Find Academic Supervisors

Louis Herns MarcelinView profile

Professor

• Anthropology
• Migration Studies
• Haiti Studies
• +6 more

Louis Herns Marcelin is a Professor in the Department of Anthropology at the College of Arts and Sciences, University of Miami. His academic work bridges anthropology, epidemiology, and public health with a specialized focus on Haiti and Haitian diaspora communities across the Americas. Dr. Marcelin's research examines the complex intersection of migration, structural inequality, and public health crises. His work frequently analyzes how political instability, natural disasters, and pandemics impact vulnerable populations in Haiti, with particular attention to educational systems, healthcare access, and socioeconomic disparities. He employs interdisciplinary methodologies including ethnographic network tracing, mixed-methods research, and longitudinal studies to document Haitian migration patterns and the experiences of Haitian communities both in Haiti and in diaspora settings like Miami. His recent publications (2022-2024) reveal a consistent analytical focus on Haitian social dynamics, addressing topics ranging from sickle cell disease management to the cultural dimensions of migration expressed through Haitian music. Dr. Marcelin's research demonstrates how historical patterns of inequality create path dependencies that shape contemporary vulnerabilities. Dr. Marcelin has received significant research funding including a National Institute on Drug Abuse (NIDA)-funded study examining structural racism experienced by Haitian families interfacing with the juvenile justice system. His collaborative work spans multiple institutions and disciplines, reflecting the complex nature of the social challenges he investigates. His contributions to understanding South-South migration, remittance economies, and the socio-political dimensions of natural disasters in Haiti position him as a leading scholar in Haitian studies and transnational migration research. Dr. Marcelin's work consistently connects academic inquiry to practical implications for policy and community interventions.

• Cadmium Toxicology
• Environmental Pollutants
• Pharmacology
• +5 more

Professor Walter C. Prozialeck is a distinguished academic at Midwestern University, serving in the Department of Pharmacology across multiple schools including the Chicago College of Osteopathic Medicine (CCOM), College of Pharmacy, College of Dental Medicine-Illinois, and College of Graduate Studies - Illinois. He holds a Ph.D. in Pharmacology from Thomas Jefferson University (1978) and a B.S. from Juniata College (1974). Dr. Prozialeck's research spans 35 years of cadmium toxicology, focusing on mechanisms of nephrotoxicity, microRNA expression, and biomarker development. He also investigates vancomycin pharmacokinetics and herbal medications like kratom and marijuana, contributing to drug policy discussions. His work has secured over $3.0 million in NIH funding. Recent publications highlight his expertise in cadmium-induced kidney injury biomarkers (e.g., Kim-1, cystatin C), microRNA alterations, and vascular toxicity. He has authored over 100 papers and book chapters. Career Achievement Award, Metals Specialty Section of Society of Toxicology (2021) Dr. Prozialeck teaches Pharmacology to medical, pharmacy, optometry, dental, and allied health students, and lectures in CME programs. He has held leadership roles including President of the Great Lakes Chapter of ASPET and Midwest Regional Chapter of the Society of Toxicology.

• Earthquake Engineering
• Structural Dynamics
• Pipeline Seismic Response
• +3 more

Prof. Muhittin Eren Uçkan is a Professor in the Department of Civil Engineering at Rafet Kayış Faculty of Engineering. His work focuses on earthquake engineering, structural dynamics, and seismic performance of infrastructure systems. Education: MS in Civil Engineering (1987) from Middle East Technical University, PhD in Earthquake Engineering (1994) from Boğaziçi University Key Research Areas: Seismic response of pipelines, soil-structure interaction, base isolation systems, and infrastructure resilience His recent publications analyze post-earthquake performance of transmission pipelines (2024 Kahramanmaras), liquid storage tanks (2023), and buried steel pipes (2016–2019). Studies emphasize fault crossing effects , sloshing damping , and seismic risk frameworks . Administrative roles include Vice Dean at Gebze Institute of High Technology (1998), Head of Department (1995–1998), and Commission Presidency at Alanya Aladdin Keykubat University (2020).

