جست‌وجوی استاد و پژوهشگر

Subodha Kumarمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Blockchain
• +۷ مورد دیگر

Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science at Temple University’s Fox School of Business. He serves as the Founding Director of the Center for Business Analytics and Disruptive Technologies and has a secondary appointment in Information Systems. His roles include Ph.D. Program Concentration Director for Operations and Supply Chain Management. With over 240 publications, he ranks #1 globally in Information Systems Research publishing and holds a robotics patent. His research focuses on AI, blockchain, healthcare analytics, and cybersecurity, with notable contributions to digital transformation and operations management. Awards include the INFORMS ISS Distinguished Fellow and POMS Fellow. He advises major journals like Production and Operations Management and organizes conferences like POMS 2018. Media mentions span NYT, WSJ, and CBS. Education: University of Washington faculty Texas A&M University faculty Research Interests: Dr. Kumar explores AI applications, blockchain in fintech, healthcare system optimization, and supply chain analytics. He investigates cybersecurity’s operational impacts and data mining strategies. His work bridges theory and practice, addressing challenges in digital transformation and platform economies. Publications Trends: Recent work emphasizes digital platforms (e.g., blockchain adoption, telemedicine impact), organizational behavior (real-time feedback systems), and healthcare operations (personalized treatment algorithms). He frequently collaborates internationally, with studies appearing in Management Science, Information Systems Research, and Decision Sciences. Awards & Recognition: Changjiang Scholars Chair (China) ISB Visiting Professorship POMS Executive Editor Grants & Labs: Secured NIH grants for health analytics projects. Leads Temple’s analytics center fostering industry partnerships. Active in editorial roles (e.g., Production and Operations Management Journal Deputy Editor). Teaching: Offers courses in data science, optimization methods, and innovation entrepreneurship at graduate/undergraduate levels.

Christopher Kananمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Deep Learning
• Continual Learning
• +۵ مورد دیگر

Christopher Kanan is a tenured Associate Professor of Computer Science at the University of Rochester, leading the AI Initiative within the Hajim School of Engineering & Applied Sciences. He holds secondary appointments in Brain and Cognitive Sciences, the Goergen Institute for Data Science and AI (GIDS-AI), and the Center for Visual Science. His research focuses on deep learning systems for artificial general intelligence (AGI), including continual learning, medical computer vision, and visual question answering. Previously, he was an Associate Professor at RIT’s Carlson Center for Imaging Science and a leader at Paige.AI, contributing to the FDA-cleared Paige Prostate system. Kanan earned his PhD from UC San Diego, completed postdoctoral work at Caltech, and worked at NASA JPL. Education: PhD in Computer Science, UC San Diego MS in Computer Science, University of Southern California Bachelor’s in Philosophy and Computer Science, Oklahoma State University Research Interests: Kanan’s work spans foundational AI capabilities like continual learning, medical imaging (pathology and radiology), multi-modal reasoning, and cognitive science-inspired models. His lab develops bias-robust AI systems and applies deep learning to healthcare and fusion research. Articles Trends: His recent work emphasizes out-of-distribution generalization, foundation models in pathology, and stability in continual learning. Key themes include AI applications in healthcare, model robustness, and neuroscience-inspired algorithms. Awards: NSF CAREER Award Senior Member, AAAI and IEEE DoE and NSF grants totaling $5M+ DARPA/ARL awards Advising & Grants: Mentored over 10 PhD students, including Robik Shrestha and Usman Mahmood. Secured grants for AI in nuclear fusion and medical imaging. Led RIT’s Center for Human-aware AI (CHAI) as Associate Director. Labs & Teams: Heads the University of Rochester AI Initiative, collaborates with Paige.AI, and leads teams advancing AI in pathology and robotics. His lab’s KLab (klab.cis.rit.edu) focuses on vision and learning systems.

