جستجوی استاد راهنمای دانشگاهی

Shounak Roychowdhuryمشاهده پروفایل

دانشیار

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Shounak Roychowdhury is an Associate Professor of Practice at the School of Information, University of Texas at Austin. He holds a PhD in Computer Engineering from the University of Texas at Austin (2013) and an MS in Computer Science from the University of Tulsa (1997). His teaching focuses on foundational and advanced topics such as Computer Vision, Database Management, Programming, and Data Science. Recent courses include INF 385T: Special Topics in Information Science: Computer Vision (2025) and I 320D: Topics in Human-Centered Data Science: Database Design (2024). Roychowdhury's research spans Computer Science , Machine Learning , and Software Engineering , with a focus on probabilistic methods, fuzzy systems, and medical informatics. His work explores areas like fault localization in software, data clustering optimization, and applying generative models to healthcare analytics. He has also contributed to fuzzy logic applications in decision-making and pattern recognition. His publications highlight interdisciplinary approaches, including Alzheimer’s disease modeling with GANs and color space analysis in art through probabilistic divergences. While no scientific awards are listed, his work reflects sustained contributions to theoretical and applied computing. Roychowdhury advises no listed students but maintains an active teaching role. No specific grants or labs are mentioned, though his research aligns with computational and data-driven methodologies.

Somali Chaterjiمشاهده پروفایل

دانشیار

• Data Science
• Digital Agriculture
• Computational Biology
• +۶ مورد دیگر

Dr. Somali Chaterji is an Associate Professor in the Department of Agricultural & Biological Engineering at Purdue University, with a courtesy appointment in the Elmore Family School of Electrical and Computer Engineering. Her research bridges data science, digital agriculture, and computational genomics, focusing on machine learning for IoT, edge computing, and scalable genomics analysis. She leads the Innovatory for Cells and Neural Machines (ICAN), developing algorithms for efficient data analytics in resource-constrained environments. Education: PhD in Biomedical Engineering (Purdue University), Postdoctoral Fellowship at University of Texas at Austin. Awards include the NSF CAREER Award (2022), ACM BCB Best Paper (2015), and Purdue Seed-for-Success Award (2016). She founded KeyByte LLC, a cloud computing startup optimizing ML workloads. Research spans IoT edge analytics (e.g., drone surveillance, embedded systems) and genomics (single-cell clustering, error correction in sequencing). She is Co-PI of the NSF CHORUS Center and A2I2 Army Institute. Over 20 students are advised, with active projects on serverless computing, federated learning, and genome engineering. Labs/Teams: ICAN Lab, WHIN project (Lilly Endowment), Purdue ABE Extension. Collaborations with Microsoft, Amazon, and Adobe Research. Active in teaching, including courses on applied ML and computational genomics with innovative pedagogy.

Peter Bloodsworthمشاهده پروفایل

مدرس

• Multi-agent Systems
• Cloud Computing
• Distributed Computing
• +۳ مورد دیگر

Dr. Peter Bloodsworth is a Lecturer and Professional Masters Programme Project Supervisor in the Department of Computer Science at the University of Oxford. He holds a PhD in Multi-agent Systems from Oxford Brookes University. His research focuses on multi-agent systems, cloud computing, distributed computing, and artificial intelligence, with applications in medical research and robotics. Dr. Bloodsworth has over a decade of academic experience, including a role as a Foreign Professor at the National University of Sciences and Technology (NUST) in Islamabad, Pakistan (2011–2016), and prior work as a Research Fellow at the University of the West of England (UWE), Bristol. He has contributed to major European projects such as the FP7-funded neuGRID project, where he acted as a workpackage leader and User Manager. His research emphasizes applying semantic technologies and multi-agent systems to solve complex problems, including medical ontology integration and grid computing in healthcare environments. Dr. Bloodsworth is a full member of the IEEE and a Chartered Member of the British Computing Society (BCS), reflecting his commitment to professional standards in computing. His recent research themes include deploying multi-agent systems for scalable cloud solutions, robotic control, and managing cloud resources through agent-based frameworks. He has over 30 publications in international journals and conferences, with notable work on cloud marketplaces, elastic multi-agent systems, and neuroimaging analysis using grid computing.

