About Me

Hi! I am 1st year PhD student in the Department of Computer Science at the University of Toronto and the Vector Institute. I am co-supervised by Prof. Marzyeh Ghassemi and Prof. Frank Rudzicz. My research is focused on developing and interrogating machine learning models in safety-critical settings such as healthcare. I also work part-time as a Research Software Engineer at Winterlight Labs. I graduated from the University of Toronto with a Master’s degree in Applied Computing in 2019. My master’s project, generously supported by Winterlight Labs and a Mitacs Accelerate Scholarship, was supervised by Prof. Marzyeh Ghassemi, Prof. Frank Rudzicz, and Dr. Jekaterina Novikova. Prior to this, I graduated with honors from the Indian Institute of Technology, Guwahati, India in 2017 where I was fortunate to get to work with Prof. Amit Sethi. I have also held research intern positions at the Technische Universität, Dresden as a DAAD-WISE scholar in 2016 where I was supervised by Prof. Carsten Rother and Prof. Stefan Gumhold, and at the Philips Innovation Campus, Bengaluru in the healthcare R&D team in 2017.

Here is my CV.

Research Interests

Developing and understanding trustworthy machine learning and natural language processing techniques for healthcare applications: particularly using unstructured and noisy human-generated data, with core machine learning methodologies such as transfer learning and reinforcement learning.



  1. Balagopalan, A., Eyre, B., Rudzicz, F., Novikova, J. (2020). To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer’s Disease Detection. Conference of the International Speech Communication Association (Interspeech) 2020. /paper/
  2. Balagopalan, A., Shkaruta, K., Novikova, J., Impact of ASR on Alzheimer’s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others, 6th Workshop on Noisy User Generated Text, EMNLP 2020 (Oral Spotlight) /paper/
  3. Balagopalan, A., Novikova, J., Mcdermott, M.B.A., Nestor, B., Naumann, T. & Ghassemi, M. (2020). Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation. Proceedings of the Machine Learning for Health NeurIPS Workshop, in PMLR 116:202-219. /paper/
  4. Novikova, J.,Balagopalan, A., Shkaruta, K., Rudzicz, F., “Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power”, 5th Workshop on Noisy User Generated Text, EMNLP 2019. /paper/
  5. Balagopalan, A., Novikova, J., Rudzicz, F., Ghassemi, M., “The Effect of Heterogeneous Data for Alzheimer’s Disease Detection from Speech.”, Machine Learning for Health (ML4H) at NeurIPS 2018. /paper/

Clinical Abstracts

  1. Balagopalan, A., Kaufman, L. J., Novikova, J., Siddiqui, O., Paul, R., Ward, M. and Simpson, W., “Early Development of a Unified, Speech and Language Composite to Assess Clinical Severity of Frontotemporal Lobar Degeneration (FLTD)”, Clinical Trials in Alzheimer’s Disease (CTAD) 2019.
  2. Simpson, W., Balagopalan, A., Kaufman, L. J., Yeung, A., and Butler, A., “The Use of a Voice-based Digital Biomarker in Patients With Depression ”, International Society for CNS Clinical Trials and Methodology 2019.
  3. Balagopalan, A., Yancheva, M., Novikova, J. and Simpson, W., 2019, “Using Acoustic and Linguistic Markers from Spontaneous Speech to Predict Scores on the Montreal Cognitive Assessment (MoCA). ”, Memory, 20, p.13., 2019.

Book Chapters

  1. Seifert, C., Aamir, A.,Balagopalan, A., Jain, D., Sharma, A., Grottel, S., Gumhold, S., “Visualizations of Deep Neural Networks in Computer Vision: A survey”, In Transparent Data Mining for Big and Small Data (pp. 123-144).Springer, Cham., 2017. /chapter/

Teaching Experience

Teaching Assistant, Department of Computer Science, University of Toronto

  1. CSC 458: Computer Networks, Fall 2017
  2. CSC 358: Introduction to Computer Networks, Winter 2018

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