Find Academic Supervisors

LIU BoxiangView profile

Assistant Professor

• Genomics
• Bioinformatics
• Machine Learning
• +3 more

Asst Prof LIU Boxiang holds the position of Assistant Professor and NUS Presidential Young Professorship at the Department of Pharmacy and Pharmaceutical Sciences, National University of Singapore (NUS), within the Faculty of Science. His research focuses on integrating multi-omics approaches with computational methods to study complex diseases such as coronary artery disease and age-related macular degeneration. He specializes in developing statistical and machine learning tools for genomic analysis, including eQTL mapping and deep learning architectures for gene expression regulation. Education: BA in Biophysics (Illinois Wesleyan University), MS and PhD in Bioinformatics (Stanford University). He contributed to the GTEx consortium and is part of the Asian Immune Diversity Atlas (AIDA) initiative. His lab develops methods like ANTseq for ancestry determination and scPrediXcan for cell-type-specific transcriptome studies. Research Interests: Functional genomics, eQTL analysis, deep learning in biomedicine, and computational tools for omics data integration. His work bridges disciplines such as natural language processing and computer vision with biological questions. Scientific Awards: NUS Presidential Young Professorship (2021). His lab's innovations include ParaMed, a biomedical translation dataset, and LinearDesign for optimized mRNA stability. Advising and Grants: Leads the Liu Lab (boxiangliulab.com), focusing on single-cell genomics, mitochondrial dynamics, and computational biomedicine. Collaborates on projects like the RESET cohort study for cardiovascular disease prevention.

Thomas A. RunklerView profile

Adjunct Professor

• Machine Learning
• Data Analytics
• Pattern Recognition
• +3 more

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

David John BakerView profile

Professor

• Philosophy of Physics
• Philosophy of Science
• Metaphysics
• +5 more

David John Baker is a Professor of Philosophy at the University of Michigan, where he also serves as Undergraduate Chair and PPE (Philosophy, Politics, and Economics) Chair. His primary research focuses on the philosophy of physics, metaphysics, and the philosophy of science, with special emphasis on quantum field theory, general relativity, and symmetry principles in fundamental physics. He holds a Ph.D. in Philosophy from Princeton University (2008) and a B.S. in Philosophy and Physics from the University of Michigan (2003). Education: 2008: Ph.D., Philosophy, Princeton University 2003: B.S., Philosophy (highest honors) and Physics, University of Michigan Research Interests: Baker’s work bridges the philosophy of physics, metaphysics, and ethics. His core research examines the ontological implications of dynamical symmetries in quantum field theory, spacetime structure, and the metaphysics of quantities. He also explores moral philosophy, particularly questions related to free will, responsibility, nuclear arms control, and mortality. Recent Work Trends: His publications address foundational issues in quantum mechanics, interpretations of supersymmetry, spacetime functionalism, and the philosophical implications of string theory. He critiques field interpretations of quantum field theory and investigates how scientific theories shape our understanding of fundamental physical quantities and symmetries. Advising & Grants: While no specific student advising records are noted here, his academic role suggests active mentorship in philosophy and interdisciplinary programs like PPE. Grants and funding details are not explicitly mentioned in the provided texts. Labs/Teams: No specific research groups or laboratories are listed, though his affiliation with the University of Michigan’s philosophy department implies collaborative work within the institution’s academic community.

Ralf SchlüterView profile

Lecturer

• Automatic Speech Recognition
• Machine Learning
• Pattern Recognition
• +4 more

Ralf Schlüter is a senior researcher and Lecturer (Privatdozent) at RWTH Aachen University, where he leads the Automatic Speech Recognition Group within the Department of Computer Science. His academic roles include serving as Academic Director and leading research initiatives at Lehrstuhl Informatik 6: Human Language Technology and Pattern Recognition. He holds a Dipl. in Physics (1995), a Dr. rer. nat. in Computer Science (2000), and a Habilitation in Computer Science (2019), all from RWTH Aachen University. His research focuses on advanced speech recognition technologies, including end-to-end architectures, acoustic modeling, discriminative training, and machine learning applications. He has led numerous national and international projects, such as EU-funded initiatives like BABEL, BOLT, and GALE. Schlüter has published extensively in top-tier conferences like Interspeech and ICASSP, contributing to advancements in neural network frameworks, feature extraction, and error analysis. He teaches courses on speech recognition, pattern recognition, and signal processing, and serves as a Subject Editor for Speech Communication . His work also involves developing the RWTH ASR toolkit, emphasizing scalable and efficient speech recognition systems. Schlüter’s contributions to speech technology have earned recognition, including the ISCA Best Student Paper Award (2019).

