Find Academic Supervisors

Anand BhattadView profile

Assistant Professor

• Computer Vision
• Computer Graphics
• Generative Modeling
• +2 more

Anand Bhattad is an Assistant Professor in the Department of Computer Science at Johns Hopkins University, starting Fall 2025. Previously, he held positions as a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC) and a visiting scholar at UC Berkeley. His research focuses on the intersection of computer vision, generative modeling, and physical reasoning, aiming to develop perception-driven and physics-aware visual models. His academic journey includes a PhD in Computer Science from the University of Illinois Urbana-Champaign under David Forsyth, with mentorship from Derek Hoiem, Svetlana Lazebnik, Greg Shakhnarovich, and Shenlong Wang. Prior to his PhD, he earned dual master’s degrees in Computer Science and Civil and Environmental Engineering at UIUC and a bachelor’s in Civil Engineering from NITK Surathkal, India. Research interests center on how generative models encode physical and perceptual knowledge, with key contributions in intrinsic image emergence, projective geometry limitations, and physics-aware relighting techniques. His work bridges classical computer vision concepts with modern deep learning, producing state-of-the-art methods for 3D scene synthesis and image editing. Articles span topics like 3P Vision , diffusion models, and 360° video datasets, reflecting interdisciplinary approaches in computer graphics and computational photography. Scientific awards include Outstanding Reviewer at ICCV 2023, CVPR 2022 Best Paper Finalist, and multiple conference service roles as workshop organizer and area chair. He designed the TTIC course Past Meets Present: A Tale of Two Visions , teaching connections between historical and modern computer vision research.

Steven A. MillerView profile

Professor

• Personality characteristics
• Individual differences
• Emotional experiences
• +5 more

Dr. Steven A. Miller is a Professor of Psychology in the Department of Psychology at Rosalind Franklin University of Medicine and Science, within the College of Health Professions. He joined RFUMS in 2013 and serves as a statistics consultant for the university. His academic background includes a PhD in Social Psychology from Loyola University Chicago, an M.S. in Psychology from Illinois State University with specialization in Clinical Psychology, and an M.S. in Mathematics from Loyola University Chicago with specialization in Probability and Statistics. PhD in Social Psychology, Loyola University Chicago M.S. in Psychology, Illinois State University (Clinical Psychology specialization) M.S. in Mathematics, Loyola University Chicago (Probability and Statistics specialization) Dr. Miller's research focuses on the intricate relationship between personality characteristics/individual differences and emotional experiences. He investigates anxiety and emotional disorders, social cognitive models of personality, and applies quantitative methodology to psychological questions. His work examines intra-individual variability in emotional responses and how situational factors interact with personality to shape emotional experiences. He employs diverse methodologies including experience sampling studies and laboratory experiments to explore these complex dynamics. His recent publications demonstrate a strong focus on psychopathy, emotion regulation, network analysis of personality, and the tripartite model of anxiety and depression. His work spans clinical, forensic, and general populations, often employing sophisticated statistical techniques. There's a clear trajectory toward more complex modeling approaches including network analysis, longitudinal modeling, and advanced psychometric techniques across his publication history. Accredited Professional Statistician (PStat®) with the American Statistical Association Chartered Statistician (CStat) with the Royal Statistical Society Dr. Miller actively mentors graduate students, with numerous student co-authors appearing in his publications. He teaches advanced statistical courses including multivariate statistics, longitudinal models, and categorical data analysis. He is currently accepting doctoral students for the 2026/2027 academic year. His collaborative research spans multiple institutions including DePaul University and Texas A&M, focusing on emerging adults, romantic relationships, and chronic illness. His research laboratory examines the fundamental relationship between personality and emotion, exploring how situational contingencies and individual expectancies shape emotional responses. Current collaborative projects investigate daily experiences of emerging adults, psychopathy in romantic relationships, and social media use among individuals with chronic illness using diverse methodological approaches.

