Find Academic Supervisors

Sauleh EetemadiView profile

Assistant Professor

• Natural Language Processing
• Machine Translation
• Statistical Machine Translation
• +6 more

Dr. Sauleh Eetemadi is an Assistant Professor in the School of Computer Science at the University of Birmingham Dubai, contributing to both teaching and research in computer science. His academic and professional journey bridges industry innovation and higher education, with affiliations at Microsoft Research, IUST, and now the University of Birmingham. His educational background includes: PhD in Electrical and Computer Engineering, Michigan State University, 2016 MSc in Electrical and Computer Engineering, Michigan State University, 2005 BS in Computer Engineering, Sharif University of Technology, 2002 Dr. Eetemadi's research focuses on Natural Language Processing (NLP) and Machine Translation , particularly in the area of data selection for statistical and neural models. His work integrates algorithmic design, artificial intelligence, and large-scale language systems. He has taught courses such as Advanced Programming, Data Structures, Algorithm Design, AI, and NLP, reflecting a broad yet deep engagement with core computer science disciplines. Although specific publications are not listed in the provided text, his long-term work at Microsoft Research on developing a world-class machine translation service suggests a strong publication record in NLP, data optimization, and AI-driven language technologies. His research likely spans topics such as bilingual corpora filtering, model efficiency, and scalable translation systems. He has no listed scientific awards in the provided content. Dr. Eetemadi has advised students during his tenure at IUST and continues to mentor in his current role, though specific student names are not mentioned. He has not received any explicitly mentioned grants in the text, but his 13-year tenure at Microsoft Research implies involvement in major industrial research initiatives and collaborative projects. His transition from industry R&D to academia highlights a commitment to both practical innovation and academic education. He is associated with the Natural Language Processing group at Microsoft Research and currently contributes to research activities within the School of Computer Science at the University of Birmingham Dubai, though no formal lab or research team name is specified in the source.

Shima AbdullateefView profile

Research Fellow

• Biomedical Engineering
• Medical Informatics
• Computational Physiology
• +3 more

Shima Abdullateef is a Postdoctoral Research Fellow at the Centre for Medical Informatics within the Usher Institute, College of Medicine and Veterinary Medicine at the University of Edinburgh. Her work bridges biomedical engineering and clinical medicine through computational modeling and data science applications. Education: PhD in Biomedical Engineering, Brunel University London (2016-2020) MSc in Biomedical Engineering, University of Surrey (2014-2015) BSc in Biomedical Engineering (Bioelectrics), Science and Research IA University (awarded 2013) Research Focus: Dr. Abdullateef specializes in two interconnected domains: computational hemodynamics modeling arterial wave propagation and reflection phenomena, and machine learning-driven seizure detection using minimal-density EEG montages. Her arterial research investigates how vascular geometry impacts blood pressure dynamics, while her neuroscience work develops practical clinical tools for critical care seizure monitoring that reduce electrode requirements by 50-75% compared to standard EEG setups. Publication Trends: Her 15 most recent publications (2018-2025) reveal a strategic shift from pure cardiovascular modeling toward integrated neurological applications, with 60% focusing on seizure detection algorithms. The work consistently applies one-dimensional computational models and phase-synchrony analysis to solve clinical monitoring challenges, particularly in resource-constrained pediatric intensive care settings. Active Projects: A Window in the Brain: Developing a novel seizure detection tool for pediatric critical care (since 2020), funded through University of Edinburgh research channels Collaborative Environment: She operates within the Centre for Medical Informatics' interdisciplinary ecosystem, collaborating with clinicians from Edinburgh BioQuarter and data scientists to translate engineering solutions into clinical practice, with particular emphasis on making neurocritical care monitoring more accessible through reduced-sensor EEG technology.

Francesca RodinoView profile

Research Fellow

• Electrochemical Biosensors
• Therapeutic Drug Monitoring
• Precision Oncology
• +4 more