Alexander SahnView profile

Assistant Professor

• Political Economy
• Urban Politics
• American Political Development
• +5 more

Alexander Sahn is an Assistant Professor of Political Science and the Thomas J. Pearsall Fellow at the University of North Carolina at Chapel Hill. He specializes in political economy and representation, focusing on subnational U.S. governments, housing policies, and urban governance. His research examines racial backlash in exclusionary zoning, public comment influence on housing approvals, and municipal civil service reforms. Education: PhD in Political Science from the University of California, Berkeley (2020); Postdoctoral Fellow at Princeton University’s Center for the Study of Democratic Politics (2020–2023). Research Interests: Political economy of housing and urban development Institutional design of local governments Racial dynamics in policy implementation Public participation mechanisms Awards: Recognized with the Susan Clarke Young Scholar Award (APSA Urban Politics) and Emerging Scholar Award (APSA American Political Economy Section). Advising/Grants: Currently mentoring students in urban politics and political methodology. No active grants explicitly mentioned, but his research often addresses applied policy questions. Labs/Teams: Active in UNC’s political science department, collaborating on projects about neighborhood representation and climate communication.

Alexander RobelView profile

Associate Professor

• Climate, Oceanography, and Weather
• Cryospheric Science
• Paleoclimate
• +6 more

Dr. Alexander Robel is an Associate Professor in the School of Earth & Atmospheric Sciences at the Georgia Institute of Technology, part of the College of Sciences. His research focuses on climate, oceanography, and cryospheric science, with a particular emphasis on glaciers, ice sheets, and sea level change. He holds a Ph.D. in Earth and Planetary Science from Harvard University (2015) and a B.S. in Physics and Earth Sciences from Duke University (2010). Research Interests: Dr. Robel’s work addresses critical questions in cryospheric science, including ice-ocean interactions, climate modeling, and the dynamics of ice sheets. His studies integrate computational and mathematical modeling to explore topics such as glacier stability, sea level rise, and the impacts of climate variability on polar regions. Key themes include the stochastic parameterization of ice dynamics, the role of subglacial hydrology, and the statistical analysis of climate forcing on glacial systems. Publications Trends: Recent work emphasizes advancing stochastic models for ice sheet projections, quantifying biases in climate-ice sheet interactions, and analyzing oceanic drivers of glacial retreat. His studies often bridge observational data with theoretical frameworks, offering insights into both near-term and long-term climate impacts. Education: Ph.D., Earth and Planetary Science, Harvard University (2015) B.S., Physics and Earth Sciences, Duke University (2010) Advising & Grants: Specific advising or grant details are not explicitly listed in the provided text. Labs/Teams: Likely affiliated with Georgia Tech’s School of Earth & Atmospheric Sciences research groups, though specific teams are not mentioned here.

Tim BiblarzView profile

Associate Professor

• Family Sociology
• Stratification and Social Mobility
• Gender and Sexuality
• +2 more

Tim Biblarz is an Associate Professor of Sociology and Gender Studies at the University of Southern California (USC), affiliated with the USC Dornsife College of Letters, Arts and Sciences and the Gerontology Research Institute in the Andrus Gerontology Center. His roles include Faculty Master of the South Residential College and former Chair of the Sociology Department (2007–2012), Director of Graduate Studies (2001–2005), and President of the USC Dornsife Faculty Council (2010–2011). Education: Ph.D. in Sociology, University of Washington, 1992 Research Interests: Focus on social inequalities, family dynamics, and intergenerational outcomes in the U.S., particularly regarding gender roles, sexual orientation, and family structure. Current projects examine parental gender impacts on child development, LGBTQ+ family structures, and social mobility patterns. Publications Trends: Recent work emphasizes LGBTQ+ family studies, intergenerational relationships, and the socio-demographic impacts of family structure. Key themes include challenging traditional family norms, analyzing post-divorce family dynamics, and exploring the societal implications of gender and sexual orientation diversity. Awards/Honors: None explicitly listed in the provided text. Grants/Advising: No specific grants or student advisees listed, though his leadership roles suggest extensive mentorship. Research has been conducted through USC’s institutions and collaborative centers. Labs/Teams: Affiliated with the Gerontology Research Institute, focusing on aging populations and intergenerational relationships.

