Find Academic Supervisors

Niall ManganView profile

Assistant Professor

• Data-driven modeling
• Biological networks
• Renewable energy systems
• +3 more

Niall Mangan is an Assistant Professor of Engineering Sciences and Applied Mathematics at Northwestern University's McCormick School of Engineering . He leads research in data-driven modeling, biological networks, and renewable energy systems. His work bridges mechanistic modeling with data science to optimize engineering solutions. Education: Ph.D. in Systems Biology from Harvard University (2013), Dual B.S. in Physics and Mathematics from Clarkson University (2008). Research Interests: Data-driven methods for complex systems design Biological network dynamics and uncertainty quantification Spatial organization in cellular biochemistry Renewable energy systems modeling Key Contributions: Developed frameworks for inferring biological networks via sparse identification of nonlinear dynamics. Advanced methods for solar cell interface analysis and wastewater surveillance epidemiology modeling. Scientific Awards: Sloan Research Fellowship DOE Early Career Award NSF Graduate Fellowship (2008) Grants & Affiliations: Member of the NSF-Simons Center for Quantitative Biology, Trienens Institute for Sustainability, and Center for Synthetic Biology. Supported by DOE, Sloan Foundation, and Northwestern grants. Labs & Teams: Leads research groups focused on systems biology and sustainable energy, collaborating with interdisciplinary teams in synthetic biology and environmental engineering.

Timo GerkmannView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +3 more

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

Neriman TokcanView profile

Assistant Professor

• Algebraic Geometry
• Multi-Linear Algebra
• Combinatorics
• +3 more

Neriman Tokcan is an Assistant Professor in the Department of Mathematics at the University of Massachusetts Boston (College of Science and Mathematics). She holds a Ph.D. in Mathematics from the University of Illinois Urbana-Champaign. Her research focuses on Algebraic Geometry, Multi-Linear Algebra, Combinatorics, Computational Biology, and Bioinformatics, with applications in tensor data analysis and medical informatics. Her work bridges pure mathematics and applied domains, such as developing algebraic methods for tensor data analysis and applying combinatorial techniques to biomedical problems. Notable contributions include studies on Newton polytopes in algebraic combinatorics and Waring ranks of binary forms. Publications highlight interdisciplinary research, including applications in postoperative cardiac care monitoring and computational methods for single-cell transcriptomics. Her recent work emphasizes tensor-based approaches for omics data analysis and patient monitoring systems. No scientific awards or grants are explicitly listed in the provided materials. She advises no students in the listed texts but collaborates widely with researchers in mathematics and computational biology. Her office is located at Wheatley 3-154-20.

Hudson SmithView profile

Assistant Professor

• Machine Learning
• Medical Imaging
• Quantum Physics
• +5 more

Hudson Smith is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Clemson University, College of Science. His research focuses on integrating domain knowledge with machine learning to address data-constrained problems in healthcare, forensics, and physics. He holds a PhD in theoretical atomic physics from Ohio State University, combining first-principles approaches with data-driven methods. Key areas of research include medical imaging analysis (e.g., ultrasound quality assessment), forensic decomposition modeling (geoFOR database collaboration), and AI detection of coordinated disinformation campaigns. His work bridges theoretical physics (quantum systems, cold atoms) with applied machine learning in healthcare and social media analysis. Publications highlight contributions to AI in trauma care, forensic science, and social media disinformation detection. Active collaborations span forensic science, biomedical informatics, and quantum physics. GitHub repositories showcase contributions to visualization tools (e.g., Schrödinger equation solvers, Voronoi-based image stylization).

Samuel CartonView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Human-Computer Interaction
• +3 more

Samuel Carton is an Assistant Professor in the Department of Computer Science at the University of New Hampshire (UNH), within the College of Engineering and Physical Sciences. His research focuses on human-centered natural language processing (NLP), emphasizing the implicit knowledge acquired by NLP models and methods to present this knowledge effectively to human stakeholders for ethical and practical use. He holds a Ph.D. in Information Science/Studies from the University of Michigan and has conducted postdoctoral research with Chenhao Tan at the University of Colorado Boulder and University of Chicago. He teaches courses including Natural Language Processing, Advanced Topics in CS, and Doctoral Research. Education: Ph.D., Information Science/Studies, University of Michigan Research Interests: Human-AI collaboration, model interpretability, ethical AI, and explainable NLP. His work bridges algorithm design and human-subject experimentation to ensure models are both effective and trustworthy. Grants & Advising: No explicit grants listed; actively involved in doctoral research supervision as seen in his course offerings. Labs & Teams: No specific lab affiliations explicitly mentioned, but his research likely involves collaborations with NLP and AI ethics groups.