Joshua Barbourمشاهده پروفایل

استاد

• Communication and Health
• Communication and Organizations
• Automation Policy and Research
• +۳ مورد دیگر

Joshua Barbour is a Professor in the Department of Communication at the University of Illinois Urbana-Champaign. He directs the Automation Policy and Research Organizing Network (APRON) Lab, founded in 2018, which focuses on data-intensive automated work systems and communication strategies. His research explores how institutional structures shape organizational communication and collaborative efforts, emphasizing macromorphic perspectives that bridge micro-level practices and macro-level frameworks. Barbour’s work spans health communication, risk management, and digital innovation, with a focus on communication design logics and institutional moorings. Education details are not explicitly listed, but his career demonstrates advanced academic qualifications in communication studies. His research interests include automation policy, organizational analytics, and conflict of interest frameworks in biomedical research. He has published extensively on topics like emergency communication infrastructure, temporal dominance in workplace cycles, and interdisciplinary collaboration in healthcare settings. Barbour’s APRON Lab collaborates across disciplines to address future-of-work challenges, particularly in healthcare and organizational resilience. His work emphasizes practical solutions for navigating regulatory constraints and fostering reliable communication systems in dynamic institutional environments. Grants and advising details are not explicitly mentioned, but his role as a professor and lab director suggests involvement in academic mentorship and funded research initiatives. The APRON Lab serves as a hub for interdisciplinary projects addressing automation and organizational communication challenges.

Sarah Kimمشاهده پروفایل

استادیار

• Pharmacometrics
• Artificial Intelligence
• Drug Development
• +۲ مورد دیگر

Dr. Sarah Kim is an Assistant Professor in the Department of Pharmaceutics at the University of Florida College of Pharmacy. She serves as the inaugural faculty hire for the UF Health Sciences Artificial Intelligence initiative. Her research focuses on pharmacometrics and AI-driven drug development, specifically targeting disease progression modeling and clinical trial optimization. Education: Ph.D. in Mathematics (Florida State University, 2015), M.S. in Applied Mathematics (Hanyang University, 2010), B.S. in Applied Mathematics (Hanyang University, 2008). Research Focus: Her work develops computational tools for neuromuscular diseases (Duchenne Muscular Dystrophy) and metabolic disorders (Type 1 Diabetes), integrating machine learning with biomedical data analysis to optimize therapeutic interventions. Awards: Excellence Award for Assistant Professors (UF, 2025) Teaching & Service Award (UF College of Pharmacy, 2024) University Scholars Faculty Mentor Award (2024) Presidential Trainee Award (ASCPT, 2018) Grants & Leadership: Principal Investigator on NIH-funded tuberculosis therapy optimization and JDRF-sponsored pancreatic imaging projects. Leads interdisciplinary teams developing AI tools for clinical trial design.

Justin Silvermanمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Genomics
• +۷ مورد دیگر

Dr. Justin Silverman is an Assistant Professor of Information Science & Technology, Statistics, and Medicine at Pennsylvania State University, with affiliations in the Department of Statistics and the Institute for Computational and Data Science. He holds an MD from Duke University School of Medicine (2020), a PhD in Computational Biology and Bioinformatics from Duke (2019), and a BS in Physics and Biophysics from Johns Hopkins University (2011). His research integrates Bayesian statistics, machine learning, and genomics to address impactful biomedical questions. Key focuses include microbiome analysis, gene expression studies, and scale-reliant inference methodologies. His work emphasizes methodological rigor and clinical relevance, with applications in personalized medicine, agriculture, and epidemiology. The Silverman Lab develops cutting-edge tools like the fido and philr packages for compositional data analysis. Collaborations span academia and industry, addressing challenges in livestock health, viral surveillance, and precision medicine. He also leads Anarres Analytics LLC, translating statistical methods into practical solutions for clients. His advising includes PhD and MS students working on topics like scalable Bayesian models, microbial co-occurrence networks, and topological data analysis in oncology. Notable projects include studies on ancient microbiomes, dairy cow treatments, and pandemic modeling. Labs and teams: Silverman Lab (PSU), Anarres Analytics. Current positions: PI, startup founder, and collaborator in multi-institutional projects.

