جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Coding Theory
• Optimization
• +۵ مورد دیگر

Arya Mazumdar is a tenured Professor of Data Science and Computer Science at the Halıcıoğlu Data Science Institute (HDSI) , part of the School of Computing, Information and Data Sciences at the University of California San Diego (UCSD). He also holds affiliations with the Computer Science and Engineering and Electrical and Computer Engineering departments at UCSD. Previously, he was an Assistant and Associate Professor at the University of Massachusetts Amherst (2015–2021), and held a postdoctoral position at MIT (2011–2012). He is an IEEE Distinguished Lecturer (2023–2024) and recipient of the NSF CAREER Award (2015–2020). Education : PhD in 2011 from the University of Maryland, College Park (advisor: Alexander Barg) Postdoctoral scholar at MIT (2011–2012, advisor: Greg Wornell) Internships at IBM Almaden (2010) and HP Labs (2008) Research Interests : Focus on algorithmic and statistical aspects of machine learning, error-correcting codes, optimization, signal processing, and distributed systems. Key areas include clustering algorithms, compressed sensing, federated learning, and information-theoretic foundations of data science. He has contributed to theoretical guarantees for learning mixtures, distributed optimization, and robust coding schemes. Recent Articles Trends : Recent work emphasizes distributed optimization (e.g., vqSGD, Byzantine-resilient algorithms), sparse recovery in high-dimensional models, and theoretical foundations of learning (e.g., support recovery, parameter estimation). His research bridges information theory and machine learning, with applications in storage systems and large-scale data processing. Awards & Roles : 2020 EURASIP JASP Best Paper Award Co-PI and co-leader of the NSF AI Institute for Learning-Enabled Optimization at Scale Editorships: IEEE Transactions on Information Theory and Foundations and Trends in Communications Grants & Labs : Leads the EnCORE Institute as UCSD Site Lead, focusing on theoretical perspectives of large language models and computational-statistical gaps. Active in organizing workshops on topics like LLMs, clustering, and distributed optimization. His research is funded by NSF and industry collaborations. Teaching : Courses include Algorithms for Data Science , Probability and Statistics for Data Science , and Coding Theory , emphasizing foundational theory and scalable methods.

Camellia Zakariaمشاهده پروفایل

استادیار

• Mobile Health
• Applied Machine Learning
• Human-Computer Interaction
• +۳ مورد دیگر

Camellia Zakaria is an Assistant Professor at the University of Toronto, holding appointments in the Biostatistics Division and Institute of Health Policy, Management, and Evaluation (IHPME) at the Dalla Lana School of Public Health (DLSPH), and the Faculty of Information (FoI). She was recently named Canada Research Chair (Tier 2) for mobile health solutions. Her research focuses on integrating mobile technologies and machine learning to develop equitable health monitoring systems. Education: University of Toronto (Present) University of Massachusetts Amherst (2020–2023) Singapore Management University (2014–2019) Research interests span mobile health, applied machine learning, and human-computer interaction, with a focus on creating inclusive health applications. Her work addresses challenges in environmental sensing, sleep monitoring, and mental health tracking through innovative sensor technologies and data analytics. Key articles (2024–2016) explore medical decision-making, aerosol emissions, and group behavior modeling. Her work has been recognized in venues like BMJ , IMWUT , and CHI . Awards : Canada Research Chair (Tier 2, 2024) Projects like StressMon and WATCHME highlight her commitment to scalable health monitoring systems. She collaborates on lab initiatives such as sensAILabs, advancing interdisciplinary health technology solutions.

