Find Academic Supervisors

Daniel CoppersmithView profile

Assistant Professor

• Clinical Psychology
• Suicide Prevention
• Digital Mental Health
• +7 more

Daniel Coppersmith is an Assistant Professor joining the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst in Fall 2025. His research program is dedicated to advancing the understanding, prediction, and prevention of suicide using innovative technologies such as smartphones and wearable biosensors. He is affiliated with the College of Natural Sciences and operates at the intersection of clinical psychology and computational behavioral science. His research interests center on the dynamic, heterogeneous nature of suicidal thoughts and behaviors. Key areas include: Describing the form and function of suicidal ideation Identifying time-varying risk and protective factors Developing personalized, scalable interventions using real-time data His work emphasizes affect regulation, temporal dynamics, and just-in-time adaptive systems to support individuals at risk. Analysis of his recent publications reveals a strong focus on digital mental health tools, computational modeling of suicide risk, and the development of real-time interventions. His work frequently employs longitudinal data, dynamic systems modeling, and mobile technology to capture intraindividual variability in suicidal thinking. Collaborations with leading figures such as Matthew K. Nock and Emily M. Kleiman underscore his integration into top-tier research networks in clinical and computational psychiatry. Although no scientific awards are currently listed, his publication record in premier journals like Proceedings of the National Academy of Sciences and Behaviour Research and Therapy highlights significant scholarly impact. His future work is expected to expand the reach of precision suicide prevention through scalable, technology-enhanced care models. Dr. Coppersmith advises graduate students in clinical psychology and related fields, though specific advisees are not yet listed. His lab likely focuses on digital phenotyping, suicide risk modeling, and intervention development. He may pursue grants from NIH, NIMH, and private foundations focused on mental health innovation and suicide prevention. His team is expected to include data scientists, clinical psychologists, and software developers working on real-time behavioral monitoring systems.

Olga ZinovievaView profile

Lecturer

• Additive manufacturing
• Metals and alloys
• Computational mechanics
• +12 more

Dr. Olga Zinovieva is a Lecturer in Mechanical Engineering and Program Coordinator at UNSW Canberra's School of Engineering and Technology. Her research focuses on computational modeling in metal additive manufacturing, particularly on processing-microstructure-property relationships. She has held research positions at the University of Bremen, Russian Academy of Sciences, and Tomsk Polytechnic University, and visiting roles in Australia, Germany, Brazil, and France. Research Interests: Modeling for additive manufacturing Multiscale methods Computational materials science Computational mechanics Microstructure evolution in 3D printing Mechanical behavior under dynamic loading Recent research trends from her publications emphasize predictive modeling of mechanical properties in additively manufactured metals, microstructure simulation, and digital solutions for advanced manufacturing. Her work integrates ICME approaches and high-performance computing to optimize alloy performance and process parameters. Scientific Awards and Grants: ARC Discovery Early Career Researcher Award (2025–2028) NSW DIN Pilot Project (2024–2025) CSIRO ON Prime Performance Bonus (2024) UNSW Start-up Grant (2022–2024) DFG-RFBR Project (2017–2022) Multiple travel and research grants from RFBR, University of Bremen, and Tomsk State University Supervision and Grants: Dr. Zinovieva actively supervises PhD and undergraduate research students in projects related to additive manufacturing modeling. She has secured over 20 grants as a Chief Investigator, including leadership in international collaborations between Germany and Russia. She mentors students through UNSW’s HDR programs and industry-linked research initiatives. Labs and Teams: She leads computational research in metal additive manufacturing at UNSW Canberra, utilizing high-performance computing resources. She collaborates with international teams at the University of Bremen and participates in editorial and advisory roles for journals such as Metals and Journal of Materials Informatics .

• 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.

• Analog and mixed-signal integrated circuit design
• Computer-aided design (CAD) of electronic circuits
• Design automation (EDA)
• +5 more

Georges Gielen is Full Professor in the Department of Electrical Engineering (ESAT) at KU Leuven, Belgium, and part-time Research Director at imec. He has held multiple leadership roles including Chair of ESAT Department (2012-2013, 2020-2024) and Vice-Rector for Science, Engineering & Technology (2013-2017). His academic career spans over 30 years at KU Leuven, progressing from Assistant to Full Professor. His research focuses on analog and mixed-signal integrated circuit design automation , with expertise in CAD tools, design optimization, sensor interfaces, and neuromorphic systems. His work bridges hardware design with machine learning, particularly in hardware-efficient AI implementations and biomedical applications. He has pioneered techniques for automated analog circuit sizing, topology synthesis, and reliability-aware design in nanometer CMOS. Gielen has received numerous accolades including the IEEE CAS Mac Van Valkenburg Award (2015), IEEE CAS Charles Desoer Award (2020), and EDAA Achievement Award (2021). He holds an ERC Advanced Grant AnalogCreate and is an IEEE Fellow since 2002. As a prolific scholar, he has chaired major conferences including DATE (2006), ICCAD (2007), and ESSCIRC (2017). He has graduated over 55 PhD students through the MICAS research group at KU Leuven, currently supervising 13 doctoral candidates. His research team collaborates extensively with imec and industry partners on cutting-edge projects in carbon-aware AI accelerators, uncertainty-aware design, and neuromorphic sensor interfaces.

