جست‌وجوی استاد و پژوهشگر

Konstantinos Pelechrinisمشاهده پروفایل

دانشیار

• Network Science
• Urban Informatics
• Sports Analytics
• +۳ مورد دیگر

Konstantinos Pelechrinis is an Associate Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. He holds a Ph.D. in Computer Science from the University of California, Riverside. His research focuses on network science, urban informatics, and sports analytics. He has been recognized with the Army Research Office Young Investigator Award for his contributions. Education: Ph.D. in Computer Science, University of California, Riverside Research Interests: Urban mobility patterns and infrastructure analysis Sports performance quantification and strategy Data-driven decision-making in transportation systems Network science applications in social and urban systems His recent work explores topics such as implicit biases in sports refereeing, anomaly detection in NFT markets, and optimizing bike-sharing systems using predictive models. He also investigates urban infrastructure resilience through projects like the Epui platform for experimental urban informatics. Awards: Army Research Office Young Investigator Award He contributes to academic outreach through courses like TELCOM2125 (Network Science and Analysis) and collaborates on initiatives like the Healthy Ride Pittsburgh bike-sharing study. His lab focuses on bridging theoretical models with real-world urban and sports datasets.

Jonathan Hauensteinمشاهده پروفایل

استاد

• Numerical Algebraic Geometry
• Scientific Computing
• Numerical Solution of Differential Equations
• +۵ مورد دیگر

Jonathan Hauenstein is the Robert and Sara Lumpkins Collegiate Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, serving as Department Chair. He holds a Ph.D. from Notre Dame (2009) and M.S. from Miami University (2005). His research focuses on numerical algebraic geometry and computational methods for solving nonlinear equations, implemented in the Bertini software package. Applications span engineering, ecology, sports science, and machine learning. Education: Ph.D., Applied and Computational Mathematics, University of Notre Dame (2009) M.S., Mathematics, Miami University (2005) Research Interests: Development of numerical algorithms for polynomial systems, real algebraic geometry, and scientific computing. Key areas include homotopy continuation methods, parameter space decomposition, and applications in mechanism design, ecological modeling, and sports biomechanics. His work bridges theoretical mathematics with practical computational tools. Awards: Sloan Research Fellowship DARPA Young Faculty Award Army Research Office Young Investigator Award Office of Naval Research Young Investigator Award College of Science Research Award Advising & Grants: Advised numerous undergraduates, graduate students, and postdoctoral researchers. Active in securing grants for computational mathematics projects, including NSF-funded initiatives. His work emphasizes interdisciplinary collaboration between mathematics and engineering. Labs/Teams: Leads computational algebraic geometry research groups at Notre Dame, focusing on software development (e.g., Bertini) and numerical methods innovation.

Lyle H. Ungarمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Affiliations & Roles Professor of Computer and Information Science at University of Pennsylvania Faculty in Graduate Groups: Bioengineering (School of Engineering) Genomics & Computational Biology (School of Medicine) Operations, Information & Decisions (Wharton School) Psychology (School of Arts & Sciences) Research Affiliations: Annenberg Public Policy Center (Distinguished Fellow) Center for Cognitive Neuroscience Institute for Translational Medicine Research Interests Focuses on explainable AI, natural language processing (NLP), and machine learning applications in psychology and medicine. Key areas include: Language analysis for well-being and mental health Spectral methods for NLP (e.g., Eigenwords) Forecasting and decision-making models Bioinformatics and genomics Teaching Teaches advanced courses in Machine Learning, Deep Learning, and AI ethics, including: CIS 5200: Machine Learning CIS 5220: Deep Learning CIS 6200: Advanced Topics in Deep Learning Key Collaborations Works with interdisciplinary teams on projects like the Good Judgment Project (forecasting) and WWBP (Well-Being and Language). Collaborators include Martin Seligman (positive psychology), Dean Foster (statistics), and Michael Collins (NLP).