Ikjot Sainiمشاهده پروفایل

استاد

• Cybersecurity
• Automotive Systems
• Vehicular Networks
• +۳ مورد دیگر

Ikjot Saini is a Professor at the University of Windsor’s Faculty of Engineering, co-leading the SHIELD Automotive Cybersecurity Centre of Excellence, Canada’s first organization addressing threats in connected transportation. Her research focuses on automotive cybersecurity, vehicular networks, and privacy-preserving technologies. She has supervised doctoral students Shiva Nejati and Kunj Dhonde, and contributed to courses in the University’s Continuing Education program, specializing in cybersecurity education for professionals. Her work includes pioneering studies on blockchain-based security for connected autonomous vehicles (CAVs), machine learning-driven intrusion detection systems, and privacy-enhancing mechanisms like pseudonym-changing strategies. She has been recognized with the K.W. Michael Siu Award from the APMA Institute for Automotive Cybersecurity (2020). Saini’s research bridges theoretical advancements with real-world applications, ensuring vehicles and infrastructure remain secure against evolving cyber threats. Her contributions span academic publications, industry partnerships, and policy recommendations, positioning her as a leader in vehicular cybersecurity. Ongoing projects emphasize eco-efficiency in cybersecurity solutions and adversarial modeling for privacy evaluation.

Wei David Daiمشاهده پروفایل

استادیار

• Robust Deep Learning
• Data Quality
• Distributed Computing Systems
• +۴ مورد دیگر

Dr. Wei David Dai is an Assistant Professor of Computer Science at Purdue University Northwest and Director of the Advanced Intelligence Software (AIS) Lab. His research focuses on robust deep learning, data quality, and public safety technologies like gunshot detection systems. He previously worked at IBM China as a senior engineer and served in Arkansas state government as a data scientist. Education: Ph.D. in Computer and Information Sciences (University of Arkansas at Little Rock, USA, 2020) M.S. in Information Science (University of Arkansas at Little Rock, USA, 2016) M.S. in Software Engineering (South China University of Technology, China, 2013) B.S. in Computer Science (Central South University, China, 2007) Research Interests: His work spans robust deep learning models, distributed computing systems, and privacy-preserving technologies. Notable projects include public safety innovations such as acoustic gunshot detection and AI-driven campus security systems. Articles Trends: Recent publications emphasize public safety applications (e.g., mass school shooting simulations) and deep learning robustness evaluation (e.g., the Accuracy-Stability Index metric). Earlier works address cloud computing optimization and data quality frameworks. Awards: Recipient of the 2024 Excellence in Research Award and multiple IBM honors for technical excellence and instruction. Grants & Advising: Leads the Indiana Space Grant Consortium-funded satellite imaging project and Purdue Provost Grant for gunshot detection. Advises doctoral and master’s students on AI ethics, distributed systems, and public safety. Labs: The AIS Lab develops AI tools for public safety, equipped with GPU resources for audio and image analysis.

Adam Doupéمشاهده پروفایل

دانشیار

• Cybersecurity
• Cybercrime
• Binary Analysis
• +۷ مورد دیگر

Adam Doupé is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence (SCAI) and Director of the Center for Cybersecurity and Trusted Foundations (CTF). He holds a Ph.D. and M.S. in Computer Science from the University of California, Santa Barbara. His research focuses on cybersecurity, vulnerability analysis, web security, and hacking competitions. Notable awards include the NSF CAREER Award (2017), Best Teacher Award, and Outstanding Assistant Professor Award from ASU's Fulton Schools of Engineering. Education: Ph.D. and M.S. in Computer Science, UC Santa Barbara (2014, 2009). Research emphasizes automated vulnerability analysis, binary analysis, and cybersecurity education. Key contributions include frameworks like SCAMNet and SENSAI for fraud detection, and tools like Fuzz to the Future for uncovering future vulnerabilities. Recent articles highlight advancements in phishing ecosystem analysis, browser fingerprinting mitigation, and compiler-aware decompilation. Awards reflect his impact in both teaching and research. Advising and grants support his work in secure systems and ethical hacking. He co-leads the SEFCOM lab with Drs. Ahn, Shoshitaishvili, Wang, and Bao, and hosts CTF Radiooo for cybersecurity discussions.