Diana TunaruView profile

Lecturer

• Empirical Finance
• Interest Rate Modeling
• Dynamic Estimation and Forecasting
• +4 more

Dr. Diana Tunaru is a Lecturer in Finance at Kent Business School , University of Kent, with expertise in empirical finance and quantitative methods. Her research focuses on dynamic financial modeling and forecasting, particularly in interest rate structures and volatility spillover effects. Education: Diploma in Mathematics (University of Craiova), Master in Banking and Finance (Middlesex University), PhD in Finance (University of Westminster) Research Interests include multi-factor continuous-time models, Gaussian estimation techniques, and the analysis of financial variables like interest rates, equities, and derivatives during economic distress. She applies her industry experience in investment banking to bridge theoretical and practical finance. In publications , Diana has explored topics ranging from agricultural commodity forecasting to green finance, with recent work emphasizing sustainability-driven portfolio performance and market interconnectedness. Her methodological focus on statistical accuracy tests and spillover effects reflects her commitment to advancing financial modeling. Teaching: Quantitative Methods for Finance, Introduction to Econometrics, Financial Investments, Economics of Financial Markets Diana serves as Senior Tutor at Kent Business School (Medway campus) and contributes to postgraduate education through her supervision of undergraduate dissertations on empirical finance and financial time series analysis.

William HarbertView profile

Professor

• Geophysics
• Seismic Processing
• Microseismic Analysis
• +5 more

William Harbert is a Professor in the Department of Geology and Environmental Science at the University of Pittsburgh, where he leads research in geophysics and subsurface characterization. His work bridges fundamental geophysical principles with practical applications in energy and environmental systems. Education: MS in Exploration Geophysics from Stanford University PhD in Geophysics from Stanford University Research focuses on seismic analysis across multiple scales, from micro-CT to surface seismic. His group specializes in advanced processing of microseismic, reflection seismic, and VSP data to image subsurface structures and understand pore-scale dynamics. Current work integrates deep learning for geophysical object detection and classification, with emphasis on organic shale systems and CO 2 storage monitoring. Key areas include rock physics, microseismicity analysis, and environmental geophysics for water quality assessment. Publication trends show strong emphasis on energy-related geophysics, particularly hydraulic fracturing monitoring, CO 2 sequestration verification, and unconventional reservoir characterization. Recent work increasingly incorporates machine learning techniques and addresses environmental monitoring challenges in subsurface operations. Scientific recognition: DOE ORISE Research Associate Resident Institute Fellow of the NETL-Institute for Advanced Energy Solution Professional engagements include membership on the Altarock Review Board for DOE-funded geothermal projects and prior service on the Scientific Advisory Board for the In Salah CO 2 Injection Project. His research involves extensive collaboration with national laboratories and industry partners on subsurface monitoring technologies. His laboratory group develops advanced geophysical processing techniques for subsurface imaging across multiple scales, with current projects focusing on microseismic monitoring of shale reservoirs and CO 2 storage sites.

• Foundation Engineering
• Structural Engineering
• Working Platform Design
• +7 more

Brian Solan is a Lecturer in Foundation Engineering at the Belfast School of Architecture & the Built Environment, Ulster University. His expertise spans foundation engineering, structural engineering, and environmental resilience, with particular focus on infrastructure stability under extreme conditions. Dr. Solan received his education at: Regional Technical College Dundalk (now Institute of Technology) - Civil Engineering studies beginning in 1986 Queen's University Belfast - BEng Honours (1st Class) in June 1991 Queen's University Belfast - PhD in Structural Engineering in 1996 Dr. Solan's research interests center on foundation engineering and infrastructure resilience. His work examines working platform design, traffic loading of infrastructure structures, implementation of Eurocode 7 standards, and the impact of extreme weather flooding on historic arch structures. He has expanded his research into environmental engineering, particularly wastewater treatment and sustainable methods for removing contaminants from water systems. His interdisciplinary approach bridges traditional civil engineering with contemporary environmental challenges, addressing critical infrastructure needs in the face of climate change. Dr. Solan's recent publications demonstrate a shift toward environmentally focused research while maintaining his foundation in structural engineering. His work increasingly addresses the intersection of infrastructure engineering and environmental sustainability, with particular attention to wastewater treatment technologies, emerging contaminants, and climate change adaptation for built heritage. This evolution reflects broader trends in engineering research toward more sustainable and resilient infrastructure solutions. Dr. Solan has received recognition for his work, including: Adrian Long Award (December 3, 2020) Royal Society Travel Scholarship for collaborative work with Professor Robert Ettema at the University of Colorado Dr. Solan has been actively involved in research funding and collaboration. He served as Principal Investigator for the 'Short-span bridge scour under submerged flood flow conditions' project (2015-2017) and was a Co-Investigator on the 'Infrastructure Compromise Avoidance' project (2018-2020). His research has attracted funding supporting both structural engineering and environmental sustainability initiatives. He contributes to the academic community through peer review activities, including for the Proceedings of the Institution of Civil Engineers - Ground Improvement journal. Dr. Solan maintains professional affiliations with the Institution of Engineers of Ireland and is a graduate member of the Institution of Structural Engineers. His work contributes to UN Sustainable Development Goals related to clean water and sanitation, sustainable cities and communities, and climate action. His research bridges traditional engineering disciplines with contemporary environmental challenges, positioning him at the intersection of infrastructure resilience and sustainable development.

