortem muscle physiology. She holds a Ph.D. in Animal Science from Texas Tech University (2000), an M.S. from Colorado State University (1996), and a B.S. from Texas Tech University (1994). Her research addresses dark-cutting beef phenomena, metabolomics, and food packaging innovations to enhance meat quality and reduce waste. Mafi teaches undergraduate and graduate courses in meat science and coordinates the Oklahoma State Meat Judging Team. She has received prestigious awards including the USDA Excellence in Teaching Award (2020) and the American Meat Science Association Distinguished Teaching Award (2019). Her leadership roles include serving as President of the American Meat Science Association (2023). Education: Ph.D. in Animal Science, Texas Tech University, 2000 M.S. in Animal Sciences, Colorado State University, 1996 B.S. in Animal Sciences, Texas Tech University, 1994 Research Interests: Dark-cutting beef mechanisms, meat color chemistry, metabolomics, food packaging technologies, and beef quality assurance. Her work bridges basic research (e.g., mitochondrial proteomics, metabolite profiling) with applied solutions (e.g., high-pressure processing, antioxidant-embedded packaging). Recent studies focus on oxygen exposure impacts, non-invasive spectroscopy for color assessment, and industry benchmarking via National Beef Quality Audits. Mafi’s grants include projects on AI-driven stress analysis in cattle and novel packaging systems to mitigate discoloration. Awards & Honors: USDA Excellence in Teaching Award (2020) American Meat Science Association Distinguished Teaching Award (2019) Signal Service Award (2021) President, American Meat Science Association (2023) Teaching & Mentorship: Leads courses in meat evaluation, animal science, and professional development. Advises graduate students and mentors the Meat Judging Team, emphasizing hands-on training in carcass and product evaluation. Key Projects: Over 20 funded projects include improving dark-cutting beef color via HPP, metabolomics-driven quality predictions, and industry collaborations on packaging and food safety. Her lab at Oklahoma State University focuses on interdisciplinary solutions to enhance meat industry sustainability and consumer satisfaction.

• Fuzzy Logic
• Artificial Intelligence
• Neural Networks
• +۶ مورد دیگر

Professor Mahdi Mahfouf holds a Chair in the School of Electrical and Electronic Engineering at the University of Sheffield. He has held academic roles since 1997, progressing from Lecturer to Professor in 2005. His research focuses on Fuzzy Logic, Control Systems, and their applications in biomedical and industrial contexts. Mahfouf leads the Intelligent Systems Research Laboratory and has contributed over 370 publications, including influential work on fuzzy modeling and predictive control. Education: Ing.Dipl. (Hons) in Control Systems MPhil in Control Systems (University of Sheffield, 1988) PhD in Control Systems (University of Sheffield, 1991) Research Interests: Fuzzy Logic applications, Artificial Intelligence, Neural Networks, Model-Based Predictive Control, Biomedical Engineering (e.g., ICU Decision Support Systems), and Manufacturing Systems (e.g., granulation processes, surface metrology). Key Achievements: Recipient of the IEE Hartree Premium Award (1992) and MEDIPEX Innovation Award. His work integrates fuzzy logic into real-time systems for aviation, healthcare, and robotics. Grants & Labs: Leads the Intelligent Systems Research Lab. Active in collaborative projects with industry (e.g., pharmaceuticals, aerospace). His research bridges theory and practice, emphasizing data-driven solutions for complex systems.

Sangtae Haمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning Systems
• Networks
• +۶ مورد دیگر

Sangtae Ha is an Associate Professor in the Computer Science Department at the University of Colorado Boulder. His research focuses on building practical computer systems spanning multiple disciplines, including machine learning/deep learning systems, networks and distributed systems, internet protocols, wireless networks, video streaming, storage systems, and security. He specializes in creating efficient and scalable solutions for real-world computing challenges. His work emphasizes interdisciplinary approaches, bridging theoretical computer science with practical system design. Key areas of exploration include optimizing neural network execution for edge computing, developing adaptive streaming protocols, and enhancing wireless network performance through novel signal processing and spectrum management techniques. Recent projects include frameworks for semantic offloading in neural networks and reinforcement learning-based cloud scheduling. No scientific awards or notable grants are explicitly mentioned in the provided materials. While no formal advisees are listed, his research team likely involves graduate students and collaborators. No dedicated labs or teams are named, though his work aligns with broader university initiatives in computer systems and networking.

Giorgia Chinazzoمشاهده پروفایل

استادیار

• Sustainable Building Design
• Indoor Environmental Quality
• Data Science Applications in Architecture
• +۵ مورد دیگر

Giorgia Chinazzo is an Assistant Professor of Civil and Environmental Engineering at Northwestern University, leading the Architectural Engineering and Design Program. She holds a PhD in Building Science from EPFL and dual master's degrees from Politecnico di Milano and Politecnico di Torino. Her research integrates engineering, architecture, data science, and health sciences to create sustainable, human-centric buildings that enhance occupant well-being while reducing environmental impact. Key focuses include thermal comfort, indoor environmental quality, and smart technologies. Education: PhD in Building Science, Swiss Federal Institute of Technology in Lausanne (EPFL) MSc in Architectural Engineering, Politecnico di Milano (Double Degree) MSc & BSc in Architectural Engineering, Politecnico di Torino Research Interests: Multi-domain human-building interactions Climate-resilient building design Data-driven occupant feedback analysis Thermal comfort innovations Wearable sensor technologies Her work emphasizes bridging lab studies and real-world applications through living laboratories, advancing methodologies for sustainable building practices. Courses taught include Building Science fundamentals and professional development seminars.