Gonzalo Guillén Gosálbezمشاهده پروفایل

استاد

• Process Systems Engineering
• Sustainable Chemistry
• Environmental Impact Assessment
• +۷ مورد دیگر

Prof. Dr. Gonzalo Guillén Gosálbez is a Full Professor at the Department of Chemistry and Applied Biosciences , ETH Zürich. He holds a PhD in Process Systems Engineering (UPC, 2005) and has held academic positions at Imperial College London (Reader), University of Manchester (Senior Lecturer), and Universitat Rovira i Virgili (Assistant/Associate Professor). His research focuses on Sustainable Chemical Processes , integrating life cycle assessment, optimization techniques, and planetary boundary analysis to evaluate and design low-carbon technologies. Current position: Full Professor, ETH Zürich (2019–present) Prior roles: Imperial College London (2016–2019), University of Manchester (2014–2016), URV Spain (2008–2014) Education: PhD (UPC, 2005), MEng/BEng (University of Murcia) His research explores CO2 valorization , green methanol synthesis , circular marine fuels , and planetary boundary compliance in energy and chemical systems. Recent work emphasizes machine learning for process modeling, single-atom catalysis , and decentralized ammonia production . Scientific contributions include 15+ peer-reviewed articles (2023–2025) in journals like Nature Chemical Engineering , ACS Sustainable Chemistry & Engineering , and Energy & Environmental Science . Key themes: Optimization of hybrid fossil/renewable carbon systems Environmental impacts of energy transitions Catalyst design for sustainable chemistry Life cycle assessment of emerging technologies Awarded UPC Top Doctoral Student Award and Top National Student Award , he combines process systems engineering with sustainability metrics to address global challenges in chemical and energy systems.

• Natural Language Processing
• Computational Linguistics
• Dependency Parsing
• +۳ مورد دیگر

Prof. Dr. Gülşen Eryiğit is a Professor at Istanbul Technical University within the Faculty of Computer and Informatics , Department of Artificial Intelligence and Data Engineering . She founded and directs the ITU Natural Language Processing Group , Turkey's leading team in Turkish-language NLP, and serves as Senior Action Editor for ACL RR , Director of ITU TÖMER (Turkish Language Teaching Center), and Co-Chair of the EU UniDive Cost Action WG3. Education: PhD in Computer Engineering from ITU (2007), MSc and BSc from ITU and Marmara University Research: Focuses on Natural Language Processing for Turkish, including dependency parsing , coreference resolution , multiword expressions , and language education technology Her recent work involves multilingual transfer learning , LLM applications for Turkish text simplification, and gamification for morphology education. She has received prestigious awards like the Siemens Excellence Award and TÜBİTAK's Above Threshold Award . Her research has produced 65+ publications and 29+ projects funded by EU, TÜBİTAK, and industry partners. Scientific Awards: Siemens Excellence Award (2007) Above Threshold Award (TÜBİTAK, 2015) Certificate of Appreciation (EU 7th Framework, 2012) Thank You Plaque (ITU, 2017) The ITU NLP Group under her leadership has developed Turkey's first licensed NLP software exported internationally. She collaborates with European institutions through COST actions and participates in ACL, CoNLL, and LREC conferences. Her lab focuses on language technology for Turkish , including sign language processing and social media normalization.

• Health Informatics
• Medical Data Privacy
• Electronic Health Records
• +۴ مورد دیگر