• Online Consumer Behavior
• Consumer Trust
• E-commerce
• +۷ مورد دیگر

Plavini Punyatoya is an Assistant Professor at the MT School of Management, New Jersey Institute of Technology (NJIT). Her research focuses on online consumer behavior, trust in digital retail environments, brand community management, and supply chain technologies. She has published extensively in marketing and business journals, with a strong citation record in areas related to consumer trust and e-commerce. Her research interests center on understanding how cognitive and affective trust influence online customer behavior, particularly in the Indian market. She investigates factors affecting loyalty towards e-tailers, including privacy and security policies, retailer reputation, and perceived risk. Additionally, her work explores the impact of online brand communities and supply chain technology internalization on firm performance. These themes reflect a strong interdisciplinary approach combining marketing, information systems, and operations management. The trend in her recent publications shows a consistent focus on digital consumer behavior and trust mechanisms, with increasing emphasis on integrated models and technological adoption in supply chains. Her work spans both theoretical frameworks like signaling theory and empirical studies in emerging markets. While no formal scientific awards are listed in the provided text, her research has garnered significant attention, as evidenced by high PlumX captures and Scopus citations, particularly for her 2019 article on cognitive and affective trust. She has collaborated with researchers such as A. Saini, D. Pattanayak, A. Satpathy, and A. Agrawal. Although specific details about student advisement or grant funding are not mentioned, her publication record suggests active research engagement. She is involved in academic communities such as the Academy of Marketing Science, as indicated by her conference proceedings.

Cliff Zouمشاهده پروفایل

استاد

• Computer Science
• Cybersecurity
• Network Security
• +۳ مورد دیگر

Cliff Zou is a Professor in the Department of Computer Science at the University of Central Florida (UCF), where he coordinates master’s programs in cybersecurity, privacy, and digital forensics. He directs the UCF Alliance for Cybersecurity and contributes to the university’s Cyber Security and Privacy Cluster. Education: Ph.D. in Electrical and Computer Engineering from University of Massachusetts-Amherst His research focuses on computer and network security , network modeling , and performance evaluation . Key areas include malware analysis, botnet defense, and adaptive cybersecurity protocols. His publications address threats like email worms, peer-to-peer botnets, and internet worms, reflecting his work in proactive threat mitigation and network resilience. Scientific recognition includes: Senior Member of IEEE Best Student Paper Award (ACSAC 2007) UCF Teaching Incentive Program (TIP) award (2013) Best Paper runner-up (PADS 2005, ICCCN 2004) First-place award for undergraduate project 'Personal Medication Monitor' His research has been featured in New Scientist Magazine , EurekAlert! , PCWorld , and The Register . Zou actively contributes to academic conferences as an organizer and program committee member.

• Trustworthy Machine Learning
• Algorithmic Fairness
• Data Privacy
• +۷ مورد دیگر

Hui Wang is a Professor and Associate Chair for PhD Studies and Research in the Department of Computer Science at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology. She also serves as the Director of the Data Science PhD Program and holds leadership roles in multiple institutional committees, including the Doctoral Committee, Faculty Mentoring Program, and Strategic Planning initiatives at both departmental and university levels. Research Interests: Dr. Wang's research focuses on building trustworthy machine learning systems by integrating privacy, fairness, and accountability . Her work aims to fortify ML models against privacy attacks, eliminate algorithmic biases, and ensure auditable decision-making. She explores intersections between machine learning, data mining, and cybersecurity, with applications across domains requiring ethical and secure AI deployment. Recent Research Trends: Her recent publications and funded projects reflect a strong emphasis on privacy-preserving machine learning , fairness-aware systems , and verifiable computing . Themes include securing graph embeddings, federated learning with fairness guarantees, and audit mechanisms for black-box models. Supported by NSF, Cisco, and Google, her work bridges theoretical rigor with practical system design. Scientific Awards: NSF CAREER Award, 2014 Advising and Grants: Dr. Wang actively mentors PhD students and hosts visiting scholars. She leads multiple NSF-funded projects, including Securing Network Embedding against Privacy Attacks and Privacy for All: Ensuring Fair Privacy Protection in Machine Learning . Her research is supported by substantial grants from the National Science Foundation, Cisco, and Google, reflecting her leadership in trustworthy AI. Labs and Teams: While not explicitly named, Dr. Wang leads a research group focused on trustworthy machine learning, advising students and collaborating with industry partners. She is deeply integrated into the Data Science PhD program and CS faculty leadership, shaping research and academic strategy at Stevens.