• Building Technology
• Renewable Energy Systems
• Thermal Engineering
• +۳ مورد دیگر

Dirk Müller is a Professor at RWTH Aachen University, holding positions as Director of the Institute for Energy Efficient Buildings and Indoor Climate (E.ON Energy Research Center) and Deputy Dean of the Faculty of Mechanical Engineering. He leads research in building climate technology, energy systems, and sustainable building design. Müller's career includes roles at Behr GmbH & Co. and Robert Bosch GmbH before academia. His research focuses on heat pumps, thermal systems, and energy efficiency in buildings. Education: Diplom in Mechanical Engineering (RWTH Aachen), Ph.D. in Heat Transfer and Climate Technology (RWTH Aachen), and MBA from Fernuniversität Hagen. He also holds a DAAD fellowship at Dartmouth College (USA). Research Interests: Advanced HVAC systems, refrigerant optimization, building energy systems, and grid integration of renewable energy. Notable projects include the SUSTAIN 2 initiative for smart building renovation and the Agentensysteme (AGENT) project for building energy control. Awards: Borchers-Plakette (2000), VDI Ehrenring (2004), and Fellow of the European HVAC Associations (2014). His work spans 150+ publications in 2023-2025, focusing on heat pump efficiency, energy storage, and indoor climate modeling. Leadership roles include Chair of the Fachkommission Gebäude-Klima and Board Member of the Helmholtz Institute IEK-10. He advises companies like Viessmann and TROX GmbH.

Manuchehr Soleimaniمشاهده پروفایل

استاد

• Electrical and Electromagnetic Tomography
• Medical Imaging
• Industrial Tomography
• +۵ مورد دیگر

Professor Manuchehr Soleimani is a Professor in the Department of Electronic & Electrical Engineering at the University of Bath, leading the Engineering Tomography Lab (ETL) since 2011. He specializes in AI-driven tomographic imaging techniques, including Electrical/Electromagnetic Tomography, X-ray CT, and Ultrasound Tomography. His research focuses on medical imaging, industrial process monitoring, and robotic touch sensing. He collaborates with CERN on projects like the TIGRE open-source software platform and leads Bath's educational initiatives, including the MSc in AI for Engineering and Design and the Robotics Engineering undergraduate program. His research interests span inverse problems, machine learning integration, and multi-modality tomography. Key applications include lung monitoring, industrial quality control, and medical diagnostics. Professor Soleimani has secured funding from Innovate UK, EPSRC, EU Horizon 2020, and industry partners. He serves on the Scientific Advisory Panel at the University of Cambridge and has supervised 31 doctoral/master’s students. His lab develops cutting-edge imaging systems, such as contactless EIT sensors and ultrasonic tomography for in-vivo imaging. Key Projects: AdvanCT (EU), TIGRE (CERN collaboration), and industrial tomography systems for steel casting and CO₂ capture. Publications: Over 378 peer-reviewed articles, with recent focus on AI-enhanced reconstruction algorithms and multi-frequency imaging. Scientific contributions include pioneering work in capacitively coupled tomography and tactile sensing systems. His work aligns with UN SDGs through innovations in health and sustainable industry practices.

Nevzat Bircan Bugdayciمشاهده پروفایل

استادیار

• Additive Manufacturing
• Advanced Manufacturing Processes
• Data-Driven Manufacturing
• +۳ مورد دیگر

Nevzat Bircan Bugdayci is an Assistant Professor of Mechanical Engineering at Michigan State University (MSU), appointed since Spring 2024. Previously, he was a Postdoctoral Research Associate at the University of Michigan's Smart and Sustainable Automation Lab (2023). He holds a Ph.D. in Mechanical Engineering from ETH Zurich (2022), an M.Sc. from Koç University (2013), and a B.Sc. from METU (2011). His research focuses on physics-based and data-driven approaches in advanced manufacturing, including additive manufacturing optimization (e.g., LPBF), milling process stability, and in-situ process monitoring using machine tool signals. Education: Ph.D., Mechanical Engineering, ETH Zurich, Switzerland (2022) M.Sc., Mechanical Engineering, Koç University, Turkey (2013) B.Sc., Mechanical Engineering, METU, Turkey (2011) Research Interests: Physics-based optimization of laser scan strategies in additive manufacturing Data-driven stability predictions in milling Non-invasive process monitoring using controller signals Residual stress reduction in LPBF processes Adaptive manufacturing systems for material inhomogeneity Teaching: Instructor: ME 391 Mechanical Engineering Analysis at MSU (Spring/Fall 2024) Prior Teaching Assistant roles at ETH Zurich and Koç University Labs/Teams: Former member of the Smart and Sustainable Automation Lab at the University of Michigan. Collaborates with industry on LPBF and machining process innovations. Future Work: Expanding SmartScan to industrial-scale additive manufacturing applications and developing real-time adaptive milling systems for heterogeneous materials.