Hendrik LensView profile

Professor

• Power Systems
• Grid Stability
• Renewable Energy Integration
• +4 more

Hendrik Lens is a **Professor for Power Plant and Grid Systems** and **Director of the Institute** at the University of Stuttgart's **Institute of Combustion and Power Plant Technology (IFK)**. He leads the **Power Generation and Automatic Control (SuA) department**, focusing on advanced grid technologies and energy system optimization. His research emphasizes **grid stability**, **renewable energy integration**, and **converter control strategies**. Key contributions include studies on grid-forming converters, inertia-aware generation planning, and dynamic network equivalents. He collaborates internationally on projects like the *VerbundnetzStabil initiative* analyzing interconnected power systems. Publications span **18 journals/conferences in 2024 alone**, with a focus on smart grid technologies and decarbonization strategies. His work addresses challenges such as frequency containment reserves, distributed generation impacts, and stochastic unit commitment tools. Operates within the Institute of Combustion and Power Plant Technology, contributing to both academic and applied research in sustainable energy systems.

Jun ZhuangView profile

Professor

• Operations Research
• Game Theory
• Hazard Response
• +5 more

Jun Zhuang is the Associate Dean for Research and Morton C. Frank Professor at the University of Buffalo's School of Engineering and Applied Sciences, Department of Industrial and Systems Engineering. With a PhD in Industrial Engineering from the University of Wisconsin-Madison, his work bridges operations research, game theory, and hazard response, focusing on cybersecurity, disaster management, and social media analysis during crises. Education: PhD, Industrial Engineering, University of Wisconsin-Madison MS, Industrial Engineering, University of Wisconsin-Madison MS, Agricultural Economics, University of Kentucky BE, Industrial Engineering, Southeast University, China His research employs game theory to model attacker-defender dynamics in homeland security, fire risk management, and misinformation control during disasters. Recent work explores machine learning applications for social media monitoring and resource allocation optimization. Jun Zhuang has supervised over 40 graduate/undergraduate researchers, with 8 PhD graduates now as faculty at institutions like Cal Poly, University of Houston, and Stevens Institute. His students' work spans terrorism interdiction, fire safety models, and pandemic-related risk management. Scientific Awards: Fellow, Society for Risk Analysis (2022) SUNY Chancellor's Excellence in Scholarship (2020-21) UB Exceptional Scholar Sustained Achievement (2019) SEAS Senior Researcher of the Year (2018) Koopman Prize, INFORMS Military Applications (2017) As advisor and researcher, he has secured multiple NSF REU awards, collaborated with international scholars from China, India, and Saudi Arabia, and contributed to policy development for disaster response and fire protection strategies.

Trevor CickovskiView profile

Assistant Professor

• Bioinformatics
• Microbiome Analysis
• GPU Computing
• +6 more

Trevor Cickovski is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences, with a focus on bioinformatics and computational biology. Holding Graduate Faculty status, he specializes in microbiome analysis, GPU computing, network analysis, and Unix systems. His academic journey began with a Ph.D. in Computer Science and Engineering from the University of Notre Dame (2008). Current roles: Associate Director, Assistant Teaching Professor Research affiliations: Bioinformatics Research Group (BioRG), ACM, IEEE Dr. Cickovski's research spans microbiome analysis, software engineering, and GPU-accelerated computing. His work explores the microbiome's role in neuropsychiatric disorders (ADHD), genetic conditions (A1AD), respiratory diseases (COPD), and environmental phenomena (red tides) through multi-omics integration. His publications in Journal of Medical Microbiology , Bioinformatics , and IEEE/ACM TCBB reflect strengths in plugin-based software frameworks (PluMA), network analysis, and computational pipeline optimization. Awards include multiple teaching excellence honors and industry funding from NVIDIA. 2020 Graduate Faculty Status 2019 FIU CAT Fellow 2019 FIU Faculty Teaching Award 2017 Teaching Excellence Award 2014 Promotion to Associate Professor As lead developer of PluMA, he enables cross-language plugin development for bioinformatics workflows. Funded by NIJ and NSF, his projects apply machine learning to epigenetics and computational approaches to vaccine discovery.