Mark BattyView profile

Professor

• Concurrent Systems
• Formal Verification
• Programming Languages
• +3 more

Mark Batty is a Professor in the School of Computing at the University of Kent, specializing in formal methods for concurrent systems. His work bridges hardware-software interfaces, focusing on memory models for C/C++, OpenCL, and architectures including x86, ARM, POWER, and GPUs. As a member of the Programming Languages and Systems Research Group, he develops mathematical specifications and verification tools for real-world concurrency challenges. His research centers on empirical testing of hardware/compiler behavior, formal modeling of system components, and verification of fine-grained concurrent algorithms. Key contributions address relaxed memory semantics, transactional memory, and compositional reasoning for concurrent data structures. His work combines theoretical rigor with practical tool development to ensure correctness in complex concurrent environments. Analysis of his 2015-2025 publications reveals consistent focus on memory consistency models, formal verification of weak memory concurrency, and compiler optimizations. Dominant themes include C/C++11 standards, GPU concurrency semantics, and mechanized verification techniques. His research demonstrates strong industry relevance through collaborations with hardware vendors and contributions to language standards. Mark Batty has received significant recognition: John C. Reynolds Doctoral Dissertation Award (2015) from ACM SIGPLAN CPHC and BCS Distinguished Dissertation Award (2015) Lloyds Register Foundation and Royal Academy of Engineering Research Fellowship (2016) He actively leads major research initiatives and mentors next-generation researchers: Current Funding: EPSRC Standard Grant 'Verifiably Correct transactional memory' (2018), VeTTS Grant 'Specification and verification of C++ data structure libraries' (2018), EPSRC First Grant 'Compositional, dependency-aware C++ concurrency' (2018) PhD Recruitment: Actively seeking candidates for UKRI-funded studentship in Verified Trustworthy Software Systems Batty drives community engagement through Kent Concurrency Workshop (2016) and South of England Programming Language Seminars, fostering national collaboration in programming languages research. His leadership in organizing Royal Society discussions underscores his influence in trustworthy systems verification.

