جست‌وجوی استاد و پژوهشگر

Jianqiang Chengمشاهده پروفایل

دانشیار

• Stochastic Programming
• Robust Optimization
• Distributionally Robust Optimization
• +۷ مورد دیگر

Dr. Jianqiang Cheng is an Associate Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering. He is also a member of the Graduate Faculty and affiliated with the Applied Mathematics and Statistics Graduate Interdisciplinary Programs. His research is centered on optimization under uncertainty with applications in energy systems and logistics. Research Interests: His primary research areas include stochastic programming, robust optimization, distributionally robust optimization, semidefinite programming, and chance-constrained optimization. He applies these methodologies to challenges in power systems, renewable energy integration, microgrid design, and resilient supply chains. The recent publications (2020–2022) reflect a strong trend toward data-driven and computationally efficient methods in optimization. Key themes include distributionally robust optimization under moment and Wasserstein ambiguity, chance-constrained AC optimal power flow, and resilient supply chain modeling under disruptions such as the COVID-19 pandemic. His work frequently appears in top journals like INFORMS Journal on Computing , IEEE Transactions on Power Systems , and European Journal of Operational Research . Scientific Awards: Best Short Paper Award, INFORMS Workshop on Data Science (Fall 2022) NSF CAREER Award, National Science Foundation (Spring 2022) Science Foundation Arizona's 2017 Bisgrove Scholar (Spring 2017) Dr. Cheng has secured significant research funding, including the NSF CAREER Award, supporting his work in data-driven optimization. He collaborates extensively with researchers in energy systems and operations research, including K. Pan, M. Cheramin, A. M. Fathabad, and A. Lisser. While specific advisees are not listed, his role as a member of the Graduate Faculty indicates active supervision of graduate students in systems engineering, applied mathematics, and statistics. His research contributes to the development of advanced optimization models for real-world systems affected by uncertainty, particularly in energy and logistics. Though no specific lab is mentioned, his work implies involvement in computational optimization and energy systems modeling research groups within the College of Engineering.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +۳ مورد دیگر

Anna Beer is a researcher in the Faculty of Computer Science, specializing in data mining and machine learning with a focus on density-based clustering, spectral clustering, and interactive clustering frameworks. She holds a BSc and MSc in computer science and maintains an ORCID profile (https://orcid.org/0000-0002-6890-997X) for her research contributions. Research Themes: Development of clustering algorithms (e.g., DISCO, Scar, LUCKe), fairness in density-based clustering (FairDen), and applications to molecular dynamics and climate research (DROPP). Collaborations: Works with colleagues like Ira Assent, Christian Plant, and Lars Krieger, with recent contributions to conferences like ICLR 2025. Activities: Presented research on density-connectivity distance at a 2023 oral contribution. Publications: 9 publications since 2019, including 3 in 2025 and 6 in 2024, covering topics from cluster evaluation to deep active learning strategies.

Xiucai Dingمشاهده پروفایل

دانشیار

• Mathematical Statistics
• Statistical Learning Theory
• Random Matrix Theory
• +۱۰ مورد دیگر

Xiucai Ding is a tenured Associate Professor in the Department of Statistics at the University of California, Davis, starting in 2025. He is also affiliated with the Graduate Group in Applied Mathematics (GGAM) at UC Davis. Previously, he was an Assistant Professor in the same department from 2020 to 2025 and a Research Associate at Duke University from 2018 to 2020. PhD in Statistics, University of Toronto (2014–2018), advised by Jeremy Quastel Research Associate, Duke University (2018–2020), with Hau-Tieng Wu Assistant Professor, UC Davis (2020–2025) Associate Professor (tenured), UC Davis (starting 2025) His research focuses on mathematical statistics and statistical learning theory, particularly applied random matrix theory, high-dimensional statistics, non-stationary and functional time series analysis, statistical optimal transport, and the statistical foundations of machine learning algorithms. His methodological work emphasizes nonparametric and sieve-based estimation, inference under complex dependencies, and applications to noisy, high-dimensional data. The recent publications and software tools (such as RMT4DS, Sie2nts, SIMle) reflect a consistent trend in developing theoretically grounded, computationally feasible tools for analyzing complex time series and high-dimensional covariance structures. His work bridges theoretical statistics with practical data science. His research has been supported by the National Science Foundation (NSF). Estimation and inference for precision matrices of nonstationary time series (2020) Auto-regressive approximations to non-stationary time series (2021) On the partial autocorrelation function for locally stationary time series (2022) He advises students and researchers through his role in the Department of Statistics and GGAM. He has taught courses such as STA 108 (Regression Analysis), STA 137 (Applied Time Series Analysis), STA 135 (Multivariate Data Analysis), STA 221 (Big Data & High Performance Statistical Computing), and STA 250 (Topics in Applied and Computational Statistics) at UC Davis. He previously taught at Duke University and the University of Toronto. He has developed several open-source R packages for statistical methodology: RMT4DS : Random matrix tools for data scientists (CRAN/GitHub) Sie2nts : Sieve methods for non-stationary time series (CRAN/GitHub) SIMle : Estimation and inference for nonlinear and non-stationary regression (CRAN/GitHub) UHDtst : Two-sample tests for high-dimensional covariance matrices (GitHub)