Johan Gustav Bellika is a Professor at the Department of Clinical Medicine, UiT The Arctic University of Norway, based in Tromsø. His work bridges clinical medicine with health informatics, focusing on practical applications that improve healthcare delivery and patient outcomes. Current Position: Professor, Department of Clinical Medicine Institution: UiT The Arctic University of Norway Location: Tromsø, Norway Contact: johan.gustav.bellika@uit.no Professor Bellika's research spans several critical areas in modern healthcare. His primary focus is on health informatics with particular emphasis on medical data privacy, electronic health records, and the application of artificial intelligence in clinical settings. He has made significant contributions to understanding chronic pain management through technology, primary care research networks, and patient-centered care models. His work often involves large population studies such as the Tromsø Study, examining how digital health tools impact healthcare utilization and patient outcomes. His research demonstrates a consistent thread connecting technological innovation with practical clinical applications. Bellika has been instrumental in developing privacy-preserving architectures for healthcare data analysis, which enable researchers to gain insights from sensitive health information without compromising patient privacy. His work on federated learning frameworks represents cutting-edge approaches to analyzing medical data while maintaining strict privacy controls. Health Informatics and Medical Data Privacy Chronic Pain Management through Technology Primary Care Research Networks (PraksisNett) Electronic Health Records and Clinical Decision Support Patient-Centered Digital Health Solutions Federated Learning Applications in Healthcare Professor Bellika's publication record shows a strong trend toward interdisciplinary research that combines clinical medicine, computer science, and public health. His recent work increasingly focuses on the intersection of artificial intelligence and healthcare, particularly how machine learning can be applied to chronic conditions while maintaining rigorous privacy standards. The geographical scope of his research extends across Norway with particular emphasis on Northern Norwegian populations, providing valuable insights into healthcare delivery in Arctic and remote regions. His collaborative approach is evident through numerous co-authored publications with researchers across multiple institutions and disciplines. While specific awards aren't detailed in the available information, his extensive publication record in reputable journals suggests recognition within his field. Professor Bellika has been actively involved in several major research initiatives including the Tromsø Study and PraksisNett, Norway's nationwide practice-based research network. His work on privacy-preserving architectures for healthcare data has significant implications for how medical research can be conducted while respecting patient confidentiality. He appears to be particularly focused on translating research findings into practical tools for clinicians, as evidenced by his work on audit and feedback systems for antibiotic prescribing.

Alanson Sampleمشاهده پروفایل

دانشیار

• Human Computer Interaction
• Cyber-Physical Systems
• Wireless Technology
• +۴ مورد دیگر

Alanson Sample is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan (since 2018), leading the Interactive Sensing and Computing Lab. His research focuses on Human-Computer Interaction, Cyber-Physical Systems, and wireless technology innovations. He holds a PhD in Electrical Engineering from the University of Washington (2011), with prior postdoctoral work there developing implantable medical devices. Before academia, Sample held senior roles at Disney Research (2013-2018) as Executive Lab Director and Principal Research Scientist, leading teams in Robotics, AI, Computer Vision, and HCI. Earlier, he worked at Intel Labs (2008-2013) on energy harvesting for wearables and IoT. Key research themes include wireless power systems, embedded sensing, and privacy-aware technologies. Over 150+ publications span topics like magnetic sensing (MagDesk), acoustic gesture recognition (HandSAW), and privacy-preserving activity tracking (PrivacyMic). His work bridges academic and industry innovation, particularly in scalable interactive systems and medical technology. Notable projects include the Quasistatic Cavity Resonance wireless charging system, RFID-based sensing platforms (WISP), and wearable health monitoring devices. His lab develops hardware-software co-design frameworks for energy-efficient embedded systems. Current research emphasizes medical applications, smart infrastructure, and ubiquitous computing interfaces.

Tarek Sayedمشاهده پروفایل

استاد

• Transportation Engineering
• Traffic Safety Analysis
• Intelligent Transportation Systems (ITS)
• +۴ مورد دیگر

Dr. Tarek Sayed is a Professor at the University of British Columbia's Department of Civil Engineering within the Faculty of Applied Science. He specializes in Transportation Engineering, focusing on road safety analysis, traffic operations, and Intelligent Transportation Systems (ITS). He serves as the Director of the Bureau of Intelligent Transportation Systems and Freight Security (BITSAFS-Engineering) and Editor of the Canadian Journal of Civil Engineering . His research addresses three core areas: improving road safety evaluation techniques, enhancing safety through traffic operations and highway design analysis, and advancing ITS technologies. Notable contributions include frameworks for safety-audits of infrastructure projects like the Sea to Sky Highway and methodologies for evaluating transit signal priority systems in Vancouver. He has authored/co-authored over 250 publications and supervised 60 graduate students. Key Roles: Professor, BITSAFS Director, Journal Editor Awards: Tier 1 Canada Research Chair, Wilbur Smith Award, Sandford Fleming Award, Prince Michael International Road Safety Award Consulting: Global projects in traffic safety and ITS for agencies like ICBC, FHWA, and Ashghal His research integrates Bayesian statistical methods, extreme value theory, and machine learning to model traffic conflicts, pedestrian behavior, and safety interventions. Current projects include real-time safety optimization using autonomous vehicle data and analyzing shared space interactions between cyclists and pedestrians. Dr. Sayed chairs national/international committees, including the U.S. Transportation Research Board’s safety data committee. His work bridges academia and practice, influencing policy and infrastructure investment decisions worldwide.