Rajeev Balasubramonianمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• DRAM/NVM Technologies
• +۷ مورد دیگر

Rajeev Balasubramonian is a Professor and Associate Director at the School of Computing, University of Utah. He specializes in computer architecture, with a focus on memory systems, emerging technologies, and energy-efficient computing. His research addresses challenges in DRAM/NVM architectures, security, and acceleration for big data and machine learning workloads. Education: PhD in Computer Science (University of Rochester, 2003), M.S. (University of Rochester, 2000), B.Tech in Computer Science (IIT Bombay, 1998). Research Interests: Memory reliability, near-data processing, cache hierarchies, transactional memory, and hardware-software co-design for emerging technologies. He has led projects on crossbar accelerators, secure memory systems, and resistive memory architectures. Recent Trends in Publications: Focus on encrypted inference (Hyena), data prefetching (PATHFINDER), and neuromorphic computing (SpinalFlow). His work bridges hardware and software, emphasizing practical acceleration and security solutions. Awards: IEEE Fellow (2021), Google Faculty Awards (2019/2020), Intel Research Award (2017), and multiple best paper awards (ISCA, ISPASS, PACT). Grants & Students: Over $4M in NSF/industry funding. Advised 15+ PhD students (e.g., Ali Shafiee, Karl Taht) and currently mentors researchers in resistive memory and security accelerators. His lab includes teams like Utah Arch Research Group. Labs & Teams: Leads the Utah Arch Research Group , organizing workshops on near-data processing and memory systems (e.g., ISCA, HPCA).

Anomadarshi Baruaمشاهده پروفایل

استادیار

• System Design and Security
• Healthcare Assistive Systems
• Industrial Control Systems (ICS)
• +۵ مورد دیگر

Anomadarshi Barua is an Assistant Professor in the Department of Cyber Security Engineering at George Mason University, leading the System Design and Security research group. His work spans hardware-software co-design for securing cyber-physical systems (CPS), robotics, and sensors. Prior Affiliation: PhD from University of California, Irvine (2023) Industry Experience: Intel Corporation, Solidigm, Nordic Semiconductor, IDEAS Research Themes: Focuses on multimodal system security (audio, visual, electromagnetic data), analog-digital signal integrity, and quantum-inspired defenses in CPS. Key applications include healthcare systems, smart grids, and industrial control systems (ICS). Recent ACSAC 2024 paper acceptance Best Paper Award at ACSAC 2022 NSF panel reviewer (2024) Labs & Collaborations: Collaborates with University of Louisville on robotics and works on Commonwealth-funded UG research (2024). Publications in ACM CCS, USENIX, CHES, and IEEE Transactions (TDSC, TIFS).

• Civil Law
• Intellectual Property Law
• Information Law
• +۳ مورد دیگر

Mustafa Aksu is a Professor at Istanbul University's Faculty of Law, Department of Civil Law. His research spans Civil Law , Intellectual Property Law , Information Law , and EU Legal Harmonization . He has advised 26 theses and published extensively on digital rights, family law, and obligations law. Research Interests: Focus on AI and law, copyright exhaustion in digital markets, EU-Turkey legal alignment, domain name regulation, and protection of computer programs. Publications: Recent works include AI's legal frameworks (2023), medical informatics law (2021), and digital exhaustion doctrine (2016). His research emphasizes cross-border IP enforcement and EU court precedents. Contact: Email aksum@istanbul.edu.tr | Office: Hukuk Fakültesi Binası, İkinci Kat, Oda No: 38, Beyazıt, Istanbul, Turkey.