Irina Benedykمشاهده پروفایل

استادیار

• Intermodal Freight Transportation
• Optimal Investment Allocation
• Freight Demand Modeling
• +۶ مورد دیگر

Dr. Irina Benedyk is an Assistant Professor at the Department of Civil, Structural and Environmental Engineering, School of Engineering and Applied Sciences, SUNY Buffalo. She holds a PhD and has over 10 years of combined industry and academic experience in marine intermodal logistics and freight transportation. Previously, she worked at CMA CGM S.A. and a major Russian forwarding company in roles ranging from import/export specialist to head of consulting. Her research focuses on intermodal freight transportation, human factors in transportation engineering, driver cognition, and sustainable transportation. She develops quantitative models to analyze freight demand, investment decisions, and emerging technologies like self-driving cars. She also investigates the impact of augmented reality (e.g., Pokémon GO) on behavior and transportation safety during extreme weather events. Her work integrates large-scale behavioral data analysis, simulation experiments, and mixed-methods approaches. Notable contributions include modeling driver physiological states using EEG and analyzing temporal instability of environmental factors during snowstorms. She collaborates on projects like vehicle-to-infrastructure communication systems for winter driving. Dr. Benedyk has received an Editor’s choice award for her 2018 paper on freight decision-making for the Northern Sea Route. Her research spans academic publications, industry partnerships, and innovative applications of AI in training and safety systems.

Murat Kantarciogluمشاهده پروفایل

استاد

• Data Privacy
• Computer Security
• Machine Learning
• +۵ مورد دیگر

Murat Kantarcioglu is an Ashbel Smith Professor of Computer Science at the University of Texas at Dallas within the Erik Jonsson School of Engineering and Computer Science. He holds visiting appointments at UC Berkeley and Harvard University, focusing on data privacy and security. With a Ph.D. in Computer Science from Purdue University (2005), he has made significant contributions to privacy-preserving data mining, blockchain analytics, and secure machine learning. Education: Ph.D. in Computer Science (Purdue, 2005) Current Roles: Ashbel Smith Professor (2021-present), Visiting Scholar at UC Berkeley (2020-present), Affiliate at Harvard (2013-present) Past Roles: Assistant (2005-2011), Associate (2011-2015), and Full Professor (2015-2021) at UTD His research focuses on data privacy , computer security , and machine learning , particularly addressing challenges in privacy-preserving distributed data mining , blockchain analytics , and adversarial machine learning . He has pioneered techniques for secure federated learning , topological analysis of blockchain networks , and privacy-utility tradeoffs in health data systems. Recent publications reveal a strong emphasis on IoT security , graph neural network vulnerabilities , and blockchain data structures . His work combines theoretical rigor with practical implementations using technologies like Intel SGX and homomorphic encryption. Notable Awards NSF CAREER Award (2009) IEEE Technical Achievement Award (2017) AMIA Homer Warner Best Paper Award (2014) Fellow of IEEE (2022), AAAS (2020), and ACM (2016) Key Projects Privacy-Preserving Genomics Data Sharing Adversarial Learning Frameworks Smart Contract Security Medical Data Protection Systems Labs Director of Data Security and Privacy Lab Collaborations with Vanderbilt, UC Berkeley (RISE Lab), and Harvard (Data Privacy Lab)

Kandethody M. Ramachandranمشاهده پروفایل

استاد

• Machine Learning
• Big Data
• Cyber Security
• +۴ مورد دیگر

Kandethody M. Ramachandran is a Professor in the Department of Mathematics & Statistics at the University of South Florida's College of Arts and Sciences. With a Ph.D. in Applied Mathematics from Brown University (1987), his research spans machine learning, cybersecurity, stochastic systems, and bioinformatics. He has directed numerous doctoral students and secured over $3.4M in research grants. Research interests include: Big data analytics and streaming data processing Game theory applications in cybersecurity and electricity markets Stochastic modeling in bioinformatics and finance Honors include the National Academy of Inventors membership and Teaching Incentive Program Award. Administrative roles include: Director of Graduate Admissions (Statistics, 2003-2015) Associate Director of Statistics Program (2010-2015)

Moi Hoon Yapمشاهده پروفایل

استاد

• Computer Vision
• Medical Image Analysis
• Facial and Gesture Analysis
• +۳ مورد دیگر