Charles WalterView profile

Assistant Professor

• Mobile and Wearable Security
• Adversarial Machine Learning
• Privacy
• +5 more

Charles Walter is an Assistant Professor of Computer and Information Science at the University of Mississippi, joining in Fall 2019. He holds a PhD in Computer Science from The University of Tulsa (2018), with prior degrees from the same institution (M.Sc 2016; B.S. 2014). His research focuses on Mobile and Wearable Security, Adversarial Machine Learning, Privacy, Malware Analysis, Fog Computing, and Self-Adaptive Systems. He leads the SPARC Lab, exploring cutting-edge topics like data privacy, malware detection, and security in fog computing environments. Education: B.S. Computer Science, University of Tulsa (2014) M.Sc Computer Science, University of Tulsa (2016) Ph.D. Computer Science, University of Tulsa (2018) Research Interests: His work addresses critical challenges in cybersecurity, including securing low-power wearable devices through fog computing architectures, developing adversarial machine learning defenses, and investigating human factors in code trustworthiness. Recent projects include studying privacy threats in diffusion models and creating frameworks for robust stability estimation in AI systems. Lab Activities: The SPARC Lab actively researches topics such as adversarial ML attacks, privacy-preserving video processing, and adaptive system security. Collaborative efforts focus on real-world applications like improving university transportation systems through smart bike rental programs.

Helma WennemersView profile

Professor

• Organic Chemistry
• Chemical Biology
• Peptide Chemistry
• +5 more

Prof. Dr. Helma Wennemers serves as a Full Professor at ETH Zurich's Department of Chemistry and Applied Biosciences, leading the Laboratory for Organic Chemistry. Her research group operates from HCI H 313 at Vladimir Prelog Way 1-5/10 in Zurich, Switzerland, with active teaching responsibilities including Organic Chemistry I and Chemical Biology - Peptides for the Fall 2025 semester. Her research program centers on the intersection of organic chemistry and chemical biology , with particular emphasis on collagen triple helix engineering , peptide-catalyzed asymmetric synthesis , and development of chemical tools for tissue remodeling diagnostics . Key focus areas include designing hyperstable collagen heterotrimers for fibrosis monitoring, creating fluorophore-based probes for collagen cross-linking visualization, and pioneering organocatalytic methodologies for complex heterocycle synthesis. Her group actively explores how hydrophobic modifications and proline derivatives influence collagen stability and cellular uptake mechanisms. Analysis of her 15 most recent publications (2024-2025) reveals three dominant research trajectories: (1) collagen structural engineering for biomedical applications, (2) innovative peptide/organocatalysis enabling stereoselective transformations, and (3) chemical probe development targeting tissue remodeling processes. These works consistently integrate synthetic chemistry with biological validation, demonstrating translational potential in fibrosis diagnostics and regenerative medicine. While specific grant details aren't provided in available sources, her research program clearly supports advanced laboratory infrastructure including peptide synthesis facilities and photochemical reaction systems like the ETHos photoreactor. Her group maintains strong industry and clinical collaborations evident in applications targeting liver cancer cells and prostate cancer diagnostics. The Laboratory for Organic Chemistry functions as an interdisciplinary hub where synthetic organic chemists collaborate with biologists to develop collagen-based diagnostic platforms and catalytic systems. Current projects focus on lysyl oxidase-responsive probes for real-time tissue monitoring and engineered peptide catalysts for sustainable chemical synthesis under environmentally relevant conditions.

• Health Sciences
• Gerontology
• Neurocognitive Disorders
• +3 more

Prof. Dr. Eling de Bruin is a Lecturer at the Department of Health Sciences and Technology (D-HEST) at ETH Zurich. His research focuses on developing and evaluating exergame-based interventions targeting neurocognitive disorders, motor-cognitive training for aging populations, and stroke rehabilitation. He leads studies on personalized exergame protocols (e.g., PEMOCS framework) and their impact on cognitive function, gait recovery, and fall prevention. His work integrates wearable sensor technology, biofeedback systems, and clinical assessment tools to improve outcomes for chronic conditions like stroke, diabetes, and sarcopenia. Key areas include exergame design, hybrid training modalities, and biomarker validation (e.g., heart rate variability for neurocognitive screening). His research also explores sports biomechanics in youth athletes and injury prevention strategies for alpine skiers. Collaborative projects involve interdisciplinary teams from rehabilitation medicine, biomedical engineering, and computer science to create user-centered exergame solutions. Current initiatives emphasize home-based interventions and tele-rehabilitation to enhance accessibility for older adults and long-term care residents. Methodological contributions include validation of motor-cognitive assessment systems using virtual reality and inertial measurement units.