Mohd Fikree Hassanمشاهده پروفایل

مدرس

• Image and Signal Processing
• Image Processing and Computer Vision
• Image Enhancement and Restoration
• +۳ مورد دیگر

Mohd Fikree Hassan is a Lecturer at the School of Information Technology, Monash University Malaysia, joining in June 2023. He holds a Ph.D. and Master's from the University of Malaya, and a B.Eng. in Electronics Engineering from Multimedia University. With over 14 years of academic experience, he is actively engaged in research, teaching, and supervision. B.Eng. in Electronics Engineering (Telecommunications), Multimedia University, 2004 M.Eng. in Engineering (Telecommunications), University of Malaya, 2015 Ph.D. in Signal and Systems, University of Malaya, 2018 His research focuses on image and signal processing , particularly in image enhancement, restoration, computer vision, and human color vision . His work contributes to improving image visibility, removing color casts, and developing algorithms for noisy or degraded images. He applies mathematical and computational techniques to solve real-world imaging challenges. The recent publication trends (2021–2025) show a strong focus on image restoration using variational methods (e.g., total variation, ℓ0 regularization), color enhancement in HSI space, and video analysis for sports applications. His work bridges theoretical optimization and practical computer vision systems. He actively contributes to the academic community through peer review for journals such as Neurocomputing , Journal of Imaging , and International Journal of Computational Intelligence Systems , as well as for IEEE conferences. Mohd Fikree is currently accepting PhD students and serves as an external examiner for academic programs. His consistent research output and editorial service reflect a growing impact in the field of image processing and computer vision. While no formal lab or team is mentioned in the text, his collaborations with researchers like R. Paramesran, T. Adam, and G. Krishnasamy suggest active research partnerships in signal and image processing.

• Battery simulation
• Cyber physical system
• Design simulation
• +۱۲ مورد دیگر

Sara Vinco is an Associate Professor at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Italy. She specializes in battery simulation, digital twins, and energy-efficient design automation for heterogeneous embedded systems, aligning with Industrial and Information Engineering (Area 0009) and ERC sectors including Computer Architecture and Machine Learning . Her research focuses on advancing cyber-physical systems through simulation frameworks like SystemC-AMS, enabling holistic modeling of analog, digital, and thermal domains. Key projects include data-driven digital twins for EV batteries and low-area digital circuits in industrial/medical applications, supported by commercial contracts such as C-based virtual prototyping. Her recent publications (2022-2023) emphasize machine learning for battery SOH/SOC estimation , energy monitoring in production lines , and multi-domain fault modeling . These works span journals like IEEE Transactions and conferences including DATE and ISLPED. Awarded the FFABR 2017 grant and IEEE FDL Best Paper Award 2011 , she also chairs editorial boards for IEEE Transactions on CAD and DATE Conference. She supervises PhD students Giovanni Pollo (Digital Circuits) and Khaled Alamin (EV Battery Twins), reflecting her leadership in smart systems design.

Mehrdad Nojoumianمشاهده پروفایل

دانشیار

• Security
• Privacy
• Trust
• +۱۰ مورد دیگر

Mehrdad Nojoumian is an Associate Professor at Florida Atlantic University's College of Engineering and Computer Science, specializing in Security, Privacy, Trust, and Human-Autonomy Interaction. His research bridges computer science with FinTech and autonomous systems, focusing on cryptographic protocols, blockchain, and IoT security. PhD, University of Waterloo (Computer Science, 2012) MSc, University of Ottawa (Computer Science, 2007) BSc, Islamic Azad University (Computer Engineering, 2002) His research explores privacy-preserving mechanisms in autonomous systems, trust modeling in blockchain, and cross-disciplinary applications of cryptography. Recent work includes patents on adaptive driving modes and ongoing studies on IoT vulnerabilities and autonomous coordination. Notable awards include the Excellence and Innovation in Undergraduate Teaching Award (2023), NAI Induction (2022), and multiple Best Paper Awards . His publications span journals like Information and Computation and conferences such as GameSec and IEEE Blockchain . He mentors graduate students in cybersecurity bootcamps, high school outreach programs, and collaborative research projects funded by NSF , ARO-AFOSR , and AFRL . His lab emphasizes diversity and inclusion, particularly supporting underrepresented groups in STEM.

Prabir Burmanمشاهده پروفایل

استاد

• Nonparametric function estimation
• Model fitting/selection
• Image analysis
• +۷ مورد دیگر

Prabir Burman is a Professor in the Department of Statistics at the University of California, Davis, with a career spanning over three decades. His research focuses on nonparametric function estimation, model fitting/selection, image analysis, time series, and discrete data. Education: Ph.D. (1982) and Master of Statistics (1977) from University of California, Berkeley; Bachelor of Statistics (1976) from Indian Statistical Institute, Calcutta. His work bridges theoretical statistics and applied problems, including ecological studies (e.g., coyote parasites, mountain lion tracking), biomedical research (e.g., metabolic syndrome in bipolar patients), and time series forecasting. He has secured multiple NSF and NSA grants for projects on multivariate analysis, shape modeling, and covariance estimation. Recent publications highlight his expertise in predictive model fitting, stock return analysis, and stroke survivor studies. While not explicitly listing awards, his editorial roles (e.g., Journal of Multivariate Analysis) and collaborative grants underscore his academic leadership.