Adriana Schulzمشاهده پروفایل

استادیار

• Computational Design
• Computer-Aided Design (CAD)
• Digital Fabrication
• +۴ مورد دیگر

Adriana Schulz is an Assistant Professor in the Department of Computer Science & Engineering at the University of Washington's College of Engineering. She leads a research group focused on computational design, computer-aided design (CAD), and digital fabrication. Her work bridges computer science with practical applications in manufacturing, robotics, and sustainable design. Dr. Schulz received her Ph.D. in Computer Science from MIT in 2018 under the supervision of Professor Wojciech Matusik. Prior to her doctoral studies, she earned a Master's degree in Mathematics from IMPA (Instituto Nacional de Matemática Pura e Aplicada) in Rio de Janeiro, where she worked with Professor Luiz Velho, and a Bachelor's degree in Electronics Engineering from UFRJ (Federal University of Rio de Janeiro). Her research interests center around computational tools that enhance design and manufacturing processes. She develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches. Her work spans multiple domains including robotics, textiles, electronics, and architecture, with a strong emphasis on creating practical tools that designers and engineers can use in real-world applications. She explores how machine learning, particularly neurosymbolic approaches, can improve design workflows and enable new capabilities in computational design systems. Analysis of her recent publications reveals a strong trend toward more intelligent and user-centered design tools. Her research increasingly integrates machine learning with traditional CAD systems to create more intuitive interfaces, supports sustainable design practices with computational tools, and develops novel fabrication techniques that push the boundaries of what's possible with digital manufacturing. She has made significant contributions to zero-waste fashion design, immersion cooling for high-performance computing, and CAD program understanding through novel representation learning techniques. Innovators Under 35 - MIT Technology Review Bolsa Aluno Nota 10 from FAPERJ Engineer 20000 award Dr. Schulz actively mentors several PhD students and postdoctoral researchers, including Haisen Zhao, Ben Jones, Yuxuan Mei, and others, often in collaboration with colleagues across different departments. Her research has attracted significant media attention, with coverage in major outlets including MIT News, BBC, IEEE Spectrum, Wired, and TechCrunch. Her work on Interactive Robogami was noted as the most read article in the International Journal of Robotics Research in its publication year. She leads a vibrant research group at the University of Washington that focuses on computational design systems, with particular emphasis on creating tools that bridge the gap between digital design and physical fabrication. Her team develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches that have practical applications across multiple industries.

Michael Baldeaمشاهده پروفایل

دانشیار

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +۳ مورد دیگر

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

Amr Youssefمشاهده پروفایل

استاد

• Applied Cryptography
• Network Security
• Cyber-Physical Systems Security
• +۴ مورد دیگر

Amr Youssef is a Professor at the Concordia Institute for Information Systems Engineering, Concordia University. His research focuses on applied cryptography, network security, cyber-physical systems security, blockchain, and privacy. He has contributed extensively to securing smart grids, IoT devices, and web applications through cryptographic protocols and machine learning-driven solutions. His work addresses critical challenges in cybersecurity, including e-voting systems, fault-tolerant differential protection, and privacy-preserving communication protocols. He explores vulnerabilities in modern systems such as SSO permissions, JavaScript exploits, and stalkerware tools, while developing frameworks like MEGR-APT for APT detection and TEE-Receipt for non-repudiation. Youssef’s research integrates interdisciplinary approaches, combining cryptography with AI (e.g., vision transformers for power quality analysis) and leveraging trusted execution environments (TEE) for secure computing. His projects often involve collaboration with industrial standards (e.g., IEC 61850), emphasizing practical implementations in real-world systems.

