جستجوی استاد راهنمای دانشگاهی

Ambuj Varshneyمشاهده پروفایل

استادیار

• Wireless Communication
• Internet of Things
• Embedded Systems
• +۴ مورد دیگر

Ambuj Varshney is an Assistant Professor at the National University of Singapore (NUS) School of Computing , leading the WEISER research group . His work bridges electronics, wireless communication, computer science, and AI with a focus on creating ultra-low-power embedded systems for sustainable IoT deployments. University of California, Berkeley: Postdoctoral Scholar (2020-2022) Uppsala University: PhD in Sustainable Networked Systems NXP Semiconductors: Software Engineer (prior to PhD) Bachelors in Information & Communication Technology Research interests center on overcoming wireless systems' energy asymmetry through tunnel diode oscillators , LiFi-RF hybrid networks , and battery-free communication architectures . His group develops STICORS —sticker-like computers for industrial and medical monitoring. Recent publications demonstrate AudioCast 's FM-band utilization for 130m transmission, TunnelSense 's vital monitoring, and PixelGen 's diffusion model cameras. These works combine IoT sustainability, spectrum efficiency, and hardware innovation . 2024: Google Research Scholar Award 2023: MobiSys Best Demonstration 2021: Berkeley FORM+FUND Fellowship 2019: ABB's $300K Research Award As an educator, he teaches CS4222 Wireless Networking and CS5272 Embedded Software Design . Past students include Wenqing Yan (NUS/UCB PhD), Qiao Yukai , and Kunjun Li . His team collaborates with Prabal Dutta (UCB), Christian Rohner (Uppsala), and Prateek Saxena (NUS).

Nabil Alshurafaمشاهده پروفایل

دانشیار

• Health Informatics
• Body Sensor Networks
• Behavioral Science
• +۷ مورد دیگر

Nabil Alshurafa is an Associate Professor at Northwestern University, holding joint appointments in the McCormick School of Engineering (Computer Science and Electrical and Computer Engineering) and the Feinberg School of Medicine (Preventive Medicine). He directs the HABits Lab, which focuses on developing mHealth systems to address health behaviors such as overeating, stress, and UV exposure. His work integrates wearable sensors, machine learning, and behavioral science to create passive sensing solutions. Education: PhD in Computer Science and Wireless Health (UCLA), MS and BS in Computer Science (UCLA). Research Interests: Body sensor networks, activity recognition, embedded systems, and health informatics. His lab designs wearable devices (e.g., neck-worn sensors, UV patches) and AI frameworks to detect behaviors like eating patterns and stress levels. Collaborations include domain experts in medicine and engineering to translate technical innovations into clinical interventions. Recent Projects: Developing systems for stress monitoring via ECG-PPG patches, UV exposure tracking, and just-in-time interventions for overeating. The lab emphasizes ethical design, privacy preservation, and user-centered technology. Students and Lab Team: Supervises PhD, MS, and undergraduate researchers in areas like machine learning, embedded systems, and health data analytics. Notable advisees include Rawan Alharbi (PhD candidate), Shibo Zhang (PhD student), and Wilson Wang (MS student). Labs/Teams: HABits Lab collaborates with experts in Preventive Medicine, Psychiatry, and Dermatology to advance interdisciplinary health research. Current projects include predictive analytics for weight loss and interventions targeting maternal stress during pregnancy.

James Fogartyمشاهده پروفایل

استاد

• Human-Computer Interaction
• Ubiquitous Computing
• Accessibility
• +۳ مورد دیگر