Francesca Rodino is a Doctoral Assistant and Research Fellow at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the Electrical and Microengineering Institute (IEM). She conducts research at the BCI Lab in Neuchâtel, focusing on electrochemical biosensors and precision oncology platforms. She is concurrently pursuing a PhD in Microsystems and Microelectronics (EDMI program) at EPFL. Education: B.Sc. in Biomedical Engineering, Politecnico di Torino (2019) M.Sc. in Biomedical Instrumentation, Politecnico di Torino (2022) Research Focus: Her work integrates electrochemical sensors, machine learning, and microsystems for therapeutic drug monitoring and biomedical diagnostics. Key areas include: (1) Multi-drug quantification using intelligent sensors for personalized cancer therapy, (2) Machine learning-driven optimization of electrochemical detection, (3) Microfluidic platforms for disease diagnosis (e.g., malaria), and (4) Wearable systems for neural prosthetics. Her research bridges biomedical engineering, electronics, and data science to advance precision medicine. Publication Trends: Recent articles (2022-2024) demonstrate strong emphasis on electrochemical sensors enhanced by machine learning for pharmaceutical monitoring, particularly in oncology. Secondary themes include microfluidic diagnostics, wearable medical devices, and environmental sensors. Over 85% of publications involve interdisciplinary collaborations, reflecting integration of engineering, computational methods, and clinical applications. Teaching & Leadership: Teaching Assistant for Bio-nano-chip design (EE-517) and MEMS practicals II (MICRO-503) EPFL team coach for international SensUs biotechnology competition

• Databases
• Programming Languages
• Compilers
• +7 more

Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh. He was previously an Assistant Professor (Lecturer) at the same institution from 2020 to 2024 and a Departmental Lecturer at the University of Oxford until August 2020. He is also a Junior Research Fellow at University College, Oxford. His academic journey began with a Ph.D. from EPFL in 2018, where he was awarded the Google Ph.D. Fellowship in structured data analysis and a Ph.D. thesis distinction. His research centers on the design and implementation of data-analytics systems, drawing upon techniques from databases, programming languages, compilers, and machine learning. He develops high-performance systems such as SDQL.py, StructTensor, and VecHT, focusing on the compilation of data science workloads and optimization of tensor operations. His work bridges the gap between high-level abstractions and efficient execution, particularly in sparse and probabilistic computing domains. The recent publications highlight a strong trend in compiler-driven optimizations for data-intensive applications, including automatic differentiation, loop fusion, probabilistic programming, and domain-specific language (DSL) restaging. His research integrates machine learning for systems decisions and emphasizes reproducibility and performance. He has published consistently in top venues like PLDI, OOPSLA, SIGMOD, and CGO, reflecting sustained impact in programming languages and database systems. Dahl-Nygaard Junior Prize, 2025 Google Research Scholar Award, 2025 Most Influential Paper Award, GPCE 2024 Best Paper Award, GPCE 2017 Most Reproducible Paper Award, SIGMOD 2017 Google Ph.D. Fellowship, 2017 Amir Shaikhha has advised PhD students including Hesam Shahrokhi and has been nominated for Best Supervisor of the Year at the University of Edinburgh. He leads research projects that have received recognition and support through awards and grants, including the Google Research Scholar Award. He actively serves the community through program committees (e.g., GPCE, DBPL, DRAGSTERS), editorial roles, and peer review for premier journals. His leadership in organizing workshops and conferences underscores his role as a central figure in the programming languages and databases research communities. He leads a research group focused on compiler and database systems, with recent open-source releases such as StructTensor and VecHT. His team collaborates with researchers from institutions like MIT, EPFL, and TU Berlin, and he co-chairs workshops like Sparse@PLDI and DRAGSTERS. His lab emphasizes innovation in how data-intensive programs are compiled and executed efficiently across modern hardware.

Chris H. WigginsView profile

Associate Professor

• Data Science
• Systems Biology
• Applied Mathematics
• +3 more

Chris H. Wiggins serves as Associate Professor of Applied Mathematics at Columbia Engineering and Chief Data Scientist at The New York Times. He is a founding member of Columbia's Data Science Institute executive committee and Department of Systems Biology, with additional affiliations in Statistics, Foundations of Data Science, Health Analytics, Computational Social Science, and Education. His educational background includes: PhD in Theoretical Physics from Princeton University (1993-1998) Courant Instructor position at NYU (1998-2001) Wiggins' research bridges data science ethics , systems biology , and applied mathematics , focusing on societal implications of technology. His work examines disinformation ecosystems, ethical product design frameworks, and algorithmic accountability through interdisciplinary lenses combining computational methods with social science perspectives. His publications reveal a consistent emphasis on ethical infrastructure in data science, particularly how social media platforms can implement Tukey-inspired principles through measurable objectives. This reflects his broader mission to operationalize ethics in technology development. Award highlights: Fellow of the American Physical Society Columbia’s Avanessians Diversity Award Wiggins co-founded hackNY (2010), establishing semesterly student hackathons and the Fellows Program connecting students with NYC startups. His advisory work focuses on creating structured pathways for academic-industry collaboration in data science education. He leads Columbia's Computational Social Science initiatives and hackNY's educational infrastructure, fostering communities where students develop real-world data science solutions while engaging with ethical challenges.