Andrew HeadView profile

Assistant Professor

• Human-computer interaction
• Programming
• Reading
• +5 more

Andrew Head is an Assistant Professor at the University of Pennsylvania's Department of Computer Science, specializing in Human-Computer Interaction (HCI) and Programming. His work bridges interactive reading , math notation accessibility , and AI-assisted code comprehension . Affiliated with Penn HCI, PLClub, and MindCORE, he co-leads research with Danaé Metaxa and Benjamin Pierce. University of Pennsylvania Assistant Professor, Computer Science Affiliations: Penn HCI, PLClub, MindCORE His research focuses on interactive reading interfaces , AI-powered programming tools , and math notation analysis . Recent projects include: FreeForm : Interactive math notation editor Tyche : Property-based testing tools Explainable Notes : Medical note interpretation systems Publications in CHI , UIST , and ICSE demonstrate his systems-centric approach combining user studies with working prototypes. Notable awards include Best Paper at UIST 2024 and CHI 2022. Advising: Ph.D. Students: Alyssa Hwang, Litao Yan, Hita Kambhamettu, Jeff Tao, Jessica Shi Grants: $1M NSF grant for Property-based Testing Tools (2024) Teaching: Spring 2025: CIS 4120/5120 - Human-Computer Interaction Fall 2024: CIS 7000 - Interactive Reading

Deanne L. WestermanView profile

Professor

• Human Memory
• Recognition Memory Processes
• Cognitive Psychology
• +3 more

Deanne L. Westerman is a Professor and Cognitive Area Coordinator in the Department of Psychology at Binghamton University. Her research focuses on human memory processes, particularly recognition memory, familiarity, and attributional factors influencing memory decisions. She holds a PhD and MA from Case Western Reserve University and a BS from John Carroll University. Her work explores how perceptual and conceptual fluency impact memory judgments, with recent studies examining effects of photos/screenshots on memory impairment and emotional image processing. She has received the Chancellor's Award for Excellence in Teaching (2002-2023). Currently accepting graduate students for Fall 2024. Key research areas: Recognition memory, metamemory, cognitive bias, and memory illusions. Methodological expertise includes experimental design, memory paradigms, and cognitive neuroscience techniques.

Raymond J. MooneyView profile

Professor

• Artificial Intelligence
• Bioinformatics and Computational Biology
• Machine Learning
• +14 more

Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has been a faculty member since 1987. He is the Director of the UT Artificial Intelligence Laboratory and affiliated with multiple research groups including the Machine Learning Research Group, UT Computational Linguistics Lab, and the UT Center for Computational Biology and Bioinformatics. He holds a B.S., M.S., and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, where his thesis was supervised by Gerald DeJong. His research spans diverse areas in artificial intelligence, machine learning, and natural language processing: Natural Language Learning Connecting Language and Perception Statistical Relational Learning Information Extraction Transfer and Active Learning Abductive Reasoning Text Mining and Clustering Recommender Systems Knowledge-Base Refinement Recent publications highlight trends in grounded language processing, human-robot interaction, and multimodal reasoning. He has been recognized with prestigious fellowships including ACL (2014), ACM (2010), and AAAI (2005). Scientific awards: Fellow of the Association for Computational Linguistics (2014) Fellow of the Association for Computing Machinery (2010) Fellow of the American Association for Artificial Intelligence (2005) Classic Paper Award (2019) Best Paper Awards (2007, 2004, 1996) He teaches graduate courses like CS 371R: Information Retrieval and Web Search (Fall 2025) and CS 395T: Grounded Natural Language Processing (Spring 2025). His research labs include: UT Artificial Intelligence Laboratory Machine Learning Research Group UT Computational Linguistics Lab UT Center for Computational Biology and Bioinformatics