Alexander J Sundermannمشاهده پروفایل

استادیار

• Epidemiology
• Infection Prevention
• Pathogen Genomic Surveillance
• +۳ مورد دیگر

Alexander J Sundermann serves as an Assistant Professor in the Department of Epidemiology at the University of Pittsburgh School of of Public Health. His research focuses on leveraging pathogen genomic surveillance and machine learning to revolutionize infection prevention practices in healthcare settings, with demonstrated impacts on outbreak detection accuracy and intervention speed. Education: 2013: BS in Microbiology, University of Rochester 2014: MPH in Infectious Diseases and Microbiology, University of Pittsburgh 2022: DrPH in Epidemiology, University of Pittsburgh Dr. Sundermann's work centers on whole-genome sequencing of pathogens to detect healthcare-associated transmission invisible to traditional methods. His research demonstrates how genomic surveillance reveals hidden outbreaks of vancomycin-resistant Enterococcus (VRE) and mucormycosis, directly linking colonization to clinical outcomes like ICU admission and mortality. He pioneers machine learning tools that analyze electronic health records to identify transmission routes, creating more efficient outbreak investigation frameworks across hospital networks. His publication portfolio (2019-2025) shows a clear trajectory toward integrated genomic-clinical surveillance systems, with recent work quantifying clinical and economic impacts while addressing implementation barriers. This interdisciplinary approach bridges epidemiology, genomics, and data science to transform infection prevention protocols. Scientific Awards: No awards listed in available information. Advising and Grants: While specific advisees and grant details aren't provided, his multi-institutional collaborations suggest active mentorship within genomic epidemiology research teams. His work with the National Healthcare Safety Network indicates engagement with major public health surveillance infrastructure. Labs and Teams: Dr. Sundermann leads cross-functional teams including microbiologists, data scientists, and clinicians across multiple healthcare systems. His UPMC outbreak investigations and national linen contamination studies demonstrate operational frameworks for real-time genomic surveillance implementation in complex hospital environments.

• Journalism and Communication
• Intelligent Communication
• Digital Media Art
• +۶ مورد دیگر

Wang Jianmin serves as Professor and Doctoral Supervisor at Tongji University's School of Art and Media, concurrently holding the position of Vice Dean since 2014. With a computer science PhD from Sun Yat-sen University, he bridges engineering and media arts through pioneering research in intelligent communication systems and digital media interfaces. His work focuses on human-centered design for emerging technologies, particularly in automotive and virtual environments. His academic foundation includes: PhD in Engineering (Computer Software and Theory), Sun Yat-sen University (2003) Master's in Computational Mathematics, Sun Yat-sen University (1999) Bachelor's in Computational Mathematics, Nankai University (1996) Professor Wang's research centers on intelligent communication systems and digital media art, with significant contributions to automotive human-machine interfaces (HMI), virtual reality applications, and user experience methodologies. His investigations into driver-robot transparency, augmented reality navigation, and mixed-reality educational platforms demonstrate interdisciplinary innovation connecting computer science, cognitive psychology, and design theory. Current projects explore AI-driven media systems for urban environments and safety-critical interaction frameworks. Analysis of his recent publications reveals a cohesive research trajectory focusing on automotive HMI (40% of output), human-robot interaction (30%), and mixed reality applications (30%). His work consistently emphasizes experimental validation through driving simulators and user studies, yielding practical design guidelines for industry implementation. The interdisciplinary nature spans computer science, cognitive ergonomics, and media studies, with increasing emphasis on AI integration in communication systems. His scientific recognition includes national and provincial awards for innovation in human-computer interaction and educational technology: 2019 China Industry-University-Research Innovation Award for automotive HMI systems 2020 China User Experience Alliance Excellence Award 2012 Guangdong Dingying Science and Technology Award Multiple national/provincial science progress awards (2001-2009) 2020 Tongji University Teaching Achievement Award for curriculum development As an educator, Professor Wang mentors graduate students in national design competitions including the 'Core Cup' Future Automotive HMI Challenge and International User Experience Innovation Competition. His research program is supported by substantial funding from diverse sources: National Grants: National Natural Science Foundation projects on driver behavior modeling and cognitive testing Ministry of Education: 15+产学合作 projects for virtual simulation labs and curriculum development Shanghai Municipal: Publicity Department funding for smart city media research Industry Partnerships: Huawei (intelligent vehicle HMI), SAIC Motor (AR-HUD design), and automotive electronics firms He directs Tongji's Media Experiment and Practice Teaching Center and the All-Media Research Institute, leading teams developing virtual simulation platforms for emergency news reporting, intelligent vehicle interaction testing systems, and mixed reality educational tools. Current initiatives focus on AI-enhanced media art for urban applications and next-generation HMI frameworks for autonomous mobility solutions.