• Computer Engineering
• Circuit Design
• Fault Tolerance
• +۶ مورد دیگر

Matteo Sonza Reorda is a Full Professor at the Polytechnic University of Turin, affiliated with the Department of Automatic Control and Computer Science (DAUIN). He holds roles such as Partnership Agreement Coordinator with SMAT and has served as Vice-Rector for Research (2021–2024). His research focuses on fault tolerance, hardware reliability, and AI acceleration, with contributions to GPU/CNN reliability, self-test libraries, and defect-oriented testing. Education: M.S. in Electronics Engineering, Politecnico di Torino (1986) Ph.D. in Computer Engineering, Politecnico di Torino (1990) Research Interests: His work spans automatic test equipment, circuit reliability, GPU fault tolerance, neural network hardening, and embedded systems safety . He leads the Electronic CAD & Reliability Group and collaborates with institutions like the ISI Foundation and the National Research Council (CNR). Publications & Impact: With over 180 publications, his recent work emphasizes AI accelerator reliability, fault injection techniques, and safety-critical systems. Key trends include GPU resilience for CNNs, self-test library optimization, and radiation fault modeling in neural networks. Awards & Recognition: IEEE Fellow (2016–) Multiple best-paper awards at IEEE conferences (DDECS, DATE, etc.) Grants & Collaborations: Coordinator of EU projects (RESCUE, TUTORIAL) Partnerships with EDF, Marelli Europe, and SMAT Lead on National HPC/Quantum Computing Spoke 1 (2022–2025) Labs/Teams: Leads the CAD Group and collaborates with the French-Italian LIA LAFISI lab on hardware-software integration.

Tengyu Maمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +۳ مورد دیگر

Tengyu Ma is an Assistant Professor of Computer Science at Stanford University. His research focuses on machine learning, deep learning, optimization, and theoretical computer science. He is particularly known for work on neural networks, reinforcement learning, and algorithmic guarantees in AI systems. His email is tengyuma@stanford.edu . Ma's research interests span foundational aspects of machine learning, including generalization theory, optimization algorithms, and the theoretical underpinnings of deep learning. He has contributed to areas such as self-play theorem provers, learning rate schedules, and robustness in low-light vision tasks. His work often bridges theoretical insights with practical algorithm design. His recent publications emphasize advancements in large language models (LLMs), theorem proving via self-play, and understanding training dynamics in deep networks. Despite prolific output, no specific scientific awards are explicitly mentioned in the provided texts. Ongoing work includes exploring in-context learning mechanisms, formal verification of AI systems, and efficient pretraining techniques. His research has implications for both theoretical understanding and real-world applications of AI.

• Digital Archaeology
• Computational Archaeology
• Machine Learning in Archaeology
• +۱۳ مورد دیگر