Jayshri SabarinathanView profile

Associate Professor

• Nano-photonic Sensors
• Remote Sensing Instrumentation
• Plasmonic Sensors
• +6 more

Dr. Jayshri Sabarinathan is an Associate Professor in the Department of Electrical and Computer Engineering at Western University's Faculty of Engineering, and a Faculty Member with the Institute for Earth and Space Exploration. She joined Western University in Fall 2003, received the NSERC University Faculty Award in 2004, and was promoted to Associate Professor in 2010. She previously served as Associate Director of Training (2019-2022) with the Institute for Earth and Space Exploration. Education: Ph.D. in Electrical Engineering, University of Michigan, Ann Arbor (2003) M.S.E. in Electrical Engineering, University of Michigan, Ann Arbor (1999) B.S.E. in Electrical Engineering and Engineering Physics, University of Michigan, Ann Arbor (1997) Her research focuses on developing novel nano-photonic sensors and miniature remote sensing instrumentation, with expertise spanning photonic crystals, plasmonic sensors, and CubeSat technology. Her work integrates nanofabrication techniques with practical applications in precision agriculture, geology, and space exploration. She has extensive experience with nanofabrication facilities including the University of Michigan Solid State Electronics Laboratory and Western's nanofabrication facility. Analysis of her 15 most recent publications reveals strong emphasis on plasmonic sensing technologies, photonic crystal applications, and nanoscale optical phenomena. Her research consistently bridges fundamental photonics with practical sensor development, particularly for environmental monitoring and space applications. The publications demonstrate progression from basic photonic crystal research to applied space instrumentation. Scientific Awards: NSERC University Faculty Award (2004) US Patent 8839683 for Photonic Crystal Pressure Sensors (2014) OSA (Optica) Senior Member Co-founder of LightSail Ltd space startup Dr. Sabarinathan actively mentors graduate students through her Nanophotonic Sensors Engineering (NPSE) and Remote Sensing Instrumentation (RSI) research groups. She has secured significant funding including Canadian Space Agency projects, notably as PI for the Western University-Nunavut Arctic College CubeSat Project Ukpik-1. Her research has resulted in three patents for micro photonic-sensors and multi-spectral camera innovations. Her labs focus on two primary research thrusts: the NPSE group developing hybrid photonics micro/nano-sensors including IR/THz plasmonic sensors and bio-photonic sensors, and the RSI group creating multispectral camera imagers for UAV/mobile robots with XRD instrumentation miniaturization for Mars rovers.

• Biostatistics
• Longitudinal Studies
• Public Health
• +4 more

Denise Esserman is a Professor of Biostatistics at the Yale School of Public Health, where she joined the faculty in 2014. She is a member of the Yale Center for Analytical Sciences and collaborates with multiple departments at the Yale School of Medicine, including the Clinical and Translational Science Award Program, Patient-Centered Outcomes Research Institute, and the Cancer Center. Her research focuses on methodological aspects of clustered randomized trials and sample size calculations. Education: PhD in Biostatistics from Columbia University (2006) MS in Statistics from University of Georgia (2001) Dr. Esserman's research interests span several critical areas in biostatistics and public health methodology. She specializes in clustered randomized trials, with particular expertise in understanding how intraclass correlation coefficients (ICC) and other factors impact sample size calculations. Her work extends to longitudinal studies methodology, randomized controlled trial design, and sampling techniques. She has contributed significantly to statistical methods for clinical trials, healthcare data analysis, and public health research. Her interdisciplinary approach bridges theoretical statistics with practical applications in healthcare settings. Analysis of Dr. Esserman's recent publications reveals a strong focus on methodological innovations in clinical trial design and analysis, particularly for cluster-randomized trials. Her work spans healthcare applications including fall injury prevention in elderly populations, opioid use disorder treatment in international settings, pain management for hemodialysis patients, and validation of medical coding algorithms. She frequently employs advanced statistical techniques including Bayesian methods, mediation analysis, and methods for handling clustered data. Her research demonstrates a consistent commitment to improving the rigor and applicability of statistical methods in public health and clinical research. Dr. Esserman serves as a reviewer for several prestigious journals including the American Journal of Epidemiology, Arteriosclerosis, Thrombosis and Vascular Biology; Statistics in Biopharmaceutical Research; Clinical Trials; and Obesity. As a member of the Yale Center for Analytical Sciences, Dr. Esserman collaborates with numerous researchers across Yale University. Her current projects include the EQuIP trial (HIC ID 2000033355), where she serves as Sub Investigator with primary completion date of 08/31/2027, focusing on mental health and behavioral research for sexual minority women.