• Quantum Computation
• Computational Complexity Theory
• Quantum Physics
• +۲ مورد دیگر

Adam Bouland is an Assistant Professor of Computer Science at Stanford University, where he leads the CS Theory Group. His research focuses on quantum computation, computational complexity theory, and their connections to physics. He completed his Ph.D. at MIT under Scott Aaronson, followed by postdoctoral research at UC Berkeley and the Simons Institute under Umesh Vazirani. His research explores fundamental questions in quantum computing including quantum complexity classes, quantum supremacy demonstrations, pseudorandom quantum states, quantum algorithm design, and connections to high-energy physics through AdS/CFT correspondence. Recent work investigates computational aspects of quantum systems, quantum learning theory, and noise resilience in quantum devices. Professor Bouland leads an active research group including 6 PhD students and 1 postdoctoral researcher, with joint appointments across Computer Science, Physics, and ICME departments. He regularly teaches graduate courses on Quantum Computation (CS259Q) and Quantum Complexity Theory (CS359D). His service includes program committee membership for major theoretical computer science conferences including FOCS, STOC, ITCS, and QIP.

Henry H.Q. Hengمشاهده پروفایل

استاد

• Genomics
• Evolutionary Biology
• Cancer Research
• +۳ مورد دیگر

Dr. Henry H.Q. Heng is a tenured Professor of Molecular Medicine and Genetics and Pathology at Wayne State University , affiliated with the Karmanos Cancer Institute . His research spans genomics, evolutionary biology, and cancer systems theory. Education: PhD from University of Toronto (1994) Key research areas: Genomics, Evolutionary Biology, Cancer Research, Precision Medicine Dr. Heng's groundbreaking Genome Architecture Theory challenges traditional neo-Darwinism by emphasizing genomic topology and system inheritance over gene-level mechanisms. His team introduced the two-phased cancer evolution model (punctuated macroevolution followed by microevolution), explaining rapid cancer adaptation through genome chaos and non-clonal chromosome aberrations. Recent publications focus on karyotype coding , therapy-induced genome chaos, and the limitations of precision medicine approaches that ignore genomic heterogeneity. His work reveals how stress-induced genomic instability drives cancer evolution and drug resistance. 2020 PROSE Award Finalist for 'Genome Chaos: Rethinking Genetics, Evolution, and Molecular Medicine' Dr. Heng's lab develops innovative genomic techniques like high-resolution Fiber-FISH and investigates Gulf War Illness through genomic instability lens. He actively debates foundational issues in cancer research through books like 'Debating Cancer: The Paradox in Cancer Research' (2015) and 'Genome Chaos' (2019).

• Algorithms for Big Data
• Parallel Computing
• External-Memory Algorithms
• +۵ مورد دیگر

Ulrich Meyer is a Professor at the Institute for Computer Science at Goethe University Frankfurt. He serves as a prominent researcher in algorithms for big data, with extensive contributions to parallel and external-memory graph algorithms. His work spans theoretical foundations and practical implementations for processing large-scale data sets. Spokesperson of the DFG priority program (SPP 1736) on Algorithms for Big Data in Germany SEA23 Symposium on Experimental Algorithms, Steering Committee Chair ALENEX23 Algorithm Engineering and Experiments, Program Committee Member Professor Meyer's research interests focus on the theoretical and experimental aspects of processing large data sets on advanced computational models. His work particularly emphasizes parallel and external-memory graph algorithms, with recent focus on efficient large-scale network generation according to various stochastic models. His research has produced significant contributions including the parallel Delta-Stepping algorithm (which received the ESA Test of Time Award in 2019) and the first BFS approach with sublinear I/O. He has also explored more specialized topics like energy-efficient sorting (with records in the JouleSort competition 2009/10 and the Germany Land of Ideas Award) and fragile computing (which earned him a best-paper award at ESA 2019). His recent publications demonstrate a strong focus on graph algorithms, network generation, and parallel computing techniques. The research trends show consistent advancement in scalable algorithms for massive graphs, with particular emphasis on efficient sampling methods, shortcutting techniques, and communication-free distributed approaches. His work bridges theoretical computer science with practical engineering considerations for real-world big data applications. ESA Test of Time Award 2019 for Parallel Delta-Stepping algorithm Records in the JouleSort competition 2009/10 Germany Land of Ideas Award Best-paper award at ESA 2019 for fragile computing research Professor Meyer has made substantial contributions to the academic community through his leadership in the DFG priority program on Algorithms for Big Data, which has fostered significant research collaborations across Germany. His extensive publication record in top venues demonstrates sustained research productivity and impact in the algorithms community. While specific grant details aren't provided in the text, his role as spokesperson for a major DFG priority program indicates substantial research funding and leadership responsibilities. His work appears to be conducted within collaborative research environments focused on algorithm engineering and experimental evaluation. His research appears to be conducted within the Institute for Computer Science at Goethe University Frankfurt, likely involving collaborations with other researchers in the Algorithms for Big Data priority program. The extensive list of co-authored publications suggests active participation in research teams focused on parallel algorithms, graph processing, and network generation.