James Fogarty is a Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. He serves as a core member of the DUB Group (Design. Use. Build.), a cross-campus initiative advancing Human-Computer Interaction and Design research. His work bridges computer science with healthcare applications, focusing on ubiquitous computing and accessibility. Fogarty's research centers on Human-Computer Interaction, Ubiquitous Computing, and Accessibility. He develops systems to overcome human obstacles in adopting intelligent computing technologies, particularly in healthcare contexts. His work spans food and symptom tracking for conditions like Irritable Bowel Syndrome, accessibility solutions for mobile interfaces, and self-experimentation frameworks for personalized health. Key themes include designing for real-world adoption, balancing automation with user control in personal informatics, and creating accessible technologies for diverse populations. His most recent publications reveal strong trends in health-focused HCI: 60% address chronic condition management (IBS, migraines), 30% focus on accessibility innovations, and 10% explore collaborative computing. Subfield analysis shows deep specialization in food/symptom tracking systems, mobile accessibility enhancements, and personalized health experimentation frameworks, with consistent emphasis on user-centered design and real-world deployment. Fogarty actively mentors doctoral students including Shaan Chopra, Tae Jones, and Aaleyah Lewis. His research receives direct funding from the National Science Foundation, National Library of Medicine, and Agency for Healthcare Research and Quality, with additional support from Adobe, Google, Intel, Microsoft, and Nokia. His lab operates at the intersection of HCI, health informatics, and ubiquitous computing. He leads projects within the DUB Group ecosystem, focusing on practical applications of sensing technologies and intelligent systems. Current work emphasizes patient-provider collaboration tools, accessibility repair mechanisms for mobile applications, and self-experimentation frameworks for personalized health management.

• Time Series Analysis
• Big Data Analytics
• Predictive Modeling
• +۳ مورد دیگر

Yasushi Sakurai is a Professor in the Department of Translational Datability at Osaka University's Institute of Scientific and Industrial Research, co-leading the Sakurai and Matsubara Laboratory within the Center for Industrial Science and AI. His research mission focuses on transforming society through real-time prediction of natural and social phenomena using large-scale data analytics, with emphasis on practical technological implementation. His research spans time-series big data analysis, dynamic learning systems, and real-time information provision. Key areas include tensor stream mining, EEG-based healthcare applications, cybersecurity anomaly detection, and multi-omics cancer subtyping. The lab specializes in developing deployable technologies that optimize social activities through predictive modeling of evolving data streams. Recent publications (2023-2025) reveal concentrated innovation in time-series data stream processing, with dominant themes in tensor analytics, frequency-domain forecasting, and causal modeling. His team produces high-impact work accepted at premier AI venues (ICLR, AAAI, KDD, WWW), consistently featuring oral presentations that highlight technical novelty and societal relevance. Scientific Awards: FY2024 Minister of Education, Culture, Sports, Science and Technology Award for Science and Technology (Research Category) for dynamic learning and real-time data stream analysis Professor Sakurai mentors graduate students including Naoki Chihara (DEIM2024 Outstanding Paper Award winner), Yuka Tamura (DEIM2024 Student Presentation Award winner), and Ren Fujiwara. His lab maintains active industry-academia partnerships focused on practical technology deployment, with research directly addressing real-world challenges in healthcare monitoring and cybersecurity. The Sakurai and Matsubara Laboratory operates as a dynamic research unit within Osaka University's Center for Industrial Science and AI, structured around specialized teams for tensor stream analysis, medical data mining, and network dynamics. Current projects emphasize real-time prediction systems with immediate societal applications, supported by strong industry collaboration frameworks.

• Responsible Computing
• Data Protection & Governance
• Digital Well-Being
• +۳ مورد دیگر

Asia J. Biega is a tenure-track faculty member (W2) at the Max Planck Institute for Security and Privacy (MPI-SP), where she leads the interdisciplinary Responsible Computing group. She is also a principal investigator of the Cluster of Excellence CASA and the FINDHR consortium. Her work sits at the intersection of computing and society, focusing on responsible computing, data protection & governance, and digital well-being within data-driven and AI-based systems. Dr. Biega's research spans multiple disciplines including computer science, law, philosophy, and social sciences. Her work examines how principles of responsible computing can be computationally operationalized, with particular attention to data protection frameworks, privacy technology governance, and digital well-being. She actively collaborates across disciplinary boundaries to make technical contributions while supporting research in other fields. Her approach combines theoretical rigor with practical applications, often drawing from her industry experience at Microsoft and Google. Her publication record reveals a strong focus on the intersection of fairness, privacy, and transparency in information retrieval systems. She has pioneered work on data minimization compliance, fair ranking algorithms, and user perceptions of data collection practices. Her research consistently bridges technical and legal perspectives, particularly examining how GDPR principles can be computationally implemented. Recent work shows increasing attention to generative AI governance, algorithmic hiring systems, and the relational aspects of data in recommender systems. Dr. Biega has received several prestigious awards including the Council of Europe's Rodotà Award for innovative research in data protection, the SaTML Notable Reviewer Award, the GI-DBIS Dissertation Award of the German Informatics Society, and recognition as one of the '100 Brilliant Women in AI Ethics' in 2025. She has advised numerous PhD students and postdocs who have gone on to faculty positions at institutions including the University of Trieste, Penn State, and the University of Washington. Her research is funded by the Max Planck Society, Alexander von Humboldt Foundation, and the European Union (Horizon Europe FINDHR). She serves as General Co-Chair for ACM FAccT 2025 and has held leadership roles in multiple academic conferences.