Sze Ming LeeView profile

Researcher

• Social Statistics
• Large-Scale Data Analysis
• Latent Variable Modelling
• +3 more

Sze Ming Lee is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on Social Statistics, with expertise in large-scale data analysis, latent variable modelling, survival analysis, and quantile regression. Lee holds a BSc in Mathematics, an MPhil in Risk Management Science, and a postgraduate diploma in Education, all from the Chinese University of Hong Kong. His academic supervisors are Dr. Yunxiao Chen and Professor Fiona Steele. His research interests emphasize methodological advancements in statistical modelling, particularly in handling high-dimensional data and latent variables. Recent publications (2023-2025) highlight contributions to quantile regression, factor analysis stability, and longitudinal data methodologies. Lee’s work bridges theoretical statistics with practical applications in social sciences and biostatistics. No scientific awards or grants are explicitly mentioned in the provided texts. He is affiliated with LSE’s Social Statistics research group and contributes to the department’s focus on data-driven social science research.

Abdus S. WahedView profile

Professor

• Personalized Medicine
• Dynamic Treatment Regimes
• Statistical Methodology
• +5 more

Abdus S. Wahed is a Professor of Biostatistics and Associate Chair in the Department of Biostatistics and Computational Biology at the University of Rochester. His research focuses on developing statistical methods for dynamic treatment regimes (DTRs), particularly in Sequential Multiple Assignment Randomized Trials (SMARTs), and their applications in hepatitis B, cancer, and obesity. He also works on longitudinal data analysis, survival analysis, and causal inference. Key Roles: Associate Chair, Professor of Biostatistics Affiliations: University of Rochester Medical Center His research interests include: Personalized medicine and adaptive treatment strategies Methodologies for analyzing longitudinal and time-to-event data Clinical collaborations in hepatitis B, liver diseases, and cancer treatment Recent work emphasizes statistical innovations in SMART trials, including interim monitoring and sample size adjustments. His collaborations span virology, oncology, and public health, addressing issues like hepatitis B treatment discontinuation and arsenic exposure effects. Awards: No specific awards listed in provided text. Advising & Grants: Active in mentoring graduate students and leading research grants focused on biostatistical methodologies. Collaborates with organizations like the Hepatitis B Research Network and the Longitudinal Assessment of Bariatric Surgery study. Labs/Teams: Engaged in multidisciplinary teams addressing chronic diseases and statistical trial design.

Kevin FloresView profile

Associate Professor

• Mathematical Biology
• Precision Medicine
• Environmental Toxicology
• +4 more

Kevin Flores is an Associate Professor in the Department of Mathematics at North Carolina State University (NC State), and Director of the Biomathematics Graduate Program. He leads the Flores Lab, focusing on developing mathematical and statistical methods for parameter estimation, uncertainty quantification, and forecasting in Precision Medicine, Environmental Toxicology, and Synthetic Biology. His work bridges computational approaches with biological systems analysis. Dr. Flores earned his PhD in 2009 from Arizona State University. His research groups include the Mathematical Biology cluster within the Department of Mathematics. His affiliations include Cox Hall 406D and the College of Sciences at NC State. Research interests emphasize interdisciplinary applications: (1) Mathematical Biology involving tumor heterogeneity, viral dynamics, and angiogenesis modeling; (2) Biostatistics focusing on parameter estimation in complex systems; and (3) Computational Tools for biomedical image analysis and machine learning in healthcare. His lab pioneered methods like biologically-informed neural networks and topological data analysis for biological systems. Recent work highlights include: (1) tumor spheroid modeling predicting clinical variability; (2) BK virus infection dynamics in transplant patients; (3) EEG-based brain-computer interface improvements using GANs; and (4) few-shot learning for plant phenotyping. His methodologies address challenges in sparse data scenarios and integrate mechanistic understanding with data-driven approaches. Awards and recognition : None explicitly listed in provided texts. Advising and grants: No specific advisees or grant details provided in texts. His lab's software tools support image segmentation and population modeling. Labs/teams: Directs the Flores Lab for Mathematical Biology at NC State, specializing in hybrid computational-experimental approaches. Collaborates across departments in biomathematics and engineering.