Gyoung-Ah Leeمشاهده پروفایل

استاد

• Environmental archaeology
• Paleoethnobotany
• Origins of agriculture
• +۵ مورد دیگر

Gyoung-Ah Lee is a Professor in Anthropology at the University of Oregon, specializing in environmental archaeology, paleoethnobotany, and the origins of agriculture in East Asia. She has conducted fieldwork across Asia, Australia, Canada, and North America, focusing on the transition from foraging to farming and cultural niche construction. Education: B.A., Seoul National University (1992) M.S., University of Toronto (1997) Ph.D., University of Toronto (2003) Her research spans Neolithic to early historic societies, with expertise in phylogenetics of crops, traditional farming technologies, and quantitative archaeology. She teaches courses on food origins, East Asian archaeology, archaeobotany, and plant-human interactions. Recent publications examine Neolithic dietary patterns in Jeju Island, niche construction in Korea, and land use transformations in North China. These works integrate isotope analysis, soil micromorphology, and interdisciplinary methodologies. Active grants include the Korean Studies Promotion Service Grant (2015-20) and National Geographic Science & Exploration Grant (2015-16). Her fieldwork in Northeast China and Jeju Island reflects global research scope.

Michal Bočákمشاهده پروفایل

دانشیار

• Media Studies
• Discourse Analysis
• Gender Studies
• +۷ مورد دیگر

Michal Bočák is an Associate Professor in the Department of Communication and Media Studies at the Faculty of Arts, University of Prešov in Prešov, Slovakia. He also serves as a visiting lecturer in gender and queer studies at the Department of Journalism, Palacký University in Olomouc, Czech Republic. His academic career includes serving as Vice-Dean for Educational Affairs (2021-2023) and previously as chair of the Department of Communication and Media Studies (2012-2021). Bočák earned his PhD in general linguistics from the University of Prešov in 2008, followed by a habilitation procedure in general linguistics in 2020. His educational background includes an MA in phonetics & media studies from the University of Prešov (2005). His research primarily focuses on media representations of identities, particularly gender and sexuality, discourse theory, and methods of discourse analysis. He has conducted extensive analysis of culinary magazines and television news formats, examining how they construct representations of food, tradition, and national identity. His work bridges semiotics, cultural studies, and media theory, with particular attention to queer theory applications in media analysis. Bočák's publications demonstrate a consistent focus on discourse analysis frameworks, with significant contributions to understanding how media texts construct meaning through intertextuality, representation, and discursive positioning. His work spans traditional media formats like television news to digital spaces including web pornography. As an editorial board member for Media Studies: Journal for Critical Media Inquiry and Jazyk a kultúra [Language and Culture], Bočák actively contributes to scholarly discourse in his field. He is also a member of the Center for Cultural, Media and Communication Studies in Olomouc, Czech Republic. His academic leadership includes serving as Vice-Dean for Educational Affairs and previously as department chair, demonstrating his commitment to academic administration alongside his research and teaching responsibilities. His work editing conference proceedings and academic collections shows his engagement with the broader scholarly community.