Ross KoppelView profile

Adjunct Professor

• Healthcare Information Technology
• Medication Errors
• Technology in Society
• +2 more

Ross Koppel is an Adjunct Professor of Sociology with expertise in healthcare information technology, medication errors, and ethics in social research. His work explores the intersection of technology and societal impacts, focusing on data governance, clinical workflows, and human factors in health IT systems. Research Interests Context-sensitive understanding of medication errors Societal implications of clinical data sharing Ethical challenges in AI and informatics Healthcare cost analysis Scientific Awards Fellow of the American College of Medical Informatics (FACMI)

Maryam GhahramaniView profile

Senior Lecturer

• Human Motion Analysis
• Machine Learning
• Rehabilitation Engineering
• +5 more

Dr. Maryam Ghahramani is a Senior Lecturer in AI & Robotics at the Faculty of Science & Technology, University of Canberra, Australia. She holds a BSc in Electrical Engineering from Shiraz University, Iran, and a PhD in Biometric Gait Analysis from the University of Wollongong, Australia. Biomedical Engineering Researcher Machine Learning Specialist Human Motion Analysis Expert Her research focuses on applying machine learning to human motion analysis for rehabilitation purposes, particularly in three key areas: Parkinson's Disease: Using fNIRS and machine learning for disease detection and motor function assessment Fall Prevention: Analyzing postural sway and risk of falls in older adults Spatial Disorientation: Studying balance in hypoxic aviation environments Recent publications demonstrate her work at the intersection of biomedical engineering, machine learning, and clinical rehabilitation. Current projects include: Young Onset Dementia Detection with 12-week Home-Based Exercise Programs Mild Hypoxia Analysis for Aviation Safety Balancing Mat Performance Evaluation

• Environmental Epidemiology
• Children's Environmental Health
• Molecular Epidemiology
• +3 more

Margaret Karagas, PhD is the inaugural Department Chair and James W. Squires Professor of Epidemiology at the Geisel School of Medicine at Dartmouth College, with a secondary appointment as Professor of Community and Family Medicine. She also serves as Director of both the Children's Environmental Health and Disease Prevention Research Center and the Center for Molecular Epidemiology at Dartmouth. Her leadership extends to the NIH-funded Environmental Influences on Child Health Outcomes (ECHO) Program, which encompasses over 90,000 participants across the United States. Dr. Karagas earned her PhD from the University of Washington in 1990 and has established herself as a leading researcher in environmental epidemiology. She collaboratively developed an innovative cross-disciplinary postdoctoral and graduate program in Quantitative Biomedical Sciences (QBS) that integrates epidemiology, bioinformatics, and bistatistics. Her research focuses on interdisciplinary studies investigating environmental exposures, host factors, and biological mechanisms affecting health from infancy through adulthood, with particular emphasis on under-studied rural populations. Dr. Karagas employs high-dimensional analytic tools along with biomarkers and sensors to study the exposome, genetic susceptibility, and biological response. Her work has uncovered adverse cardiometabolic, neurodevelopmental, and immune-related health outcomes associated with drinking water contaminants, food-borne toxicants (including in infant first foods), and exposures from woodstoves. Analysis of her recent publications reveals a strong focus on environmental health impacts across the lifespan, with significant contributions to understanding how environmental exposures affect child development, chronic disease risk, and health disparities. Her work frequently employs advanced molecular and analytical techniques to assess environmental exposures and their biological consequences. Dr. Karagas serves on numerous prestigious international consensus panels and committees, including those for the United Nations, World Health Organization, International Agency for Research on Cancer, European Food Safety Authority, US National Institutes of Health, and National Academies of Science, Engineering and Medicine. As an academic leader, she has mentored diverse investigators at all career stages and established innovative training programs. Her research has directly contributed to practice and policy changes regarding environmental health threats. She maintains active collaborations across multiple centers at Dartmouth including the Dartmouth Toxic Metals Superfund Research Program and Dartmouth SYNERGY.