Palanichamy NaveenView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +7 more

Dr. Palanichamy Naveen is an Assistant Professor in the Department of Electronics and Communication Engineering at Dr. N.G.P. Institute of Technology, India. His research lies at the intersection of computer vision, machine learning, and signal processing, with applications in healthcare, control systems, and academic policy. He actively collaborates with researchers globally, including institutions in the Czech Republic, and has contributed over 60 publications to academic literature. His research interests span Computer Vision , Deep Learning , Image and Video Processing , Signal Processing , and Optimization Algorithms . He also explores interdisciplinary topics such as academic performance appraisal and sports analytics. His work reflects a strong commitment to both technical innovation and institutional improvement in engineering education. The recent trend in his publications demonstrates a dual focus: applying AI to solve real-world problems in medical imaging and industrial control, while also contributing to academic governance through models for faculty evaluation and research incentivization. This blend of technical and administrative research highlights his broad impact in engineering academia. Scientific Contributions: Developed lightweight transformer models for retinal disease detection from OCT scans Proposed novel bio-inspired optimization algorithms for PID controller tuning Designed frameworks for faculty performance appraisal and research contribution assessment Created a relative grading system for ODI cricket player rankings Dr. Naveen is actively involved in academic discourse and knowledge dissemination through preprint platforms and peer-reviewed journals. While no formal advising or grant information is available in the text, his extensive publication record and international collaborations suggest active mentorship and research leadership. He is also engaged with academic networks like SciProfiles and Multidisciplinary Digital Publishing Institute, contributing to the broader scholarly ecosystem.

Yang Janet LiuView profile

Assistant Professor

• Computational Linguistics
• Natural Language Processing
• Discourse Analysis
• +5 more

Yang Janet Liu is an Assistant Professor in the Department of Linguistics at the University of Pittsburgh. Previously, she was a Postdoctoral Researcher at the MaiNLP research lab at the Center for Information and Language Processing (CIS) at LMU Munich, where she was also affiliated with the Munich Center for Machine Learning (MCML). Dr. Liu earned her Ph.D. in Computational Linguistics from the Department of Linguistics at Georgetown University. During her doctoral studies, she was advised by Amir Zeldes and was a member of both Corpling@GU and Computational Linguistics @ Georgetown (GUCL) research groups. She was also a student research affiliate of NERT, directed by Nathan Schneider. Her research focuses on tackling text variation in NLP, studying model internals for discourse-level linguistic phenomena and generalization, and analyzing discourse-level phenomena across genres using computational, statistical, and corpus-based methods. She investigates NLP applications involving discourse structure and understanding, particularly summarization for genre-diverse texts. Dr. Liu is also deeply involved in cross-framework discourse understanding and unifying discourse resources, having co-organized the DISRPT shared task. Her work extends to multilingual annotation projects involving discourse-level phenomena. Dr. Liu's recent publications demonstrate a consistent focus on discourse analysis, multilingual capabilities, and genre diversity in NLP. Her work bridges theoretical linguistics with practical NLP applications, particularly in discourse parsing, summarization, and multilingual generalization. She has made significant contributions to creating diverse datasets for discourse analysis and understanding the limitations of current discourse parsing systems. Dr. Liu is actively involved in the academic community, having co-organized workshops such as the First Workshop on Bridging NLP and Public Opinion Research at COLM 2025. She regularly presents her work at major NLP conferences including ACL, EMNLP, and INLG. Dr. Liu maintains connections with her previous institutions and collaborators, including her postdoctoral advisor Prof. Dr. Barbara Plank at LMU Munich and her doctoral advisor Amir Zeldes at Georgetown University. Her work continues to build on the foundation established during her time at these institutions.

Mohammed ShafaeView profile

Assistant Professor

• Cyber-physical systems security
• Smart manufacturing systems
• Statistical process monitoring
• +7 more