Athena NghiemView profile

Assistant Professor

• Hydrology
• Biogeochemistry
• Environmental Geochemistry
• +3 more

Athena Nghiem is an Assistant Professor at the University of Wisconsin-Madison, starting Fall 2024, specializing in biogeochemistry and hydrology. She currently holds an ETH Postdoctoral Fellowship at ETH Zürich, focusing on redox processes in groundwater systems. Education: PhD in Earth and Environmental Sciences from Columbia University, BA in Geophysics and Statistics from UC Berkeley Research interests: Environmental variability in hydrology, redox processes, groundwater contamination, data science in environmental research, and reactive transport modeling Her research combines traditional laboratory/field methods with data science to study trace element cycling, particularly arsenic release in aquifers. Recent work includes quantifying sulfate reduction's role in arsenic contamination and evaluating mitigation strategies. Notable awards: ETH Postdoctoral Fellowship (2022-2024), NSF Graduate Research Fellowship (2018-2021), and multiple academic honors during her UC Berkeley BA studies. Current advisees at UW-Madison: Juyong Bak, Savannah Finley, Logan Goulette. She actively encourages applications from diverse backgrounds for future lab positions.

• Artificial Intelligence
• Human-Computer Interaction
• Computer Vision
• +4 more

Prof. Dr. Julius Schöning is a Professor at the Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences. His research focuses on Artificial Intelligence , Human-Computer Interaction , and Computer Vision within agricultural contexts. 2019–Present: Professor, Hochschule Osnabrück 2018–2019: System Architect, ZF Friedrichshafen AG 2014–2018: Researcher, University of Osnabrück 2009–2013: Project Lead/System Engineer, CLAAS Harsewinkel Education: M.Sc. in Intelligent Embedded Microsystems (Freiburg), B.Eng. in Mechatronics (DHBW Stuttgart) His research spans smart agriculture , quantum NLP , and explainable AI systems , with recent work on vibrotactile warning systems, AI compliance frameworks, and hybrid dataset applications in farming. Publications emphasize interdisciplinary approaches bridging technology and agricultural practice . Scientific honors include: DAAD Stipendium for conference participation Best Paper Award (2018) IEEE GHTC Student Paper Contest Winner (2016) Sonderpreis für gute Lehre (2017) Finalist/Falling Walls Lab (2015)

Julia LenView profile

Assistant Professor

• Applied Cryptography
• Computer Security
• Secure Communication
• +7 more

Julia Len is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where she conducts research in applied cryptography and computer security. She co-leads the Cryptography Group with Saba Eskandarian and is currently recruiting Ph.D. students for Fall 2026. She earned her Ph.D. in Computer Science from Cornell University in 2024 under the supervision of Tom Ristenpart, following a B.S. in Computer Science from UC San Diego in 2018, where she worked with Mihir Bellare. Before joining UNC, she was a METEOR postdoctoral fellow at MIT. Her research focuses on improving the security and privacy of deployed cryptographic protocols, particularly in end-to-end encrypted messaging. Key areas include interoperability, abuse prevention, key transparency, and authenticated encryption. She applies principled approaches ranging from identifying flaws in existing protocols to designing new cryptographic schemes and definitions. Her recent publications span top venues such as USENIX Security, CCS, Eurocrypt, and Crypto, demonstrating a strong and consistent research output in applied cryptography. Trends in her work show increasing focus on real-world protocol design, security analysis of widely used schemes, and privacy-preserving moderation mechanisms for secure communication platforms. METEOR Postdoctoral Fellow at MIT Julia Len has advised and collaborated with numerous researchers; current advisees are being recruited for Fall 2026. She has received research support through collaborations with industry partners including Zoom, Microsoft Research, and Meta. Her work on Partitioning Oracle Attacks has led to updates in Shadowsocks, age, OPAQUE, and HPKE, demonstrating significant real-world impact. She co-leads the Cryptography Group at UNC Chapel Hill, fostering research and education in cryptography. She also serves on the program committees of IEEE S&P 2026, USENIX Security 2025, and CATS 2023, contributing to the broader academic community.