• Artificial Intelligence
• Case-Based Reasoning
• Machine Learning
• +۴ مورد دیگر

Prof. Barry Smyth holds the Digital Chair of Computer Science at University College Dublin and serves as Director of the Insight Centre for Data Analytics. A Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) since 2003 and Member of the Royal Irish Academy since 2011, he previously directed the Clarity Centre for Sensor Web Technologies (2008-2013) and led UCD's School of Computer Science and Informatics as Head of School. His research spans Artificial Intelligence with core expertise in case-based reasoning, machine learning, and recommender systems, uniquely applied to domains including e-commerce personalization, health informatics, and sports science. Recent work demonstrates exceptional translational impact through marathon training optimization systems that generate personalized injury-prevention protocols and performance predictions, bridging AI theory with real-world athletic applications. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary AI applications: 60% focus on sports science (particularly marathon running), 25% on privacy-enhanced recommender systems, and 15% on financial time-series analysis. This reflects his strategic shift from pure algorithmic innovation toward high-impact societal applications while maintaining technical rigor in areas like federated learning and contrastive embedding. Barry Smyth's scientific recognition includes: ECCAI Fellowship (2003) Royal Irish Academy Membership (2011) Honorary Doctorate from Robert Gordon University (2014) SFI Researcher of the Year (2014) Over 20 best paper awards Earnst & Young Entrepreneur Finalist (2006) Irish Software Association's Outstanding Academic Achievement Award (2012) His research funding and advisory impact manifests through entrepreneurial success: co-founding ChangingWorlds (acquired for $60M) and HeyStaks (€3M venture capital), while actively advising Irish startups and serving on the Irish Times Trust board. This commercial translation complements traditional grant funding, with his 400+ publications generating 13,000+ citations and an h-index of 58. Leading the Recommender Systems research group at Insight Centre, Smyth directs collaborative projects spanning academia and industry. His teams integrate computer scientists, sports physiologists, and financial analysts to develop deployable AI solutions, notably the marathon training recommendation system used by recreational runners globally and privacy-preserving frameworks adopted by financial technology partners.

Wouter Lueksمشاهده پروفایل

استاد

• Applied Cryptography
• Anonymous Communication
• Privacy-Preserving Systems
• +۳ مورد دیگر

Wouter Lueks is a faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. His research focuses on designing secure, practical, and privacy-friendly systems using applied cryptography, anonymous communication, and systems engineering. He leads a lab and actively mentors PhD and PostDoc researchers. Education: PhD in Computer Science (Radboud University, Netherlands), advised by Jaap-Henk Hoepman and Bart Jacobs. Postdoctoral Researcher at EPFL, Switzerland (SPRING Lab). Research Interests: Privacy-preserving systems for humanitarian aid and public health. Cryptography in decentralized networks and anonymous communication. Secure protocols for identity management and voting systems. Key Publications: Recent work includes privacy-preserving digital wallets for aid distribution (IEEE S&P 2025), biometric deduplication systems (IEEE S&P 2024), and decentralized proximity tracing protocols (Commun. ACM 2022). His research emphasizes practical applications of zero-knowledge proofs and attribute-based credentials. Awards: Distinguished Paper Award at IEEE S&P 2023. Runner-up Casper Bowden PET Award 2021 and CNIL-Inria Privacy Award 2021. Advising & Grants: Supervises PhD/PostDoc researchers and collaborates on projects funded by EU and industry grants. Active in open-source contributions, including the zksk library and Lightnion anonymous communication tool. Labs/Teams: Principal investigator at CISPA, collaborates with the SPRING Lab (EPFL) and leads interdisciplinary teams in privacy-enhancing technologies.