Karsten Lambers is Professor of Digital and Computational Archaeology at the Faculty of Archaeology, Leiden University, where he leads research and teaching in the application of computational methods to archaeological data. His work integrates machine learning, remote sensing, text mining, and citizen science to advance archaeological prospection and heritage management. He is affiliated with the Department of Archaeological Sciences and plays key roles in research groups and university-wide initiatives such as SAILS and ARCHON. His research interests span Digital Archaeology , Machine Learning in Archaeology , Remote Sensing , Geoarchaeology , and Human-Environment Interaction . He investigates how computational tools can extract meaningful archaeological information from large datasets, including LiDAR imagery and excavation reports. His fieldwork spans Central Europe and Latin America, with a focus on prehistoric landscapes and cultural heritage. The analysis of his recent publications reveals a strong trend toward automated detection using deep learning (e.g., R-CNN, WODAN), named entity recognition in archaeological texts (e.g., ArcheoBERTje), and citizen science integration for data validation. His work bridges archaeology with computer science, geomatics, and environmental science, emphasizing interdisciplinary collaboration and methodological rigor. His scientific awards include: Best Thesis Award (University of Zurich, 2005) EUROPA NOSTRA Award (2020, 2022) Membership in the German Archaeological Institute (since 2022) Lambers actively supervises students and leads major research projects such as ABMA, EXALT, and Heritage Quest. He has secured substantial research funding and collaborates widely with computer scientists, geophysicists, and palaeoecologists. His teaching includes digital methods, modeling, and simulation, often linked to ongoing research. He has also contributed to open educational resources and digital textbooks in archaeology. He leads or participates in several research labs and teams, including the Digital Archaeology Research Group (which he chairs), the Heritage Quest citizen science project, and interdisciplinary teams focusing on alpine terraces and Iraqi prospection. His work emphasizes the integration of digital tools into practical archaeological workflows, advocating for complementary human-computer strategies.

Noah Smithمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Computational Linguistics
• +۳ مورد دیگر

Noah Smith is the Amazon Professor of Machine Learning at the University of Washington's Paul G. Allen School of Computer Science & Engineering. He holds adjunct appointments in Linguistics and serves as Senior Director of NLP Research at the Allen Institute for Artificial Intelligence. Previously an Associate Professor at Carnegie Mellon University, Smith earned his Ph.D. from Johns Hopkins University and dual BS/BA degrees from the University of Maryland. His research focuses on developing algorithms for language/music data processing, AI methodology improvement, and human capability augmentation. He co-directs the OLMo open language modeling initiative and leads the NSF/NVIDIA-funded 'Open Multimodal AI Infrastructure to Accelerate Science' project. Research spans machine learning foundations, NLP applications, and computational social science methodologies. Smith's publications demonstrate consistent focus on transformer architectures, language model optimization, and AI ethics. Recent work addresses length extrapolation techniques, self-supervised alignment, domain adaptation, toxicity evaluation, and linguistic transfer learning. ACL Fellow (2020) Amazon Alexa Prize (2017) UW Innovation Award (2016-2018) NSF CAREER Award (2011-2016) 15+ best paper awards at major conferences He has graduated 32 PhD students and mentored 18 postdocs, with 28 alumni in faculty positions globally. Leads the Noah's ARK research group and UW NLP lab, focusing on open-source AI infrastructure development.

Grant Hoمشاهده پروفایل

استادیار

• Security and Privacy
• Machine Learning for Security
• Data Science
• +۳ مورد دیگر

Grant Ho is an Assistant Professor in the Computer Science Department at the University of Chicago. His research focuses on computer security, particularly at the intersection of data and security. Prior roles include a CSE Postdoctoral Fellowship at UC San Diego, a Visiting Researcher position at Corelight Labs, and a PhD in Computer Science from UC Berkeley (advised by Vern Paxson and David Wagner). He holds a B.S. in Computer Science from Stanford University. **Research Interests**: Enterprise security, AI/ML applications in security, large-scale threat analysis, and data-driven cybersecurity practices. His work spans detecting attacks (e.g., phishing, ransomware), improving security measures, and evaluating the efficacy of security policies. **Awards**: 2023 IEEE Euro S&P Best Paper Award, 2019 and 2017 USENIX Security Distinguished Papers, 2017 Internet Defense Prize, NSF and Facebook Fellowships, and the 2015 IEEE S&P Distinguished Practical Paper Award. **Advising & Teaching**: Current advisees include Aniket Anand (PhD), Christiana Marchese (PhD), and Robert Liu (B.S.). Taught courses include Introduction to Computer Security (CMSC 23200) and seminars on AI/ML and NLP in cybersecurity. Collaborates with industry partners like Barracuda Networks and Dropbox. **Labs & Teams**: Leads a research group focused on enterprise security challenges, emphasizing practical impact and interdisciplinary approaches.