Miguel R. RuedaView profile

Associate Professor

• Electoral Manipulation
• Civil Conflict
• Money in Politics
• +3 more

Miguel R. Rueda is an Associate Professor in the Department of Political Science at Emory University, specializing in electoral manipulation, civil conflict, money in politics, and political methodology. He holds a PhD from the University of Rochester (2014), an M.Sc. in Economics, and a B.Sc. in Economics and Mathematics from La Universidad de los Andes. Before Emory, he was a visiting scholar at Princeton University's Center for the Study of Democratic Politics (2013–2014). In Fall 2024, he will serve as a Visiting Associate Professor at Vanderbilt University. His research has been published in top journals like the American Political Science Review , American Journal of Political Science , and Journal of Conflict Resolution . Key themes include electoral fraud mechanisms, civil war dynamics, and the intersection of political methodology with empirical policy analysis. Rueda's work spans theoretical models of strategic behavior (e.g., foreign aid allocation, partisan poll-watching) and applied analyses of electoral systems, conflict outcomes, and governance challenges. His methodological contributions address econometric issues like post-instrument bias and omitted variable effects. Contact: miguel.rueda@emory.edu , 315 Tarbutton Hall, Emory University, Atlanta, GA 30322.

Anjalie FieldView profile

Assistant Professor

• Natural language processing
• Computational social science
• AI ethics
• +3 more

Anjalie Field is an Assistant Professor in the Computer Science Department at the Whiting School of Engineering, Johns Hopkins University. She is also affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). Dr. Field completed her PhD at the Language Technologies Institute at Carnegie Mellon University under Yulia Tsvetkov, where she was a member of TsvetShop. Prior to joining Johns Hopkins, she was a postdoctoral researcher in the Stanford NLP Group and at the Stanford Data Science Institute, working with Dan Jurafsky and Jennifer Eberhardt. She also spent time as a visiting student at the University of Washington from 2021 to 2022. Her research focuses on the ethics and social science aspects of natural language processing, developing computational models to address societal issues like discrimination and propaganda while critically assessing and improving privacy, transparency, and fairness in AI pipelines. Her work spans social media analysis, bias detection in multilingual content, police accountability systems, and ethical considerations in large language model applications. Dr. Field's publications demonstrate a consistent focus on identifying and addressing biases in language technologies, with particular attention to racial, gender, and cultural dimensions. Her recent work has expanded into domain-specific applications of NLP in astronomy, healthcare, and social justice contexts, showing the breadth of impact that ethical AI considerations can have across disciplines. Among her notable recognitions are: AI2050 Fellow by Schmidt Sciences 2022 Wikimedia Foundation Research Award of the Year Best Paper nomination at Socinfo (2020) Dr. Field teaches AI Ethics and Social Impact (Fall 2023; Fall 2024) and NLP for Computational Social Science (Spring 2024; Spring 2025). She plans to take PhD students for the 2024-2025 admissions cycle. Her work bridges technical NLP research with important social considerations, making significant contributions to both the technical community and broader societal discourse around AI ethics. She is an active member of the Center for Language and Speech Processing, where she collaborates with researchers working at the intersection of language technologies and real-world applications. Her research focuses on developing methods that not only advance NLP capabilities but also ensure these technologies serve diverse communities equitably.