• Organic Chemistry
• Photoredox Catalysis
• Electrochemistry
• +6 more

Magnus A. Rueping is a highly distinguished Professor of Chemistry at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. With an impressive h-index of 107 and over 35,502 citations from 430 documents, he stands as a leading figure in modern synthetic chemistry. His research group maintains active collaborations with 651 co-authors worldwide, reflecting his significant impact on the chemical sciences community. Professor Rueping's research spans multiple cutting-edge areas in organic chemistry and catalysis. His work primarily focuses on developing novel sustainable methodologies including photoredox catalysis, electrochemical synthesis, and mechanochemistry. He has made significant contributions to the fields of $$\text{C-H}$$ functionalization, late-stage modification of complex molecules, and sustainable chemical transformations. His research group explores the intersection of traditional organic synthesis with emerging technologies to create more efficient and environmentally friendly chemical processes, with particular emphasis on nickel catalysis and metal-organic frameworks. Analysis of Professor Rueping's recent publications (2023-2025) reveals a strong trend toward integrating multiple activation modes in single catalytic systems. His work increasingly combines photochemistry, electrochemistry, and mechanochemistry (particularly resonant acoustic mixing) to develop novel catalytic platforms that minimize waste and energy consumption. A notable research direction involves the application of copper nanoclusters and cerium-based metal-organic frameworks as heterogeneous photocatalysts for challenging organic transformations. His group has also pioneered methods for $$\text{C-Ge}$$ and $$\text{C-S}$$ bond formation with exceptional selectivity. Professor Rueping's research has attracted substantial funding and recognition, as evidenced by his high citation metrics and publication record in top-tier journals including Nature Communications, Journal of the American Chemical Society, and Angewandte Chemie. His work bridges fundamental chemical research with practical applications in pharmaceutical development and sustainable manufacturing. As a dedicated mentor, Professor Rueping has supervised numerous graduate students and postdoctoral researchers who contribute to his diverse research portfolio. His laboratory operates state-of-the-art facilities for advanced organic synthesis, photochemistry, electrochemistry, and materials characterization. Current research directions include developing new methodologies for late-stage functionalization of pharmaceutical compounds, creating sustainable approaches to chemical manufacturing, and engineering novel catalytic materials for energy applications. His group's recent expansion into diagnostic technologies (nanobody-based lateral flow assays) demonstrates the versatility and interdisciplinary nature of his research program.

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.

• 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.

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.

Jocelyn Alcántara-GarcíaView profile

Associate Professor

• Conservation Science
• Cultural Heritage Materials
• Textile Conservation
• +5 more

Jocelyn Alcántara-García is an Associate Professor in the Department of Art Conservation at the University of Delaware, with a joint appointment in the Department of Chemistry and Biochemistry since 2023. She specializes in conservation science, focusing on the forensic analysis and preservation of cultural heritage materials, particularly textiles and archival objects. Education: B.S. in Chemistry – National Autonomous University of Mexico (UNAM) Ph.D. in (Inorganic) Chemistry – National Autonomous University of Mexico (UNAM) Postdoctoral Fellowship – Johns Hopkins University (Andrew W. Mellon Postdoctoral Fellow) Conservation Studies – National School of Conservation, Restoration and Museography (ENCRyM) Internship in Polymers – Brno University of Technology, Czech Republic Conservation Science Fellowship – National Museum of Contemporary Art Reina Sofia, Spain Research Focus: Dr. Alcántara-García’s work is deeply interdisciplinary, bridging chemistry, conservation science, and cultural heritage studies. Her research is structured around three core areas: forensic analysis of historical materials, reverse engineering and degradation mechanisms of heritage textiles, and the development of non-destructive techniques for studying cultural artifacts. She also integrates innovative pedagogical approaches to attract and retain students in STEM fields, using cultural heritage as a focal point for teaching chemistry and conservation science. Publications Overview: Her scholarly output reflects a consistent focus on the application of advanced analytical techniques to the study of historical materials. Topics include the non-destructive identification of dyes and pigments, the degradation of copper-based pigments, and the use of spectroscopic and chromatographic methods in conservation science. Her work often involves collaboration with conservators, historians, and scientists, and is published in high-impact journals in the fields of conservation, chemistry, and cultural heritage. Scientific Awards & Honors: Andrew W. Mellon Postdoctoral Fellowship Phi Kappa Phi Honor Society Membership Sigma Xi Scientific Research Society Membership Advising & Mentorship: Dr. Alcántara-García is actively involved in mentoring students across multiple departments and institutions. She works with undergraduate and graduate students in Art Conservation, Chemistry and Biochemistry, and the Winterthur/University of Delaware Program in Art Conservation (WUDPAC). Her mentorship emphasizes interdisciplinary research and hands-on experience with analytical instrumentation in cultural heritage contexts. Labs & Affiliations: She is affiliated with the Winterthur Museum, Garden and Library, where she conducts research and teaches WUDPAC students. She also engages with students and collaborators in Old College and Lamont DuPont/Brown/Drake facilities at the University of Delaware.