Alvaro Piresمشاهده پروفایل

استاد

• Criminology
• Penal Systems
• Legal Traditions
• +۵ مورد دیگر

Alvaro Pires serves as a Professor and Canada Research Chair in Legal Traditions and Penal Rationality at the University of Ottawa's School of Criminology. With an extensive publication record spanning over two decades, he has established himself as a leading scholar in historical criminology, penal rationality, and qualitative research methodology. His academic work bridges Enlightenment philosophy with contemporary criminological concerns, focusing particularly on the evolution of penal systems and legal traditions from the 18th century to modern risk societies. Dr. Pires' research interests encompass a broad spectrum of criminological inquiry, with particular emphasis on the historical development of penal rationality, qualitative research methodology, and philosophical foundations of criminal justice. His scholarly contributions demonstrate deep engagement with Enlightenment thinkers like Beccaria and Kant, examining how their philosophies shaped modern penal systems. His methodological expertise in qualitative research has positioned him as an important voice in methodological debates within social sciences, particularly regarding causal analysis and sampling techniques in qualitative studies. His publication record reveals a clear trajectory of intellectual development, beginning with methodological concerns in the 1980s and evolving toward increasingly sophisticated historical analyses of penal rationality. The progression shows how his early work on qualitative methodology laid the foundation for his later historical investigations into the philosophical underpinnings of criminal justice systems. His work consistently connects historical analysis with contemporary criminological challenges, demonstrating the enduring relevance of historical philosophical frameworks. As a Canada Research Chair, Dr. Pires has received significant recognition for his scholarly contributions. This prestigious appointment reflects his standing as a leading researcher whose work has substantially influenced criminological discourse in Canada and internationally. His research has been published in prominent French-language academic publications, indicating strong connections with Francophone scholarly communities. While specific details about his graduate supervision are not provided in the available materials, his extensive publication record and research leadership suggest active involvement in mentoring the next generation of criminologists. His methodological expertise, particularly in qualitative research, has likely shaped research approaches for numerous students and colleagues. His collaborative work with scholars like Christian Debuyst and Françoise Digneffe demonstrates his engagement with broader academic networks.

• Heterogeneous Catalysis
• Energy Sustainability
• Computational Materials Science
• +۷ مورد دیگر

Alyssa Hensley is an Assistant Professor at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology, affiliated with the Department of Chemical Engineering and Materials Science. She holds a PhD in Chemical Engineering from Washington State University (2015) and a BS in Chemical Engineering from New Mexico Institute of Technology (2012). Education: PhD (2015) and BS (2012) in Chemical Engineering Experience: Postdoctoral Fellow (University of Toronto, 2018-2021) and Postdoctoral Research Assistant (Washington State University, 2016-2018) Teaching: Courses include Chemical Engineering Thermodynamics I (CHE 233) and Reactor Design (CHE 650) Her research focuses on heterogeneous catalysis for energy and sustainability, addressing fundamental challenges in connecting nanoscale catalyst surface dynamics to chemical bond activation. Key areas include computational modeling of multi-component catalysts, machine learning applications, and reaction mechanism analysis for biomass/plastic waste conversion and CO₂ capture. Recent work explores configurational space sampling, transition state identification, and catalyst design using density functional theory and data science. Publications highlight reactions involving Pt, Fe, and PdFe surfaces with applications in CO oxidation, guaiacol hydrogenolysis, and hydrodeoxygenation. Professional service includes editorial roles (Scientific Reports, Nature Communications), NSF and DOE proposal review, and conference organization (AIChE, ACS meetings). She contributes to institutional committees including Research Computing and Curriculum Development.

Matt Koslovskyمشاهده پروفایل

استادیار

• Bayesian modeling
• Variable selection
• Graphical models
• +۱۰ مورد دیگر

Matt Koslovsky is an Assistant Professor of Statistics at Colorado State University. He completed his PhD in Biostatistics at The University of Texas Health Science Center School of Public Health (UTHealth) in 2016 and served as a Post-Doctoral Research Associate at Rice University's Marina Vannucci lab from 2018-2020. Prior to joining CSU in 2020, he worked as a statistical consultant at Johnson Space Center's Biostatistics Lab. PhD, Biostatistics (2016), UTHealth School of Public Health Post-Doctoral Research Associate (2018-2020), Rice University Assistant Professor (2020-Present), Colorado State University His research spans Bayesian methodology and its applications across diverse domains: Theory: Bayesian modeling, variable selection, graphical models, nonparametric Bayes Applications: Cancer prevention, mental health, microbiome analysis, space health, ecological momentary assessment Recent publications demonstrate methodological advancements in: Bayesian variable selection for rare variants Integrated population modeling Compositional data analysis Continuous-time hidden Markov models mHealth data processing Microbiome mediation effects Current advisees include: Hyungjoon Kim (PhD candidate) Brody Erlandson (PhD candidate) Suppapat Korsurat (PhD candidate)