Professor Moi Hoon Yap is a Professor of Image and Vision Computing and Lead of Human-Centred Computing at The Manchester Metropolitan University. Her research focuses on computer vision, deep learning, and medical image analysis, particularly in early cancer detection and diabetic foot ulcer tools. She leads projects funded by The Royal Society, EU, EPSRC, Innovate UK, and industry partners. She is an Associate Editor of the Journal of Computers and Programs in Biomedicine and has developed datasets and grand challenges for reproducible research, such as the DFU-2021 challenge. Her work includes facial analysis, gesture interpretation, and human behavior quantification. Key Projects: FAST Healthcare NetworksPlus Award (2020-2021), Royal Society PhD Studentship (2018-2022), FootSnap Cloud Infrastructure (2019-2021). Awards: Royal Society Industry Fellowship, Marie Skłodowska-Curie Innovative Training Network Grant. Research Interests: Medical imaging, facial/gesture analysis, human behavior analysis, dataset creation, and sustainable software development. Professor Yap supervises PhD students and teaches Data Analytics, Deep Learning, and related courses. She is actively involved in international collaborations to advance biomedical data sharing and federated learning mechanisms.

Nasser Kehtarnavazمشاهده پروفایل

استاد

• Signal and Image Processing
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor and Director of the Embedded Machine Learning (EML) Laboratory at the University of Texas at Dallas's Erik Jonsson School of Engineering and Computer Science. He holds a PhD from Rice University (1987) and has over 40 years of academic and industrial experience. His research focuses on signal/image processing, machine learning, biomedical applications, and real-time embedded systems. He has authored/co-authored 11 books and over 450 publications, and supervised 37 PhD and 35 MS students. Leadership roles: Editor-in-Chief of Journal of Real-Time Image Processing , SPIE Conference Chair, IEEE Fellow (2013), AAIA Fellow Industrial collaborations: Texas Instruments, AT&T Bell Labs, US Army TACOM Research Lab Key contributions: Smartphone-based signal processing labs, hearing aid amplification algorithms, and FPGA-based embedded systems Research interests include real-time implementation on embedded processors, biomedical signal analysis, and smartphone-based laboratories. He has pioneered educational tools like anywhere-anytime labs using smartphones for signal processing education. Recipient of numerous awards including IEEE Region 5 Professional Leadership Award (2015), Outstanding Engineering Educator Award (2013), and community recognition from SPIE (2020-2021). His funded projects (62 as PI/Co-PI) span healthcare tech, energy systems, and semiconductor manufacturing. Director of the EML Lab and previously the Signal and Image Processing Lab. Teaches graduate courses like Machine Learning and Pattern Recognition and undergraduate courses in signals and systems.

Payam Khazaeinejadمشاهده پروفایل

دانشیار

• Computational solid mechanics
• Bio-inspired materials design
• Architected & smart materials/structures
• +۷ مورد دیگر

Payam Khazaeinejad is Associate Professor of Solid Mechanics at Kingston University London, where he also serves as School Director of Learning & Teaching and leads the IMechE-accredited Monitored Professional Development Scheme. A Chartered Engineer & Scientist, he is Senior Fellow of Advance HE, Fellow of IMechE, Senior Member of AIAA and sits on multiple national boards including IMechE Council and EPSRC Peer Review College. Education & Continuous Development PhD Engineering, University of Edinburgh (2016) MSc Mechanical Engineering – Applied Mechanics (2006) BEng Mechanical Engineering – Solid Mechanics (2004) MIT Professional Education – Machine Learning for Materials Informatics (2024) Chartered Manager Bootcamp, CMI Level 5 Diploma (2023) Certificate in Innovation in Teaching, Laspau/Harvard (2021) Chartered Engineer & Chartered Scientist status (2021, 2024) Research Interests His research integrates computational solid mechanics with bio-inspired design to create architected materials and structures that are lightweight, adaptive and resilient. Using high-fidelity finite-element and discrete-element modelling he investigates failure mechanisms under extreme thermomechanical environments, with applications spanning pharmaceutical tablet compaction, biomedical scaffolds for bone regeneration, floating solar-panel arrays, fire-exposed steel plates and subsea riser systems. Across 50+ peer-reviewed works he has advanced analytical solutions for functionally graded shells, nonlinear thermo-elastic plates, vibration of micro-composite skew plates and efficient riser elements, while recent outputs concentrate on additive-manufactured aerospace mounts, 3-D printed biodegradable scaffolds and data-driven engineering curricula. Honours & Recognition Provost's Award for Learning & Teaching Excellence 2025 Enterprising Value Award, Kingston People Awards 2024 Senior Fellow, Advance HE Fellow, Institution of Mechanical Engineers Senior Member, AIAA Chartered Engineer & Chartered Scientist (UK Engineering & Science Councils) Leadership & External Examining He is Chief External Examiner at UWE Bristol (2024-present) and External Examiner at Manchester Met and University of East London, having served on programme-revalidation panels nationwide. At Kingston he directs learning & teaching for the School of Engineering, chairs the IMechE Structural Technology & Materials Group, and founded the Engineering Simulation Group.