• Machine Learning
• Fairness in Artificial Intelligence
• Federated Learning
• +۷ مورد دیگر

Yang Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at the Baskin School of Engineering, University of California, Santa Cruz. Previously, they were affiliated with Harvard University and earned their PhD in 2015 from the Department of EECS at the University of Michigan, Ann Arbor. Their research lies at the intersection of machine learning, fairness, and trustworthy AI, with a strong focus on large language models, federated learning, and causal reasoning. Their research interests include: Machine Learning and Fairness Federated and Privacy-Preserving Learning Large Language Model Safety and Unlearning Causal Inference and Counterfactual Reasoning Anomaly Detection and Robust Forecasting Human-AI Interaction and Ethical AI Recent publications (2024–2025) demonstrate a strong trend in developing methods for machine unlearning, fairness in LLMs, and robustness under label noise and distribution shifts. Their work frequently appears in top-tier venues such as NeurIPS, ICLR, ICML, AAAI, and KDD, often in collaboration with researchers like Zhaowei Zhu, Mingyan Liu, Jiaheng Wei, and Kun Zhang. Themes include algorithmic fairness, model accountability, and human-aligned AI systems. Scientific contributions include: Frameworks for LLM unlearning and model editing Methods for fair classification and recourse Robust time series forecasting under anomalies Test-time adaptation in multimodal models Causal approaches to debiasing and policy learning While no formal advising list is provided, the depth and volume of collaborative work suggest active mentorship of graduate students and postdocs. Their research program is highly active, with numerous ongoing projects in trustworthy and socially responsible AI.

Alfred Taudesمشاهده پروفایل

استاد

• Operations and Supply Chain Management
• Cryptoeconomics
• Blockchain Technology
• +۷ مورد دیگر

Alfred Taudes is a Full Professor at the Department of Information Systems and Operations, Institute for Production Management, Vienna University of Economics and Business (WU Vienna). He holds a doctoral degree and a Habilitation from WU Vienna in Business Administration and Management Information Systems, and a Magister degree from Vienna University. He has held assistant professorships at WU and visiting professorships at Augsburg, Münster, Essen, and Tsukuba University, Japan. He joined WU permanently in 1993 and served as head of the Department of Information Systems and Operations from 2010 to 2016. His research spans Operations and Supply Chain Management , Marketing Engineering , Knowledge Management , and the impact of Big Data and Blockchain on production systems. Using Complexity Science and Cryptoeconomics , he investigates digital production, integrated value chains, and market designs. He teaches undergraduate and graduate courses including Operations Strategy, Data Science, and IT seminars in WU’s International Supply Chain Master program, and has also taught at Japanese universities. His recent publications focus on blockchain privacy (e.g., CoinJoin analysis), CBDCs, MiCAR regulation, decentralized federated learning, and digital custody, reflecting a strong trend toward cryptoeconomics and blockchain-based systems in operations and finance. These works appear in top journals and conferences in information systems, security, and operations research. Cooperation Officer of the Year 2013/14 Distinguished Paper Award WI 2009 VHB Best Paper Award Nomination VHB Best Paper Award Dr. Wolfgang Houska - Recognition Award Alfred Taudes has coordinated major research projects such as the WWTF-project “Integrated Demand and Supply Chain Management” and the Special Research Area Adaptive Models in Economics and Management Science. He currently leads the research group on Cryptoeconomics at WU and chairs the scientific board of the Austrian Internet Offensive . His leadership extends to project management in initiatives like the Austrian Blockchain Center and research on decentralized finance and digital assets. He is actively involved in academic service, including organizing conferences like DEXA 2022, serving on editorial boards, and advising on research policy. His lab and research group focus on blockchain applications, digital transformation in operations, and the societal implications of big data.