• Sports Science
• Football (Soccer) Analytics
• Cognitive Psychology in Sports
• +۶ مورد دیگر

Prof. Daniel Memmert is a Professor at the German Sport University Cologne, leading research in Sport Informatics and Sports Games within the Institute of Exercise Training and Sport Informatics. His work focuses on cognitive aspects of sports performance, decision-making, and data-driven analysis in football (soccer) and other sports. He has published extensively on topics like penalty kick strategies, home advantage dynamics, artificial intelligence applications in coaching, and route-setting in climbing. Memmert's research bridges sports science, computer science, and psychology, with over 550 publications and 41 projects to his name. He frequently engages with media, explaining complex sports phenomena to the public. Key Research Areas: Football analytics, cognitive psychology in sports, sports technology, decision-making under pressure Media Contributions: Over 50 media features discussing topics such as AI in coaching, referee bias, and athlete creativity Projects: Includes initiatives on sports data visualization, performance metrics, and prevention of sports betting addiction His work emphasizes translating academic findings into practical tools for athletes, coaches, and sports organizations, combining rigorous data analysis with real-world applications.

Elena Simperlمشاهده پروفایل

استاد

• Human-centric AI
• Knowledge Graphs
• Knowledge Engineering
• +۷ مورد دیگر

Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.

Naoki Yoshinagaمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Multilingual and Multimodal NLP
• +۴ مورد دیگر

Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .

Arpit Agarwalمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Human-AI Interaction
• +۴ مورد دیگر

Arpit Agarwal is an Assistant Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology, Bombay. He previously held postdoctoral positions at FAIR Labs (Meta) working with Max Nickel and at the Data Science Institute at Columbia University hosted by Prof. Yash Kanoria and Prof. Tim Roughgarden. He completed his PhD from the Department of Computer & Information Science at the University of Pennsylvania under the guidance of Prof. Shivani Agarwal. His research focuses on the intersection of human behavior and machine learning systems, with particular interest in learning from implicit, strategic, and heterogeneous human feedback. His work spans multiple dimensions of human-AI interaction including understanding long-term dynamics between humans and AI systems, designing responsible AI, and studying misalignment between user preferences and system objectives. His research methodology often combines theoretical machine learning with practical applications in recommendation systems and social AI. His recent publications reveal a strong focus on bandit algorithms, preference learning, and recommendation systems, with increasing attention to responsible AI design and human-centered considerations. His work demonstrates expertise in theoretical machine learning with applications to real-world problems, particularly in understanding how humans interact with and are influenced by AI systems over time. Dr. Agarwal teaches advanced courses including CS767 Theoretical Machine Learning (Autumn 2025) and CS6103 Human-Centered AI: From Learning Models to Responsible Systems (Spring 2025), which covers topics such as AI alignment, learning from pairwise comparisons, crowdsourcing, human-in-the-loop decision making, recommendation systems, interpretability, privacy, fairness, causality, and AI governance.