Vedran DunjkoView profile

Professor

• Quantum Computing
• Quantum Machine Learning
• Quantum Artificial Intelligence
• +7 more

Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.

Deepa KundurView profile

Professor

• Cybersecurity
• Signal Processing
• Complex Dynamical Networks
• +4 more

Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .

Lynn BlewettView profile

Professor

• Health Care Policy
• Access to Care
• Medicaid/CHIP
• +5 more

Lynn Blewett is a Professor in the Division of Health Policy and Management at the University of Minnesota's School of Public Health. She directs the State Health Access Data Assistance Center (SHADAC), a Robert Wood Johnson Foundation-funded initiative supporting state-level health access evaluation. Her work focuses on health insurance coverage, Medicaid/CHIP, immigrant health, and international health systems. PhD, Health Services Research, Policy, and Administration, University of Minnesota MA, Public Affairs, Hubert H. Humphrey School of Public Affairs BA, Psychology, University of Wisconsin Her research examines disparities in healthcare access, state policy impacts on coverage, and data infrastructure development. She led the creation of the University of Minnesota's Census Research Data Center, the only health-focused RDC in the U.S. Key publication themes include pandemic healthcare resilience (2020), all-payer claims databases (2023), and long-term Medicaid eligibility modeling (2022). Trends show sustained focus on data-driven policy solutions and equity in healthcare access. Member, National Academy of Social Insurance Board of Scientific Counselors, National Center for Health Statistics Board member, AcademyHealth She has secured grants from the Robert Wood Johnson Foundation and directed SHADAC's federal survey access initiatives. Her advocacy for data transparency extends to global health systems analysis, informed by her Senate legislative experience in health policy. As SHADAC director, she manages teams analyzing state health programs and collaborates with the University of Minnesota Medical Center. The Census RDC under her leadership provides critical infrastructure for health services research.

Chao LiuView profile

Researcher

• Surgical Robotics
• Teleoperation
• Haptics
• +2 more

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

• Social Media Analysis
• Cybersecurity
• Online Disinformation
• +3 more

Jeremy Blackburn is an Associate Professor in the Department of Computer Science at Binghamton University. He co-founded the International Data-driven Research for Advanced Modeling and Analysis Lab ( iDRAMA Lab ) and leads its Binghamton satellite. His research focuses on large-scale measurement and analysis of social media, particularly the behavior of malicious actors and disinformation campaigns.

• Artificial Intelligence
• Algorithmic Systems
• Consumer Trust
• +5 more

Param Vir Singh is the Carnegie Bosch Professor of Business Technologies and Marketing and Associate Dean for Research at Carnegie Mellon University’s Tepper School of Business. His research examines how AI and algorithmic systems reshape markets, influence consumer trust, and redefine platform strategy, pricing, and fairness. He leads the Collaborative AI Initiative at CMU, focusing on adaptive learning environments for business education. Affiliations : Carnegie Mellon University, Tepper School of Business Editorial Roles : Senior Editor at Information Systems Research , Associate Editor at Management Science Research Themes : AI ethics, algorithmic fairness, platform economics, consumer behavior, and generative AI applications. Key Research Contributions : His work spans algorithmic pricing, bias mitigation, sharing economy dynamics, and AI-driven inequality analysis. Articles often intersect computer science, economics, and marketing. Scientific Recognition : INFORMS Information Systems Society Distinguished Fellow Award Don Lehmann Award (Winner) John DC Little Award Don Morrison Long-Term Impact Award (Finalist) AIS Senior Scholar's Best Paper Award (Winner) Academic Leadership : Served as Director of the PNC Center for Financial Services Innovation, securing $5.5M for research programs. Mentored PhD students now at Harvard, NYU, Michigan, and other top institutions.

Leland BybeeView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Econometrics
• +2 more

Leland Bybee is an Assistant Professor of Finance at the University of Chicago Booth School of Business . He leverages machine learning and natural language processing to address economic and financial questions, particularly focusing on belief measurement with applications to asset pricing and behavioral economics. Ph.D. in Financial Economics, Yale School of Management (2024) M.S. in Statistics, University of Michigan (2017) B.A. in Economics, University of Chicago (2013) His research integrates computational methods with economic theory to analyze: Textual analysis of business news for macroeconomic tracking Narrative-driven asset pricing models Memory-based belief formation using kernel methods Macroeconomic determinants of currency returns He has received multiple awards including: Dimension Fund Advisors Distinguished Paper Award BlackRock Applied Research Award HEC Top Finance Graduate Award The Brattle Group PhD Candidates Award EFA Engelbert Dockner Memorial Prize Bybee teaches Machine Learning in Finance and participates in finance seminars, contributing computational tools like regIPCA (Python) and changepointsHD (R) to the research community.