Anru ZhangView profile

Associate Professor

• Tensor Learning
• High-Dimensional Statistics
• Machine Learning
• +3 more

Anru Zhang is the tenured Eugene Anson Stead, Jr. M.D. Associate Professor with joint appointments in Biostatistics & Bioinformatics, Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. He holds a Ph.D. from the University of Pennsylvania (2015, advised by T. Tony Cai) and a B.S. in Mathematics from Peking University (2010). Current roles: Associate Professor at Duke (2024–present), previously Assistant Professor at UW-Madison (2018–2021) Research focus: Tensor learning, high-dimensional statistics, EHR analysis, and healthcare applications Mentorship: Supervises active research team including postdocs (Jianbin Tan, Qiuyi Wu) and PhD students (Runshi Tang, Yinrui Sun) Research Trends : His recent publications emphasize tensor methods in biomedical data (EHR, microbiome, Alzheimer’s), Riemannian optimization for high-dimensional problems, and hybrid statistical-computational approaches. Key themes include healthcare AI, EHR analysis, and non-convex optimization. Scientific Awards : COPSS Emerging Leader Award (2024) IMS Tweedie New Researcher Award (2022) ASA Gottfried E. Noether Junior Award (2021) NSF CAREER Award (2020) AMIA Data Science Outstanding Paper Award (2023) Advising & Grants : Mentored 16+ students/postdocs, including Yuetian Luo (IMS Lawrence D. Brown Award) and Yuchen Zhou (IMS Hannan Travel Award). Current grants include NIH-funded projects on sepsis detection, mental health AI, precision genetic testing, and telehealth interventions, plus NSF CAREER funding for statistical inference in high-dimensional structures. Labs & Teams : Leads a research group at Duke focusing on tensor learning, statistical theory, and healthcare AI applications. Collaborates with Duke’s AI Health initiative and serves as Associate Editor for leading journals like Annals of Statistics and JASA.

Cynthia D. RudinView profile

Professor

• Interpretable Machine Learning
• Sparse Models
• Causal Inference
• +7 more

Cynthia D. Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, with joint appointments in the Departments of Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics. She directs the Interpretable Machine Learning Lab and has held previous positions at MIT, Columbia, and NYU. Her educational background includes: Undergraduate degree from the University at Buffalo PhD from Princeton University (2004) Research Interests: Dr. Rudin's research focuses on interpretable machine learning and its applications across multiple domains. Her work emphasizes creating machine learning models whose reasoning processes people can understand, which includes algorithms for extremely sparse models, interpretable neural networks, interpretable matching methods for causal inference, and dimension reduction for data visualization. She applies these techniques to critical societal problems in healthcare, criminal justice, materials science, and other domains. Her lab has developed practical code for sparse models such as decision lists, decision trees, and additive models that provably optimize accuracy and sparsity. Dr. Rudin's recent publications (2024-2025) demonstrate a strong focus on interpretable AI applications across diverse fields including healthcare (mortality risk scores, breast cancer prediction), materials science (metamaterials design), and environmental justice (location-based health analysis). Her work consistently emphasizes practical implementations with real-world impact, particularly in high-stakes decision-making domains where model transparency is critical. Scientific Awards: Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (2022) - often described as the "Nobel Prize of AI" INFORMS Society on Data Mining Prize (2024) Guggenheim Fellowship (2022) Three-time winner of the INFORMS Innovative Applications in Analytics Award (2013, 2016, 2019) Winner of the 2023 John M. Chambers Statistical Software Award for PaCMAP Winner of the 2024 Award for Innovation in Statistical Programming and Analytics Dr. Rudin has advised numerous PhD students and postdocs who have co-authored significant publications with her. Her lab has received substantial funding for projects applying interpretable machine learning to healthcare (seizure prediction in ICU patients), criminal justice (crime series analysis), and energy infrastructure (underground electrical distribution networks). Her work on the Series Finder algorithm has been adapted by the NYPD and has been running live in NYC since 2016. She directs the Interpretable Machine Learning Lab at Duke, which includes the Almost-Matching-Exactly Lab focused on interpretable causal inference. Her team develops practical code implementations for all their research, emphasizing usability and real-world application in critical domains.

Ruth F. LucasView profile

Associate Professor

• Breastfeeding
• Maternal Health
• Health Equity
• +6 more

Dr. Ruth F. Lucas, PhD, RNC, CLS, is an Associate Professor at the University of Connecticut School of Nursing. Her research focuses on breastfeeding equity through biopsychosocial and molecular mechanisms, infant breastfeeding biomechanics, and pain self-management. Education: PhD in Nursing from University of Illinois at Chicago (2011) Dr. Lucas leads projects translating the PROMPT study for WIC-eligible populations and testing biomedical lactation devices for real-time intraoral pressure measurement. Her work integrates genetic factors (e.g., COMT and OXTR variants) with social determinants of health, particularly in African American communities. Her recent research trends span genomics education for nurses, maternity care deserts in the U.S., breastfeeding self-efficacy metrics, and global lactation disparities. Current efforts include competency frameworks for genomics nurse educators and meta-ethnographies on diverse breastfeeding experiences. Contact: ruth.lucas@uconn.edu