• inflammation-hemostasis interface
• biomaterials
• cellular immunotherapeutics
• +7 more

Brian R. Smith is Professor of Laboratory Medicine, Biomedical Engineering, Internal Medicine (Hematology) and Pediatrics at Yale University. He serves as Deputy Dean for Clinical and Translational Research at the Yale School of Medicine, Co-Director of the Yale Center for Clinical Investigation, and Chair of the Department of Laboratory Medicine. Dr. Smith is also Chief of Laboratory Medicine and Attending Physician at Yale New Haven Hospital, as well as an attending physician at the Connecticut VA Medical Center and Bridgeport Hospital. Dr. Smith's research focuses on the inflammation-hemostasis interface, particularly in relation to biomaterials, and cellular immunotherapeutics. His work spans the full translational spectrum from basic wet bench research through clinical and epidemiological trials (T1-T4). He has been continuously funded by the NIH at the PI-level for over 35 years, demonstrating sustained excellence in translational research. His investigations have particular emphasis on hematologic malignancies, diagnostic accuracy, and the educational frameworks needed to train future laboratory medicine specialists. Analysis of Dr. Smith's publications reveals a consistent focus on advancing laboratory medicine through both clinical research and educational innovation. His work addresses critical issues in diagnostic accuracy, physician-scientist training pathways, and the integration of new technologies into clinical practice. The publications span oncology, hematology, medical education, and healthcare systems, reflecting his interdisciplinary approach to advancing laboratory medicine. Evans Award for Outstanding Contributions to Laboratory Medicine (2010) President, Academy of Clinical Laboratory Physicians & Scientists (2007-2008) Chair (elected), Research Committee and Member of the Council, Association of Pathology Chairs (2010) Dr. Smith has been instrumental in developing Yale's research infrastructure, overseeing implementation of research core facilities in Translational Immune Monitoring, Flow Cytometry, and Clinical Sample Real Time Acquisition. He chairs the Clinical Research Technology Committee and has guided the development of a Cellular Therapy core resource. As an educator, he has personally mentored over 50 MD, MD/PhD, and PhD trainees, most of whom hold tenure-track positions. He is the initiator and long-standing PI of Laboratory Medicine's post-doctoral T32 training program in Immunohematology. Dr. Smith has played a key national role in laboratory medicine education, developing curricula and physician-scientist training paradigms. Through his leadership in the Association of Pathology Chairs, he helped establish a specific Physician-Scientist residency pathway with the American Board of Pathology, addressing a critical need for specialized training in academic pathology.

Katja RöslerView profile

Professor

• Automotive Engineering
• Modeling and Simulation
• Driver Modeling
• +12 more

Prof. Dr. Katja Rösler is a Professor of Automotive Engineering at the Institute of Mechanical Engineering, Ruhr West University of Applied Sciences since March 2012. Her career spans academic research and industrial development with key positions at TU Braunschweig, Volkswagen AG, and Fraunhofer Institute. Education: Industrial Mathematics degree completed under standard period Doctorate: Engineering (Driver Modeling) from TU Braunschweig, 2008 Her research focuses on automotive engineering with special emphasis on modeling/simulation, vehicle dynamics, driver assistance systems, accident research, alternative drives, and mobility concepts. She actively combines simulation with experimental verification and has significant involvement in Formula Student projects. Recent publications highlight her work in intelligent mobility systems (2018-2020), with particular attention to electromobility, accessibility solutions for elderly/disabled populations, and micromobility analysis. Earlier works established her expertise in driver modeling, vehicle measurement technology, and simulation-experiment correlation. Labs: Automotive Engineering Lab Teaching: Mechanics (Statics, Strength of Materials, Dynamics), Vehicle Dynamics, Driver Assistance Systems

• Software Engineering
• Artificial Intelligence
• AI Trustworthiness Assessment (SE4AI)
• +5 more

Gias Uddin is an Associate Professor at York University's Lassonde School of Engineering and an Adjunct Professor at the University of Calgary . His research bridges Software Engineering (SE) and Artificial Intelligence (AI) , focusing on AI Trustworthiness Assessment (SE4AI) and AI-Driven Productivity Tools (AI4SE) . PhD in Software Engineering & AI, McGill University (2018) MSc in Software Engineering, Queen’s University (2008) BSc in Computer Science & Engineering, Bangladesh University of Engineering and Technology (2004) His research explores: Metamorphic Relations for LLM Hallucination Detection AI-Enhanced Software Documentation Foundational Models for Runtime System Modernization Developer-Centric AI Tooling Recent article trends show expertise in LLM Trustworthiness , Low-Code Platforms , and IoT Developer Communities . Awards include Distinguished Paper at FSE 2025 , multiple IBM Champion recognitions, and York Research Award . He leads the Data Intensive Software Analytics (DISA) Lab and mentors PhD students in SE-AI Intersections .