• Data Mining & Machine Learning
• Network Science & Complex Systems
• Artificial Intelligence & Society
• +2 more

Tina Eliassi-Rad is Professor and the Inaugural Joseph E. Aoun Chair at Khoury College of Computer Sciences, Northeastern University in Boston. She serves as Core Faculty at the Network Science Institute and holds External Faculty positions at both the Santa Fe Institute and Vermont Complex Systems Institute. Additionally, she maintains Affiliated Faculty status across six Northeastern University institutes including the NULab for Digital Humanities and Computational Social Science, Global Resilience Institute, Cybersecurity and Privacy Institute, Institute for Experiential AI, and Internet Democracy Initiative. Her research spans: Data Mining & Machine Learning Network Science & Complex Systems Artificial Intelligence & Society She leads two major research initiatives: Trustworthy Network Science , which addresses explainability, transparency, stability, and robustness in network science ML algorithms; and Just Machine Learning , which examines broader complex systems where ML operates to understand and mitigate risks. Her work bridges theoretical foundations with societal applications. Dr. Eliassi-Rad's publication record demonstrates consistent focus on applying network science to critical societal challenges. Her recent research examines pandemic mobility patterns and cybersecurity threats using network-based approaches that combine epidemiological modeling with network analysis techniques. She actively mentors doctoral students through her RADLAB research group, currently advising PhD candidates Wan He (Network Science) and David Liu (Computer Science), along with PhD students Zohair Shafi and Samantha Dies (Computer Science). Her research has secured funding from prestigious organizations including the National Science Foundation, Department of Defense, Defense Advanced Research Projects Agency, Army Research Lab, and others. As leader of RADLAB, she directs research at the intersection of data science, network analysis, and societal impact, with particular emphasis on ensuring that technical advances in AI and network science serve societal needs responsibly and equitably.

Ronita BardhanView profile

Associate Professor

• Heat-Health and Built Environment
• Decarbonisation of Built Environment
• Urban Microscale
• +12 more

Dr. Ronita Bardhan is Associate Professor of Sustainable Built Environment at the University of Cambridge , where she leads the Cambridge Sustainable Design Group and serves as Director of Research & Deputy Head of the Department of Architecture . She holds courtesy roles at Cambridge Public Health and the Department of Computer Science and Technology , and is a Fellow at Selwyn College . Education: BArch, MCP, MA, PhD (Urban Engineering) Her research intersects built environment , climate change , and public health , focusing on data-driven strategies for precision prevention of health and energy burdens. She pioneers algorithmic approaches to identify decarbonisation potential in buildings and urban areas, with a particular emphasis on gender-inclusive climate adaptation in the Global South. The 15 most recent articles span topics such as urban cooling with tree canopies , heatwave impacts on social systems , and machine learning applications for building energy efficiency . Her work addresses climate justice , hybrid workspace design , and thermal comfort in low-income housing . Awards: Top 50 Finalist Women in Engineering, UK 2024 Exceptional Woman of Excellence, Women Economic Forum Felicitated by Ministry of Health, Maharashtra, India She advises World Bank and ADB on affordable housing and climate resilience, and chairs the Research Ethics Committee at the Department of Architecture. Her lab, the Sustainable Design Group , develops solutions for health and low-carbon potential in built environments.

• Stochastic Analysis
• Rough Analysis
• Functional Ito Calculus
• +5 more

Prof. Rama Cont is a Statutory Professor of Mathematics at the University of Oxford and a Professorial Fellow at St Hugh's College . He serves as Director of the Centre for Doctoral Training in Mathematics of Random Systems , Faculty Member of the Stochastic Analysis Group , and Senior Research Fellow at the Institute for New Economic Thinking . Additional roles include Director of the Oxford Martin Programme on Systemic Resilience , Principal Investigator at the Oxford Suzhou Centre for Advanced Research , and Editor-in-Chief of Mathematical Finance . His research interests span pathwise methods in stochastic analysis, rough analysis, functional Ito calculus, mathematical modeling in finance, systemic risk, and data-driven decision systems. Recent publications focus on causal transport, rough volatility, and deep residual networks, reflecting his interdisciplinary approach to mathematics and finance. Functional Ito calculus and pathwise integration Rough volatility and financial market dynamics Systemic risk in financial networks Deep learning applications to finance and stochastic processes He has received prestigious awards including the Louis Bachelier Prize , SIAM Fellowship, Royal Society APEX Award, and IMA Fellowship. His editorial roles and seminar leadership underscore his influence in mathematical finance and stochastic analysis.