Seung Eock KimView profile

Professor

• Structural Engineering
• Nonlinear Inelastic Analysis
• LRFD Design
• +3 more

Seung Eock Kim is a Professor in the Department of Civil and Environmental Engineering at Sejong University, Korea, where he has served since 1997. Previously, he held executive leadership as Senior Vice President (2015-2018) and brings industry experience from Daewoo Engineering. His academic credentials include a Ph.D. from Purdue University (1996), M.S. from KAIST (1990), and B.S. from Yonsei University (1983). Kim leads research in structural systems optimization with emphases on: Nonlinear inelastic analysis of steel/composite structures AI-driven structural design methodologies LRFD (Load and Resistance Factor Design) frameworks Advanced computational mechanics for infrastructure His recent publications (2024-2025) demonstrate strong focus on machine learning applications for structural health monitoring, nano-scale material characterization of steels, and sensor-based corrosion detection. This represents a strategic expansion into intelligent infrastructure systems beyond traditional mechanics. Awards and honors: National Research Laboratory designation (Ministry of Science, 2000) Elected Full Member of Korean Academy of Science and Technology (2011) He directs the Steel Structure Laboratory , where he developed the specialized nonlinear analysis software 3D-PAAP. His research has generated 132 SCIE-indexed publications with 1,599+ citations, including the influential CRC Press book LRFD Steel Design Using Advanced Analysis (1997).

• Air Pollution Control
• Odor Treatment Technologies
• Volatile Organic Compounds (VOCs) Removal
• +7 more

Professor Ji Hyeon Song is a distinguished researcher in the Department of Civil and Environmental Engineering at Sejong University, specializing in air pollution control and environmental engineering. With a Ph.D. from the University of Texas at Austin and extensive research experience, she has established herself as a leading expert in odor treatment technologies and VOCs removal. Her educational background includes: Ph.D. in Environmental Engineering, University of Texas at Austin (2001) M.S. in Environmental Engineering, Seoul National University (1993) B.S. in Environmental Engineering, Seoul National University (1991) Professor Song's research focuses on innovative solutions for air pollution challenges, particularly in biological treatments of air pollutants using microorganisms, advanced oxidation processes including catalytic ozone oxidation and electrochemical oxidation, and combined systems for air pollution control. Her work bridges fundamental science with practical applications for VOCs and odor control technologies. Her research has significant implications for environmental sustainability and public health. An analysis of her recent publications reveals a strong focus on catalytic oxidation processes for VOC removal, hydrogen sulfide treatment in wastewater systems, and innovative combined approaches for simultaneous pollutant removal. Her work consistently emphasizes practical engineering solutions that can be implemented in real-world environmental systems. Professor Song has maintained an active research program with consistent publication output over more than two decades, demonstrating sustained scholarly productivity and impact in her field. Her research group develops and tests various technologies including electrochemical oxidation systems, catalytic ozone processes, and biofiltration methods for air and water treatment applications. Current projects appear to be focused on optimizing catalyst systems for room-temperature pollutant removal and developing integrated approaches for simultaneous treatment of multiple contaminants.

Suyong LeeView profile

Professor

• Artificial intelligence for food systems
• Alternative food processing
• Food Texture Design
• +5 more

Suyong Lee is a Professor in the Department of Food Science and Biotechnology at Sejong University, leading research at the Carbohydrate Bioproduct Research Center. His work bridges food science, artificial intelligence, and advanced processing technologies to develop innovative food systems. His research focuses on Artificial intelligence for food systems , Alternative food processing , and Food Texture Design , with particular emphasis on hydrocolloids, oleogels, and plant-based analogues. His laboratory specializes in rheo-processing technologies that integrate machine learning with hyperspectral imaging for non-destructive food analysis. Analysis of his recent publications reveals strong trends in AI-driven food characterization, with 60% of his 2023-2025 work applying machine learning to predict food properties. His research spans from fundamental carbohydrate chemistry to applied product development, particularly in sugar reduction, fat replacement, and gluten-free systems. Over 130 peer-reviewed research papers More than 30 patent applications h-index of 47 with 6548 citations He actively supervises research in food texture engineering and maintains strong industry collaborations, particularly in developing plant-based meat and dairy alternatives using hydrocolloid-based technologies. His laboratory operates cutting-edge facilities for rheological analysis, hyperspectral imaging, and AI modeling of food systems.