Laurie Williamsمشاهده پروفایل

استاد

• Software Security
• Agile Software Development
• Software Supply Chain Security
• +۴ مورد دیگر

Laurie Williams serves as a Goodnight Distinguished University Professor in the Computer Science Department within the College of Engineering at North Carolina State University. She co-directs both the NCSU Secure Computing Institute and the NC State Science of Security Lablet, demonstrating deep institutional leadership in cybersecurity research. With over 260 refereed publications, her work establishes her as a prominent figure in software security academia. Her research spans critical areas including software security, agile development practices (particularly continuous deployment), software reliability, and software supply chain security. Williams focuses on practical security solutions addressing modern challenges like malicious dependencies in open-source ecosystems, AI-generated code vulnerabilities, and runtime protection mechanisms. Her work bridges theoretical security principles with industry-relevant applications. Recent publications reveal strong trends toward software supply chain security, with multiple 2024-2025 papers addressing vulnerability exploitability, malicious commit detection, and metrics-driven security control selection. Her research increasingly incorporates machine learning for threat detection while maintaining focus on human factors in secure development practices. IEEE Fellow (2018) National Science Foundation CAREER Award (2004) ACM SIGSOFT Influential Educator Award (2009) Multiple IBM Faculty Awards (2002-2012) NCSU Alumni Association Outstanding Research Award (2015-2016) Williams leads multiple major NSF-funded projects including the $5.7M SaTC Frontiers grant on secure software supply chains and the Science of Security Lablet with $3.6M in DoD funding. Her research emphasizes practical industry impact through collaborations with Cisco and Laboratory for Analytic Sciences. She actively mentors through the NCSU Research Leadership Academy and maintains significant educational outreach in software security. Her laboratory work centers on the Secure Computing Institute and Science of Security Lablet, where her team develops frameworks for vulnerability prediction, supply chain risk assessment, and secure development methodologies. Current projects focus on machine learning integrity, cognitive modeling for security decisions, and empirical analysis of build/deployment logs for anomaly detection.

• Statistical signal processing
• Robust statistical methods
• Array processing
• +۶ مورد دیگر

Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.

Mark S. Handcockمشاهده پروفایل

استاد

• Statistics
• Social Network Analysis
• Survey Sampling
• +۳ مورد دیگر

Mark S. Handcock is a Distinguished Professor in the Department of Statistics and Data Science at the University of California, Los Angeles (UCLA), where he leads research at the intersection of statistical methodology and applied problems in social sciences, epidemiology, and environmental science. His work bridges theoretical statistics with real-world challenges through innovative methodological development. His primary research interests encompass statistical models for social networks, network inference, methodology for hard-to-reach population surveys, spatial processes, demography, and environmetrics. Handcock has pioneered advances in exponential-family random graph models (ERGMs) and developed foundational R packages like ergm and tergm within the statnet suite, enabling sophisticated network analysis across disciplines. Analysis of his recent publications (2023-2025) reveals three dominant research thrusts: (1) Antarctic sea ice modeling using Bayesian reconstruction and temporal variability analysis, (2) epidemiological modeling of infectious disease transmission dynamics (particularly COVID-19), and (3) methodological innovations in network inference and causal analysis over stochastic networks. His work consistently integrates advanced computational statistics with domain-specific applications in climate science, public health, and social systems.