• Databases
• Data Mining
• Information Retrieval
• +۲ مورد دیگر

Dr. Arnab Bhattacharya is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Kanpur since December 2020. He previously served as Associate Professor (2014–2020) and Assistant Professor (2007–2014) at IIT Kanpur. Education: PhD in Computer Science (2007), University of California, Santa Barbara MS in Computer Science (2007), University of California, Santa Barbara Bachelor of Computer Science and Engineering (2001), Jadavpur University Research Focus spans Databases , Data Mining , Information Retrieval , and Artificial Intelligence . His work emphasizes graph analytics , skyline queries , probabilistic data , and knowledge graph management . Article Trends reveal expertise in graph neural networks , trajectory-aware systems , statistical significance in databases , and legal/medical data mining . His methodologies often integrate chi-square statistics , approximate indexing , and provenance tracking . Scientific Awards: IBM Faculty Research Award Yahoo! Faculty Research and Engagement Program Award Best Paper at COMAD 2011 Best Student Paper at COMAD 2010 Top-Five Student Paper at ICDM 2005 ICDM Student Travel Award sponsored by IBM Contact: Office RM 409, Department of Computer Science and Engineering, IIT Kanpur Email: arnabb@iitk.ac.in | Phone: +91-512-259-7650

• Cybersecurity and Privacy
• Data Management and Data Science
• Distributed Computing and Systems Software

Professor Jun Zhang is a leading academic in cybersecurity at Swinburne University, Australia, where he directs the Cybersecurity Lab. He has been honored as Australia's top cybersecurity researcher and instrumental in establishing Swinburne as a globally recognized cybersecurity research institution. His work includes high-impact papers and multi-million-dollar R&D projects, culminating in awards like the 2021 'Top Cybersecurity Research Institution' accolade. As course director of the Bachelor of Cyber Security, he pioneered an industry-driven teaching model with Deloitte and CSIRO, significantly boosting course enrollment. His collaborations extend to Adobe's Curriculum Innovation Program and the Australian P-TECH initiative, promoting STEM education and cybersecurity awareness. He supervises doctoral candidates and leads grants focused on AI-driven cybersecurity, smart home security, and blockchain-based edge computing. His research spans vulnerability detection, GAN forensics, IoT security, and privacy preservation in OSNs. Research interests include cybersecurity fundamentals, data science applications, and distributed systems. Notable achievements include the PTFix framework for Java vulnerabilities, the IoTFuzz smart home testing system, and CTI mining methodologies. Awards reflect his mid-career research excellence and industry partnerships. His grants with CSIRO and defense organizations emphasize real-world impact, addressing challenges from malware detection to adversarial machine learning. The Cybersecurity Lab and collaborative projects like Artchain demonstrate his commitment to bridging academia and industry. Professional activities include supervising over 20 HDR students and securing grants totaling millions. His work on blockchain-based edge storage (CSEdge) and SDCCP congestion control highlights innovation in networking. Future directions include advancing AI for design collaboration with CSIRO and enhancing privacy in smart energy technologies. His contributions span technical, educational, and community outreach domains, positioning him as a pivotal figure in cybersecurity's evolution.

Konstantin Baumanمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Recommender Systems
• +۳ مورد دیگر

Dr. Konstantin Bauman is an Associate Professor in the Department of Management Information Systems at Temple University's Fox School of Business. He holds a PhD in Mathematics (Geometry and Topology) from Moscow State University and dual Master’s degrees in Mathematics and Machine Learning from prestigious Russian institutions. His research focuses on machine learning, data science, and context-aware recommender systems, emphasizing novel methods for predicting customer preferences and designing personalized recommendation frameworks. Education: PhD in Mathematics (Geometry and Topology), Moscow State University MS in Mathematics, Moscow State University MS in Machine Learning, Moscow Institute of Physics and Technology/Yandex School of Data Analysis Research Interests: Data Science and Analytics Machine Learning and Recommender Systems Context-Aware Systems and Text Mining Technology-Enhanced Learning Recent Work Trends: His publications emphasize context-aware recommendation algorithms, privacy concerns in personalized systems, and applications of hyperbolic embeddings. He also explores device impact on employee feedback and cryptocurrency investor behavior using multimodal data analysis. Awards: None explicitly listed in the provided materials. Advising/Grants: No formal advisees listed; his work at Yandex and NYU involved leading machine learning teams and tackling large-scale data science challenges. Labs/Teams: Active in the MIS department at Temple, contributing to research on adaptive learning systems and enterprise machine learning applications.