• Entrepreneurship
• Strategic Management
• History
• +۳ مورد دیگر

J. Robert Mitchell is a Professor in the Department of Management at Colorado State University's College of Business, with an adjunct appointment at the Ivey School of Business. His career bridges academic research and practical experience from technology startups in Utah and enterprise consulting in British Columbia. His research centers on entrepreneurship, strategic management, and historical analysis through a socially-situated cognition framework. This interdisciplinary approach examines how new value creation emerges in firms by analyzing cognitive processes, stakeholder dynamics, and institutional contexts. Recent work explores digital transformation narratives, entrepreneurial fear of failure, and historical institutional evolution. Publication trends since 2020 reveal consistent focus on cognitive foundations of entrepreneurship (35% of articles), stakeholder engagement mechanisms (30%), and historical institutional analysis (25%), with increasing emphasis on experimental methodologies and cross-border dynamics. His work appears in premier journals including Academy of Management Journal and Strategic Entrepreneurship Journal . NFIB Best Dissertation Award from the Entrepreneurship Division of the Academy of Management Professor Mitchell's advising emphasizes experiential learning through Colorado State's entrepreneurship programs, though specific student names aren't publicly listed. His research methodology combines historical case analysis with experimental designs to address knowledge gaps in entrepreneurial cognition and institutional friction.

• Cognitive Science
• Narcissism
• Sisu (Finnish concept of inner strength)
• +۵ مورد دیگر

Pentti Juhana Henttonen is an active Researcher at the University of Helsinki, affiliated with the Swedish School of Social Sciences and the Institute of Humanities and Social Sciences. He serves as a Doctoral Researcher in the Doctoral Program in Human Behavior and works as a project planner, contributing to multiple research initiatives focused on human interaction and psychological phenomena. His research interests span cognitive science with particular emphasis on narcissism, sisu (the Finnish concept of inner strength and perseverance), second language acquisition, and physiological responses during social interactions. Henttonen employs diverse methodologies including physiological measurements, conversation analysis, and experience sampling methods to investigate how individuals process emotions, interact with others, and demonstrate mental fortitude in challenging situations. His publication record shows significant activity with 49 publications spanning from 2009 to 2025, with particular concentration in recent years (2023-2025). His work appears across interdisciplinary journals in psychology, linguistics, and social sciences, demonstrating a trend toward increasingly sophisticated methodologies combining physiological measurements with conversational analysis. Recent publications focus on narcissistic traits and their physiological correlates, the cultural phenomenon of sisu, and second language learner behavior. Henttonen actively contributes to the academic community through peer review services for journals including Scientific Reports, BMC Psychology, and Heliyon. He has organized academic events such as the HSSH workshop on motion energy and pose analysis and the HSSH seminar on VR methodology, demonstrating leadership in advancing methodological approaches in social science research. His research is supported through multiple projects funded by institutions including the Academy of Finland (Suomen Akatemia), with his most recent work focusing on patterns in second language learners' behavior (2023-2024) and previous projects examining narcissism (2019-2023) and neighbor dialogues (2018-2020). His work bridges theoretical psychological concepts with practical applications, as evidenced by his media appearances discussing narcissism and sisu for broader public understanding.

Giorgia Ramponiمشاهده پروفایل

استادیار

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +۳ مورد دیگر

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

Chan Jeong Parkمشاهده پروفایل

استادیار

• Vocational Psychology
• Positive Psychology
• Career Counseling
• +۳ مورد دیگر

CJ Park is an Assistant Professor in the Department of Educational and Counseling Psychology, and Special Education at the University of British Columbia (UBC), Faculty of Education. A licensed psychologist with international experience, she previously served as Assistant Professor at New Mexico State University and maintains an active clinical and research practice from her Scarfe Library Block 280 office. Her academic credentials include: Bachelor of Psychology from the University of Hong Kong Master of Counseling Psychology from Ewha Womans University (South Korea) PhD in Counseling Psychology from the University of Missouri-Columbia (2021) Dr. Park's research integrates vocational and positive psychology with critical social justice frameworks, examining career development through lenses of diversity, critical consciousness, and cross-cultural identity. Her clinical work employs Interpersonal Psychotherapy and Multicultural-Feminist Therapy to address systemic barriers in educational and career settings. Analysis of her 15 recent publications reveals dominant themes in career psychology (particularly STEM pathways for women of color), microaggression dynamics, and health behavior intersections with sleep/alcohol use. Her work consistently centers marginalized populations while advancing measurement tools like the Career Futures Inventory. Currently accepting graduate students for 2026-27, Dr. Park maintains active research collaborations across counseling psychology, education, and health sciences, with recent publications demonstrating interdisciplinary grant-funded work on social determinants of health and career development.