Joshua J. Coonمشاهده پروفایل

استاد

• Bioanalytical Chemistry
• Mass Spectrometry
• Proteomics
• +۴ مورد دیگر

Joshua J. Coon is a Professor at the University of Wisconsin-Madison with appointments in the Department of Biomolecular Chemistry and the Department of Chemistry. He leads the Coon Group, focusing on advancing mass spectrometry technologies for proteomics, metabolomics, and lipidomics. His research addresses fundamental questions in cell biology, including stem cell differentiation, epigenetic regulation, and cancer biomarker discovery. Affiliations : Director of the NIGMS National Center for Quantitative Biology of Complex Systems. Research Emphasis : Instrumentation development, data analysis software, ion chemistry, and biological applications of proteomics. Laboratory : Located in the Genome Center of Wisconsin with a dozen hybrid mass spectrometers, including Orbitrap systems. Collaborations : Long-term partnership with Thermo Fisher Scientific and the Wisconsin Alumni Research Foundation (WARF) for technology commercialization. Training : Mentored 27 Ph.D. students since 2009, emphasizing interdisciplinary research and professional development.

• Branching processes
• Random trees
• Reinforcement processes
• +۹ مورد دیگر

Cécile Mailler is a Reader in Probability at the University of Bath, where she is a member of the probability group Prob-L@B. She has held significant research positions including an EPSRC postdoctoral fellowship (2018-2021) titled "Random trees: analysis and applications" and previously worked as a postdoc at Prob-L@B (2013-2016) as part of Peter Mörters' EPSRC project "Emergence of Condensation in Stochastic Networks". She earned her PhD under the supervision of Brigitte Chauvin and Danièle Gardy at the Laboratoire de Mathématiques de Versailles. Her research focuses on probability theory with emphasis on branching processes, random trees, reinforcement mechanisms, Pólya urns, stochastic approximation, random networks, and statistical physics. She has made significant contributions to understanding preferential attachment models, zero-range processes, and random Boolean trees. Her work bridges theoretical probability with applications in statistical physics and combinatorics. Analysis of her recent publications shows a strong focus on random tree structures, branching processes, and reinforcement learning algorithms, with applications spanning from network theory to statistical mechanics. Her research demonstrates sophisticated mathematical techniques applied to complex stochastic systems, particularly those with reinforcement mechanisms and memory effects. Associate Editor of the Applied Probability Trust (since October 2020) Associate Editor of Stochastic Processes and Their Applications (since March 2022) Author of a general introduction to Pólya urns for the LMS Newsletter (November 2020) Co-organizer of the "Random Walks: Applications and Interactions" conference at CIRM (January 2026) She actively supervises PhD students working on topics including the multi-city ants process, Pólya urns with growing initial composition, large deviations for the Monkey walk, and competing growth processes. She has secured research funding through EPSRC fellowships and has been involved in multiple collaborative projects with prominent researchers in probability theory. Mailler regularly teaches mini-courses on advanced probability topics at international summer schools and workshops, demonstrating her commitment to knowledge dissemination in the field.

Dan Dongseong Kimمشاهده پروفایل

دانشیار

• Cybersecurity
• Cybersecurity Modeling and Analysis
• Moving Target Defense
• +۷ مورد دیگر

Associate Professor Dan Dongseong Kim is Deputy Director of UQ Cybersecurity and an Associate Professor at The University of Queensland (UQ), Australia. Previously, he held permanent academic positions at The University of Canterbury (UC), New Zealand (2011-2018) as a Senior Lecturer and Lecturer. His research focuses on Cybersecurity and Dependability for AI, IoT, Autonomous Vehicles, Cloud Computing, and Moving Target Defenses (MTD). Doctor of Philosophy in Computer Engineering from Korea Aerospace University Postdoctoral Research at Duke University (2008-2011) Visiting Scholar at University of Maryland (2007) Dan's work explores Graphical Security Models , Moving Target Defense for proactive resilience, and AI-Driven Cybersecurity with emphasis on adversarial robustness and interpretable models. His recent publications (2024-2025) span journals like IEEE Transactions on Dependable and Secure Computing and conferences such as DSN , addressing automated defense, evolving attacks, and hardware-aware security frameworks. He has advised 15 Ph.D. graduates, including researchers now at institutions like RMIT University, La Trobe University, and CSIRO's Data61. Current supervision includes projects on Automated Penetration Testing , AI-Based Intrusion Response , and Moving Target Defense . Dan's research is funded by agencies including the Republic of Korea's Agency for Defence Development and US Army Research Lab . His professional roles include Associate Editor for IEEE Communications Surveys and Tutorials and Steering Committee Chair for IEEE PRDC .