Amy K JohnsonView profile

Associate Professor

• Adolescent Sexual and Reproductive Health
• Sexually Transmitted Diseases
• HIV Prevention
• +3 more

Amy K Johnson, PhD, serves as Associate Professor in Pediatrics (Adolescent and Young Adult Medicine) at Northwestern University's Feinberg School of Medicine, with clinical affiliation at Ann & Robert H. Lurie Children's Hospital of Chicago. She holds key roles in the Institute for Public Health and Medicine (IPHAM) - Center for Dissemination and Implementation Science, Northwestern University Clinical and Translational Sciences Institute (NUCATS), and Robert J. Havey, MD Institute for Global Health (IGH). Dr. Johnson earned her PhD from the University of Illinois, Chicago in 2016. Her research centers on adolescent sexual and reproductive health with dual emphases on HIV/STI intervention implementation and ethical dimensions of community participation in cure research. She develops prevention packages targeting adolescents—particularly young women—and addresses surveillance gaps in sexually transmitted diseases through implementation science frameworks. Analysis of her 2024-2025 publications reveals a dominant focus on PrEP implementation across diverse populations and geographies. Her work consistently addresses health disparities through mixed-methods approaches, examining structural barriers among Black women in Chicago, migrant communities in France, and youth in Nigeria. Common threads include innovative delivery strategies (text messaging, peer navigation), cultural adaptation of interventions, and ethical considerations in community engagement—demonstrating her commitment to translating research into equitable prevention solutions. Her scientific contributions have been recognized with the Excellence in Scholarship award from Lurie Children's Hospital (2023) and Best Poster Award from the American Sexually Transmitted Disease Association (2016). Excellence in Scholarship, Lurie Children's Hospital (2023) Best Poster Award, American Sexually Transmitted Disease Association (2016) Dr. Johnson actively shapes public health practice through leadership in the Illinois PrEP Working Group, Getting to Zero Illinois, and as Vice Chair of Epidemiology for the Illinois Public Health Association. Her editorial role at Frontiers in Reproductive Health and memberships in the American College of Epidemiology and American Public Health Association reflect her influence in advancing methodological rigor and equity-focused HIV prevention science. Current projects like iCARE Nigeria and POWER Up demonstrate her commitment to scalable interventions addressing the HIV care continuum.

Saikat DuttaView profile

Assistant Professor

• Software Engineering
• Machine Learning
• Artificial Intelligence
• +5 more

Saikat Dutta is an Assistant Professor in the Department of Computer Science at Cornell University. He is affiliated with the Software Engineering Group and focuses on the intersection of Software Engineering and Machine Learning . His work aims to enhance the reliability of ML-based systems while applying ML techniques to solve software engineering challenges. PhD in Computer Science from University of Illinois Urbana-Champaign (Summer 2023) Postdoctoral Researcher at University of Pennsylvania Bachelor's in Computer Science and Engineering from Jadavpur University Research Interests: Dr. Dutta's research spans several key areas: Automated test generation and debugging for ML/DL libraries Using AI/ML for automated software engineering tasks Improving performance of regression tests in ML libraries Static and dynamic analysis for probabilistic programming Article Trends: His recent publications emphasize: Automated testing of ML systems Security vulnerability detection using LLMs Probabilistic program analysis Neurosymbolic learning frameworks Flaky test management in stochastic environments Stochastic regression test optimization Scientific Awards: Meta AI LLM Evaluation Research Grant (2025) Mavis Future Faculty Fellowship (2022-23) Facebook PhD Fellowship (2020-22) 3M Foundation Fellowship (2019-2020) Advising & Grants: Dr. Dutta actively recruits PhD students and postdocs. He leads research projects supported by grants from Meta AI and participates in program committees for top conferences like ICSE and ISSTA. His lab focuses on neurosymbolic systems and ML-based software verification.