• Defects in Nanomaterials
• Physics Education
• Single Photon Emitters
• +3 more

Zack Ward serves as a Lecturer in the Department of Physics, Applied Physics & Astronomy at Rensselaer Polytechnic Institute (RPI), teaching Physics I (PHYS 1110/1100) and Physics II (PHYS 1200) across Summer 2025, Spring 2025, and Fall 2024 semesters. His office is located in J-ROWL 3C06, with contact email wardz3@rpi.edu and phone 845-453-1829. Ward's research spans two primary domains: defects in nanomaterials and physics education innovation. In nanomaterials, he investigates single photon emitters and develops predictive models for defect energy/stability in 2D materials. His recent educational focus targets introductory physics courses, emphasizing curriculum redesign through enhanced lab manuals, Discord-based communication systems, and refined lecture materials to improve student comprehension and engagement. His publication record (2021-2024) reveals strong interdisciplinary work at the intersection of materials science and computational methods, with emphasis on defect engineering in hexagonal boron nitride for batteries, optical properties of transition metal dichalcogenides, and machine learning applications for perovskite bandgap tuning. Ward's recognition includes: Walter Eppenstein Award (2020) - Best graduate teaching assistant in Physics at RPI As an instructional-focused lecturer, Ward concentrates on classroom innovation rather than traditional research advising. His current initiatives include developing comprehensive Physics I lab manuals with visual enhancements, implementing Discord channels for real-time student-instructor interaction, and designing assessment questions that capture nuanced conceptual understanding across engineering and physical science disciplines. He actively shapes the Physics I/II instructional labs at RPI, with recent work centering on restructured lab manuals featuring detailed imagery, highlighted critical variables, and activity sequences designed to optimize experimental learning outcomes for diverse student cohorts.

Lujie Karen ChenView profile

Assistant Professor

• Applied Machine Learning
• Data Mining
• Statistical Modeling
• +3 more

Dr. Lujie Karen Chen is an Assistant Professor in Information Systems at UMBC's College of Engineering and Information Technology. She directs the Lab for Informatics for Human Flourishing, integrating 20 years of experience in applied machine learning, data mining, and analytics. Research bridges computational methods with human-centered applications in education and social systems. Prior industry experience includes roles in accounting and business analytics. Received PhD in Information Systems from Carnegie Mellon University, where she was a PIER Fellow funded by the U.S. Department of Education. Maintains research collaborations with CMU's Auton Lab.

• Clinical Prediction Modeling
• Health Informatics
• Big Data Analysis
• +4 more

Dr. Muhammad Faisal is an Associate Professor at the Centre for Digital Innovations in Health & Social Care, University of Bradford. He holds a PhD in Biostatistics from the University of Vienna and has over 15 years of applied health research experience. His work focuses on developing equitable clinical prediction tools and leveraging big health data for improved healthcare outcomes. Key contributions include leading the CARSS (Computer Aided Risk Scoring System) project, highlighted in the Goldacre review, and co-founding the NHS-R community to advance R-based analytics in healthcare. Education: MSc in Statistics (Bahauddin Zakaryia University, 2006), PhD in Biostatistics (University of Vienna, 2012). Professional Affiliations: Royal Statistical Society (RSS), International Society for Clinical Biostatistics (ISCB). Awards: Wolfson Centre for Applied Health Research Fellow, Fellow of Advance HE. Research Interests: Clinical prediction modeling, machine learning applications in healthcare, big data analytics, and translational research for population health. He leads the £5.8M Yorkshire & Humber Patient Safety Research Collaboration and evaluates interventions for cancer screening in South Asian Muslim women (funded £440,699). Teaching: Courses include Health Data Science, Health Informatics, and Clinical Prediction Modelling. Grants: Reviewed NIHR and UKRI grants, served on UKRI Peer Review College and NIHR HS&DR committees. Notable Publications: Over 100 peer-reviewed articles, with one achieving top 5% Altmetric attention (17 news stories). Labs/Teams: CARSS Research Group, NHS-R community. Future Work: Expanding equitable prediction tools and digital health innovation.

Ramanathan SubramanianView profile

Associate Professor

• Human-centered computing
• AI systems
• Non-verbal behavioral analysis
• +7 more

Ramanathan Subramanian is an Associate Professor in the Department of Information Technology & Systems, AI and Robotics at the University of Canberra. His past affiliations include IIT Ropar and UIUC, Singapore. He has received notable awards including the IEEE Transactions on Affective Computing Best Paper Award (2019) and Multimedia Rising Star nomination (2015). His research focuses on Human-centered computing, AI systems leveraging non-verbal behavioral cues, and applications in health analytics and virtual reality. He actively supervises PhD and Master’s students in areas like Human-Computer Interaction, Data Science, and VR for healthy aging. Research interests: Human perception modeling, intelligent interfaces, health-focused VR, and applied machine learning Key projects: Driver sentiment prediction, distraction awareness campaigns, and robotics for waste management His work contributes to UN SDGs related to health and well-being, particularly through technologies improving aging care and mental health monitoring. Recent projects include developing explainable AI for emotion inference and multimodal data fusion systems. Awards: IEEE Best Paper Award, Multimedia Rising Star Grants: Lead or Co-Investigator on three major grants totaling over AUD 4M Labs/Teams: Collaborates extensively with the University's robotics and health tech groups, contributing to interdisciplinary projects in AI ethics and medical signal processing.