Mohammed Shafae is an Assistant Professor in the Department of Systems and Industrial Engineering at the University of Arizona, where he has been serving since 2018. He is also a member of the Graduate Faculty, contributing to advanced research and mentorship in engineering disciplines. Education: PhD in Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States MS in Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States MS in Production Engineering, Alexandria University, Alexandria, Egypt BS in Production Engineering, Alexandria University, Alexandria, Egypt His research centers on cyber-physical systems security , smart manufacturing systems , and data-driven quality control , with a strong emphasis on leveraging manufacturing data for process monitoring, modeling, and securing machining and additive manufacturing systems. His work integrates statistical process monitoring, machine learning, and advanced metrology to enhance manufacturing resilience and efficiency. His recent publications (2022–2025) reflect a clear trend toward securing Industry 4.0 systems, with a focus on digital twin security, attack detection in machining, and anomaly detection in additive manufacturing using photodiode and thermal data. He also explores futuristic applications such as in-situ lunar manufacturing using regolith-based materials. Scientific Awards: First Place in the ASCEND Propel Pitch Competition (AIAA, Fall 2020) Virginia Tech Teaching Excellence Award (Spring 2017) Harold Schneikert Graduate Fellowship (Spring 2016) David H. Burrows Graduate Fellowship (Fall 2015) Best Track Paper Award (IEOM 2012) Prize for Excellence in Senior Design Project (Cairo, 2009) Dr. Shafae actively mentors graduate students, co-authoring numerous publications with advisees in areas such as cybersecurity, additive manufacturing, and process monitoring. While specific grant details are not listed, his research is clearly funded through competitive fellowships and likely external grants given the volume and quality of his outputs. He has no listed labs or teams in the provided text, but his collaborative work suggests active participation in research groups focused on smart manufacturing and cybersecurity.

Thomas LengauerView profile

Professor

• Computational Biology
• Bioinformatics
• Applied Algorithmics
• +10 more

Prof. Dr. Thomas Lengauer is a leading figure in computational biology and applied algorithmics at the Max Planck Institute for Informatics, part of the Max Planck Society in Saarbrücken, Germany. He heads the Department of Computational Biology and Applied Algorithmics, where he drives research at the intersection of computer science, genomics, and medicine. His work integrates algorithm development with biological applications, particularly in viral genomics, epigenetics, and personalized treatment prediction. Research Interests: His research focuses on computational methods for analyzing complex biological data. Key areas include HIV and hepatitis virus evolution, antiretroviral therapy outcome prediction, DNA methylation and epigenomic analysis, machine learning applications in medicine, and the integration of big data in biological research. He has made significant contributions to understanding viral drug resistance and host-pathogen interactions through computational modeling. The recent publications (2019–2025) reflect a strong trend toward integrating temporal genomic data, machine learning, and public health surveillance. His work spans from fundamental algorithm development (e.g., RnBeads, MeDeCom) to applied clinical research (e.g., dolutegravir resistance, SARS-CoV-2 interventions). Key domains include epigenomics , virology , machine learning in healthcare , and biological database systems , increasingly incorporating AI-driven approaches. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: While no formal list of students is provided, Prof. Lengauer leads a large collaborative research group, evidenced by frequent co-authorship with researchers such as Walter, Bock, Müller, Kaiser, and Pirkl. He participates in major consortia (e.g., DEEP Consortium, Respiratory Virus Network), suggesting leadership in funded collaborative projects. His work is likely supported by Max Planck Society core funding and competitive third-party grants, though specific grants are not listed. Labs and Teams: He leads the Computational Biology and Applied Algorithmics group at the Max Planck Institute for Informatics. The team develops computational tools for epigenomic data analysis (e.g., RnBeads, DecompPipeline), viral resistance prediction, and public health modeling. The group collaborates extensively with clinical and biological researchers across Europe, functioning as a hub for interdisciplinary bioinformatics research.

• Mathematical Economics
• Actuarial Sciences
• Financial Mathematics
• +3 more

Graziella Pacelli is a Full Professor in the Department of Social Sciences at the Faculty of Economics, Università Politecnica delle Marche (UNIVPM), Italy. Her academic work focuses on quantitative methods in economics, actuarial sciences, and financial modeling. Her research interests lie in the application of mathematical and statistical techniques to economic and financial systems. Key areas include econometrics, actuarial risk modeling, and financial mathematics. She contributes to advancing methodological frameworks used in economic analysis and decision-making processes within uncertain environments. While no recent publications are listed in the provided text, her scientific sector (STAT-04/A) suggests a strong orientation toward theoretical and applied statistics in economics and finance. There are no listed scientific awards at this time. She advises students in quantitative economics and related fields, though specific advisees are not named. There is no mention of grants or funded research projects in the available data. Graziella Pacelli is affiliated with the Department of Social Sciences, which supports interdisciplinary research in economic theory, policy, and quantitative analysis.