Giovanni Dalmassoمشاهده پروفایل

دانشیار

• Computational Biology
• Developmental Biology
• Biomedical Image Analysis
• +۴ مورد دیگر

Giovanni Dalmasso is an Associate Professor in the Department of Mathematics and Data Analytics at the IQS School of Engineering, Ramon Llull University. His research focuses on computational modeling of biological systems, including morphogenesis, cellular homeostasis, and biomedical image analysis. He specializes in developing tools for 3D/4D visualization of developmental processes and agent-based models of cellular dynamics. Key research areas include: 3D/4D reconstruction of embryonic development (e.g., limb bud morphogenesis) Biological shape correspondence algorithms (μMatch) Systems biology approaches to mitochondrial function and stress responses Mathematical modeling of calcium signaling dynamics His work contributes to UN Sustainable Development Goals related to innovation and infrastructure through advances in bioimaging and computational tools. Collaborations span institutions globally, including projects with the University of Edinburgh and University Pompeu Fabra. He has developed open-source software like LimbLab and veda for scientific visualization. Research outputs emphasize interdisciplinary methods combining engineering, mathematics, and biology to address fundamental questions in developmental biology and cellular systems. Current efforts focus on integrating moral decision frameworks into AI agents through contractualist models.

Steven J. Rehseمشاهده پروفایل

استاد

• Biomedical Physics
• Analytical Chemistry
• Medical Diagnostics
• +۳ مورد دیگر

Dr. Steven J. Rehse is the Department Head and Professor in the Department of Physics at the University of Windsor, specializing in Experimental Biomedical Physics and AMO (Atomic, Molecular, Optical) Physics. His research focuses on laser-induced breakdown spectroscopy (LIBS) for rapid bacterial pathogen detection in clinical specimens such as blood, urine, and cerebrospinal fluid. He has led a prolific research group, mentoring over 20 graduate and undergraduate students, and has presented at international conferences including LIBS XIII, SciX, and the Canadian Association of Physicists (CAP) Congress. His work bridges physics, biomedicine, and chemistry, aiming to develop faster diagnostic tools for infectious diseases. Dr. Rehse holds a Ph.D. from Colorado State University and has held academic positions at the University of Western Ontario and Wayne State University. His awards include the Roger Thibert Teaching Excellence Award (2013) and multiple NSERC grants. His lab collaborates with medical professionals and industry, advancing LIBS applications in clinical settings. Recent projects include emission enhancement techniques using silver thin films and interdisciplinary outreach with pre-optometry students. Key achievements include developing LIBS protocols for urinary tract infections and bacterial meningitis diagnosis, with publications in Spectrochimica Acta, Applied Spectroscopy, and other journals. He advocates for undergraduate research through programs like the Outstanding Scholars initiative and has engaged the public via media appearances and science outreach events.

Seongho Songمشاهده پروفایل

استاد

• Bayesian Statistics
• Biostatistics
• Statistical Modeling
• +۵ مورد دیگر

Seongho Song is a Professor and Undergraduate Program Director in the Department of Mathematical Sciences at the University of Cincinnati. He holds a Ph.D. in Statistics from the University of Connecticut (2005). His research focuses on Bayesian statistical methods, biostatistics, and computational biology, with applications in genetics, environmental science, and healthcare. Song has led or collaborated on grants totaling over $4.7M, including projects funded by DARPA, NIH, and the Korea Institute of Ocean Science and Technology. Education: Ph.D. in Statistics, University of Connecticut (2005) Positions: Assistant (2005-2011) → Associate (2011-2019) → Full Professor (2019–Present) Research interests include Bayesian hierarchical models, longitudinal data analysis, and bioinformatics. He has published over 30 peer-reviewed articles in journals like Biostatistics , Frontiers in Genetics , and Journal of Productivity Analysis . Recent work involves modeling circadian rhythms, lung disease progression, and oceanographic systems. Teaching responsibilities include graduate courses in Bayesian analysis, statistical computing, and linear models. He has served on editorial boards for Sankhya Series B and IEEE Transactions on Computational Biology , and chairs the EAC-ISBA Special Interest Group.