Patrik RydénView profile

Professor

• Mathematical Statistics
• Data Analysis
• Ambulance Care Optimization
• +3 more

Patrik Rydén is a Professor of Mathematical Statistics at the Department of Mathematics and Mathematical Statistics, Umeå University, where he applies statistical methods to complex datasets. He also directs the Industrial Doctoral School for Research and Innovation , fostering academia-industry collaboration. His research spans healthcare (ambulance logistics, cancer diagnostics), industrial optimization (automotive quality control), and bioinformatics. Research Focus Rydén develops data-driven methodologies for: Healthcare : Optimizing ambulance deployment, predicting patient care times, and modeling disease spread. Industry : Statistical process control for manufacturing and quality improvement. Bioinformatics : Cancer subtype identification and vaccine efficacy modeling. Publication Trends His recent work emphasizes applied statistics in healthcare (40%), industrial machine learning (30%), and genomic analysis (30%), with strong themes in predictive modeling, spatio-temporal analysis, and high-dimensional data interpretation. Leadership & Projects As director of the Industrial Doctoral School, he bridges academic research with industry needs. Key projects include: Predicting post-anesthesia care time using ML (2022–2027) Data-driven prehospital care optimization (2018–2028) Statistical learning for industrial defect detection (2017–2023)

Hamid Alinejad-RoknyView profile

Senior Lecturer

• Machine Learning
• Bioinformatics
• Genomics
• +7 more

Dr. Hamid Alinejad-Rokny is a Scientia Senior Lecturer at UNSW Sydney and Adjunct Associate Professor at Concordia University. He leads the UNSW BioMedical Machine Learning (BML) Lab within the Graduate School of Biomedical Engineering. His research focuses on applying machine learning, bioinformatics, and statistical methods to understand genomic mechanisms underlying diseases like cancer and neurodevelopmental disorders. Dr. Rokny holds a PhD in Biostatistical Machine Learning from UNSW and has secured over $13M in grants as a principal or co-investigator. He has published 80+ papers, including 10 as first author and 45 as senior author. Education: Bachelor’s in Software Engineering (2004-2009), Master’s in Artificial Intelligence (2009-2012), PhD in BioMedical Machine Learning (UNSW, 2014-2018), Postdoc at Harry Perkins Institute (2017-2019). Research interests include medical AI, deep learning, genomic data analysis, and systems biology. He actively supervises 20+ PhD/Master’s students and collaborates with industry partners like CSIRO and 23Strands. Awards include the DECRA 2023, NHMRC MERIT, and International Autism Fellowships. He also serves as a keynote speaker at conferences like HUGO and an Honorary Lecturer at Macquarie University. Grants total $2.75M as lead investigator and $10.6M as co-investigator. Industry partnerships include PORSPA Advance ($4.7M) and Australian Digital Domains ($3.6M). His lab develops tools like MaxHiC and DeepGenePrior for genomic analysis. Labs/Teams: Director of UNSW BML Lab, Health Data Theme Leader at UNSW Data Science Hub. Active in mentoring 13 researchers globally and co-supervising international teams.

Marta AriasView profile

Associate Professor

• Machine Learning
• Data Mining
• Design and Analysis of Algorithms
• +3 more

Marta Arias is an Associate Professor in the Computer Science Department at Universitat Politècnica de Catalunya (UPC), Barcelona, and a member of the LARCA research group. She has held academic positions since 2007, including roles at Columbia University and the University of Edinburgh. Her research focuses on Machine Learning, Data Mining, Algorithm Design, and Logic in Computer Science. She teaches advanced courses in Machine Learning, Information Retrieval, and Complex Networks at undergraduate and master's levels, including the Erasmus Mundus program in Data Mining and Knowledge Management. Educations: PhD in Computer Science from Tufts University (2000-2004), Postgraduate Studies at University of Edinburgh (1999-2000), B.Sc. in Computer Science from UPC (1992-1997). Professional experience includes software engineering and research roles in New York and Barcelona. Research highlights include developing algorithms for real-time ranking systems, Twitter-based financial forecasting, and causal network analysis. Notable contributions include the 'Best Paper Award' at AIPESW@ECAI 2020 and a 3rd place award in football performance research. Her work spans theoretical foundations (e.g., Horn clause learning) and applied domains like cybersecurity, healthcare analytics, and energy systems. Teaching responsibilities include labs and lectures on Machine Learning, Information Retrieval, and Programming, with active involvement in curriculum design and pedagogical innovation. Ongoing projects include enterprise risk analysis, synthetic data generation, and network modeling.