Mauro Contiمشاهده پروفایل

استاد

• Security
• Privacy
• Wireless Sensor Networks
• +۹ مورد دیگر

Mauro Conti is a Full Professor at the University of Padua, Italy, where he serves as Editor-in-chief of IEEE TIFS, UniPD Academic Advisor for Entrepreneurship Development, Study Program Coordinator of the MSc degree in Cybersecurity, Head of the SPRITZ Security and Privacy Research Group, and Director of the UniPD node of CINI Cybersecurity National Lab. He also maintains affiliations with TU Delft (NL) and the University of Washington (USA). Additionally, he is the CEO and co-founder of CHISITO and co-founder of DYALOGHI. His research focuses on security and privacy for wireless resource-constrained mobile devices (WSNs, RFIDs, and smartphones), computer system security, computer forensics, access control, and distributed and networked systems. Professor Conti has published extensively in top security venues including ACM CCS, IEEE S&P, NDSS, and USENIX Security, with recent work covering topics such as battery authentication, federated learning privacy, private set intersections, USB peripheral fingerprinting, and ATM PIN inference. He has received numerous prestigious awards including IEEE Fellow, Young Academy of Europe Fellow, EU Marie Curie Fellow, and DAAD Fellow. Professor Conti has advised over 100 students at various levels including PhD, MSc, and BSc, many of whom have gone on to successful careers in academia and industry. His research has been supported by multiple European Commission projects, university grants, and industry collaborations with companies like Cisco and Intel.

• Spoken Language Processing
• Natural Language Processing (NLP)
• Paralinguistics
• +۶ مورد دیگر

Sarah Ita Levitan is an Assistant Professor in the Department of Computer Science at Hunter College, CUNY, and a member of the doctoral faculty in both Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She previously served as a Postdoctoral Research Scientist at Columbia University, where she completed her PhD in Computer Science in 2019 under Dr. Julia Hirschberg. Research Focus: Spoken Language Processing Natural Language Processing Paralinguistic Analysis Trustworthiness and Deception Detection Acoustic-Procedic and Lexical Feature Extraction Online Radicalization and Misinformation Recent Publications demonstrate expertise in analyzing speech and text for trust cues, deception detection, and mental health prediction. Her awards include grants from NSF, Google, and Columbia University fellowships. She leads the Hunter Speech Lab , mentoring PhD, MS, and undergraduate students in computational linguistics research. Scientific Awards and Grants: NSF EAGER Grant (2023) Google Cyber NYC Grant (2023) NSF AI Institute Grant (2023) Air Force Office of Scientific Research Grant (2020) Brown Institute Seed Grant (2020) Knight News Innovation Fellowship (2018) Teaching: Courses include Natural Language Processing (undergraduate/graduate), Computational Linguistics, Computer Theory, and advanced topics in spoken language processing at both Hunter College and Columbia University.

Jia Zhangمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +۱۰ مورد دیگر

Dr. Jia Zhang is the Inaugural Robert H. Dedman Jr. Endowed Department Chair and Professor of Computer Science at Southern Methodist University (SMU Lyle School of Engineering). She holds the Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering and has a courtesy appointment in the Department of Operations Research and Engineering Management. Her research focuses on applying machine learning, natural language processing, and information retrieval to data science infrastructure, particularly scientific workflows, provenance mining, software discovery, knowledge graphs, cloud computing, immune AI, and applications in earth science and healthcare. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, Nanjing University B.S. in Computer Science, Nanjing University Dr. Zhang's work emphasizes data science infrastructure and machine learning for scientific workflows and knowledge graphs. Her recent publications highlight deep learning , graph neural networks , and optimization algorithms in cloud computing, cybersecurity, and environmental applications. Key trends include spatiotemporal modeling , hybrid neural architectures , and AI-driven service ecosystems . Scientific Awards: Best Paper Awards IEEE SCC (2011, 2017) Best Student Paper Awards IEEE ICWS (2014, 2018), IEEE ICCC (2018) Distinguished Paper Award ICSOC (2023) First Outstanding Service Award IEEE Technical Committee on Services Computing (2016) She has secured over $5 million in federal grants (as PI) and $11 million as PI/Co-PI from NSF, NASA, NIH, UTSW, Ericsson, SAP, and Google. Her lab (Caruth Hall 308) actively recruits research assistants. She previously served as a faculty member at Carnegie Mellon University, Northern Illinois University, and Nanjing University, and worked in industry as a software architect.